There is hardly any industry operative in the world today that is oblivious to the high revenue and value offering potential that artificial intelligence comes packed with. A declarative fact that is evident from the promising AI technology trends for 2020 and later.

This quick adoption, while on one hand has come with a lot of benefits for both businesses and end-users, it is on the other hand is on a very native stage. Meaning, businesses are yet to find concrete use cases and return effectiveness. This nascency and benefit combination has given birth to a number of queries around how to manage your AI projects. 

Seeing how complexity lies at the very center of the AI project management solutions, it is important to understand the intricacies of managing AI projects.

In this article, we answer every question and element surrounding how we, at Appinventiv, perform AI project management and the steps we follow to successfully transform a Proof of Value (POV) to efficient AI solution & services.

Table Of Content

1. How is an AI Project Different from Traditional Projects?
2. Splitting AI Into Two Distinct Categories
3. A Slight Detour: Understanding the Pillars of AI Project Success
4. The Challenges of AI Project Development: Why AI Projects Fail
5. Answering the Question of the Hour: How to Manage Your AI Projects
6. FAQs About the Steps of AI Project Management

How is an AI Project Different from Traditional Projects? 

AI project management calls for a different approach when parallels are drawn between them and traditional mobile app project management. Meaning, the differences between AI projects and traditional IT projects are manifold. 

The traditional mobile app development process is a solution specified. Whenever it gets difficult to specify a solution, the results become uncertain and risky. This development type falls under the top-down programming. 

Opposedly, in case of AI projects’ Proof of Value (POV), a bottom-up approach is followed. In that case, AI draws conclusions from its own rules and processes of working with an extensive data set. 

The AI development landscape also tends to open up several opportunities as the cycle matures. Meaning, for a project to be deemed complete, it has to cross several stages of exploration and hits and trials. While the outcome of the approach is almost always high revenue friendly, it often leads to high development cost and extended development timelines. 

The last part of the question surrounding how to manage your AI projects lies in making change management an integral part of the Agile process. The principle that AI program managers generally work on is fail-fast, wherein the idea is to explore expeditiously and fail right at the beginning of a wrong approach, instead of at a later stage in the development process. 

Splitting AI Into Two Distinct Categories

The first part of planning your AI project starts with our team understanding the category it belongs to. Category one deals with projects that are common in nature, like translating a language in another, or converting images into words. Category two is more complex. It handles tasks like detecting heartbeat or monitoring sleep. 

The two categories call for two distinct solutions – incorporation of an existing AI or creating custom AI project management solutions.  

Existing Artificial Intelligence Solutions

There are a number of events where the inclusion of AI has become common and mainstream. Meaning, there are ready-made tools that our engineers only have to integrate AI into the applications. A few of the platforms that our team generally use include Microsoft Azure AI, Google AI Platform, and Amazon Machine Learning, etc. 

Custom Artificial Intelligence Solutions

In case there is a complex project in hand, like recently we made a neural network driven healthcare app that gave users insights into their health on the basis of their voice, we have to resort to custom AI solution development. To make the process easier, Android 11 will be using its new Neural Networks API 1.3, as an effort to make your Machine Learning apps run smoothly on devices.

A Slight Detour: Understanding the Pillars of AI Project Success

Our journey with Artificial Intelligence started back in 2019. It took us an extended delivery timeline to understand that the secret to an AI project success lies in two pillars – people and data. Only in the presence of the two pillars, AI is able to improve customer experience to its entirety. 

We started with bringing in experts from different sections that the application adhered to – irrespective of whether or not they had a technical expertise. It was necessary to feed in domain specific data into the algorithm to make the AI system efficient and unbiased. 

The next part – the second pillar – was data. Data, when not rightly stored or when not in its entirety is utterly useless. Now, there are two types of data that a business ejects – structured (ones like date of birth, address, etc.) and unstructured data (invoices, voice recordings, emails, etc.). When in the process of AI project management, you have to consider both the data types. 

There are certain steps that a data has to go through to become one that can be used for deep learning or Artificial Intelligence. Ones that our team of data engineers work on when we develop Artificial Intelligence (AI) solutions for scale-ups and enterprise clients.

The faster data finds a place in this pyramid, which is based on Maslow’s Hierarchy needs, the faster your AI project will start churning and the greater would be the possibility of engineers working on modeling instead of keeping their focus on data filtration. 

The result of our exploration journey was an understanding of the different issues that come across when answering what creates a valuable A.I solution. Let us counter those issues before walking you through the stages of managing AI projects in a way that their Proof of Value (POV) reflects into the end system. 

The Challenges of AI Project Development: Why AI Projects Fail

If we sit down to make a list of what challenges do companies face when implementing AI, the list will be very extensive. But at the core of it all of why Proof of Values fail, lies two prime causes – misaligned expectations and insufficient data management capabilities. The causes that hold back businesses from making money in AI.

Misaligned Expectations 

More often than not, the majority of AI projects don’t see the light of the day because of the attached misalignment in expectations. The root cause of challenges of artificial intelligence in business often emerges because of heightened short term expectations from a technology which inherently operates on a long-term mode. 

Next instance of misaligned expectations can be seen in businesses assuming their AI based solution will be accurate enough to meet different user perceptions. For example, in case of a music streaming application, assuming that the “next song” your AI is suggesting is exactly what the user believes to belong to the genre is a problem area. This is the reason why businesses often use the word ‘may’ when showcasing products or services that their users might be interested in, next. 

Inefficient Data Management

AI tends to make wrong decisions on the basis of wrong datasets. The problem in AI project management solutions emerge when the data is incorrect or incomplete – in short, not prepared to fit into the AI model. 

For an AI system to work as expected, it is necessary to have refined data which the system can use to learn and analyze patterns. When we build an AI-ready data set our focus is primarily on dividing the structured and unstructured information following the modern data collection strategy.

Answering the Question of the Hour: How to Manage Your AI Projects

1.  Identifying Problem

The first step for us when it comes to managing AI projects is identifying the problem. We start with asking our partners two questions: “what is it you are willing to solve?” and “what is the desired outcome for you?”

When settling on a problem statement it is important to understand that AI in itself is not a solution but a means/tool to meet the need. Noting that, we choose multiple solutions, which can be built upon with the help of AI and not be dependent on it.

2.  Testing the Problem Solution Fit 

This stage, ideally answers how to start an AI project. Before we initiate the AI project development process, it is first important to test and be sure that people are willing to pay for what you are building. 

We test the problem-solution fit through a number of techniques like traditional lean approach and Product Design Sprint. 

One of the best things about AI technology is that it is very fairly easy to create a base level version of a solution by using real humans or MVP. The benefit of this is not just easy analysis of a solution but also within time guarantee that the product actually needs an AI solution.

3.  Preparing and Managing Data 

Having reached the point where we know that there exists a customer base for your solution and you have the confidence that the AI can be built, we initiate managing machine learning projects by gathering data and handling their management. 

We start by dividing the available data in structured and unstructured forms. Although the stage is fairly easy when we are working with a startup or a company that doesn’t have multiple data, building multiple applied AI solutions for enterprise is what is tricky. Generally, big firms have huge proprietary database data which might be ready for AI and what could make it all the more difficult is the fact that the data might be stored in silos. 

Our data engineers start with organizing and cleaning up the data, where in principle, they define a chronological order and add labels where needed.

4.  Choosing the Right Algorithm

Although, to keep the essence of the article, we won’t mention the technicalities of AI algorithms here, but what is important to know is that there are different types of algorithms, which vary on the basis of the learning you do. 

• Supervised Learning

At its core, classification predicts a label and regression predicts the quantity. We generally choose classification algorithms when we want to understand the chances of an event occurrence, e.g. the chance of rainfall tomorrow. 

On the other hand, we go with regression algorithms when we have to quantify the scenario, e.g. when we want to know the chance of an area drowning. 

There are several other algorithms that our engineers choose from depending on the project requirement – naïve Bayes classification, random forest, logistics regression, and support vector machine. 

• Unsupervised Learning 

The choice of algorithm would be very different here since the data is not organized or follows a certain type. We might use clustering algorithms for grouping objects together or association algorithms when finding links between different objects, etc. 

5.  Training the Algorithms 

Once we have selected the algorithm we move on to training the model wherein we input data into the model, keeping the importance of model accuracy into consideration. 

Our team of engineers understand that setting the minimum acceptable threshold and applying statistical discipline are the key steps to accelerate the development of AI, in a way that it calls for minimal fine-tuning later.   

For training the algorithms and taking the next developmental steps, we employ tech experts who are experts in Python, R, Java, and C++. Depending on the project needs, we also involve experts who understand Julia – the top language for machine learning app development. 

6.  Deployment of the Project 

We generally advise our partners to go with ready-made platforms like Machine Learning as a Service for their product launch and deployment needs. These platforms are developed to simplify and facilitate Artificial Intelligence and aid the deployment phase of an AI project. They also provide cloud-based advanced analytics that can be used to add in different languages and algorithms. 

[Also Read: Consider Important Steps to Write a Masterful project Plan]

FAQs About the Steps of AI Project Management

Q.  How To Get Started with Artificial Intelligence And Machine Learning

There are six steps that are covered in the process of AI project management: Identification of the problem, testing the problem solution fit, data management, selecting the right algorithm, training the algorithm, and deploying the product on the right platform.

Q.  What is a good idea for an artificial intelligence project?

AI has gotten a scope across a number of industries. What is necessary is to find a use case that incorporates the technology in a way that generated data is organized and converted into actionable analysis. It is important to be realistic about your expectations from the AI solutions in terms of treating it as a tool helping in the advancement of your service, instead of it becoming a service itself.

Q.  Are AI projects better than traditional IT projects?

It depends from situation to situation. There are indeed some projects that do better with AI inclusion, while there are other applications that become unnecessarily complex with the technology’s integration. Ultimately, it depends on the use case and how valued it would become with artificial intelligence.

The post How to Manage AI Projects: From POV to Ready-to-Execute Solution appeared first on Appinventiv.

Though you are aware what type of wallet you need, but don’t know the cost or time taken to get the customized version.

Same is the case with machine learning projects. And to help you out with this dilemma, we have provided detailed information for you to have a successful project.

Machine Learning is like a coin that has two-sides. 

On one side, it helps to eliminate uncertainties from processes. But on the other side, its development is full of unsurity. 

While the end result of almost every Machine Learning (ML) project is a solution that makes businesses better and processes streamlined; the development part of it has a completely different story to share. 

Even though ML has played a massive role in changing the profit story and business model of several established mobile app brands, it still operates under nascency. This newness, in turn, makes it all the more challenging for mobile application developers to handle an ML project plan and make it production ready, keeping the time and cost constraints in mind. 

A solution (probably the only solution) to this difficulty is black and white Machine Learning app project estimate of the time, cost, and the deliverables. 

But before we head on those sections, let us first look into what makes the difficulty and burning of the night candles worth it. 

Why Your App Needs A Machine Learning Framework?

You might be thinking how come we are talking about framework in the middle of time, cost, and deliverables estimations.

But the real reason behind the time and cost lies here, that informs us about our motive behind app development. Whether you need machine learning for:

For Offering Personalized Experience

For Incorporating Advanced Search m

For Predicting User Behaviour

For Better Security 

For Deep User Engagement 

Based on these reasons, the time, cost and deliverable will depend accordingly. 

Types of Machine Learning Models

What type of model would you consider to adjust the time and cost? If you don’t know, we have provided information for you to understand and choose models, depending on your requirements and budget.

Machine learning amidst its different use cases can be categorized into three model types, which play a role in turning rudimentary apps into intelligent mobile apps – Supervised, Unsupervised, and Reinforcement. The knowledge of what these Machine Learning Models stand for is what helps define how to develop an ML enabled app. 

Supervised Learning 

It is the process where the system is provided with data where the algorithm’s inputs and their outputs are labeled correctly. Since the input and output information are labeled, the system is trained to identify the patterns in data within the algorithm. 

It becomes all the more beneficial for it is used to predict the outcome on the basis of future input data. An example of this can be seen when social media recognizes somebody’s face when they are tagged in a photograph. 

Unsupervised Learning 

In the case of unsupervised learning, the data is fed in the system but its outputs are not labeled like in the case of supervised model. It allows the system to identify data and determine patterns from the information. Once the patterns are stored, all the future inputs are assigned to the pattern for producing an output. 

An example of this model can be seen in cases where social media gives friends suggestions on the basis of several known data like demography, education background, etc. 

Reinforcement Learning

 Like in the case of unsupervised learning, the data which is given to the system in reinforcement learning is also not labeled. Both the machine learning type differs on the ground that when correct output gets produced, the system is told that the output is right. This learning type enables the system to learn from the environment and experiences. 

An example of this can be seen in Spotify. Spotify app makes a recommendation for songs which the users then have to either give a thumbs up or thumbs down. On the basis of the selection, Spotify app learns users’ taste in music.  

Lifecycle Of A Machine Learning Project

The lifecycle of a Machine Learning project deliverables timeline usually appears like this –

ML Project Plan Setup 

• Define the task and requirements
• Identify the project feasibility 
• Discuss the general model tradeoffs
• Create a project codebase

Collection and Labeling of Data 

• Create the labelling documentation 
• Build the data ingestion pipeline 
• Validation of data quality 

Model Exploration 

• Establish the baseline for model performance 
• Create a simple model with initial data pipeline 
• Try parallel ideas during the early stages
• Find the SoTA model for the problem domain, if any, and reproduce results. 

Refinement of Model

• Do model-centric optimizations
• Debug models as complexity gets added
• Conduct error analysis for uncovering failure modes.

Test and Evaluate

• Evaluate the model on test distribution 
• Revisit the model evaluation metric, ensuring it drives desirable user behaviour 
• Write tests for – model inference function, input data pipeline, explicit scenarios expected in the production. 

Deployment of Model 

• Expose the model through REST API 
• Deploy the new model to a subset of users to ensure that everything is smooth before the final rollout. 
• Have the ability to roll back the models to its previous version 
• Monitor the live data. 

Model Maintenance

• Retrain the model for preventing model staleness
• Educate the team if there is a transfer in the model ownership

How to Estimate the Scope of a Machine Learning Project?

The Appinventiv Machine Learning team after perusing the Machine Learning type and the developmental lifecycle goes on to define the Machine Learning app project estimate of the project following these phases:

Phase 1 – Discovery (7 to 14 days)

The ML project plan roadmap begins with the definition of a problem. It looks into the issues and operational inefficiencies which should be addressed. 

The goal here is to identify the requirements and see if Machine Learning meets the business goals. The stage requires our engineers to meet with the business people on the client side to understand their vision in terms of what issues they are looking to solve. 

Secondly, the development team should identify which kind of data they have and if they would need to fetch it from outside service. 

Next, developers have to gauge if they are able to supervise algorithms – if it returns the correct response every time a prediction is made. 

Deliverable – A Problem Statement which would define if a project is trivial or or would be complex. 

Phase 2 –  Exploration (6 to 8 weeks)

The goal of this stage is to build upon a Proof of Concept which can then be installed as API. Once a baseline model is trained, our team of ML experts estimate the performance of the production-ready solution. 

This stage gives us the clarity on what performance should be expected with the metrics planned at the discovery stage. 

Deliverable – A Proof of Concept 

Phase 3 – Development (4+ months)

This is the stage where the team works iteratively till they reach a production ready answer. Because there are far less uncertainties by the time the project reaches this stage, the estimation gets very precise.  

But in case the result is not improved, developers would have to apply a different model or rework on the data or even change the method, if needed. 

In this stage, our developers work in sprints and decide what is to be done after every individual iteration. The outcomes of every sprint can be predicted effectively. 

While the sprint outcome can be predicted effectively, planning for sprints in advance can be a mistake in case of Machine Learning, for you will be working on uncharted waters.

Deliverable – A production ready ML solution

Phase 4 – Improvement (continuous)

Once deployed, decision makers are almost always in a hurry to end the project to save costs. While the formula works in 80% of the projects, the same doesn’t apply in Machine Learning apps. 

What happens is that the data changes throughout the Machine Learning project timeline. This is the reason why an AI model has to be monitored and reviewed constantly – to save it from degradation and provide a safe AI enabling mobile app development. 

The Machine Learning centered projects require time for achieving satisfying outcomes. Even when you find your algorithms beating the benchmarks right from the beginning, chances are that they would be one strike and the program might get lost when used on a different dataset.

Factors That Affect The Overall Cost

The way to develop a machine learning system has some distinguishing features such as data related issues and performance related factors which decide the last expense.

Data-related Issues

The development of reliable machine learning depends not just on phenomenal coding, but the quality and quantity of the training information also plays a crucial part.

1. Lack of Suitable Data
2. Complex Extract, Transform, Load Procedures
3. Unstructured Data Processing

Performance-related Issues

The adequate algorithm performance is another important cost factor, as a high-quality algorithm requires several rounds of tuning sessions.

1. Accuracy Rate Varies
2. Performance of Processing Algorithms

How We Estimate the Cost of a Machine Learning Project?

When we talk about the estimation of the cost of a machine learning project, it is important to first identify which project type is talked about. 

There are majorly three types of Machine Learning projects, which hold a role in answering How much does Machine Learning cost:

First – This type already has a solution – both: model architecture and dataset already exists. These types of projects are practically free, so we won’t be talking about them. 

Second – These projects need fundamental research – application of ML in a completely new domain or on different data structures compared to mainstream models. The cost of these project types are usually one which the majority of startups cannot afford. 

Third – These are the ones we are going to focus on in our cost estimation. Here, you take model architecture and algorithms which already exist and then change them to suit the data you are working on. 

Let us now get to the part where we estimate the cost of the ML project. 

The data cost 

Data is the primal currency of a Machine Learning project. Maximum of the solutions and research focuses on the variations of the supervised learning model. It is a well known fact that the deeper the supervised learning goes, the greater the need for annotated data, and in turn, the higher is the Machine Learning app development cost.

Now while services like Scale and Amazon’s Mechanical Turk can help you with gathering and annotation of data, what about Quality? 

It can be extremely time consuming to check and then correct the data samples. The solution to the issue is two faced – either outsource the data collection or refine it in-house.

You should outsource the bulk of the data validation and refinement work and then appoint one or two people in-house for cleaning the data samples and labeling it.

The research cost 

The research part of the project, as we shared above, deals with the entry level feasibility study, algorithm search and the experimentation phase. The information which usually surfaces from a Product Delivery Workshop. Basically, the exploratory stage is the one every project goes through before its production. 

Completing the stage with its utmost perfection is a process that comes with an attached number in the cost of implementing ML discussion. 

The production cost

The production part of Machine Learning project cost is made up of infrastructure cost, integration cost, and maintenance cost. Out of these costs, you will have to make the least expenses with the cloud computation. But that too will vary from the complexity of one algorithm to another. 

Integration cost varies from one use case to another. Usually, it is enough to put an API endpoint in the cloud and document it to then be used by the rest of the system. 

One key factor that people tend to overlook when developing a machine learning project is the need to pass continuous support during the entire lifecycle of the project. The data which comes in from APIs have to be cleaned and annotated properly. Then, the models have to be trained on new data and tested, deployed. 

In addition to the points mentioned above, there are two more factors that carry an importance on the estimation of the cost to develop an AI app/ML app. 

Challenges in Developing Machine Learning Apps

Usually, when a Machine Learning app project estimate is drawn, the developmental challenges associated with it are also kept into consideration. But there can be instances where the challenges are found mid-way of the ML powered app development process. In cases like these, the overall time and cost estimation automatically increases. 

The challenges for Machine Learning projects can range from:

• Deciding what set of features would become machine learning features
• Talent deficit in AI and Machine Learning domain 
• Acquiring data sets is expensive 
• It takes time to achieve satisfying results

Conclusion 

Estimating the manpower and time needed to finish a software project is relatively easy when it is developed on the grounds of modular designs and is handled by an experienced team following an Agile approach. The same, however, becomes all the more difficult when you work on creating the time and efforts wise Machine Learning app project estimate. 

Even though the goals might be well-defined, the guarantee of whether or not a model would achieve the desired outcome is not there. It is not usually possible to lower the scope and then run the project in time boxed setting through a predefined delivery date. 

It is of prime importance that you identify that there will be uncertainties. An approach that can help mitigate delays is ensuring that input data is in the right format for Machine Learning. 

But ultimately, no matter which approach you plan to follow, it will only be deemed successful when you partner with a Machine Learning app development agency that knows how to develop and deploy the complexities in their simplest form.

FAQs about Machine Learning app project estimate 

Q. Why use Machine Learning in developing an app? 

There are a number of benefits that businesses are able to avail with the incorporation of Machine Learning into their mobile apps. Some of the most prevalent ones are on the app marketing front – 

• Offering personalized experience
• Advanced search 
• Predicting user behaviour 
• Deeper user engagement 

Q. How machine learning can help your business?

The benefits of Machine Learning for businesses goes beyond marking them as a disruptive brand. It ripples down to their offerings becoming more personalized and real-time. 

Machine Learning can be the secret formula that brings businesses closer to their customers, just how they want to be approached. 

Q. How to estimate ROI on developing a Machine learning project?

While the article would have helped you in establishing the Machine Learning app project estimate, calculating ROI is a different game. You will have to keep into consideration the opportunity cost in the mix as well. Additionally, you’ll have to look into the expectation that your business has from the project. 

Q. Which platform is better for an ML project?

Your choice of whether to connect with an Android app development company or with iOS developers will depend entirely on your user base and the intent – whether it is profit making or value centric. 

The post Estimating the Time, Cost, and Deliverables of an ML App Project 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.

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