The environment of development of artificial intelligence, however, works differently. In the case of AI projects, development is centred around identifying the data source and collecting data, cleansing it, and turning them into insights. Such an approach calls for a different mindset and skill sets. 

This unconventionalism that is networked into Artificial Intelligence projects comes with a whole new set of issues and answers for how to solve AI development challenges. 

Our team of artificial intelligence development specialists have worked on around 7 full-fledged solutions and 17+ POCs, with no two belonging to the same industry. The work exposure has made a few things very clear to us – 

1. You cannot expect your AI software development project outcome to be the same as a conventional product’s, for with AI, the game is more around hit and trials.
2. You will be able to best implement AI strategies and programs in your business when the entire team is on board, and not just the techies. 
3. Like in the case of non-AI app projects, the limitations in case of AI projects too vary from one idea to another. But there are some AI developmental challenges and solutions that are similar across products. 

Digging at the third learning, there are issues which are similar across products, no matter which idea backs them. No matter which application we were developing, we encountered these issues, making it safe to assume that these are recurrent. 

In order to imbibe in entrepreneurs a proactive approach, or data engineers have listed down the commonly occurring issues to adopting AI development services along with their insight against every individual artificial intelligence difficulties and opportunities.

AI Developmental Challenges and Solutions

1.  Data collection & management issues

The statement that the AI system is only as good as the data it is based on, while common, comes with several inherent issues. The issues that come on this front are mainly in terms of data gathering and its refinement. But there are other challenges as well, like – 

A.  Data quality and quantity 

As mentioned above, the quality of the AI system depends heavily on the quantity and quality of data that is fed into the system. In order to identify patterns and behave like what is expected of it, AI needs a lot of quality data. 

At Appinventiv, we start the process to implement AI strategies and programs by listing down the data that we have and the data which the model needs to operate. To do that, we use both open data and Google’s dataset search for getting access to the data that helps train the model.

• Labeling of data

Till a few years back, the majority of the data was textual and structured. But with the inception of omni-channel customer experience and Internet of Things, the data type that is being feeded in the business system is majorly unstructured. The problem is that the majority of the AI systems are trained to work around supervised datasets.

At Appinventiv, we use multiple approaches to handle data labeling, revolving majorly around data programming and synthetic labeling, feedback loop system, etc. when answering how to solve AI development challenges.

• Data biasness

The stories around AI being biased are widespread. Question is how does that happen, especially since the technology is not conscious and thus cannot have bad intentions, right?

Biasness fosters from wrongly collected data. This is the bottomline. When the source of the data is biased, the system becomes discriminatory. 

We look at all the data, ensuring they are unbiased from the very beginning. This way, when they enter the AI system, there is no scope of biases in the picture. 

• Case-focused learning

Human intelligence allows us to apply experience from one field to another. It is not something that AI can handle with ease. 

AI-powered tools for business are specialized. It is supposed to carry out a single-handed task. Going by its core complexity, it can be very difficult for AI to use the experience that it derived from one project to use it in another. 

We use a Transfer Learning approach where we train the AI model to carry out a task and then apply the learning to a similar activity. It means that the model devised for task A can later be used as the starting point for task B model.

2.  People centric issues

Even amidst widespread AI adoption, the human resources who are comfortable with working around the technology are counted. This, in turn, causes a number of persistent challenges for businesses both in the short and long term when they create AI-based applications. 

• Absence of understanding among non-technical employees

AI implementation calls for the management to understand AI technologies, their opportunities and limitations, etc. the absence of know-how hinders the right adoption of AI in business, in places where it can, in actuality, have an impact. 

• Rarity of field specialists

What the AI industry needs is experts who have the blend of technical understanding and market know-how for AI problems and techniques. The problem is that finding full-time in-house resources who have the blend of both is really difficult, especially with FAMGA group hiring talent having the core skills needed for AI software development. 

This is the number one reason why businesses often outsource their AI solution development to AI app development company like ours which are made of a team of experts who also have an in-depth knowledge of industries.

3.  Integration challenges

Adding or integrating Artificial Intelligence in your current system is a lot more complicated a process than adding a plugin in your browser. There are multiple elements and interfaces which are to be set up to address your business needs. 

Our team of data scientists consider your individual data infrastructure needs, data labeling, storage, and the process of feeding data in the system, so that you don’t have to face any startup AI app implementation challenges. We also work on training the model and testing effectiveness of the AI, thus developing a feedback loop for improving the models on the basis of people’s actions.

4.  Infrastructure capabilities 

Handling data and its computation, storage, scaling, security, extensibility etc are all necessary for businesses to deploy AI solutions. The success of a business when they deploy an AI solution begins with answering how suitable their infrastructure environment is and how well does it support the workloads and AI applications. The answer, sadly, is also one of the biggest enterprise AI challenges. 

There are a few things that our business analysts take note of at the very early stages:

• The right blend of high speed storage and processing capabilities for supporting deep learning and machine learning models. 
• The best software which can be optimized and tuned for fitting the underlying hardware. 
• An interface which manages most of the moving components and parts. 
• An infrastructure that can be deployed in the cloud or on premise data centre for optimized performance.

5.  Lack of multi-tasking abilities 

Deep Learning models are extremely trainable. Once the training ends, you can be sure that the solution will do its irrespective task best, whether it is identifying objects or recommending products on the basis of your customers’ search history. 

This is one of the biggest problems in AI when you want the system to multi-task. For example, when you want the AI to identify the person in a video and track the origins of the song that is playing in the background, the efficiency will be lost. 

A solution to this problem, that our data engineers have identified, is the use of progressive neural networks. It means, connection of separate deep learning models in a way that the bits of information can easily pass. Although we are yet to apply the model in practice, the method is proven to be extremely useful in robotic arms development – speeding their learning from weeks to only one day.

This was our take on the AI developmental challenges and solutions. But the tips to overcome AI development difficulties doesn’t just end with these. As you deep dive into the AI project devise and deployment world, you will find that the implementation of AI problems to solve and provide answers to business ultimately comes down to the skillset and technical + business understanding that your partnered Artificial Intelligence development company has.

6. Human-level interaction

This is possibly the main challenge in AI, one that has saved researchers on edge for AI services in organizations and new businesses. These organizations may be boasting above 90% exactness, however people can improve in all these situations. For instance, let our model predict whether the picture is of a dog or a cat. The human can foresee the right output everytime without fail, wiping up a staggering accuracy of above 99%.

For a profound learning model to play out a similar performance would require remarkable finetuning, hyperparameter advancement, huge dataset, and a well-defined and accurate algorithm, alongside robust processing power, continuous training on train data and testing on test data. That sounds like a ton of work, and it’s in reality multiple times more troublesome than it appears.

A one way solution you can try not to do all the difficult work is simply by utilizing a specialist organization, for they can prepare explicit deep learning models utilizing pre-trained models. They are trained on a huge number of pictures and are tweaked for greatest precision.

7. Data scarcity

With major companies such as Google, Facebook, and Apple facing charges regarding unethical use of user data generated, various countries such as India are using stringent IT rules to restrict the flow. Thus, these companies now face the problem of using local data for developing applications for the world, and that would result in bias.

With big organizations, like Google, Facebook, and Apple dealing with indictments in regards to unethical utilization of user data generated, different nations, like India are using severe IT rules to limit the data flow. Hence, these organizations presently deal with the issue of using nearby local information for creating applications for the world, and that would bring bias result.

The data is a vital aspect of AI, and the labeled information is utilized to train machines to learn and make predictions. A few organizations are trying to invent new strategies and are centered around developing AI models that can give precise outcomes regardless of the scarcity of data. With one-sided data or biased information, the whole system could get defective.

Concluding Thoughts

With an ever growing demand for adaptable, secure, and unique applications, there is tremendous tension in the development community. In such cases, adopting AI technology will give basic solutions and a favorable place to breed innovation. Artificial intelligence and machine Learning are without a doubt the future of programming and software development, and embracing them is the best choice for the organizations to make.

The App development process comprises a number of activities and an expert to perform it. The development significantly contributes to the different factors of AI development based on the location, such as the pricing factor, development, tools, etc. depend from place to place from AI development services in USA to services in other parts of the globe.

FAQs About AI Developmental Challenges and Solutions

Q.  What challenges do companies face when implementing AI?

There are a number of issues that companies face when they implement AI in their business. Here are a few of them – 

• Data collection and refinement 
• Lack of skill set 
• Integration challenges 
• Infrastructure capabilities

Q.  How to resolve AI development challenges?

The solutions to AI developmental issues ultimately come down to the partnership with a team of skilled AI experts and understanding of the users and the market the solution will be focusing on.

Q.  What are major ethical concerns about the use of AI?

These are some of the most prominent ethical concerns surrounding artificial intelligence – loss of jobs, biasness, the scope of AI making grave mass-scaled mistakes, the probability of people tampering with the datasets to meet their ulterior motives.

The post How We Solve AI Development Issues You’re Bound to Encounter appeared first on Appinventiv.

A ripple effect of this is that mobile apps have started embracing AI capabilities to ultimately emerge as Intelligent apps.

So, what exactly are intelligent mobile apps and how are they different from those apps that we use in our daily lives? 

Intelligent mobile apps are basically AI-enabled applications that deliver an adaptive, rich, and personalized user experience. These apps combine the power of predictive and prescriptive analytics, cutting-edge technologies, consumer data, and operational data with latest user-centric design and application development tools to make a high-end user experience.

Now that we have covered what are intelligent apps, let’s turn towards the best types of i-apps to understand them in a better way.

Best Intelligent Apps to Use in 2021 and Future

1. SwiftKey Keyboard

SwiftKey Keyboard is an AI-powered keyboard which auto-corrects your wrong text. Additionally, the app also offers features like changing font color, design, theme, and style, and adding fun to your conversation with emojis.

2. Elsa Speak

ELSA Speak is another popular Artificial Intelligence app that helps users learn English quickly and effectively. The application has listed down tests designed by the experts which enables users to learn and speak English fluently in a very less time span. Also, this one the best intelligent apps for Android and iOS app encourages them to continue further by keeping a track of their progress. Recently, this platform raised a $15 million Series B. The capital will be used to expand ELSA’s operations in Latin America and build a scalable B2B platform

3. Socratic

Introduced by Google, Socratic is an AI-based intelligent application that is simplifying the learning process. The application empowers students to ask questions by taking a photo of questions or asking verbally. This one of the popular artificial intelligence apps  comes up with optimal Q&A sections, blog links, and YouTube videos, which makes the studying process faster and simpler.

4. Hound

Hound is yet another perfect example of how Artificial Intelligence in mobile apps can streamline your life. The app takes voice commands to perform different actions such as getting familiar with the best hotel, restaurant, or doctors.

Another interesting feature of this i-app is that it translates speech to text and vice-versa.

5. IRIS

Another example of implementation of artificial intelligence in mobile apps for better services is IRIS.

Developed by Paint Technology Solutions, it is one of the most downloaded intelligent apps for Android and iOS used by managers, team leaders, and co-workers of any particular project. This one of the best artificial intelligence apps enables them to get access to the report of any team member. And the best part is that multiple users can access the same document at the same time and that too from different sync devices.

6. FaceApp

FaceApp is yet another application that uses AI and Neural network technology to create freaky, hilarious, yet most realistic face transformations.

[Know more about FaceApp – Its working and Reason Behind Popularity here.]

7. Youper

Available on both Android and iOS, Youper is a user-friendly Artificial intelligence app that caters to health and fitness problems. The application is based on cognitive behavioral therapy, encourages users to share their social phobia and get real-time motivating advice. And this way, improve their social interactions and lifestyle effectively.

8. Flo

Flo is also one of the popular artificial intelligence apps that is primarily used in the process of photo and video editing. The app comes with a built-in voice assistant that enables users to give a voice command regarding which footage to pick, and on what basis (on the basis of location, date, or tags). And eventually, merge them optimally with high-quality music.

The application also employs Machine learning algorithms that provide options like choosing the best moments, detecting faces, locations, and more.

9. Google Assistant

Launched in 2016, Google Assistant, an AI-powered virtual assistant, has made significant strides since its inception. It is available on a wide range of devices such as smartphones, headphones, and cars. Google Assistant supports both voice and text entry and uses natural language processing. This AI assistant performs a variety of tasks for its users like searching for a favorite restaurant, playing music, and even assisting them in obtaining vital information from Google.

10. Replika

Replika converses with her users as if she were a person. You can form an emotional connection, share a laugh, or get real with an AI that’s so good it almost seems human. Replika can help you understand your thoughts and feelings, track your mood, learn coping skills, calm anxiety and work toward goals like positive thinking and stress management. The app was initially created for iOS users, but it is now available for Android users as well.

11. Databot

Databot is a virtual artificial intelligence software for Android, iOS and Windows 10. It is also available on Android tablets, iPod, iPad, and windows phone. The app answers your questions and addresses the issues that are important to you. This intelligent mobile app design provides you with images, multimedia presentations, and information based on the topic of your interest. The app uses Wikipedia, RSS networks and other sources to give you the desired information. Databot can understand and speak multiple languages like Spanish, English, French, etc.

Now, as we have gone through some examples of artificial intelligence software for Android and iOS, let’s move to the features that make these apps a must-have element for enterprises to navigate the new-normal.

Prime Features of Intelligent Mobile Apps

1. Action-Oriented

The foremost characteristic of intelligent app solutions is that these applications do not wait for users to make every move. Rather, they study user behavior and deliver personalized and actionable results using the power of Predictive Analytics. 

In this way, they cut down the hassle of users and prompt them to take desired actions.

The finest example to understand this trait of Intelligent applications is Hound app. It shows the finest nearby restaurants, hotels, and more in real-time. This gives users a push towards making a booking, which might not have been possible if they were asked to make a search manually.

2. Data-Driven

Delivering a data-driven output is also one of the key features of Intelligent apps.

The intelligent apps gather data from different sources – be it IoT sensors, mobile apps, websites, beacons, or user interactions – and analyze it in real-time. A result of which is that you get the right results for almost everything, when asked.

3. Contextual

Since Intelligent mobile apps study users’ behavior and desires, they easily differentiate between which information is relevant and which is not. Because of this, these apps provide highly contextual and relevant information and suggestions to users.

 In addition to this, they notify users about the issues they might face depending on their current situation. An example of this would be Cleo application – an AI powered budgeting app that examines people’s expenses and notifies them of their spending capabilities. The downside of it though is that the cost of a cleo like app is fairly high

4. Adaptive in Nature

Backed by Machine learning algorithms, the intelligent mobile app design is adaptive in nature. Meaning, they can easily upgrade their knowledge as per their surroundings.

5. Exclusion of Keyboard Inputs

Since delivering high convenience is a core objective of i-apps, these applications also facilitate the opportunity to give commands without using keyboards. Meaning that these applications respond to commands given in the form of speech, gesture movement, or image also.

6. Omnichannel

Last but not least, delivering an Omnichannel experience is also one of the main features of Intelligence apps. 

These apps understand that users expect a common experience, irrespective of which platform they are interacting with. And thus, ensures that they get the same experience on all the communication channels.

If you want to know more about how apps can deliver an omnichannel experience, you can read this article here. 

With all such features, the emergence of intelligent mobile apps are paving the way to do business more effectively and profitably. They are enhancing the efficiency of the workforce, accelerating decision making process, gathering better insights, and thus, delivering long-term benefits. Something we will cover in detail in the next section of this article, related to the usage of intelligent applications in different domains.

Use Cases of Intelligent Apps in Different Business Domains

1. Healthcare

AI-based intelligent applications are transforming healthcare. These applications are bringing better opportunities for both patients and medical practitioners and simplifying their interactions.

These applications are easing the path to find the best nearest doctor, book a consultation, keep reminder of medication, getting a basic knowledge of each medication, and more. Whereas, it is helping doctors with remaining updates with real-time status of consultations, assigning duties to staff, ensuring the availability of equipment, maintaining a proper temperature for medicines, and more.

2. Fintech

Another industry enjoying the finest results from the usage of intelligent apps is Fintech 

The Fintech industry is using AI-based intelligent applications to woo millennials, alongside providing futuristic opportunities to all. These applications are analyzing the past and present expenses of users and providing them with better budget-managing tips. 

Also, they are giving insights about every stock’ history helping everyone with deciding whether to make an investment or not.

3. Education

These apps are bringing learners closer to the sources of knowledge. These apps are empowering students to make notes in the form of speech and images, along with text format. They are also providing assistance in determining which formula/principle to consider for solving a problem.

4. Restaurant

Intelligent apps are also opening new avenues for the food industry. These applications are not just helping users in locating the nearest restaurant, booking a table, and repeating an order, but are also delivering data based on user’s behavior to restaurant owners and helping them in giving a personalized experience to customers.

An effect of this is that various Entrepreneurs are putting efforts into making their restaurant apps smarter with the help of AI.

5. Hospitality

Hospitality is yet another industry that is making profits by the right usage of intelligent apps.

The Hotel staff is using AI-enabled intelligent apps to improve their customer experience strategy. They are using these apps to know their guests better, offer predictive personalized experience, give an option of self-service, use sentiment analysis for better marketing, and more.

6. Enterprise

When it comes to Enterprise, intelligent apps for mobile are proving to be a revolutionary addition.

These apps are helping the workforce with sensing the urgency of any task and allocating work to individuals accordingly. They are also aiding in the decision-making process and ensuring value generation before competitors.

Above all, these AI-enabled mobility solutions are giving a smart ecosystem experience to the workforce by providing functionalities like optimizing their work, access report files in real-time, performing micro-activities, and more.

Various business organizations in these sectors, which have already invested in mobile app development to introduce an i-app to their processes, are enjoying competitive advantage over others. Whereas, many other businesses are discussing how to get ahead of this growing trend towards intelligent apps.

So, to ensure that you do not lag behind, let’s have a walkthrough of how to develop intelligent mobile apps.

The Process to Create an Intelligent Mobile Application

When it comes to the development of intelligent mobile apps, the process begins with understanding the decisions that core business constituents must consider in support of their business operational objectives.

Once you create intelligent mobile models, it is necessary to focus on embracing key app technology and architecture potential while developing next-gen apps:-

1. Data lake that supports:-

• Real-time data gathering, indexing, and management.
• Enhanced data accessibility across the ecosystem.
• Safe and Secure data management services.
• Flexible data delivery options.

2. Data-as-a-Service that favors:-

• User self-service possibilities.
• Simple and Quick data discovery and exploration mechanism.
• Accelerated data management, monitoring, and tracking options.
• Integrated data alignment and transition services.

3. Analytic-as-a-Service that encourages:-

• Embeddable and Shareable analytics and Visualization.
• Real-time maintenance of data records for smarter decisions.

4. Intelligent app development process that comes up with:-

• Agile development possibilities with modern Devops and Paas techniques.
• Availability of analytic insights at the time of decision.
• Constant relevance of information.
• Delivery of insights in Omnichannel environment.
• Now, although we have discussed the prototype of developing intelligent mobile apps, the process is much more advanced and challenging. It is always better to hire a tech mobility expert to taste the future of mobile apps, i.e, intelligent apps.

Frequently Asked Questions about Intelligent Mobile Apps

1. How AI is creating an intelligent app ecosystem?

Artificial Intelligence is creating an intelligent app ecosystem by bringing the power of gathering real-time data, understanding user behavior, and performing predictive analysis in the mobile app domain, and eventually making better decisions. 

2. How to create an intelligent app suited to one’s business needs?

The process of building intelligent solutions depends entirely on the business needs and the mobile app architecture and tech stack that they consider. This includes focusing on:-

• Data lake,
• Data-as-a-Service, 
• Analytics-as-a-Service, and
• Intelligent App development process

3. How much does an intelligent app cost to build?

The cost of developing an intelligent app depends on the level and complexity of the app. Typically, AI integration will boost the cost of an app by anywhere between 20 and 40%.

4. What are the benefits of intelligent apps?

Some of the benefits of using intelligent apps are that they are adaptive, responsive and driven by data.  They also deliver a rich and personalized user experience.

Final Note

With the rise of new technologies like artificial intelligence,  the demand for smart and intelligent mobile applications is expected to rise exponentially. Intelligent applications make smart decisions. Thus, it ensures a personalized user experience with its adaptive nature. Besides, these applications have entered all the industries in this digital era. The user-centric design of the application makes intelligent mobile app development the future of mobile applications.  In case you want to develop one for your business, it’s advisable to hire a team of AI app developers. 

The post Intelligent Mobile Apps: The New Face of Mobile Industry appeared first on Appinventiv.

Ever since the advent of Artificial Intelligence, the fundamentals of Industries started to change for the better. Now, every other AI development company, irrespective of their industry-type, wants in on this one miracle of technology.

The extent of the popularity of AI is that, According to SemRush, the AI market globally is predicted to increase in the next few years, reaching a $190.61 billion market value in 2025.

Another AI 2021 prediction statistics shown by Gartner states that by 2021, as many as 15% of customer service interactions worldwide will be fully powered by AI.

According to artificial intelligence growth statistics, the global GDP will grow by $15.7 trillion by 2030.

That’s why companies are integrating AI technologies to achieve their business goals.

To witness the exponential growth and latest AI developments in industries like Medical & Healthcare, Banking & Finance, Transportation, Retail & Commerce, Social Media, Manufacturing, and Business, we have dissected the latest AI trends into specific industries.

Why Is Artificial Intelligence A Disruptive Technology?

The scope of artificial intelligence is vast and that’s why it is a key empowering agent for disruptive innovation that prompts game changing products and services ready to serve low-end or unserved purchasers and relocate to the mainstream market.

As automation gradually is turned into sophistication, there’s no doubt that AI is currently in the process of disrupting the industry and markets.

This disruptive AI technology is a main thrust behind the disruptive innovation that we are presently witnessing.

For instance, AI empowered the sharing economy as a new business model where people can share their own resources with others as administrations to utilize these resources and to create benefits. This model is effectively applied in various domains, for example, ride sharing, smart prototyping, smart grids, and so on.

What Are The Emerging Technologies In AI?

Generative AI

The latest innovations in artificial intelligence have permitted numerous organizations to create algorithms and instruments to generate 3D and 2D pictures automatically. These calculations basically structure Generative AI, which empowers machines to utilize things like content, sound, and images to make content.

Generative AI, the latest innovations in artificial intelligence can likewise help in medical care by rendering prosthetic limbs, organic molecules, and different things from scratch when activated through 3D printing, CRISPR, and different other potential advances. It can likewise empower early distinguishing proof of possible malignancy to more effective treatment plans.

For example, on account of diabetic retinopathy, generative AI offers a pattern based theory as well as constructing the scan and creating content, which can assist with informing the doctor’s next steps.

Federated Learning

As indicated by Google’s research paper named Communication-Efficient Learning of Deep Networks from Decentralized Data, federated learning is characterized as a learning method that permits users to altogether procure the benefits of shared models prepared from rich information, without the need to centrally store it. In more technical speech, it disseminates the ML process over to the edge.

Medical associations for the most part are unable to share information because of privacy restrictions. Federated learning can help address this concern through decentralization by eliminating the need to pool information into a single area and preparing in various cycles at various sites.

Neural Network Compression

Generally, a neural network contains far more weights, represented at higher precision than are required for the specific task, which they are trained to perform. If we wish to bring real-time intelligence or boost edge applications, neural network models must be smaller. For compressing the models, researchers rely on the following methods: parameter pruning and sharing, quantization, low-rank factorization, transferred or compact convolutional filters, and knowledge distillation.

With the increasing size of the deep neural network for carrying complex computation, the storage needs are also rising. To address these issues, researchers have come with an artificial intelligence future idea and technique called neural network compression.

A neural organization contains undeniably more loads, addressed at higher accuracy than are needed for the particular assignment, which they are trained to perform. If we wish to bring real-time intelligence or boost edge applications, neural organization models should be smaller. For compressing the models, researchers depend on the accompanying strategies: boundary pruning and sharing, low-rank factorization, and knowledge refining, among others.

 So, here are some amazing AI Technology Trends bearing witness to the opportunities it brings into these sectors, and also the impact of AI in 2021.

Technologies Used With AI

1. Internet of Things

AI in IoT is beneficial for both real-time and post event processing. In the former one, AI helps in identifying patterns in data sets and running predictive analytics. While in Real-time processing, it assists in giving quick responses to conditions and collecting knowledge of decisions about those events, for example, remote video camera capturing images of license plates for the parking payments. 

There is a whole world out there where integration of AI in IoT connects every device with each other to enable them to perform additional functions. 

2. Blockchain

Blockchain is another hyped technology which has created momentum in all industries. Now AI with Blockchain is simply the best of both worlds. It is so, for you to get benefits such as Better transactions, high-quality data, decentralized intelligence, lower market entry barriers, greater transparency, improved artificial trust provided by blockchain app development.

The impact of AI on Blockchain is such that we only expect this partnership to deliver us more remarkable technologies and features.

3. Augmented Reality

Thanks to Artificial Intelligence’s deep neural networks, it is now possible to detect vertical and horizontal planes, estimate and analyze depth and segment images for realistic occlusion, moreover, even infer 3D positions of things in real-time. It is due to such features and functions that AI models are replacing some traditional computer vision methods holding back amazing AR experiences.

Medical and Healthcare with AI

4. Medical Imaging & Diagnostics

One of the advantages of Artificial Intelligence in healthcare is Medical imaging; the process where images of the internal aspects of a body are revealed through a noninvasive process of imaging. This helps in diagnosing and treating disease. Smartphone penetration along with evolved image recognition is making mobile phones as an all-in-one tool for at-home diagnostics. We are also looking forward to FDA approving AI in Healthcare as a medical device.

5. Clinical Trial Enrollment

Enrolling the right set of people is prominent for the success of any clinical trial, and it is not, in any way, an easy process. However, with some assistance from AI Technology, it would become possible to access and extract information from the medical records and then compare them with the ongoing studies. This way, the studies suggested to doctors and patients would be more relevant and reliable. 

6. Improved Healthcare Biometrics

With AI’s neural networks, scientists are analyzing the atypical risk factors that were too complicated to quantify. AI in Healthcare wields the power to develop the industry in numerous ways such as by enabling retinal scans, examining and recording skin color changes, and whatnot. AI technology’s proficiency in finding patterns will make possible the unlocking of new diagnostic methods and anticipate unknown risk factors.

7. Efficient Drug Discovery

To put a full-stop on the tediously long drug discovery cycle, traditional pharma organizations are now placing their hopes and faith into new AI biotech startups. Though many startups are on a rudimentary funding stage, they are still swamped by a myriad of clients already. We can see the trend of investment in AI biotech by pharma incumbents continuing into 2020.

Retail/eCommerce and AI

8. Search Technology

One of the biggest benefits of AI―Search technology, is a boon for any business, perhaps it is why many industry incumbents have started to invest in it. Now that the contextual understanding of search terms is no longer in an experimental phase, its global adoption is still a long way. Nevertheless, numerous SaaS agencies are coming forth to deliver search technologies to third-party retailers, and investment in this particular tech stack makes it the latest AI trends of 2021.

9. Retail―Checkout-Free

Companies like Standard Cognition and AmazonGo are the very first players of the Check-out free AI system. Meaning, customers can shop without checking out or scanning the products. This is expected to keep tabs on theft and other problems. The adoption of this AI based system will depend on its deployment and other inventory loss costs due to technical glitches. As of mid-2019, there are 11 AmazonGo stores in operation, going cashier-less.

10. Logistics for Warehouse

Logistics is said to be the developing branch and recent trends in artificial intelligence. In warehouses of the future, logistics will play an eminent role, as the warehouses will be developed to accommodate not humans, but highly proficient robots who will be able to work 24X7 without the need of even basic facilities such as lighting.

{Bonus read: How Much Does On-demand Logistics App Development Costs?}

11. Peer-to-peer Networks 

AI Machine Learning requires enormous amounts of data to enable machines to make informed decisions. Peer-to-peer networks, like the ones deployed by cryptocurrencies, offer organizations of all sizes the advantage to run AI programs by channeling the power of networked personal computers. This network amalgamated with AI will promote transparency in search engines.

The global AI market size, i.e.  Artificial Intelligence in retail and e-commerce market size is predicted to increase at a CAGR of 42.8% during the forecast period 2019-2025 and acquire a revenue of $19.37 billion by the year 2025.

Government/Public sector and AI

12. Facial Recognition

Facial recognition is a dominant form of biometric authentication. Due to the extensive research in this area, this AI application’s readability and accuracy rate is improving expeditiously. One of the advantages of Artificial Intelligence’s Facial recognition feature is that it will help security agencies in identifying and removing rogue elements from society. Not just this, but businesses are rapidly integrating this AI feature into their apps and other solutions used in processes, according to their business model. 

13. Cyber Threat Hunting

Cyber threat hunting is a proactive approach combined with high-security functions, to detect stealth attackers and stop the malicious activities initiated by such hackers. One of the benefits of AI integrated with this technology is that by using machine learning, detecting such foreign elements and security breaches would become a piece of cake.

14. Surveillance via Computer Vision

Computer Vision is an area of AI research which is based on algorithms. A prominent use case of the facility can be seen in the Japanese machine learning algorithm, AI Guardman, which detects suspicious behavior of shoppers and also alerts the store owner on mobile.  Similarly, this computer vision added to the drones will help in surveillance of crowded places. AI in the Government sector is expected to rise in the coming future as new ways are about to be explored.

Transportation and AI

15. Self-driving Vehicles

Self-driving technology will increase AI market size from $54 billion in 2019 to a $556 billion market by 2026, growing at a 39% CAGR, as per a report by Allied Market Research. Moreover, by the year 2025, AI in transportation is estimated to deliver $173 billion in cost savings across the entire automotive OEM supply chain.

16. Traffic Management

AI Technology trends of 2021 also consist of the traffic management with the help of applications that predicted and detected probable traffic mishaps. This was accomplished by transforming traffic sensors into “intelligent” agents using cameras. A successful use case for this is Rapid Flow Technologies.

17. Smart Tracks

Smart Tracks is an initiative started by China, named the ART- Autonomous Rail Rapid transit. This doesn’t require any track as the train follows the virtual track made by painted dashed lines. This astounding artificial intelligence trends is predicted to spread in the coming years, globally.

18. Digital Number plates for Vehicles

Recent trends in artificial intelligence would interestingly enable the smart technology facelift to transport number plates. The digital number plates, created of course with AI technology, will help transport authorities on so many levels, from alerting authorities in case of accidents or use GPS for location detection.

Manufacturing and AI

19. Predictive Maintenance & Algorithms

Using the AI algorithms, manufacturers would be able to better predict unanticipated machine failures. This will save millions of dollars for incumbents. The predictive maintenance algorithms deploy constant data collection to forecast equipment failures before they happen. Due to dropped sensor costs, edge computing, AI advancements, and Predictive Maintenance are now more widely available for use. 

20. Computer Vision for Defect Detection

We have already discussed how computer vision will help in surveillance. In a similar manner, it will prove as a holy grail for manufacturing industries to supervise their production process and point out even the minutest of discrepancies in the products without the inclusion of human factor. The camera in computer vision is extremely precise in catching defects which are half the width of a hair. 

21. Collaboration of Humans and Robots

Though it sounds very futuristic, it is not so much. There will be more than 1.7 million robots deployed in factories around the world by 2020, as per some reports. They would soon work alongside human workers and enhance the productivity level. The AI-powered robots will be designed to take over manufacturing tasks and the human workers will be trained for higher positioned jobs in design, programming, and maintenance.

22. The advent of Quality 4.0

Quality 4.0 is a product of the integration of AI in Manufacturing industry.  This involves AI algorithms to notify the manufacturing teams about detected production faults, so that an appropriate action can be taken to stop the production of faulty products and save loads of capital. Said faults can be a deviation from preset recipe, changes in machine behavior, change in raw materials and more.

Gaming and AI

23. Improved Visualization Technology

Recent trends in artificial intelligence are the gaming sector. With the help of deep learning and incessantly growing data at the disposal, AI can improve visual quality in video games. The game environment and characters would become more realistic and seem natural like never before. AI has the quality to highly improve how characters move and express themselves to make the whole gaming experience all the more realistic.

24. Real-world experience with Voice Assistants

Voice Assistants, being a significant branch of AI, are now added to the games such as Destiny 2, where it helps the players access certain game features without accessing the game menu every time. Not just this, but it also offers a wide range of interactive games where the player will not be passive anymore.

25. Personalized Play for Each Gamer

After acquiring the data on individual game players, AI algorithms will enable the developers to create game worlds that are more individualized and reactive to individual players in-game. With future AI technology, we could access MMORPGs that guide players in the direction of quests and other players that compliment their style. A lot is possible in this area, and we are excited to see what will comprise the AI technology trends of  2021.

Business and AI

26. Elevating Customer Experience

As the customers are demanding more facilitated and developed services, businesses are looking to up their game as they certainly do not want to lose customers. Now, AI is taken as an effective technology which is helping several businesses keep their head out of the water. 

The future of artificial intelligence in Customer experience is being improved by using Chatbots, 24X7 support, Virtual assistance, and self-help VR systems, as natural-language processing improvises and learns more from the expanding data pool of past experience.

27. Advanced Hiring Process

AI technology solutions such as X.ai and ClearFit can assist recruiters in scheduling interviews and find the most ideal candidate for the job. Now, the recruiting process is not mechanical which is why AI is being developed by adding human factors such as empathy, personality and other human traits that will help the AI algorithm in analyzing and then selecting a candidate just like we humans do. Impact of AI on business is certainly evident and is only expected to rise by 2020.

28. Business Models Transformation

The future of artificial intelligence in business is changing the fundamentals of business functions. The transformation in this sector amounts to the integration of AI features such as Data Analysis, performance prediction, computer vision, and whatnot. With such incredible features at disposal, it is not long that the business organizations will find new and innovative ways to develop this technology into something new and set unparalleled future AI technology trends.

Finance and AI

29. Credit Risk Assessment

In the Finance sector, keeping records on customer data is sort of a second nature. Now, wouldn’t it be amazing if we could use all that data to examine a customer’s record and capacity of paying off loans and credit cards they already possess. AI in Finance is going to do just that and more. Machine Learning and AI are taking the place of a human analyst to determine the risks involved in the financing sector and also provide finance development solutions for them. 

30. Easy Fraud Detection

Fraudulent elements are one of the major concerns of the Banking and Finance Industry. To eliminate such risks, AI in Finance uses its machine learning and algorithms to recognise a pattern and if something odd happens, it can detect the discrepancy very easily. For instance, say that a credit card has been used in another country, right after it was used elsewhere, it will alarm the institution to take action. Moreover, it can be developed in a way to recognize what can be considered fraud and what not.

31. Safe Trading with Predictions

Like every other area, trading will also be facilitated with AI’s machine learning and algorithms combined with its other branches. By analyzing the AI market size and conditions from the past data in the cloud, organizations and individuals will be able to overcome unanticipated risks, be it buying or selling of stock and shares.

32. Auto Claims Processing

Many times, Finance companies are faced with a difficult  situation where they have to pay the customer for their insurance. To combat the fraud problem and make sure the genuity of the situation, insurers and startups are using AI to compute a vehicle owner’s “risk score”, examine accident imagery, and also keep tabs on driver behavior. 

Social Media and AI

33. Improving Social Network

AI plays a pivotal role in making social media platforms what they are. AI in Social media has shaped the whole industry since Facebook adopted AI in 2013. Now, be it finding a friend via neural networks learning to tag to image recognition, or identifying incorrect news, AI does it all.

34. Empowering Marketers

Marketers are leveraging the advantages of AI in Social Media, to understand and analyze the customers’ buying persona and their preferences. This will help them personalize products suggestions based on their buying habits and what drives them to make their decisions. 

35. Effective Audience Targeting

Marketers get yet another advantage of AI, which is to expand their campaign beyond linear segmentation and target audience similar to your current client base. Moreover, effective targeting is the trump card for paid ad campaigns across all social media platforms, as the activities of people are stored online. Leveraging the AI technology to access this data will provide them with useful insights on Social media usage, online user behaviors, etc.

36. AI-Supported Content Designing

It is no news that Content is the king in Social Media marketing and optimizing it with AI is the best thing that can happen to this industry. With AI tools studying brands’ pattern of posting content and its type, it can suggest what kind of content is demanded and which content needs to be optimized.

37. AI-Powered Chips become popular

If you thought about the future of artificial intelligence, then you have entered it. Artificial Intelligence depends heavily on specialized processors. Seeing the current demand of AI, chips are being manufactured and integrated with AI Technology to perform functions for which we need separate devices as of now. These chips will be utilized by enabling AI tools in them such as computer vision, speech recognition, and natural language processing. 

What Is The Future Of Artificial Intelligence?

After witnessing these incredible AI industry trends, we can pretty much surmise how enormous this AI technology is going to be and the inevitability of it immersing every sector of all industries. Even to date, businesses are using AI to develop next-gen mobile apps to enhance their customer engagement and eventually expand their business.

The disruptive AI technology and artificial intelligence future ideas that organizations are willing to invest heavily in when considering the guarantee it has shown up until now!. We at Appinventiv, we are helping organizations with our AI development company in USA to be at the forefront by implementing AI in their existing tech-structure to reap the benefits and advantages that it brings.

At this point, it is hard to imagine a future without Artificial Intelligence. As 2022 approaches, we are excited to see what awesomeness it will bring to the table.

Frequently Asked Questions (FAQs)

Q. What is the scope of artificial intelligence in the future?

AI industry trends is the latest in which companies are working, and it certainly plays a prominent role in our future. This technology will facilitate us on so many levels and in so many ways from enabling AI-Powered chips which will make travelling token free, to cashier-less shopping marts. You imagine it and AI will do it within a time span of a few years.

Q. Which industries will be affected by AI?

Because of the features AI comes loaded with, almost all of the industries are adopting this technology at their own pace and in their own ways. Industries, namely, Healthcare, Finance, Business, Manufacturing, Retail & eCommerce, Government and Public sector, social media, transportation, and Gaming are reaping the benefits of AI integrated with technologies like IoT, Blockchain, AR, and Cloud.

Q. How Artificial Intelligence Is Transforming Business?

With the advent of AI in business, its core fundamentals changed. Now, it is possible to hire personnel effectively without human involvement, offering better customer services and enhancing customer experience has really evolved by using chatbots and other AI features. Moreover, technologies like computer vision, performance prediction, and data analysis have added to the list of future of artificial intelligence and benefits that businesses are getting with the incorporation of AI technology.

The post Top AI Technology Trends For 2021 and Upcoming Years appeared first on Appinventiv.

Now the answer that the internet has to offer to which are the most profitable artificial intelligence startup ideas and other semantic questions is a mesh. One that sends entrepreneurs in the direction of value offering, competitor analysis, and pricing models. 

All ending with very few truly useful insights. 

Ensuring that we are not another non-useful dead end, let’s walk you through the exact sectors and categories which are and are poised to continue making money in AI. Ones that would give you a headstart of where your Artificial Intelligence startup ideas journey should start from. 

But before we head on to the stage of revelation, let us look at the market state of the technology which has unanticipatedly made its place in the Profit and Loss statement of various businesses across industries, across the globe. 

Here’s your answer to how big is the market highlighting the benefits of AI in business

AI Market Structure

The world wide web is filled with data and statistics talking about the many facets of Artificial Intelligence – its sub-technologies, use cases, adoption rate, etc. 

And one commonality that has time and again surfaced itself in all the researches is Artificial Intelligence Growth. 

The growth trip that AI is on for the past many years has made it a part of not just the many innovation-centric AI business ideas but also of the list of top tech trends. 

It is this value and growth based nature of AI that has made the technology a trump card tech offering of businesses looking for a competitive advantage and high revenue count. 

Now even though the present and artificial intelligence future have established it as a gold-mine-like technology that favors every industry and domain that invests in AI business ideas, there are only a few which make the most money and become successful AI business models. 

In fact, this preference is not just limited to which industry will make the most money through AI. The classification is present in multiple other categories, such as which country will benefit the most from AI, which the AI sub-categories would be most profitable, etc. But in order to understand the best business ideas using AI, it is important to know the two segments which generally dominate the AI landscape. 

Core AI solutions – These are business models that keep AI at the center. They cannot function without the technology

AI tools – These companies provide AI infrastructure and tools for other companies to use like IBM Watson or Google AI algorithms

Product or service enablers – These Artificial Intelligence business ideas revolve around using the technology to better the service or product. Take for example Netflix using AI to give recommendations to the users

Let us start with looking into the different categories individually, giving you – an entrepreneur – the insight into which industry or which country would deem the most profitable as AI startup business models. 

What Is the Industry Wise Impact of AI?

The total yearly value of AI across 19 different industries in the context of the global economy came out to be $3.5 trillion and $5.8 trillion. The number is around 40% of the total $9.5 trillion and $15.4 trillion yearly impact which can be enabled by using analytical techniques in its entirety. 

Even though the benefits of AI in business can be seen across a wide spectrum of industries, there are a few which are forecasted to generate the most revenue – ones that answer which is the best industry to make money in AI. And unlike what was presumed, the profit projection is not just limited to the Healthcare sector or to Artificial Intelligence in Financial Markets. There are a number of other sectors in line to gather their intelligence-driven profit. 

The figure below highlights the same, giving you a peek into which sectors would prove to be the best AI business ideas.

Taking the discussion back to the 19 industries that we mentioned at the start of this section, here are the industry-wise impact of AI, both in terms of the average dollar impact and the percent effect on the industry’s revenue. 

Which Countries Should Your AI Business Strategy Include?

The true artificial intelligence industry growth lies in the country it is operating in. 

A report by Accenture – Artificial Intelligence is the future of growth – studied the scope that AI carries in affecting nation-wide economies across the globe. 

Here are their findings –

* Baseline shows the expected annual economic growth rate under current assumptions about the Artificial Intelligence future. 

** AI scenario is the valuation that shows the expected economic growth once the impact of AI has been absorbed into the economy. 

While the graph highlights that the USA is the nation that is most inclined to be benefitted from the advent of Artificial Intelligence, other nations are also not falling behind. 

There are a number of nations that will be seeing the result of artificial intelligence’s impact on the economy. 

Other nations like Japan, the UK, France, and Germany have all individually made announcements around their AI strategies. 

For example,  President Macron announced that the French government would be spending around $1.85 billion over the coming five years for supporting the growth of the AI ecosystem, which would include the development of large-scale datasets. 

Companies like Google’s DeepMind and Samsung are committed to opening up new labs in Paris, while Fujitsu is also planning to expand its Paris-based research center. The UK also announced its plan to push $1.4 billion into AI, which would be used for funding 1000 PhDs in the AI domain. 

The crux of all these worldwide AI adoptions is that while the inclusion will be global, there are some nations that are most likely to get maximum profit for AI based startups compared to their counterparts

Which AI sub-technologies Should Your Startup App Development Process Include?

There are many companies operating in the world today that claim to be using AI in their services or applications. However, AI in its broader sense is simply an application that mimics the cognitive functions that humans associate with.

On a much lower level, AI is a programmed rule which determines machine to act in a specific way in specific situations. However, in revenue generation and true business benefitting sense, when we talk about Artificial Intelligence, we are mainly talking about its two main technology sub-parts – Deep learning and Machine Learning. 

There are other important AI technologies as well that are the drivers of the revenue that businesses and governments can generate through AI inclusion in their systems like Regression Analysis, Descriptive statistics, etc.  

Measuring the future of all these AI-powered technologies, a study by McKinsey Global Institute established that there are three technologies that will primarily be used in AI applications – Deep Learning, Reinforcement Learning, and Transfer Learning. 

Another study by Allied Market Research identified that Machine Learning will be one of the top revenue generators for an AI startup in the coming time, followed by NLP and Image Processing technologies.

Which Business Domain Will Make Money in AI?

It is not just the industries that AI is carrying a glaring impact on but also the individual business domains. 

Now although the impact of AI on business models is not limited to any or a specific set of job areas, there are a few that are expected to carry maximum value potential once incorporated with the power of AI. 

Here are the business domains that are able to make maximum money at the back of AI –

In case you are planning to offer enterprise-level AI solutions, covering these domains can have a positive impact on your revenue charts to a great extent. 

Which use cases will make money with Artificial Intelligence

A study by Tractica Research forecasts that the revenue count which is generated from the direct or indirect application of AI in a system is going to reach $36.8 billion by 2025. 

When we talk about the inclusion of AI in a system, what we are actually talking about are the different use cases that surround the AI in a business ecosystem. Here are the use cases of AI-based mobile app for startups that are guesstimated to generate maximum revenue for their parent company.

If you look at the graph closely, you will find that around 60% of the cases are based on Big Data while 40% surround object and image recognition. Meaning the companies offering either of the two services are poised to see a very steep growth in the coming time. 

What Are the Most Profitable AI Startup Ideas?

Healthcare startups

There are a number of use cases around inclusion of AI in healthcare setup. For all the three subsets of the sector – patients, doctors, and healthcare agencies, AI has proven to be a boon. Whether you look at AI backed automation in EHR, scheduling for doctors, or health tracking for patients, the role of AI in the domain is massive. 

Security solutions 

We are going to see a rise in security focused AI use cases in the coming time. Businesses are using the technology for tracking change in user patterns, abnormalities in user journey, and incorrect pin entries, etc. We are expecting that in the age of hacks and breaches, the combination of AI and security will also grab investors’ attention in the coming years.

Energy sector systems 

The data-driven energy sector will be focused on improving forecasting, efficiency, trading, and accessibility. The application of the technology in the energy sector will be around – electricity trading, intelligent power consumption, intelligent energy storage, energy storage facilitation, etc. 

Fintech startups 

The role of AI in the Fintech domain is in an expansive mode. Right from payments to fraud detection, the technology has found multiple use cases in the sector. 

Even with the abundance of use cases around AI in Fintech, the sector is still ripe for data-centric innovation. For an entrepreneur looking to enter the financial domain, now would be the right time to make the move. 

What Do These Economic Values Mean?

The fact that the Artificial Intelligence industry is set to offer monetary profits across a wide spectrum, is a sign that we are entering a golden era that will be based on Artificial Intelligence capabilities. 

Standing at today, it is safe to say that no matter what the AI idea of app development for startup is, the time to come will entitle you to monetary benefits. 

However, the different aspects that we read above, when dissected, appear to be carrying a theme. A theme that would define how the economic values are moving and where’s the money in AI.

Here’s the crux of the prevalent theme. 

• While global tech giants have the expertise to drive the AI movement and create a sort of rulership on the major chunk of money that the ArtificiaI Intelligence industry is bound to make, Artificial Intelligence startups are not lagging very behind too. 
• Startups that attend to the use cases which are cash cows of the Artificial Intelligent domain carry the probability to come across as winners. 
• The government bodies, like companies, are also now finding themselves competing in the AI race. While on one hand China and the USA have declared their intent to be the world leader in AI by the next 10 years, European nations are measuring the restrictions that their strong data privacy would pose. 
• Technologies wise, Machine Learning and Deep Learning have placed themselves at the top revenue-generating position. And this position has made companies that offer these services a gold mine which is going to make money for a very long term. 

Knowing which categories hold the answer to how to make money with AI is not enough. It all remains incomplete until you know how your AI startup would make money. And it won’t just happen with you launching a product or service belonging to the money-making industry. 

How Do AI Startups Make Money?

The only true answer to how do AI startups make money is the combination of their access to data sets and capabilities. 

For your AI project management practices to be truly successful, you will have to make three facets of your business very strong.

Data Sets

The success of your AI startup and the answer to how do AI companies make money ultimately depends on the data set that they work with. The more quantitative it is, the greater will be the playing field for AI engineers. 

But, gathering data is not easy. Everyday security breaches like the Facebook-Cambridge Analytica instance have made it all the more difficult for a startup app development company to ask for data. 

The solution to this crippling restriction is a partnership. Several tech giants are known to partner with hospitals or payment companies or even enterprises to get data for them to then analyze. 

Domain Expertise 

More often than not, insights that create a breakthrough innovation comes from having an in-depth idea of the industry or domain. Only when you know an industry inside out, you will be able to identify areas where AI will create a breakthrough and get the answer to how to make money in AI. 

Skilled AI Talent

Your team of AI engineers and researchers will ultimately drive your AI business. The more skilled your associated manpower is, the greater the chances of you delivering intelligent services to industries that will make money with Artificial Intelligence. 

Now that you have the exact know-how of how to make money in AI, it is time for the Next Step. 

The step that will bring you on the path of earning guaranteed high revenues and business popularity. Get in touch with our AI engineers to start your AI journey. 

The post A Comprehensive List of Artificial Intelligence Business Ideas appeared first on Appinventiv.

More and more businesses are showing interest in understanding the basic mechanisms of AI and ways to use the technology for enhancing customer engagement and experience. 

But, is technology really effective? And can it really make a difference in upgrading your customer experience strategy? How are companies integrating AI technologies to achieve their business goals?

Let’s find answers in this article – starting from the very basic, i.e, why you should pay attention to Customer experience.

Table of Content

1. Why Should Businesses Focus on Customer Experience?
2. Role of AI In Customer Experience
3. Different Industries Delivering Higher Customer Experience with AI
4. Future of Artificial Intelligence in Customer Experience
5. Steps to Use AI for Delivering Better Customer Experience
6. Other Technologies that are Innovating Customer Experience in 2019 & Beyond
7. Frequently Asked Questions (FAQs) about AI in Customer Experience

Why Should Businesses Focus on Customer Experience?

“Customer Experience is the new battlefield” –  Chris Pemberton, Gartner

With people understanding the difference between User Experience and Customer Experience, the latter term is becoming the key to unlock unparalleled opportunities in the business market. It has become imperative to the process of understanding your customers and planning a marketing strategy using these insights to give a personalized experience. Thus becoming imperative to gain higher success in the marketplace.

And this can be clearly proven from the following statistics:-

Now as we have taken a glance of why to focus on customer experience, let’s jump directly into where AI stands in all of this. What does AI means to the CX world in 2019. Or better say, what are the advantages of using Artificial Intelligence in your Customer Experience strategy.

Role of Artificial Intelligence (AI) in Customer Experience

1. Know Your Customer

One of the foremost reasons why you should use AI to improve customer experience strategy is that it serves you with ample of real-time user data. It helps you gather and analyze user data in real-time and in this way, enable you to remain familiar with the change in their behavior and expectations.

2. Simplicity, Efficiency, and Productivity

Another reason for using AI to improve customer experience is that it adds simplicity, efficiency, and productivity to the business processes.

The technology, in the form of Chatbots and self-driving software, automates repetitive processes which means the efforts and time required for performing repetitive tasks cut down to a half. 

It also gathers and analyzes the user data in real-time to help you introduce the features and concepts that they want and in the way, they wish to interact with. Moreover, the inclusion of AI in quality assurance helps you to design an innovative mobile application with a higher scope of efficiency and simple structure.

Besides, these AI-powered bots and platforms perform most of the routine work and give the workforce an opportunity to perform other productive tasks. 

3. Better Decision Making

Artificial Intelligence is also acting as the right companion for business in terms of decision-making process. The technology looks into the user interaction history as well as the current market trends, which makes it easier for businesses to predict future. This eventually provides them with a clarity of what feature/functionality to introduce in their business solution for gaining a huge momentum in the market.

4. Streamline Purchase Process

In the present scenario, various customers add products into their cart but never proceed due to slow loading, complicated check out process, and more. Artificial Intelligence, in this context, helps in understanding the challenges faced by the customers and deliver a seamless purchase experience – something that helps businesses to lower down app cart abandonment rate. 

5. Fraud Detection

One of the prime uses of Artificial intelligence in finance, retail, and other industries, in terms of customer experience, is that it helps in detecting fraud. The technology, using its potential to gather, store and compare user data in real-time, is making it easier to identify any change in the actions of users, and thus, helping with taking a timely preventive measures against frauds.

6. Customer Analytics

Artificial Intelligence is also showing a remarkable significance in the customer data analytics process. The AI-enabled tools and platforms are simplifying the process to gather a heap of user data from different sources and arrange them effectively as per the key factors.

Furthermore, Artificial Intelligence is making it possible to predict the context of user interactions and build better customer engagement strategies using the right use cases of the technology and insights gained from the data quickly and precisely.

[ALSO READ: ThoughtSpot raises $248 M in Series E round to speed up AI-driven data analytics process]

7. Self-Service

Many customers these days prefer doing everything on their own rather than hiring an agent or taking help from any machine. This is yet another reason why investing in AI is becoming the need of the hour.

Artificial Intelligence, as we already know, provides you with valuable insights about where customers get stuck and what doubts/queries make them connecting with your support team. Using these insights, you can provide users with some options or FAQs that gives them a feel that they have find out the solution to their problem without any interaction, or better say, on their own.

8. Visual, Text, and Voice engagement

AI-powered platforms are also providing the opportunity to deliver optimal customer experience to the targeted audience based upon their voice or facial expressions.

The technology, using Facial recognition and Virtual assistants, is making it easier to get an idea of the users’ emotions and sentiments at any particular time, and identify ways to deliver an instant positive effect to them through offers or refunds, etc.such that businesses gain long-term profits.

9. Predictive Personalized Experience

Last but not least, AI is making it easier for startups and established brands to analyze the user interaction history and predict their next move and hence, use the information gained to provide them with a perfect marketing offer. And in this way, gaining higher customer engagement and profits.

While this is all about how Artificial Intelligence (AI) improves Customer experience in general, let’s figure out what the technology mean to different business verticals and their customer experience efforts in 2020 & beyond.

Different Industries Delivering Higher Customer Experience With AI

1. Retail

When talking about industries that AI is transforming, the very first business domain that comes into the limelight is Retail. 

The technology, using a heap of transactional data and machine learning, is making it possible to track and analyze purchase history and behavior of customers, which in turn is helping with determining when and what promotional offer/message to be delivered for getting attention of customers and thus, gain higher ROI.

A clear evidence of the impact of AI in retail is that, as per a survey of 400 retail executives by Capgemini, it was highlighted that the technology will save around $340B annually for retailers by the year 2022.

The survey also revealed that the use of Artificial Intelligence in Retailing customer experience has resulted in a 9.4% increase in customer satisfaction and a 5.0% decrease in user churn rate. An example of how brands are focusing on the usage of AI for bettering customer experience can be seen in Nike’s acquisition of Celect for predicting users’ shopping behaviour.

2. Healthcare

AI is transforming healthcare in different ways – with customer experience being on the top. 

The technology is proving to be the nervous system of the healthcare user experience ecosystem by making it easier to analyze the patient health history and come up with medical treatment (or surgery) that offers higher chances of success. 

It is also helping healthcare organizations in providing the best assistance to every patient in the form of Virtual Nursing assistants and thus, taking care of everything – right form notifying about the medicine intake timings to sharing real-time health data with the corresponding doctors.

An impact of this is that the AI health market is predicted to cross $6.6B by the year 2021, with a CAGR of 40%.

3. Entertainment

AI and its subset, Machine Learning are also leaving no stone unturned in delivering exemplary customer experience in the Entertainment domain. Clear evidence of which is Netflix.

The Entertainment platform is able to get a clear idea of the user behavior, needs, and expectations, and thus, showcase personalized options onto the screen. This is improving the customer retention rate as well as boosting customer loyalty – eventually resulting in higher profits.

To know further about the use of Artificial Intelligence in delivering impeccable customer experience on the Netflix platform, check out this video:-

4. Mobile Banking and Finance

Artificial Intelligence is also revamping user experience in mobile banking and finance apps. The technology, in the form of Chatbots, is providing 24×7 assistance to users and helping them in determining the right financial plan for themselves. It is also detecting and lowering down the risk of fraud in the processes – ultimately resulting in better customer engagement and retention rate.

As we have covered in this article so far, Artificial Intelligence is helping industries in revamping customer experience one way or the other. But, will this continue to happen in the future also? Will AI be a part of customer experience in upcoming years?

Let’s look into what is the future of AI in the field of Customer Experience to find definite answers to these questions.

Future of Artificial Intelligence in Customer Experience

The AI market has grown exponentially in the past few years. Over 1,500 companies including Microsoft, Google, IBM, and Amazon have invested their efforts into developing next gen apps for delivering higher customer experience and it is expected that many more will join the bandwagon. Many more companies will trust the AI’s ability to boost productivity and reduce the time and cost involved – something that can be predicted from the statistics shared below.

The technology will revolutionize the future of the business world and the customer experience in numerous ways, such as:-

1. It will automate routine work and encourage humans to focus on creative things. It will help pay attention to their vision and not on every minor detail of production.
2. It will make the business-customer interactions go from ‘one click’ to ‘zero click’ – giving a seamless and timeless experience to the target user base.
3. AI will also leave a significant impact on connectivity networks. It will encourage the idea of pattern analysis to troubleshoot any problem, pull out important user information from multiple channels to quickly and effectively get an idea of what users need.
4. Above all, Artificial Intelligence will also put the practice of gaining biased data to an end, eventually resulting in a better quality of information gained.

Now as we have covered what, when and how Artificial Intelligence drives customer experience, it’s the best time to head towards how businesses can integrate this technology to gain better insights and improve customer experience in 2020 and beyond.

Steps to Use AI for Delivering Better Customer Experience

1. Design a Customer Experience (CX) Strategy

Before looking into how AI improves customer experience, it is necessary to have a clear understanding of your CX vision and strategy. So, bring your team on board to discuss your ‘CX-based’ expectations and ways you follow to meet those expectations. And, based on the insights gained, create/update a robust Customer Experience strategy.

2. Plan and Analyze User Journeys

Right from discovery to pre-sales, sales, customer support, and beyond, a user connects with your brand at different touchpoints and platforms. So, invest your time and effort into getting a comprehensive knowledge of all those connecting points, and deliver an AI-based omni-channel customer experience.

3. Have a Clear Understanding of AI solutions

The first step of AI project management lies in understanding that the technology can be used in different forms to improve customer experience strategy, such as Recommendation engines, Virtual assistants, Predictive search engines, Computer vision, Sentimental analyzing tools, etc. However, not all can be the right fit for your business needs and expectations.

So, the next step to employ AI in your Customer experience strategy is to determine what all forms of technology can be integrated into your business model.

4. Decide Whether to Create/Buy AI solutions

When talking about how to improve customer experience using AI, the next step to consider is to determine whether to integrate AI in your existing application or invest in a pre-made CX/AI solution. 

Here, the former one will be the right option for your business, if you have a well-qualified AI expert team in-house or have a partnership with the right AI specialized mobile application development agency. Whereas going with the latter option can be a profitable deal when you have less time to develop an application and the vendor understands your customer issues and has the caliber to focus on critical points.

5. Track and Measure Success

Lastly, taking the backseat just after incorporating Artificial Intelligence in your CX strategy is not enough. It is imperative to keep a watch on key performance indicators (KPIs) and metrics to track the success ratio of combining Artificial Intelligence (AI) and customer experience. And hence, improve your strategy for a better future.

ALSO READ: Key Metrics to Evaluate Your Chatbot’s Performance

While this is all about how the use of Artificial Intelligence in Customer Experience can bring better outcomes and what steps to consider for implementing it in your strategy, let’s take this conversation further by exploring other possibilities.

Or better say, let’s look into what all other technologies can aid in the process to improve customer experience strategy in 2019-2020 and beyond.

Other Technologies That Are Innovating Customer Experience in 2019-2010 & Beyond

1. Internet of Things (IoT)

In 2019-2020, the number of connected IoT devices will reach 26 billion. Besides, the 5G technology will become more significant in the market with high-speed, lower latency, and other such features. 

This will open new doors for universal connectivity – making it possible for the companies to find better insights to understand customer behavior and lifestyle and thus, come with valuable data points and strategies to deliver memorable customer experience.

Or better say, it will help companies to work with facts and not just assumptions about customer needs and expectations, and eventually redefine their Customer experience strategy.

2. Machine Learning

With a rise in IoT-based solutions, the volume of data points will also increase gradually. Clear evidence of which is that there will be around 45,000 Exabytes of data volume in the market the year 2020.

Now, with an increase in data volume, the process of gathering, optimizing, and operating data will become a challenge – something that Machine Learning will help with.

Machine learning, with its self-learning algorithms, will enable companies to perform better actions on the data and find new approaches to improve customer experience.

3. Blockchain

Blockchain is also acting as a catalyst in the process of improving customer experience. The technology, with its key features like decentralization, transparency, and immutability, is making it possible for companies to store user behavioral and demographic data on blocks securely, make them portable and letting users decide with whom to share their immutable details with. The technology enables users to know what exactly is happening with their personal information and thus, experience a sense of security and trustability throughout the process.

ALSO READ: Blockchain and AI: What Happens When the Technologies Merge?

4. Voice Technology

Not only Artificial Intelligence, but Voice technology will also be seen playing an indispensable role in improving customer experience. 

The technology, in the form of Voice search and Digital assistants, will continue to help businesses in delivering a faster, seamless and flexible experience to their target audience. It will enable businesses to engage users in a profitable manner and facilitate them with better actions.

And this can be proven by a study by Pindrop, which states that around 28% of companies have already embraced voice technology in their CX strategy while 57% are planning to deploy in the next one year. Also, another 88% believe that voice technology will give a competitive advantage in enhancing user experience.

5. AR/VR

Lastly, AR/VR is also one of the technologies that are reshaping the world of customer experience. 

The technology takes users to the virtual world and enhance their customer journey effectively. It presents feedback form in different ways and increases the chances of getting a positive reply. And above all, it helps in product testing by exposing user/product to different situations and places. 

With this, we have covered all about the process and use of Artificial Intelligence in Customer Experience. We have also unveiled what is the future of AI in the CX world as well as what all other technologies will disrupt the world of Customer Experience.

If you still have any doubts, feel free to check the FAQs shared below or directly get in touch with our AI mobility experts.

Frequently Asked Questions about AI in Customer Experience

1. What is the Role of AI in Customer Experience?

AI plays a crucial role in improving customer experience in the business domain in terms of automating repetitive tasks, streamlining processes, reducing the risk of fraud, and above all, delivering personalized options to every individual.

2. Why use AI to improve Customer Experience?

Artificial Intelligence, with its power to gather and analyze customer data in real-time, is helping in getting a better understanding of customer behavior and needs, and eventually creating a personalized customer experience strategy.

3. How AI and Machine Learning are improving Customer Experience?

AI and Machine learning are enhancing customer experience in multiple ways, including streamlining shopping experience, reducing the risk of fraud, and delivering personalized marketing schemes.

4. How to Start using AI to improve Customer Experience?

There are four steps to start using AI to improve customer experience:-

• Design a Customer Experience (CX) Strategy
• Plan and Analyze User Journeys
• Have a Clear Understanding of AI solutions
• Decide Whether to Create/Buy AI Solutions
• Track and Measure Success

5. How AI will shift Customer Experience to the Next Level?

AI will bring a drastic shift in Customer experience in the future in the following ways:-

• It will encourage users to focus more on their vision and creativity, rather than looking into minor details of production.
• It will turn ‘One Click’ experience to ‘Zero Click’, providing target audience with a quick and seamless experience.
• It will improve connectivity networks.
• It will encourage the idea of gathering and employing unbiased society data and deliver quality to all.

The post How AI can Improve Customer Experience Strategy? [2019-2020 Guide] appeared first on Appinventiv.

Designing, almost in all its different forms is driven by keeping the human part of process at a much higher ground than the analytical and data driven side. While there are some domains like CAD design or Product Design that leaves some space open for machine learning to enter, when the design form in question is mobile app design, the gap seems to become negligible.

However, Artificial Intelligence, like a number of other industries have found a place in the Mobile App Design vertical as well, giving birth to the concept of artificial intelligence user interface design. A concept that is ought to bring a new level to the relationship between artificial intelligence and customer experience.

While, the answer to whether machine would replace designers is next to impossible, there are ways that the designer community has started taking AI user experience together in their journey to designing memorable mobile apps in multiple ways, like –

• Getting time-taking manual works like image resizing automated
• Making designs localized by taking help of AI based translation
• Bring system consistency between users and products
• Give insights into which elements are users interacting with, which needs attention

This participation that the deisgning industry is witnessing coming in from the AI driven UI domain is something that is showing to have a huge impact on the industry’s present, while paving the way to a world where AI and the future of design is much better linked.

Now that we have seen the impact that AI carries on Mobile app Design and how it is soon becoming one of the proven tips to enhance mobile app design, the next step is to look at the principles that guide their unison in the domain of designing AI experiences

The Guiding Principles that Combine Mobile App Design with Machine Learning

Develop a Shared Language

Elements like user experience review, product vision, and business goals are something that needs to be understood and shared by the complete team. You would only be able to create a meaningful and truly intelligent user experience if the mobile app design and machine learning development methods complement each other through shared concepts and common language.
The machine learning experts and user experience designers should come together to develop a common blueprint which includes data pipelines and user interfaces, with the aim to set a blueprint that grounds the team’s product planning with the users’ reality.

Focus on Use Case

The important thing when developing a consumer facing app, as the top software designers would tell you, is not the technology that backs it but the business goal and the user experience that you plan on achieving. And so, it is extremely important that you crystallize the use case.
With a separate focus on the use case, you can then put your intricate attention on the user flow, which then allows the team to identify the main points where machine learning can be added to enhance the experience.

A clear understanding of the use case also enable teams of the mobile app design company to determine the right KPI for the development of user experience program, which in turn is aligned with machine learning metrics.

Mix Quantitative and Qualitative Data

In order to understand the true impact of combining the machine learning solution and user experience design, it is important that both qualitative and quantitative data is considered. You should make use of qualitative research methods like questionnaires, interviews, etc to measure how the users are experiencing your app.

The reason why we are emphasizing on using a combination of quantitative and qualitative data is because when designing a new app, it is possible that you meet unexpected factors that affect machine learning developmant and user experience. Factors like: Effectiveness of feedback loop, ability of data point capturing intention and user behaviour, which are must to know parts of Artificial Intelligence app design can best be answered only after a deep consideration of both the data types.

Bring Your Combined Data to Real Life Setting

How do you make sure that machine learning is actually used to develop comprehensible and fluent user experience? By setting up an end to end solution that shows how machine learning and user experience fit together in real world.
An MVP that includes the working data pipeline along with the machine learning models makes it easy to iterate the AI assisted design together and helps in getting a direct feedback from the users via beta or user testing.

When both UX designers and Machine Learning experts of your partnered AI app development company share the understanding of product design issues, iteration is productive and fast. While on the other hand, user experience designers become aware of possibilities that surrounds machine learning: when it can be used to improve the user experience and how.

Be Transparent About Collecting Data

Designing for AI and with it, needs a constant effort and for it to be absolutely on point, it is important that you give a special focus to the data you have collected.
It is very important to consider the end user side in this cycle of collect data – convert data into information – iterate design.
Tell users that their data is being used to feed the AI and give them the option to alter the collected information in a way that the best context comes through. In addition to giving users the option to change what data is collected by the AI, you should also give them the option to change what the AI learns – to ensure that the predictions are what the users desire.  

While these principles that we just saw help in giving some clarity into how the combined AI and UX design should function, let us look at how some of the famous designing and editing tools that are backed by the developers community across the globe are using the technology to offer better mobile app user experience.

Tools That Use Artificial Intelligence for Design

Tailor Brands

The Tailor Brands logo maker is a famous product used by businesses to get professional logo in a small budget. The AI designs are built upon with your input coming in form of information that would be entered in logo.

Adobe Photoshop

The Select Subject functionality that Photoshop offers make use of AI for memorizing the shape, and then shifting, changing, and editing them with much ease. The tool works on an internal AI system known as Sensei that enables changing backgroungds by recognizing the different subjects in the image.

Prisma and Deepart

Both the famous image editing tools/AI design software make use of artificial intelligence for identify the different aspects of your video and photo and transforming them in a style of your choosing. They give you the option to work around filters and colours among other things.

Let’s Enhance

One of the most frequently arising issues in the designing industry is low quality images. Let’s enhance, powered by AI improves the quality of images using three filters.
Anti-JPEF filter converts image to high quality PNG while Boring filter scales up image to around 4 times without any compromise on the image quality. Magic, the third filter allows you to add detailing inside the image.
Making Artificial Intelligence a primary part of the Mobile App Design process is something that comes packaged with several add on factors that have to be considered to ensure that that User Interface and User Experience is intact.

And this in turn is not an easy process.

Packaging your app’s user experience with Artificial Intelligence in a way that the whole process gets translated into Artificial Intelligence design patterns calls for a lot of homework, which in itself is heavily dependent on the information that the users provide with consent.

If you are just starting with making your designs smarter, there are some UI patterns that would help you start on the intelligent journey.

A. Criteria Sliders

A number of apps use machine learning algorithms to predict an outcome or pass recommendations. A criteria slider comes in handy here for it helps userss adjust and then fine tune recommendations on the basis of criteria that is meaningful to them.
Here, you will have to ensure that the criteria that the users are manipulating with is mapped correctly to data which the machine is using in algorithms.

B. Like and Dislike Button

A simple like and dislike button help better the user experience that someone shares inside the application. Wwhen you ask users to feed in their experience even through a simple like and dislike button, you give them the option to not just build upon the recommendation system but also give feedback on what they don’t like and why.

C. Confidence Inducing Tips

More often than not, users not just not know how the whole prediction and artificial system works, but also they don’t know how much confidence they can place in the system. When you ask users to feed in their data or anser questions in return of something – better matched clothes choice, next show to follow option, etc.
The confidence quotient increases even more when you give users the result and let them approve or disapprove it. Doing this makes your users in charge of the charge – something that automatically instills confidence in the app.

D. Give them an In and Out Option

Not all users would want to feed in data for you to fetch and feed in the artificial intelligent system or even want to take the smart route. So, give them the option to opt in and out of the smart options as and when it suits them.
Doing this, they would not just have a more positive outlook towards your app but also, wknowing that they have an out option, they will be more willing to add in their data in the future.

Now that you have seen the ways AI powered UX is impacting the app design industry, the guiding principles of designing for AI, tools that are already using AI, and the UI patterns that you should add in your design manifesto to make your users open to the idea of AI, there is only one last thing left to do.

And that last thing is to make AI an active part of your mobile app design process. Let our team of UI/UX designers help you with that.

[Also Read: How We Solve AI Development Issues?]

The post How to Use AI to Design Better Mobile App User Experience? appeared first on Appinventiv.

The AI impact on business can help you streamline your processes across all domains in a way that every outcome is real-time and efficient. 

While the world is filled with instances of how Artificial Intelligence can transform even the most traditional of the areas like Education or Real Estate, the general impression that has gotten created is that AI is for the industries and businesses that work in multi-million revenue cycle and a team size of hundreds of employees divided into the fifties of the team. 

But, what we aim to achieve here today is that AI is not just a rich man’s dream of expansion, it is something that businesses of all sizes can employ in their process and make themselves more efficient and over time add them into the league of the leaders – a reason why you should start your search for AI development companies.  

In this article, we will look at what AI’ impact on business is, how AI can fit into every single domain of your business and make it better.  

Let’s begin and help you understand what makes AI the need of the hour for making businesses become efficient and intelligent by looking into the role of AI in business domains.

But before we look into how AI helps businesses, let us look into a holistic level view of the benefits of artificial intelligence in business. 

What Is Leading To The Growing Impact Of AI On Business?

• Customer and market insights – Whether it’s coming from your system matrix, social media, or web matrix, there is no limitation of the data about your customer and market. The data act as raw materials needed to make AI systems efficient, which in turn helps make crucial decisions about your business and product marketing. 
• Process automation – Automation of tasks that takes up crucial manpower resources is one of the biggest AI benefits in business. Across industries, businesses have been using automation capabilities to not just lower the workload on their employees but also prevent the chances of data entry issues. 
• Better customer experience – The role of AI in business goes beyond automation and process efficiency. The technology plays a huge contributory role in bettering the customer experience. Some of the benefits of AI in business in the customer experience front can be seen in:
• Personalization 
• Streamlining of the purchase process 
• Fraud detection
• Effective self-service 
• Real-time text, visual, and voice engagement

Now that we have looked into the many ways how AI can benefit businesses and why should businesses hire AI developers on an asap mode, the next part is to dive into the answer of how AI is changing businesses across domains. We will be looking into the use of AI in business across HR, Finance, Operations, and Marketing processes. 

Impact of Artificial Intelligence on Human Resources and Recruitment 

Talent Acquisition

The artificial intelligence impact on business most visible in the field of talent acquisition as compared to any other field. Artificial Intelligence is being used in a number of areas falling in the Talent Acquisition superset like – sourcing the candidates, screening their resumes, employing chatbots to engage with the candidates, and then using face recognition AI-powered software to recognize the emotion that the candidate is showing. 

Employee Engagement

With the advent of NLP, chatbot technology, and sentiment analysis, it is now a lot easier for companies to analyze and get real-time feedback from their employees in terms of taking the right action. All the while answering how can AI be adopted in business. 

Talking of employee engagement, one of the biggest concerns that employees tend to show on this front is in terms of them meeting their reporting managers once a year to discuss how the work is going and discuss their performance. A solution to this concern is given by Peakon, an AI-based software that enables all employees to reach their fullest potential. 

HR Management

There are a number of AI products in the market today to help HRs in the management of administrative tasks. Personnel teams around the globe are now using Chatbots to answer employee queries and using Big Data to develop employee schedules, which in turn are helping businesses with prediction and meeting of demand via fair and effective staff rotas. 

Career Management

When talking of usage of AI for learning and development, there are a number of applications that come up – career pathing, personalized training recommendation, coaching delivered by the chatbots, and manager development led by real-time feedback from the team. 

An example of AI for bettering learning and development can be seen through Gweek. The platform helps improve the presentation and communication skills of the users. There is another site called Sidekick that enables confidential coaching for the employees through the medium of messaging platforms. 

Performance Management

Because the AI-driven assessments happen instantaneously, in real-time (with algorithms monitoring the quotas, targets, and how they are varying on a day to day basis for every employee), it becomes a lot easier to note all the good and the poor performances instances, ultimately helping in the correct measure of performance and giving an answer to how will AI benefit business internally. 

The real-time access and monitoring also help in flagging the shortcomings on a per-day basis, enabling businesses to take action before a problem worsens. 

Impact of AI on Marketing and Sales

Research and Development 

Artificial Intelligence comes loaded with the ability to develop a deep understanding of a range of different industries and customer bases. By gathering and analyzing the humungous amount of data that floats about a business and market helps businesses research issues and build solutions that weren’t thought of before. 

In addition to automating tasks, AI can open avenues for new discoveries, methods of product improvement, and finding ways to accomplish a task better. 

Customer Support

The most common answer to how is artificial intelligence used in a business environment lies in Chatbots. Deep Learning powered AI-powered Chatbots allows businesses to access the layers of data from the neural networks such as customer data and information, which have been built up over time. 

When filled with real-time access to the preferences that customers come with and their purchase history, gives chatbots an edge over the human counterpart. 

Content Creation

There is a chatbot for every content marketer who is bored of developing the monthly content across spreadsheets. There is a software, Wordsmith, which is known to transform a series of structured data in the written doc, with great success. 

While at present the use case of AI in content creation is limited to articles having a rigorous format, there are a few agencies that have used platforms like Wordsmith for the development of content for fantasy football drafts. 

Heightened User Experience

The answer to why is artificial intelligence good for business lies in this one statement – Your customers and consumers are a lot more demanding than they were ever before. They need a huge amount of products, information, and services – all at a real-time and lightning speed mode – all in one place. And when using AI, you offer all the services in one place in an instantaneous mode, which ultimately helps in elevating your users’ experiences. 

Another thing that AI makes possible is Personalization. By incorporating artificial intelligence in mobile app development, businesses get a chance to study the customers’ preferences. AI technology makes it possible for marketers is to send personalized content in time and space that suits the customers. And there is nothing more user experience elevating in the world of marketing than personalization. 

Emotion Recognition

The capability to identify human emotions is known to be the biggest challenge for an AI – a question that is solved by the answer of how is AI helping businesses. When an AI-powered Chatbot when backed by the NLP facility can gauge when someone is getting frustrated and can adjust its offering and tone automatically – by either giving them a discount or forwarding the call to a human consultant.

Sales Forecasting

Prediction is a forte of Artificial Intelligence. By analyzing the past sales performance and trends, it can help predict what would the sales figure be this time around or what deals would work and which won’t. Ultimately, an inclusion of predictive AI in app development made around the sales domain will help the sales professionals plan their tactics between upselling and downwelling based on qualified data coming in from their past trend. 

Optimization of Lead Generation

Rather than someone from your sales team going through the potential clients’ on Google, or social media, you can incorporate Artificial Intelligence to review them for you. 

In addition to reviewing the prospects, you can use AI for two more crucial sales tasks: A. To identify the right job title and brands to target and B. To analyze sentiments made on competitor’s sentiments made on email or social media. It means that AI-powered intelligent mobile apps can help identify the unhappy set of clients while giving you insights into whether the client is unhappy with the service they are getting from the competitor or are seeking something new from the market. 

When you get a database of prospects who are unhappy with the service they are being offered by someone else, the probability of your closing the deal by offering them what they need, increases by manifold. 

Impact of Artificial Intelligence on Finance

Accounts Payable

There are a number of AI-based invoice management system that makes invoice processing a lot more streamlined because of the digital workflows which are implemented. To achieve this, the machine learning algorithms are designed in a way to learn accounting codes that are appropriate for invoice creation and management. 

Supplier Onboarding

Through the combined power of Artificial Intelligence and Machine Learning, AI development companies can help businesses shortlist suppliers on the basis of their tax information and credit scores and set them in systems without any human intervention. 

Procurement

The purchase and procurement process of organizations are generally filled with a lot of paperwork and make use of different files and systems which more often than not are not compatible with each other. By taking the help of the combination of APIs and AI to integrate and process the unstructured data through AI powered mobile apps, the procurement process will become a lot more paperless and would call for less human intervention. 

Audits

The digitalization of the audit process helps in increasing security by allowing a digital trail of when the file was accessed and by whom. 

Using Artificial Intelligence, auditors will get real-time access to the digital files, thus removing the need to search the file cabinet for documentation – something which not just lowers the time gap in getting access to the information but also makes the whole process a lot more efficient. To make your audit process intelligent, you should get in touch with an AI app development company that specializes in the whole powered Audit processes.  

Expense Management

Review and approval of expenses in a way that it is ensured that they are compliant with the organization’s policies is a time-consuming task for any accounting team. While though AI, people can employ machines to read the receipts, audit the expense, and then alert the finance team in case there’s any discrepancy. 

Impact of AI in Operations Management

Log Analysis

Analysis of log is the biggest use case of AI-powered Operations. Every layer of the stack – operating system, server, hardware, and applications – leaves traces of data stream which can be gathered, stored, processed, and then analyzed by the Machine Learning algorithms. The data then is used for performing root cause analysis of events. 

By incorporating the power of AI in Log Analysis systems, businesses can find lacks in the system even before a failure happens, marking a use case of AI benefits for business. 

Capacity Planning

IT Architects spend a lot of time planning the resource needs of the applications. It can be very challenging for them to define server complications for the development of a multi-tier, complex application. Every physical layer of application should be matched with CPU cores, the amount of storage capacity, ROM, and the network bandwidth. 

Artificial Intelligence comes in handy here by helping architects define the right specification of the hardware or for selecting the right instance type in the public cloud. These algorithms tend to study the present deployment and performances for recommending an optimal configuration for every workload. 

Infrastructure Scaling

Auto Scaling can be configured to be reactive and proactive. Under the reactive mode, the monitoring infrastructure will be able to track the key metrics like memory usage and CPU utilization for initiating scale-out operations. And when load returns to normalcy, the scale-in operation takes place bringing the infrastructure back to its original form. 

In the proactive mode, admins schedule scale-out the operation before an event. 

One of the AI benefits for business lies in the fact that through Artificial Intelligence, IT administrators can configure the predictive scaling which learns from previous usage pattern and load conditions. This way, the system becomes intelligent to decide when it should scale with no mentioned rules. This new mechanism complements capacity planning through adjustment of runtime infrastructure needs. 

Cost Management

Assessment of infrastructure cost plays an important role in the IT architecture. And when you work with a public cloud mechanism, the forecast and cost analysis becomes a lot more difficult and cloud providers tend to charge for a number of components like – VMs usage, storage capacity, external and internal bandwidth, API calls made through apps, and IOPS. 

Through the analysis of workload and the usage patterns, Artificial Intelligence can estimate the cost of infrastructure by offering cost breakup around a number of applications, components, subscription amounts, and departments – something which would help the operation unit in securing the IT budgets accurately. 

Performance Tuning

Once the application gets deployed in the production, a good amount of time is then spent in tuning the performance, specifically in the case of the database engines which deal with a good amount of transactions as they experience the most reduction in performance over time. 

By analyzing logs and time utilized in attending to tasks like processing of a query or responding to request, Artificial Intelligence algorithms developed by a sound AI software development company can offer an exact fix to the issues. It comes very handy in augmenting log management through taking respective action in place of escalating issues to the operations team, which have a direct impact on the cost of support and on running the enterprise IT help desks. 

Building Maintenance

The last point in our list of the benefits of AI in business operations is the help it offers with building maintenance. The technology can help facilities managers better the energy use while keeping their occupants’ comfort into consideration. 

One example of this can be seen in the building automation services that an AI development company offers. In this, AI is merged with IoT to help manage the buildings’ equipment, light, cooling/heating system, etc. in addition to using computer vision for monitoring the building.  

So here were the four domains which are considered to be the pillars of any business. The domains that the AI development services providers believe can have a massive impact on and can make more efficient. If you too wish to reap the benefits of AI by making your processes efficient, streamlined, and high revenue-generating, contact our team of AI Developers today. 

The post How AI Carries an Impact On Your Business, Across Domains? appeared first on Appinventiv.

For those who are new to the world of Amazon Go, it is a grab and go retail model, where all you have to do is download the app, take up an item from the shelf and leave the shop. The items will automatically get added in your cart and the amount will be debited from your Amazon account.

The concept with which Amazon Go has been launched is something that the retail world has never seen. With Artificial Intelligence at its center tracking what items users are buying and the amount that has to be charged from them, Amazon go is venturing into the traditional retail setup and has introduced the technology yet again in its retail setup.

The popularity that Amazon Go Retail has been able to achieved in just a few months of its official launch, led to a number of other retail stores investing in deep learning and artificial intelligence technologies to follow the lead that Amazon has set. Walmart and Microsoft are also apparently planning to level the cashier-less retail outlet space which is presently only dominated by Amazon.

AI-powered Amazon Go venturing into traditional retail plans has proved itself to be a hit with the brand hoping to expand its business to over 300 stores and making itself one of the major AI technology trends that the retail houses will be looking at in 2021 and beyond. But AI’s capability as the medium to bring Retail industry transformation is not restricted to just one disruptive use case that Amazon Go retail is. There are a number of other ways AI in Retail is changing the industry’s outlook. In this article, we are going to dissect it in its many facets and extent. 

Table of Contents:

1. Why Do You Need Artificial Intelligence in Retail Industry
2. How Can Artificial Intelligence and Retail Be Clubbed
3. Artificial Intelligence (AI) Use Cases in Retail: how major retailers are currently using AI
4. Futuristic Approach
5. FAQs About the Digital Transformation of the Retail Industry with AI

Stats Showing Role of Artificial intelligence in Future Retail Market

Now that we have looked into the role of AI in the future retail industry, let us move on to why the need of AI even arises in the retail domain. 

Why Do You Need Artificial Intelligence in Retail Industry

The use of AI in the retail industry is mainly talked about in the front of their applications. What is comparatively less discussed is the need of using AI in retail. 

• Captivate more customers 
• Create insights from dissimilar data
• Synchronize online and offline retail experience, a.k.a Omnichannel Retail 
• Better flexible logistics network
  

The need cum benefits of AI in retail goes as far as how well you manage the AI project in your retail business. 

How Can Artificial Intelligence and Retail Be Clubbed

With 7,000 retail stores around the world announcing that they are ceasing to exist in 2018 and thousand other announcing their plan to close down their operation after being succumbed to not being able to deliver the same output as online retail stores, the brick and mortar retail world as we know it is facing what is called a ‘Retail Apocalypse’.

Seeing the horror that now stands to face the future of Retail, a number of retail giants have started applying the power of Artificial Intelligence in retail market to keep them afloat.

Let us look into a few areas where the combination of Retail and AI is marking a strong appearance.

1. Streamlining supply chain

AI in supply chain management is the most popular adaption of the technology in the Retail industry.

One of the major elements of the mere concept of a factory of the future is made of smart, connected, and very efficient supply chain. The fact that there are a vast number of stakeholders participating in the supply chain cycle — manufacturers, suppliers, retailers, distributors, and the customers — the supply chain itself presents a number of opportunities for introducing automation in the business process, something that the supply chain focused AI solutions for retail is known to handle effortlessly.

Insights taken from big data analytics can help companies predict and then react to the changes in demand, the amount of available inventory, the environmental condition, the supplier capabilities, the environment condition, and even the customer needs, which all ultimately ensures better productivity and a timely product and service delivery.

The prominent role of AI in supply chain planning is what IDC estimates will bring half of the mature supply chains to incorporate AI by the time we reach 2020. Even in the present day, there are a number of ways AI and Retail amalgamation is revolutionizing Supply Chain Management.

Ways AI in Supply Chain and Logistics is Revolutionizing Retail

• Improving the accuracy of demand forecasting
• Lowers the operations and inventory costs along with providing faster response times to the customers.
• Forecasting the demand for new products
• Improving the factory scheduling and production planning accuracy by accounting for various constraints and then optimizing the supply chain for each
• Reducing freight costs, improvement in the supplier delivery performance, and finally, a minimization of the supplier risk.

2. Understand your customers

Machine Learning gives the retail houses a complete idea of who their customers are. Upon studying the multiple customer data and reviews, it gives businesses an idea of what their customers want and what is keeping them away from making repeat purchases. 

The insights drawn this way lays the future of AI impact on retail and how it can be your way to get direct insights into your customers – all lost, present, and new. The information can further be used for running targeted marketing campaigns, which is known to better the customer retention numbers across several retail houses.

3. Customized customer experience

Answering the shoppers’ unique requirement is one of the most full-proof ways to grow and maintain the customer loyalty count. Something conveniently possible through the blend of Artificial Intelligence and Customer Experience.

In a survey, 70% of respondents revealed how they would be a lot more loyal to the brands which added the features of personalization in their outlets. With the transactional data and machine learning, brands can easily track and then analyze past purchase, customer behavior, and loyalty cards etc. to deliver more customized offerings.

An example of this customer oriented artificial intelligence in retail market size use cases would be what Sephora is doing with Color IQ. It is the brand’s artificial intelligence driven, an in-store offering which scans the skin surface and recommends makeup products on the basis of the skin color.

4. Low out of stock instances and markdowns

Using AI in retail stores, the seller can get direct insights into the sales pattern in the stores and lower the safety stock usage. The extent of how AI is revolutionizing the retail industry can be seen in how handy it comes while allocating or replenishing the inventory as retailers will no longer have to be dependent on seasonal markdowns.

An example of AI in the retail industry can be seen in the case of H&M that declared the plan to adopt Big Data and AI for analyzing the store returns and receipts for the evaluation of purchase per location and then stock the inventories on the basis of these insights.

Employing AI for the purpose of analyzing the inventory will help lower the occurrence of product excess and instances where stores order bulk items of products that are not in demand, which ultimately gets wasted and have to be returned – bring an unnecessary hike in the purchase and shipping cost.

5. MAP analysis 

The Minimum Advertised Price (MAP) is the lowest price which any retailer can quote for the sale of a product. The products which are sold at a rate lower than MAP can adversely affect business for the retailers. Thus, it is necessary to track sellers who play around with the MAP price. The Retail analytics firms like Intelligence Node make use of AI for helping brands selling online prevent violation of their MAP pricing by tracking and monitoring products in real-time.

6. Virtual trial rooms revolutionizing the in-store customer experience

By integrating smart mirror technology – an AI in retail examples and use cases that is growing in popularity – in the store, clothing brands will be able to help visitors check how the piece of clothing is looking on them, while all they have to do is hold the piece of garment in front of their body.

The mirror technology – one of the most popular use of AI technology in the retail industry – can be made more advanced, more conversion friendly by giving the visitors option to wear with their chosen piece along with the cost of each and an embedded checkout option which would be in sync with the system placed in the checkout zone.

7. Detecting non-scans

Retailers, on a yearly basis, lose out around $45 Billion in name of non-scans and shrinkage. The catastrophic checkout problem can easily be solved with AI. By using computer visions and AI algorithms retailers can analyze the video feed and detect the instance where the product is left unscanned and inform the checkout staff in real time.

One of the retail-focused AI software development company offering technology to solve this very problem is StopLift. It makes use of video analytics and computer vision to identify scams and mistakes at the time of checkout. It compares the items detected on the video to the real POS data for tracking items which have not been scanned, thus saving a great number of scam instances.

8. Smart product searches

AI in retail can likewise simplify product searches for the customers. Customers can now take a photo of an item or product they like in reality and afterward utilize the image to search for a retailer selling it on the web. Here machine learning classifies a large number of things from different merchants and sorts them for the customers within seconds, making item search and comparison simpler than ever.

9. Behavior analytics and forecasting

AI technology is believed to be vital for retail businesses. Utilizing this innovation can assist retailers with sorting out the customer’s requests and needs, which will make them more customer-centric.

With the assistance of information procured through AI-driven analytics, retailers can make well informed business choices about the amount of stock to be ordered based on the customers’ conduct. It won’t just build productivity but additionally save time and money for the organization.

Utilizing AI calculations, retail organizations can run focused marketing and advertising campaigns based on customers’ locale, inclinations, gender, and buying habits. It will help in improving customer loyalty and retention as a customized experience is an incredible method to show them that you care.

Now that we have looked at the different ways AI can be incorporated in your brick and mortar retail space, even when your plan is not as ambitious as Amazon Go, let us look at the use cases of AI in Retail Industry by looking at some big name retailers that have successfully implemented AI. Businesses setting the best examples of AI to improve the retail experience.

Artificial Intelligence (AI) Use Cases in Retail: How Major Retailers Are Currently Using AI?

1. Pepper robot

Pepper is a humanoid robot which can interact with the walk-in customers while perceiving the human emotions, is the best example of the use of AI in retail. The robot is already a hit in Japan where it is being used as customer greeter and rep in over 140 SoftBank mobile stores. A pilot of Pepper in California stores – Palo Alto and Santa Monica – received a 70% rise in the foot traffic in Palo Alto, along with 50% of Neo-pen sales in the Santa Monica region.

2. Domino’s robotic unit

Integrated with artificial intelligence technology used for military combat training, the DRU sensors help it in the navigation of the best path for delivery of pizza. Seeing the amount of investment that Domino’s is making behind the technology, the future when delivery persons will be replaced by robots is not very far.

3. Amazon’s drone

Coined as Amazon Prime Air, drones are a major futuristic part of Amazon delivery system. While the launch date is not announced yet, the future where the items we buy are delivered to our home through drones is not very far. A future that will set a benchmark for AI use cases in retail.

Want yours to be the next name in the list as a disruptive use case of AI in future retail market? Contact our team of developers specializing in the adoption of Artificial Intelligence in Retail.

Artificial intelligence retail solutions in one form or the other has placed themselves as an important part of the brick and mortar retail industry.

4. Sephora’s color IQ

Sephora helps you find the best makeup shade the moment you step inside the store. Their Color IQ AI solution makes you try shades on the screen and test how they would look on you without having to try them in reality. 

5. Neiman Marcus’s visual search 

The luxury department store makes use of AI for making it convenient for their customers to search items. Through their Snap. Find. Shop. App users can take pictures of items they saw when outside the store and then search the Neiman Marcus inventory to get the item. In place of using unclear search terms to search for an item, the photos help them find similar items.

While its integration is comparatively a lot easier in case of mobile retail or mCommerce apps, integration of the disruptive technology in store would call for a major overhaul of your business model and would only deem to be useful when the integration that you have made is technically advanced – something that can only be achieved when you have partnered with an equally sound AI app development company who understand the technology to its core.

Futuristic Approach

 From making more jobs in the market to improved and more customized customer experience, it is right to say that Artificial Intelligence is the eventual fate of the retail business.

Though predictive analysis is certainly not a new idea to retailers yet over the last couple of years, with the progressions in technologies, retail brands are presently ready to value those analysis and use it for the better development of their business. The AI-empowered retail world assists customers feel more associated and social, and make ways for countless choices.

Wrapping up the article with the answer to the question we started with – Is Amazon Go the Start of Fully-AI Retail? Yes. The adoption of AI in Retail Stores that had already started will now move in full swing with the trend that Amazon Go has now set in the Retail industry.

After getting to know how AI in retail is helpful, now it is time to select the appropriate AI software development services partner to make your dream a reality. It is advisable to refer to a trustworthy and reliable AI development company in USA, – A company like Appinventiv that will help you expand your AI journey.

FAQs About the Digital Transformation of the Retail Industry with AI

Q. How AI is changing the retail industry?

AI, in its many forms and facets is changing many industries. As for the retail industry, it is making it a lot more predictive, forecasting, and preventative. It is helping businesses understand their customers better, estimate the item requirements, map the price they should be sold at, etc.

Q. What’s the future Of AI in retail?

The future of AI in the Retail which is estimated to be worth $5,034 million, is likely going to see itself getting combined with other disruptive technologies like AR/VR, Blockchain etc. with the aim to make the retail store smart and future driven.

Q. Does AI improve retail and help increase sales?

Yes. AI gets retail houses complete information into their customers – their purchase preferences, what irks them, etc. It also gives them information about the stocks, which they should reorder or stop ordering for a while, among other such insights which are directed at their long term sales and profit generating capability.

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The answer is the technology they operate on – Artificial Intelligence.

AI, after much ado, have finally stepped out of the offices of IBM and Amazon and have entered the consumers lives through the device they are always active on, Mobile. And, the power it has come with is the one that is too large to contain.

By using its subfields like Machine Learning, Predictive Analysis, and Deep Learning, AI has helped businesses that are on mobile take personalization to the next level, both in terms of the features that they offer and the marketing efforts that they follow. In fact, the benefit of these are so prevalent that next-gen apps have become a way for enterprises to chart the new normal.

And it doesn’t end there.

Here are the Six Use Cases of Artificial Intelligence Application in Mobile Apps –

1. Automated reasoning

The feature is a blend of the science and art of making apps that uses logical and analytical reasoning to solve issues, it was what help machines prove theorems, win chess matches, and solve puzzles. It is through this feature that AI machines are able to judge the number of patients that will check-in in the hospital, are able to do stock trading and even play Jeopardy.

There are a number of mobile app companies that have also incorporated the feature. One such company is Uber. The ride sharing app uses logical reasoning so as to optimize the drivers’ routes and help the riders reach their destinations sooner. The reasoning algorithm studies trillions of portions of data collected from the Drivers who have used the routes – both time wise and directions wise – and take the time to reach information.

2. Recommendation services 

It is probably the most effective and simplest application of AI technology in mobile apps, something that is seen in almost all mCommerce applications.

The number one reason behind app failures only within a year from its launch is the failure to offer relevant content that would continuously engage the users. Even though you must be continuously adding new products in your site, until and unless users see the ‘Customers who bought this also bought’ option, the chances are you will continue seeing a low app session and conversion rate.

By gauging the users’ choices and putting in the data in your learning algorithm, mobile apps make the recommendations, which the users are most likely to be appealed to buy. It is one strong stream of revenue for a number of mCommerce apps like Amazon and entertainment mobile app like Prime Video and Netflix. Even though, the AI type is mostly used by mCommerce and Entertainment industry, any business which indulge in upselling or cross-selling of content can use this AI type.

3. Learning behavior patterns

Most of the platforms have the ability to know about users’ buying patterns so as to make the upcoming session much more smoother. For example, the half-human, half-bot hotel booking service, Snaptravel uses NLP (natural language processing) and machine learning for having real sounding conversations with the users, one that suits their needs. When interacting with human, whenever bots get stuck, human team take over and teach the bot of how to handle the situation next time around.

Another most seen example where AI learns users behavior and then uses the information is fraud detection in case of online payments. AI’s pattern-detection mechanisms go through the credit card details and purchase history as and when they happen, and uses the learning to check if someone has made a purchase recently that is not in line with the purchases you make.

4. Decoding the App Sentiments

By tracking what is been talked about your app everywhere – on the stores, on social media, on forums, or even on messaging platforms, AI’s Sentiment Analysis feature gives you an insight into how users are interacting with your app, with what competitors are they comparing you, etc.

Sentiment Analysis gives you a direct information of what feature needs to be added and which needs to be removed from your suite of app features. In addition to getting you information on how users are interacting with your app, AI will also help you get access to information that is related to your users’ behaviour across different platform. You’ll then get to know which platform your users frequent, at what time, for what purpose etc.

5. Ability to Personaliz

Have you ever wondered how some taxi booking apps give you the discount message right around the time of your usual ride? Or how is it that your favorite restaurant app suggests food that you like to eat? It is all because of AI.

One of the biggest benefits of having this AI feature integrated in a mobile app is that it gives the brands a full access to the user location of device usage data – the time when they are active, the kind of app they use, people they talk most, platforms they visit most, etc.

Using the information, companies then offer discount in the same app to some while focus on giving cashbacks to another. AI allows brands to take personalization to another level altogether, thus helping redefine their CRM model.

6. Adding Predictiveness to the Ever Changing User Life

The much talked about Machine Learning subfield of Artificial Intelligence is what comes in handy when doing predictive analysis on your app users. Suppose you have an on demand medicine delivery app, now with the help of predictive analysis, you will be able to roll out notification to your users that their medicine is about to end and that they should reorder.

Predictive analysis is what apps like period tracker or weather applications are based on.

If you are just starting, you can use the features in two ways – either base the whole app on predictive analysis or use it to keep rolling out product or discount information, to keep the active in your mobile app. Or, you can also launch an extension in your messaging app, which would make use of neural network to send automated replies, like what Google does. If you are confused with the many options, contact your partner app development company for some clarity.

There are a number of other use cases of what happens when Artificial Intelligence meets mobile apps. When it comes to the combination of AI and apps, there is rarely a chance that something would go wrong and in a direction that is opposite to company’s growth.

But, in order to allow an app to take complete advantage of the power that AI brings with it, there are some things that the mobile app developers need to take into consideration as well. Let’s look at what they are –

• Instead of going with the flow and making your whole app a definition of AI and Machine Learning, incorporate them in some portions of your application. Suppose you have a taxi booking app, now instead of being creepy and telling people where they want to go, use AI to calculate the time and distance or for offering them discount on the basis of their ride history.
• Know the in-depth features of all the available platforms that are used in developing Machine Learning based mobile apps. While a lot, here are the 5 we commonly use – Api.ai, Wit.ai, IBM Watson, Microsoft Azure, and Tensorflow.
• Invest in developing algorithms that know how to divide structured and unstructured data and how to convert them in meaningful information that would help take the app far, globally.

Till now we have talked about the ways AI makes common Mobile Apps a game changing apps and then we looked into some tips that app developers should consider when working around with AI. What next? Contact our team of AI developers to know the best way to incorporate artificial intelligence in your next mobile app.

The post Ways You Can Use AI To Develop Next-Gen Mobile Apps appeared first on Appinventiv.

According to the latest report, Alphabet put around $30 billion for unleashing AI technology, while Baidu has invested nearly $20 billion in the technology last year. Not just this, the China government is also focusing on AI development with the perspective of controlling the future cornerstone innovation.

With all these things into consideration, it can be easily said that the business world is progressively turning towards Artificial Intelligence, and we will see various significant changes in the industrial world in 2018.

While AI is bringing the next revolution in various business verticals, including healthcare, finance, education and travel, the most significant change is in the mobile economy. The technology is disrupting the mobile app industry, paving a new way to meet the users’ expectation and reap the benefits of app development.

Curious to know how will AI transform the mobile market?

Here are the 7 ways AI is Reforming the Mobile App Industry in Terms of User Engagement and Developer App Revenue

1. Better Conversational Experience

Artificial Intelligence is filling the gap between the consumers and brands. The technology, in the form of Chatbots, is offering exceptional conversational experience to the customers. It is not only managing multiple clients at a time but is also proving to be the right solution for satisfying their needs with personalized products/services. According to a survey, 95% of smartphone users feel that customer service has been improved with the inception of chatbots. Another survey revealed that 15% of users have communicated with a bot in the past 12 months, while 35% of customers wish to see more companies investing in chatbot development. In addition to these, it has been expected that 80% of businesses will employ bots by 2020.

This clearly defines that AI/Chatbots are here to stay for a long, and disrupt the market in a positive way.

2. App Personalization

By integrating AI into their mobile apps, developers and brands are able to facilitate personalized experience to their users.

Apps will trace the user’s location and provide location-based results automatically; the user need not enter the location every time. Secondly, the technology will let the brands gather in-detailed information about customers via different means, such as online traffic, mobile devices, PoS machines, etc. The collected data along with the recorded user behavior will be further used to provide personalized results. This will improve the user retention rates. Besides, the mobile applications will let the user enjoy exceptional services without making many efforts. For example, Starbucks AI-powered app, My Starbucks Barista allow customers to place an order by just speaking the product they want.

On a larger scale, AI will revamp the search algorithm, shifting focus to richer contextual and personalized app experience.

3. Seamless Search Experience

AI is streamlining the user experience by empowering them to make their searches not only via text but also through images and voice. In fact, the technology is also enabling them to make searches in their natural language. This has taken the mobile app experience to the next level, by making it necessary for the app developers to integrate image recognition, voice recognition, and app localization features into their mobile apps.

4. Onboard Experience and Gamification

Nearly 25% of app users never return to the app after the first try. To minimize these numbers, app developers are turning towards AI. The technological effort understands human psychology and enables the UI/UX designers to design accordingly. It prevents users from memorizing all the details by securely storing with it, which indicates AI is enhancing the app usability. Besides, it let the users have access to various in-app gestures, which turns their app experience to be more enticing.

5. Improved App Security

AI technology will cater the biggest concern of mobile app developers, i.e, security. With parallel technologies like Predictive Analysis and Machine learning, the technology will help in making predictions related to app security and vulnerability levels. The technology will keep an eye on the user behavior pattern and alert is anything suspicious is found. Not only this, the technology will also recommend or implement changes to improve the security of the app. It will also introduce new ways of logging into/out of the mobile application.

In addition to evaluating and implementing improvements in the security mechanism, the Artificial Intelligence technology will also let the app developers find potential security “holes” and “backdoors”, reducing the risk of intruder attack by employing these security leakage points.

6. Enhanced App Marketing

Not only the app development and engagement, the Artificial Intelligence will also refine the app marketing scenario. The AI-enabled machines and apps study the market trends and user behavioral pattern to provide real-time, detailed demographics. By this, these machines and apps not only cut down the efforts of marketers in gathering data but also reduces the chances of an error. By serving with real-time data, the technology enables the marketers to make marketing strategies with a futuristic approach. This improves the conversion rates and sales.

According to a Gartner Research, 30% of companies will employ AI for sales by 2020, which defines investing in AI to be the need of the hour.

7. AI and IoT

AI is also playing a pivotal role in the growth of IoT. The technology enables the connected devices to collect real-time data and take a decision on their own. In other words, AI-powered mobile apps empower connected devices to learn from the information exchange pattern and act accordingly.

According to statistics, there will be more than 50 billion connected devices by the year 2020, which means a higher demand for AI apps to control these devices.

Artificial Intelligence technology is slowly and gradually revamping the mobile economy with its potential to collect real-time data, understand human emotions and provide a personalized experience. And now, with the announcement of AI chips in the 5G smartphones, the future of the mobile economy is going to be more intelligent and interesting.

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