In 2021, the focus on digitalization is as strong as ever before. Machine learning and AI help IT leaders and global enterprises to come out of the global pandemic with minimal loss. And the demand for professionals that know how to apply data science and ML techniques continues to grow.
In this post, you will find some career options that definitely will be in demand for decades to come. And there is a twist ― AI has stopped being an exclusively technical field. It is intertwined with law, philosophy, and social science, so we’ve included some professions from the humanities field as well.
Popular ML jobs to choose in 2021
Programmers and software engineers are some of the most desirable professionals of the last decade. AI and machine learning are no exception. We have conducted research to find out which professions are the most popular and what skills you need for each of them (based on data from Indeed.com and Glassdoor.com).
1. Machine learning software engineer
A machine learning software engineer is a programmer who is working in the field of artificial intelligence. Their task is to create algorithms that enable the machine to analyze input information and understand causal relationships between events. ML engineers also work on the improvement of such algorithms. To become an ML software engineer, you are required to have excellent logic, analytical thinking, and programming skills.
Employers usually expect ML software engineers to have a bachelor’s degree in computer science, engineering, mathematics, or a related field and at least 2 years of hands-on experience with the implementation of ML algorithms (can be obtained while learning). You need to be able to write code in one or more programming languages. You are expected to be familiar with relevant tools such as Flink, Spark, Sqoop, Flume, Kafka, or others.
2. Data scientist
Data scientists apply machine learning algorithms and data analytics to work with big data. Quite often, they work with unstructured arrays of data that have to be cleaned and preprocessed. One of the main tasks of data scientists is to discover patterns in the data sets that can be used for predictive business intelligence. In order to successfully work as a data scientist, you need a strong mathematical background and the ability to concentrate on uncovering every small detail.
Bachelor’s degree in math, physics, statistics, or operations research is often required to work as a data scientist. You need to have strong Python and SQL skills and outstanding analytical skills. Data scientists often have to present their findings, so it is a plus if you have experience with data visualization tools (Google Charts, Tableau, Grafana, Chartist. js, FusionCharts) and excellent communication and PowerPoint skills.
3. AIOps engineer
AIOps (Artificial Intelligence for IT Operations) engineers help to develop and deploy machine learning algorithms that analyze IT data and boost the efficiency of IT operations. Middle and large-sized businesses dedicate a lot of human resources for real-time performance monitoring and anomaly detection. AI software engineering allows you to automate this process and optimize labor costs.
AIOps engineer is basically an operations role. Therefore, to be hired as an AIOps engineer, you need to have knowledge about areas like networking, cloud technologies, and security (and certifications are useful). Experience with using scripts for automation (Python, Go, shell scripts, etc) is quite necessary as well.
4. Cybersecurity analyst
A cybersecurity analyst identifies information security threats and risks of data leakages. They also implement measures to protect companies against information loss and ensure the safety and confidentiality of big data. It is important to protect this data from malicious use because AI systems are now ubiquitous.
Cybersecurity specialists often need to have a bachelor’s degree in a technical field and are expected to have general knowledge of security frameworks and areas like networking, operating systems, and software applications. Certifications like CEH, CASP+, GCED, or similar and experience in security-oriented competitions like CTFs and others are looked at favourably as well.
5. Cloud architect for ML
The majority of ML companies today prefer to save and process their data in the cloud because clouds are more reliable and scalable, This is especially important in machine learning, where machines have to deal with incredibly large amounts of data. Cloud architects are responsible for managing the cloud architecture in an organization. This profession is especially relevant as cloud technologies become more complex. Cloud computing architecture encompasses everything related to it, including ML software platforms, servers, storage, and networks.
Among useful skills for cloud architects are experience with architecting solutions in AWS and Azure and expertise with configuration management tools like Chef/Puppet/Ansible. You will need to be able to code in a language like Go and Python. Headhunters are also looking for expertise with monitoring tools like AppDynamics, Solarwinds, NewRelic, etc.
6. Computational linguist
Computational linguists take part in the creation of ML algorithms and programs used for developing online dictionaries, translating systems, virtual assistants, and robots. Computational linguists have a lot in common with machine learning engineers but they combine deep knowledge of linguistics with an understanding of how computer systems approach natural language processing.
Computational linguists frequently need to be able to write code in Python or other languages. They are also frequently required to show previous experience in the field of NLP, and employers expect them to provide valuable suggestions about new innovative approaches to NLP and product development.
7. Human-centered AI systems designer/researcher
Human-centered artificial intelligence systems designers make sure that intelligent software is created with the end-user in mind. Human-centered AI must learn to collaborate with humans and continuously improve thanks to deep learning algorithms. This communication must be seamless and convenient for humans. A human-centered AI designer must possess not only technical knowledge but also understand cognitive science, computer science, psychology of communications, and UX/UI design.
Human-centered AI system designer is often a research-heavy position so candidates need to have or be in the process of obtaining a PhD degree in human-computer interaction, human-robot interaction, or a related field. They must provide a portfolio that features examples of research done in the field. They are often expected to have 1+ years of experience in AI or related fields.
8. Robotics engineer
A robotics engineer is someone that designs and builds robots and complex robotic systems. Robotics engineers must think about the mechanics of the future human assistant, envision how to assemble its electronic parts, and write software. Thus, to become a specialist in this field, you need to be well-versed in mechanics and electronics. Since robots frequently use artificial intelligence for things like dynamic interaction and obstacle avoidance, you will have plenty of opportunities to work with ML systems.
Employers usually require you to have a bachelor’s degree or higher in fields like computer science, engineering, robotics, and have experience with software development in programming language like C++ or Python. You also need to be familiar with hardware interfaces, including cameras, LiDAR, embedded controllers, and more.
Bonus: AI career is not only for techies
If you don’t have a technical background or want to transition to a completely new field, you can check out these emerging professions.
1. Data lawyer
Data lawyers are specialists that guarantee security and compliance with GDPR requirements to avoid millions of dollars in fines. They know how to properly protect data and also how to buy and sell this data in a way that avoids any legal complications. They also know how to manage risks arising from the processing and storing of data. Data lawyer is the professional of the future; they stand at the intersection of technology, ethics, and law.
2. AI ethicist
An AI ethicist is someone who conducts ethical audits of AI systems of companies and proposes a comprehensive strategy for improving non-technical aspects of AI. Their goal is to eliminate reputational, financial, and legal risks that AI adoption might pose to the organization. They also make sure that companies bear responsibility for their intelligent software.
3. Conversation designer
A conversation designer is someone who designs the user experience of a virtual assistant. This person is an efficient UX/UI copywriter and specialist in communication because it is up to them to translate the brand’s business requirements into a dialogue.
How much does an ML specialist make?
According to Indeed.com, salaries of ML specialists vary depending on their geographical location, role, and years of experience. However, on average an ML specialist in the USA makes around $150,00 per year. Top companies like eBay, Wish, Twitter, and AirBnB are ready to pay their developers from $200,000 to $335,000 per year.
At the time of writing, the highest paying cities in the USA are San Francisco with an average of $199,465 per year, Cupertino with $190,731, Austin with $171,757, and New York with $167,449.
Industries that require ML/AI experts
Today machine learning is used almost in every industry. However, there are industries that post more ML jobs than others:
Transportation. Self-driving vehicles starting from drones and ending up with fully autonomous vehicles rely very heavily on ML. Gartner expects that by 2025, autonomous vehicles will surround us everywhere and perform transportation operations with higher accuracy and efficiency than humans.
Healthcare. In diagnostics and drug discovery, machine learning systems allow to process huge amounts of data and detect patterns that would have been missed otherwise.
Finance. ML allows banks to enhance the security of their operations. When something goes wrong, AI-powered systems are able to identify anomalies in real-time and alert staff about potentially fraudulent transactions.
Manufacturing. In factories, AI-based machines help to automate quality control, packing, and other processes, while allowing human employees to engage in more meaningful work.
Marketing. Targeted marketing campaigns that involve a lot of customization to the needs of a particular client are reported to be much more effective across different spheres.
The health insurance sector is finding itself in a whirlwind of change. For insurers, this means getting an understanding of the domain’s benefits and tech stack powering the change.
New-age healthcare space or digital health ecosystem, as it’s usually called, is engineered to keep patients on a pedestal. The transformation of healthcare has created an ecosystem where every medical industry stakeholder and process has been operating to keep patients at the center stage.
While this patient-first system is coming organically to the caregivers, insurers – the third key stakeholder of the healthcare sector – are finding the transition a bit challenging. In this article, we are going to look into the capabilities that payers need to inhibit to survive the growing competitiveness in the digital health insurance world.
The insurance industry is staring at a cultural shift that will put people’s well-being at a primary stage. The shift has forced the sector to move from paying financial compensation to injury, illness, or loss towards the end goal of mitigating or preventing them.
A key role in bringing this shift to health insurance digital transformation has been brought along by technology adoption by every custom healthcare software development company.
In this article, we will dive into the technologies insurance companies can adopt in the digital health services landscape. But first, let us look into the benefits that would make the domain an active participant in the changing landscape of digital transformation in healthcare.
The Benefits of a Digitalized Insurance Sector
1. Digital solutions help people make healthy decisions
Digital healthcare solutions powered by wearables and insurance-inclined apps help people: prevent diseases, better diagnostic, manage a chronic condition, limit lifestyle risks. A number of modern users expect their health and care providers, insurers to provide them digital wellness solutions.
New-gen insurers, by offering consumer-centric programs like health quantification, etc. can meet this consumer expectation.
2. Digital platforms help insurers connect with the users
Digital tools and digital health platforms provide insurers the ability to give their clients a welcoming, interactive platform using which the insurers can create an ‘I care’ strategy. The platforms do not just act as a mode to have positive interactions with the customer but also gather data that can help make processes and values efficient.
3. Insurance digitalization help lower risk and cost
Digital healthcare services help make a detailed picture of users’ overall health. Upon the data, the insurers can create an approach that can be used for personalizing premiums through dynamic pricing. The data-driven approach also enables risk assessment that enables accelerated underwriting.
According to a Capgemini report, the use of data-driven assessment helps insurers adopt a more value-driven care model where the digital tools provide a transparent picture of the dynamic pricing model.
4. Digitalization in the insurance space optimize customer experience
Digital space helps healthcare insurance service providers optimize the customers’ experience by increasing the number of touchpoints and providing new services. Here are some of the use cases of how it happens:
Digital health insurance plans for managing personal health
Virtual healthcare delivery
Connected vehicle and smart-home solutions
Voice technology for advising customers.
5. Digital tools can secure data dominance
The insurance sector is producing more data than ever before. They have been building and scaling big data ecosystems to create visualized data sets that can suggest benefits plans according to the success factors.
Providers and healthcare app developers that are best able to use big data are delivering personalized customer experience, bettering retention, and are creating more accurate quotes – all at a lot faster speed compared to traditional approaches.
These and a plethora of other benefits are a guarantee that is dependent on the adoption of a new-gen technology set. Technology in health insurance has become a driving force that can take the domain towards end-to-end digitalization.
Let us take a closer look at some of the technologies we suggest our insurance sector partners, in our capacity as a healthcare app development company USA and India, to integrate into their solutions.
3 Technologies Aiding Insurance Digitalization
Machine learning
Insurers, as they process claims, gather a large amount of data from multiple sources. Incorporation of machine learning in the process and healthcare mobility solutions can help build customer relationship management systems that offer targeted service and eliminate churn.
The industry is also exploring ways to use the technology for helping doctors deliver proactive, timely, and cost-effective care to patients.
Internet of Things
With over 1 billion wearable devices active in the market, the healthcare industry is no more unsure about the technology’s capabilities. The biosensors present inside the wearables are getting more sophisticated to become links in the digitalized healthcare system which lays emphasis on home-based treatments.
Through the data collected by the sensors, insurance companies can help wary customers of problem signs before they develop into diseases.
Distributed ledgers
When a patient interacts with a healthcare system, they talk to a number of parties: doctors, pharmacists, insurance companies, etc. A distributed ledger can enable all the stakeholders to be on the same platform, making the interactions and following transmissions, transactions transparent and real-time.
An example of this can be seen in how the patients no more have to wait for the insurance companies to release payment since the latter is updated of the stage where they are at in the healthcare system.
One of the key resources in the healthcare economy is data – a resource that places insurers in the central of the provider side of the digitalization landscape. A number of insurance companies are making use of data to become active participants in healthcare transformation, while there are still a number of providers who are waiting for the digitalization lighting to strike.
Whatever the case may be on the insurer end, the other side of the reality is that members are appreciating insurers’ digitalization efforts by increasing the usage of value-added services. For an insurance company, it means that now is the golden time to enter the space. We can help you.
Our team can help you make an infrastructure that can bring you on a shared transparent, value-plus platform as your customers. Contact our team of insurance industry digitalization experts.
For years, many industries have explored ways to incorporate artificial intelligence (AI) into their services because it provides a competitive edge. Since it is an evolving technology, the exploration of AI has brought about sub-concepts. One of the more significant concepts of AI is machine learning (ML), coined by Arthur Samuel in 1959 as “the field of study that gives computers the ability to learn without being explicitly programmed.”
MACHINE LEARNING
So, let’s talk a little more about machine learning and what it is. Just as Samuel described, machine learning is a subset of AI where computer algorithms are used to automatically learn from data and information, without being explicitly programmed.
By learning from data and information, the system is able to change and improve its algorithms on its own. The learning algorithm enables the system to identify patterns in observed data and build predictive models based on the observations. Because ML is used in situations where perfection is not expected, the goal is not to achieve perfect predictions but to achieve predictions that are good enough to be useful.
Just like the rest of the tech world, machine learning is not a simple concept. It’s important to note that the concept of ML is broken down into smaller subsets based on how much data the system is provided: supervised learning, unsupervised learning, and reinforcement learning. The type of learning is determined on if the information it’s fed is labeled or not.
PREDICTIVE ANALYSIS
The most popular application of ML is predictive analysis, which uses historical data to make predictions or recommendations for future events. You know how your phone starts to provide suggestions of words to use as you’re typing out a text? That’s predictive analysis at work. The system has recorded patterns of the words you actively use in order to provide suggestions for responses in the future.
Predictive analysis is used across a multitude of apps, like e-commerce, social media, finance, and even transportation. E-commerce apps use predictive analysis to provide consumers recommendations for other products to consider purchasing. The system detects patterns in items that are commonly purchased together and generates suggestions based on these patterns. Social media does the same, but instead of suggesting products, it recommends people to follow. As mentioned earlier, the system collects demographic data from users and (using unsupervised learning) creates patterns to make these suggestions to users.
There are tons of budgeting apps that will use your banking information (after giving permissions to link) to help you manage income and spending. The system analyzes your transaction history and detects patterns to offer appropriate suggestions for spending. That maps app you use to find the best route to get from work to home uses ML, too. The system records past traffic patterns associated with the time of day to provide recommendations for your commute. ML is in use all around you, and you may not have even realized it until now.
You’re probably asking yourself how the system undergoes training. The type of ML that you’re trying to use determines how much training the system has to undergo. The amount of training for the system is determined by how much data is initially provided to the system. Data is the center of ML, without it, the system wouldn’t know how to do its job. Before we dive into why and how we use the different types of ML, let’s talk about what they are first.
SUPERVISED LEARNING
Supervised learning is the process of ML when the system is initially provided data where the algorithm’s inputs (x) and their respective outputs (y) are correctly labeled. Because the input and output data are labeled accordingly, the system is trained to recognize patterns in the data with the algorithm. In future scenarios, this allows the system to receive inputs and produce correctly labeled outputs based on the pattern. Supervised learning is beneficial because it can be used to predict outcomes based on future input data without human interference, like when social media automatically recognizes someone’s face after you’ve tagged them in a picture. When you tag your Aunt Sally in a picture (y), social media stores the facial features of Aunt Sally (x). When you upload pictures of Aunt Sally in the future, social media recognizes her facial features (x) and automatically tags Aunt Sally (y).
UNSUPERVISED LEARNING
In unsupervised learning, data is fed to the system, but the outputs are not labeled accordingly like they are in supervised learning. Unsupervised learning allows the system to observe the data and determine patterns with the information it is given, rather than being trained to recognize the pattern. Once the system has stored the patterns it created, future inputs are assigned to a pattern (created by the system) to produce an output. Unsupervised learning is beneficial because it can show patterns in data that may have been overlooked when observed by humans. Unsupervised learning is used when social networks make recommendations of friends to follow; these recommendations are based on patterns created by demographic data individuals share online. If you went to University X and studied calculus, the algorithm is trained to recognize and recommend other individuals that went to University X and studied calculus as people you may know.
REINFORCEMENT LEARNING
Though it is a separate classification, reinforcement learning is a type of unsupervised learning. Similar to unsupervised learning, the data provided to the system is not labeled, so the system is left to create its own patterns. Where reinforcement differs from unsupervised is that when a correct output is produced, the system is told that this output is correct. This type of learning allows the system to learn from its environment and its experiences to explore a full range of possibilities. It is quite literally, learning through reinforcement. When Spotify makes a recommendation for a song (based on a pattern it noticed in your music selection) you are given the option to “thumbs up” or “thumbs down” the recommendation. When you indicate “thumbs up” or “thumbs down,” Spotify utilizes reinforcement learning because it’s learning your music taste based on what you tell their system.
MACHINE LEARNING APPLICATIONS
Why all the hype about machine learning? Well, because it is the next step in achieving artificial intelligence, and is a big step for app developers. ML gives apps the ability to improve and adjust based on user data, without developers influencing it to do so. This technology is saving time for developers (which saves you money) by enhancing the user experience with accuracy on what users want. While there is a multitude of uses for machine learning, two significant ones are image processing and predictive analysis.
IMAGE PROCESSING
Image processing is a very common application of ML, one that you probably see every day. It is used through supervised learning where an algorithm is used to detect various objects in a given image. Much like a child is taught that shapes with five sides are pentagons, the machine is taught to recognize objects in images. The machine is trained by providing a set of labeled images containing different objects; when given future inputs, the machine will identify objects within those inputs and label them. Apple’s Face ID feature is an example of image processing; when you set up Face ID, there is a series of steps you undergo to train the system (iPhone) to recognize your face. These steps include taking photos of your face from multiple angles so the system can analyze your face store this data. After being trained, the system recognizes your facial features as a means to unlock the phone.
PREDICTIVE ANALYSIS
The most popular application of ML is predictive analysis, which uses historical data to make predictions or recommendations for future events. You know how your phone starts to provide suggestions of words to use as you’re typing out a text? That’s predictive analysis at work. The system has recorded patterns of the words you actively use in order to provide suggestions for responses in the future.
Predictive analysis is used across a multitude of apps, like e-commerce, social media, finance, and even transportation. E-commerce apps use predictive analysis to provide consumers recommendations for other products to consider purchasing. The system detects patterns in items that are commonly purchased together and generates suggestions based on these patterns. Social media does the same, but instead of suggesting products, it recommends people to follow. As mentioned earlier, the system collects demographic data from users and (using unsupervised learning) creates patterns to make these suggestions to users.
There are tons of budgeting apps that will use your banking information (after giving permissions to link) to help you manage income and spending. The system analyzes your transaction history and detects patterns to offer appropriate suggestions for spending. That maps app you use to find the best route to get from work to home uses ML, too. The system records past traffic patterns associated with the time of day to provide recommendations for your commute. ML is in use all around you, and you may not have even realized it until now.
Any machine learning algorithm requires some training data. In training data we have values for all features for all historical records. Consider this simple data set
Height Weight Age Class
165 70 22 Male
160 58 22 Female
In this data set we have three features for each record (Height, Weight and Age).
Any algorithm takes into account all the features to be able to learn and predict. However all the features of the data set may not be relevant.
Suppose we have 1000 features in a large training data set, using all features may exhaust all the system memory and computational power. So we must choose most relevant features and transform them according the the input required to algorithm. After this process we may find that only 100 of 1000 features are contributing to labels.
We can prepare training data by following two techniques
Feature Extraction
Feature Selection
Feature Extraction
Feature extraction is the process of extracting important, non-redundant features from raw data. Suppose you have 5 text documents. Suppose there are 10 important words that are present in all 5 document. Then these 10 words may not be contributing in deciding the labels for those documents. We can omit these words and create new features excluding those words.
TF-IDF technique
TF-IDF technique is a feature extraction technique based on frequency of features in documents
Suppose there is set of documents D(d1, d2, d3)
TF(t,d) is term frequency = frequency of a term/feature value t in document d.
IDF(t,d,D) is inverse document frequency of term t for document d in set D. Here N is the number of documents N = |D|.
TF(t,d) x IDF(t,d,D) is a measure based of which we can say that term ‘t’ is important in document ‘d’. The words for which this measure is really low, we can omit those words in features.
Feature Selection
Feature selection process tries to get most important features that are contributing to decide the label.
Chi Square Test
Chi Square test is a test that tests feature’s independence from class/label and then select ‘k’ features that depend most on class.
Example
Suppose there are some people living in four neighborhoods A, B, C and D and they are labelled as White Collar, or Blue Collar or no Collar
Chi Square test may determine that 90 people living in neighbourhood A are living by chance and not because they are White collar. Chi Square test depends on probabilities.
Chi Square is calculated by below formula for a feature.
Where O values are observed and E are expected.
In the above example, expected values for neighbourhood A for each class can be calculated as
E(A,White Collar) = (150/650) * 349
E(A,Blue Collar) = (150/650) * 151
E(A,No Collar) = (150/650) * 150
After Chi Square has been calculated, its closest value is located in below table for probability in the degree of freedom row.
In the above example for neighbourhood A the chi Square is equal to 2 (rounded off) and degree of freedom here are (no of rows – 1) x (no of columns – 1) = 6
We get 2.20 in 6th row that is most close to 2, it gives us p = 0.9.
This means there is a 90% chance that deviation between expected and observed is due to chance only.
So we can conclude that labelling doesn’t really depend on neighbourhood A.
On the other hand if p values come to 0.01, it means there is only 1% probability that deviation is due to chance, most of the deviation is due to other factors. In this case we can not really ignore Neighbourhood A in our predictive modelling and it has to be selected.
When we talk about the present, we don’t realize that we are actually talking about yesterday’s future. And one such futuristic technology to talk about is how to implement ML and how to add AI to your app. Your next seven minutes will be spent on learning what is the role of Machine learning and Artificial intelligence in the mobile app development industry and what you can do to take advantage of it.
The time of generic services and simpler technologies is long gone and today we’re living in a highly machine-driven world. Machines which are capable of learning our behaviors and making our daily lives easier than we ever imagined possible, all the way, making it necessary for us to understand the process of integrating Machine Learning and Artificial Intelligence into apps.
Technological realm today is fast-paced enough to quickly switch between Brands and Apps and technologies if one happens to not justify their needs in the first five minutes of using it. This is also a reflection upon the competition this fast pace has led to. Mobile app development companies simply cannot afford to be left behind in the race of forever evolving technologies.
Today, if we see, there is Artificial Intelligence and Machine Learning incorporated in almost every mobile application we choose to use. Which makes it all the more important to know How to integrate machine learning and artificial intelligence in mobile apps.
For instance, our food delivery app will show us the restaurants which deliver the kind of food we like to order, our on-demand taxi applications show us the real-time location of our rides, time management applications tell us what is the most suitable time to complete a task and how to prioritize our work.
In fact, Artificial Intelligence and Machine Learning that were once considered top complicated technology to work on or even comprehend is something that has become an everyday part of our lives without even use realizing of its presence. A proof of which is the following functionalities offered by the top brand apps.
The wide inclusion of the two related technologies has made the need for worrying over simple, even complicated things cease to exist because our mobile applications and our smartphone devices are doing that for us.
The below provided stats will show us that ML and AI powered mobile apps are a leading category among funded startups and businesses.
Allied Market Research has predicted that the market for ML will reach $5,537 million in 2023 further demonstrating its growing prevalence.
According to the 2019 CIO Survey by Gartner, the number of companies implementing AI technologies in some form has grown by 270% in the past years.
According to Microsoft, 44% of organisations fear they’ll lose out to startups if they’re too slow to implement AI.
Research by Fortune Business Insights predicts that $117.19 billion is the expected value of the global machine learning market by 2027 at a CAGR of 39.2% during the forecast period.
The Wall Street Journal, states that the advancements in AI and machine learning have the potential to increase global GDP by 14% from, now until 2030.
The idea behind any kind of business is to make profits and that can only be done when they gain new users and retain their old users. The difficult task can be made easy through AI as it comes as one of the benefits or advantages of integrating machine learning and artificial intelligence in apps.
Ways To Implement AI and ML
There are three primal ways through which the power of Machine Learning and Artificial Intelligence can be incorporated in mobile apps to make the application more efficient, sound, and intelligent. The ways which are also the answer to how to add AI and ML to your app.
Reasoning
AI and ML are two proficient technologies that imbibe the power of reasoning for solving problems. Applications like Uber or Google Maps that are used by individuals to travel to different areas, many times change the course or route based on the traffic conditions. This is where AI works – by harnessing its thinking capacities. This facility is what makes AI beat a human at chess and how Uber makes use of automated reasoning for optimizing routes to get the users to reach their destination faster.
Hence, real-time quick decisions are presently being controlled by AI to provide the best customer service.
Recommendation
As you are familiar with OTT platforms like Netflix, Amazon, and others; the streaming features of these platforms acquire a large number of customers with high rates of user trust and retention. Both Netflix and Amazon have implemented AI and ML into their applications that examine the customer’s decision based on age, gender, location, and their preferences. The technology based on the customer’s choices then suggests the most popular alternatives in their watch playlist or that individuals with similar tastes have watched.
Giving the users insight into what they would require next has turned out to be the secret of success of some of the top brands in the world – Amazon, Flipkart, Netflix, amongst others have been using the Artificial Intelligence backed power for a very long time now. This is an amazingly popular technology for streaming services and is currently being executed into numerous other applications.
Behavioral
Learning how the user behaves in the app can help Artificial Intelligence set a new border in the world of security. Every time someone tries to take your data and try to impersonate any online transaction without your knowledge, the AI system can track the uncommon behavior and stop the transaction there and then.
These three primal bases that answer what are the best ways to incorporate machine learning and AI in application development can be used in multiple capacities to enable your app to offer a lot better customer experience.
And now that we have looked at how to integrate AI in android apps along with integration of ML, let us answer the why?
Why should you integrate machine learning and AI into your mobile app?
Why to Integrate Machine Learning and AI Into Your Mobile App?
Personalization
Any AI algorithm attached to your simpleton mobile application can analyze various sources of information from social media activities to credit ratings and provide recommendations to every user device. Machine learning application development can be used to learn:
Who are your customers?
What do they like?
What can they afford?
What words they’re using to talk about different products?
Based on all of this information, you can classify your customer behaviors and use that classification for target marketing. To put simply, ML will allow you to provide your customers and potential customers with more relevant and enticing content and put up an impression that your mobile app technologies with AI are customized especially for them.
To look at a few AI ML examples of big brands who are setting standards on how to implement Machine Learning in apps?
Taco Bell as a TacBot that takes orders, answers questions and recommends menu items based on your preferences.
Uber uses ML to provide an estimated time of arrival and cost to its users.
ImprompDo is a Time management app that employs ML to find a suitable time for you to complete your tasks and to prioritize your to-do list
Migraine Buddy is a great healthcare app which adopts ML to forecast the possibility of a headache and recommends ways to prevent it.
Optimize fitness is a sports app which incorporates an available sensor and genetic data to customize a highly individual workout program.
Advanced search
Through the AI and Machine learning based app development process, you will get an app that lets you optimize search options in your mobile applications. AI and Machine Learning makes the search results more intuitive and contextual for its users. The algorithms learn from the different queries put by customers and prioritize the results based on those queries.
In fact, not only search algorithms, modern mobile applications allow you to gather all the user data including search histories and typical actions. This data can be used along with the behavioral data and search requests to rank your products and services and show the best applicable outcomes.
Upgrades, such as voice search or gestural search can be incorporated for a better performing application.
Predicting user behavior
The biggest advantage of AI based machine learning app development for marketers is that they get an understanding of users’ preferences and behavior patterns by inspection of different kinds of data concerning the age, gender, location, search histories, app usage frequency, etc. This data is the key to improving the effectiveness of your application and marketing efforts.
Amazon’s suggestion mechanism and Netflix’s recommendation works on the same principle that ML aids in creating customized recommendations for each individual.
And not only Amazon and Netflix but mobile apps such as Youbox, JJ food service, and Qloo entertainment adopt ML to predict the user preferences and build the user profile according to that.
More relevant ads
Many industry experts have exerted on this point that the only way to move forward in this never-ending consumer market can be achieved by personalizing every experience for every customer.
According to a report by The Relevancy group, 38% of executives are already using machine learning for mobile apps as a part of their Data Management Platform (DMP) for advertising.
With the help of integrating machine learning in mobile apps, you can avoid debilitating your customers by approaching them with products and services that they have no interest in. Rather you can concentrate all your energy towards generating ads that cater to each user’s unique fancies and whims.
Machine Learning app development companies today can easily consolidate data intelligently that will in return save time and money went into inappropriate advertising and improve the brand reputation of any company.
For instance, Coca-Cola is known for customizing its ads as per the demographic. It does so by having information about what situations prompt customers to talk about the brand and has, hence, defined the best way to serve advertisements.
Improved security level
Besides making a very effective marketing tool, Artificial Intelligence and machine learning for mobile apps can also streamline and secure app authentication. Features such as Image recognition or Audio recognition makes it possible for users to set up their biometric data as a security authentication step in their mobile devices. ML also aids you in establishing access rights for your customers as well.
Apps such as ZoOm Login and BioID have invested in ML and AI application development to allow users to use their fingerprints and Face IDs to set up security locks to various websites and apps. In fact, BioID even offers a periocular eye recognition for partially visible faces.
Now that we have looked at the different areas in which application of AI and ML can be incorporated in the mobile app, it is now time to look at the platforms which will make it possible, which we in our capacity has experienced AI app development company have been relying on, before we head on to the strategy that a business should devise to ensure a smooth implementation.
User engagement
The AI development services and solutions engage organizations to offer balanced customer support and a span of features. Few apps provide small incentives to the customers so that they utilize the application consistently. Also just for entertainment purposes, chatty AI assistants are there to help the users and hold a discussion at any hour.
Data mining
Data mining, also known as data discovery, includes analyzing the vast set of data to gather helpful information and collect it in different areas, including data warehouses and others. ML offers data algorithms that will generally improve automatically through experience based on information. It follows the way of learning new algorithms that make it quite simple to find associations inside the data sets and gather the data effortlessly.
Fraud detection
The cases of fraud are a worry for every industry, particularly banking and finance. To solve this problem, ML utilizes data analysis to limit loan defaults, fraud checks, credit card fraud, and more.
It also assists you with determining an individual’s capability to take care of a loan and the danger related with giving the loan. E-commerce apps frequently exploit ML to discover promotional discounts and offers.
Object and facial recognition
Facial recognition is the most loved and latest feature for the mobile apps. Facial recognition can help improve the security of your application while additionally making it faster to login. It also helps in securing the data from unknown sources.
With the improved security, facial recognition can be utilized by medical professionals to evaluate the health of patients by examining a patient’s face.
Best Platforms to Develop a Mobile App with Machine Learning?
1. Azure
Azure is a Microsoft cloud solution. Azure has a very large support community, and high-quality multilingual documents, and a high number of accessible tutorials. The programming languages of this platform are R and Python. Because of an advanced analytical mechanism, the AI app developers can create mobile applications with accurate forecasting capabilities.
2. IBM Watson
The main characteristic of using IBM Watson, is that it allows the developers to process user requests comprehensively regardless of the format. Any kind of data. Including voice notes, images or printed formats is analyzed quickly with the help of multiple approaches. This search method is not provided by any other platform than IBM Watson. Other platforms involve complex logical chains of ANN for search properties. The multitasking in IBM Watson places an upper hand in the majority of the cases since it determines the factor of minimum risk.
3. Tensorflow
Google’s open-source library, Tensor, allows AI application development companies to create multiple solutions depending upon deep machine learning which is deemed necessary to solve nonlinear problems. Tensorflow applications work by using the communication experience with users in their environment and gradually finding correct answers as per the requests by users. Although, this open library is not the best choice for beginners.
4. Api.ai
It is a platform that is created by the Google development team which is known to use contextual dependencies. This platform can be very successfully used to create AI based virtual assistants for Android and iOS. The two fundamental concepts that Api.ai depends on are – Entities and Roles. Entities are the central objects and Roles are accompanying objects that determine the central object’s activity. Furthermore, the creators of Api.ai have created a highly powerful database that strengthened their algorithms.
5. Wit.ai
Api.ai and Wit.ai have largely similar platforms. Another prominent characteristic of Wit.ai is that it converts speech files into printed texts. Wit.ai also enables a “history” feature which can analyze context-sensitive data and therefore, can generate highly accurate answers to user requests and this is especially the case of chatbots for commercial websites. This is a good platform for the creation of Windows, iOS or Android mobile applications with machine learning.
6. Amazon AI
The famous AI based platform is used to identify human speech, visual objects with the help of deep machine learning processes. The solution is completely adapted for the purpose of cloud deployment and thus allowing you to develop low complexity AI-powered mobile apps.
7. Clarifai
The solution based on AI analyzes information with the help of complicated and capacitive algorithms. The apps made using the platform (which can be integrated in-app using REST API) – can adapt to individual user experience – which makes it the most preferred choice for the developers who wish to invest in Artificial intelligence for app development to enter the world of intelligent assistants.
With this, you now know that the ways your mobile app can become an AI app and the tools that will help with Machine learning and AI app development. The next and the last and the most important part that we are going to discuss now is how to get started.
How to Start Implementation of AI into Apps?
Implementation of Artificial or Machine Learning in an application calls in for a monumental shift in the operation of an application that works sans intelligence.
This shift that is asked for by AI is what demands to look at pointers that are very different from what is needed when investing in the usual mobile app development process.
Here are the things that you will have to keep into consideration when managing an AI project:
Identify the issue to solve through AI
What works in case of applying AI in a mobile app, as we saw in the first illustration of the article is applying the technology in one process instead of multiple. When the technology is applied in a single feature of the application, it is much easier to not just manage but also to exploit to the best extent. So, identify which is that part of your application that would benefit from intelligence – is it recommendation? Would the technology help in giving a better ETA? – And then collect data specifically from that field.
Know your data
Before you look forward to AI app development, it is important to first get an understanding of where the data would come from. At the stage of data fetching and refinement, it would help to identify the platforms where the information would come from in the first place. Next, you will have to look at the refinement of the data – ensuring that the data you are planning to feed in your AI module is clean, non-duplicated, and truly informative.
Understand that APIs are not enough
The next big thing, when it comes to implementing AI in a mobile app is understanding that the more extensively you use it, the more unsound Application Programming Interfaces (APIs) would prove to be. While the APIs that we mentioned above are enough to convert your app into an AI app, they are not enough to support a heavy, full-fledged AI solution. The point is, the more you want a model to be intelligent, the more you will have to work towards data modeling – something that APIs solely cannot solve.
Set metrics that would help gauge AI’s effectiveness
There is hardly a point of having an AI or Machine Learning feature implemented in your mobile app until you also have the mechanism to measure its effectiveness – something which can only be drawn after getting an understanding of what exactly do you want it to solve. So, before you head out to implement AI or even ML in your mobile app, understand what you would like it to achieve.
Employ data scientists
The last most important point to consider is employing data scientists on either your payroll or invest in a mobile app development agency that has data scientists in their team. Data scientists will help you with all your data refining and management needs, basically, everything that is needed on a must-have level to stand and excel your Artificial Intelligence game.
This is the stage where you are now ready to implement the intelligence in your mobile application. Since we talked about data a lot in the last segment and because data is an inherent part of Artificial Intelligence, let us look at the solution of problems that can arise out of data as the parting note.
Feasibility and practical changes to make
Now that you have known the which, why and how about the implementation of AI and Machine Learning apps, you might have an idea regarding a plan in mind like what steps should be taken as a top priority and how your application would work/appear, once the changes are made. Along these lines, it is an ideal opportunity to perform a couple of checks prior to moving ahead, for example, –
Perform a quick possibility test to know if your future execution will profit your business, improve user experience and increase engagement. A fruitful upgrade is the one which could make the existing users and customers happy and attract more individual towards your product. If an update is not expanding your efficiency, then there is no reason for putting in effort and money for it.
Analyze if your current group can deliver what is required. If there is less or no internal team capacity then you need to hire new employees or outsource the work to a reliable and expert artificial intelligence development company.
Data integration and security
While implementing Machine Learning projects for mobile applications, your app will require a better information configuration model. Old data, which is composed in a different way, may influence the effectiveness of your ML deployment.
When it is decided what abilities and features will be added in the application, it is important to focus on data sets. Efficient and well organized data along with careful integration will help in providing your app with high-quality performance in the long run.
Security is another basic issue, which can’t be overlooked. To keep your application strong and secure, you need to think of the correct arrangement to integrate security implications, clinging to standards and the needs of your product.
Use strong supporting technological aids
You need to pick the right technology and digital solutions to back your application. Your data storing space, security tools, backup software, optimizing services, and so on should be strong and secure, to keep your app consistent. Without this, the drastic decrease in performance may occur.
Solutions to the Most Common Challenges in AI Technology?
Like any other technology, there is always a series of challenges attached to AI as well. The basic working principle behind machine learning is the availability of enough resource data as a training sample. And as a benchmark of learning, the size of training sample data should be large enough so as to ensure a fundamental perfection in the AI algorithm.
In order to avoid the risks of misinterpretation of visual cues or any other digital information by the machine or mobile application, the following are the various methods which can be used:
1. Hard sample mining
When a subject consists of several objects similar to the main object, the machine is ought to confuse between those objects if the sample size provided for analysis as the example if not big enough. Differentiating between different objects with the help of multiple examples is how the machine learns to analyze which object is the central object.
2. Data augmentation
When there is an image in question in which the machine or mobile application is required to identify a central image, there should be modifications made to the entire image keeping the subject unchanged, thereby enabling the app to register the main object in a variety of environments.
3. Data addition imitation
In this method, some of the data is nullified keeping only the information about the central object. This is done so that the machine memory only contains the data regarding the main subject image and not about the surrounding objects.
Concluding Thoughts
Now that you know the reasons and how to implement mobile apps, it is time to apply the top-notch performance and quality for AI and ML together to bring out the best in the application. AI and ML together are the future of advancement of mobile app development.
If you are still confused and want to clear your doubts, you can contact us. If you are looking to develop an app that is advancing with the time and technology and want to update your existing app with all the latest technology features, then you should partner with ML and AI development company that is well-adapted with the changing market needs. You can also opt for professional development providers in your area like AI development services USA or other regions. But make sure you choose the best to get quality results.
Rapid innovation and productivity breakthroughs require an accelerated digital transformation strategy that melds people, business processes, advanced analytics, and new human/machine interaction technologies.
Today, it is the supervised machine learning segment of AI that is generating the most economic value. But as digital transformation accelerates, the abundance of data that AI can consume will drive the speed of AI adoption even faster, including its unsupervised learning segment.
Ask Alexa to summarize the meeting minutes
We need only look at how quickly conversational AI (CAI) has become part of our everyday lives as we query Alexa, Siri or Cortana. But in the enterprise the interactions can be extremely complex, such as “Hey <CAI>, summarize the minutes and action items from the recording of the last board meeting.” We are limited by only our imagination and — significantly — access to high-quality, well-organized data.
The accelerated AI adoption will in turn drive better understanding of how to customize AI for the relevant business context and drive digital transformation to new levels. It will provide instant measures of business performance down to the smallest task, leading to more predictable business outcomes, as well as enhance productivity and 24×7 business operations through automation of business processes and algorithmic work.
Manage advanced analytics as assets
As AI permeates every facet of the organization, organizations will need industrialized AI with strong governance and data quality. They will need to manage analytics models as assets to avoid algorithmic bias, retrain analytics models in a timely manner and ensure that data privacy and regulatory policies are properly implemented.
As we become better at blending advanced analytics technologies with how we think and work, there will be massive implications for how we run our companies and live our lives. It will be up to all of us to make sure that advanced analytics are used for ethical purposes.
Organizations should define their long-term AI objectives, clearly understand where and how new business value will be created, and design their digital journey maps. Once a business outcome and measurable business value is identified, organizations should proceed with developing analytics and AI/Machine Learning models and implement them in business operations.
Successful AI implementations rarely hinge on the unique innovation of a specific algorithm or data science technique. Those are important factors, but even more foundational to successful AI enablement are the core data operations and enabling platforms. These act as the fuel and chassis of the AI machine that a business must build and evolve for continued competitive advantage.
Here are the five foundational elements to be addressed to enable a successful transformation to an AI-empowered business:
1. Define an integration strategy for embedding AI and analytic insights into business operations
Successful digital transformations focus on evolving and optimizing business operations through the better use of data assets combined with modern technologies such as machine learning, AI, and robotics. These paradigm shifts result in the creation of new operating patterns rather than simply more efficient legacy operations. In this way, digital transformation represents the enterprise operations in the way the business wants to be run, rather than the way it has been running due to technical and operational limitations and barriers constraining it.
To go beyond siloed or single-use insights and fully benefit from AI and analytics, it must first be decided how the business desires/needs to function in the future. Determining your business transformation priorities then evaluating the advanced technology and data science options for addressing them is a key step towards maturing and evolving to a data-driven enterprise. This understanding will identify the type of AI and analytics that will be the most beneficial for your business and the technology required to accomplish it. Additional thoughts on overall data strategy can be found in the white paper “Defining a data strategy: An essential component of your digital transformation journey.”
2. Establish a holistic data and analytics platform
Selecting and configuring an integrated set of technologies to support data management and applied analytics is a complex challenge. Fortunately, solutions to such technical integration have matured in recent years into pre-built core platform components and best practices that can be accelerated and augmented further through value-added third party software and partner services.
Cloud-based modular platform environments bring together technical flexibility and financial elasticity with an ever-maturing technical set of capabilities, including interoperability across hybrid environments that include legacy on-premises deployments and geographical federation. In addition to open source components, such platforms include the option to integrate select native modules and commercial technology components for broader flexibility and a customizable architecture that can be deployed as prebuilt services for simpler adoption and integration.
The tools to support and enable AI integration into business operations are beginning to leverage the same capabilities they enable. For example, data pipeline tools are beginning to use machine learning (ML), metadata tools are using AI and ML to identify content and auto-generate the metadata on the fly, and user interfaces are embedding chatbot and digital assistant AI technology to guide end-users through the complexities of data science for accelerated insights. By adopting toolsets and platforms that have embedded AI and analytics in their core, the use and integration of AI into business operations will be more natural and accelerated across the enterprise community.
3. Know your data
Fully understanding the data your enterprise has access to may seem like a fundamental need when supporting operational reporting and analytics within the enterprise. Many organizations, however, stop with simple source systems listings and maybe some high-level business definitions and schemas.
Truly knowing your data includes a lineage-based view of where the data comes from and what business process it represents, what operations are performed on it prior to your access, what transformations are performed thereafter, the associated level of quality and, of course, the core “Vs” of big data; volume, velocity, variety, veracity and value.
Building an easily searchable, enterprise-wide data catalog of information is one of the first steps towards empowering the enterprise with data. Exposing the catalog to a crowdsourced editing model ensures richer content and wider adoption of such information across the enterprise.
4. Control and govern your data
Understanding the types of controls and governance your data needs is a natural extension of knowing your data. By reviewing the types of data and their business content with associated metadata, enterprises can align and define proper governance and compliance policies related to internal policies and to external standards such as HIPAA for healthcare, PCI DSS for secure payments, and PII and GDPR for data privacy.
It is also important that source data retains its original state integrity without over processing or over-filtering it. Aligning to the data pipeline workflow principles of “ingest, refine, consume” allows the same data to be leveraged efficiently for different uses with different policies and operational needs while ensuring security. Such controls can also be extended to support and define quality standards required for using the available data and to trigger any necessary control processes to correct or adjust for deviations in such standards.
You can safeguard proper policy compliance, improve ease of use and increase trust and adoption by the end user community by ensuring that governance controls are built into your data management operations from the start.
5. Simplify access to your data
To further expand the adoption of AI and analytics, it is important to simplify and automate data workflows and the use of analytical tools. Reducing manual process overhead can significantly improve time to market and quality of results. Providing clear and flexible governance allows enterprises to control such access without it becoming a barrier for use.
Self-service leads to rapid user community adoption and better integration of data and insights into business operations. By reducing the dependency on IT resources for complex data integration and preparation tools, average business users can interact with the data through simple common interfaces and receive results in simple and easily consumable formats.
Once these foundational elements are in place, organizations can take full advantage of the unique value proposition offered by advanced analytics and AI. And they can do so with the confidence that the resulting solutions are enterprise-grade in their scalability, security, quality and usability. It is this kind of confidence that leads to business user adoption and, in turn, successful digital transformation.
Artificial Intelligence (AI) and Machine Learning (ML) are rapidly evolving to help companies with both digital transformation and innovation. There has been a lot of hype and discussion about these topics, and, in a very short time, we have moved the conversation from “AI is cool” to “AI can drive specific business outcomes.”
Experience has allowed companies to clarify the economics of AI and reduce time to value. In addition, the technology has continually improved, with advancements including:
extracting unstructured data for improved insights and processes
moving from simple chatbots to more sophisticated conversational assistants that are smart, use more natural language interaction (text and/or voice), and enable the initiation of transactions
integrating operational and knowledge management systems
As our clients start to understand more about AI/ML, there are 2 key questions typically asked:
Question 1: “How do AI and Machine Learning differ from traditional programmed systems?”
There are three different capabilities that AI/ML typically extends and enhances into existing applications. One aspect is understanding – enabling systems to understand language, other unstructured data, including pictures, just like humans do. A second aspect is reasoning – enabling systems to grasp underlying concepts, form hypotheses, and infer and extract ideas, similar to humans. The third aspect is focused on learning – improving over time and avoiding repeated mistakes. These capabilities, often referred to as U-R-L (understand, reason, learn), are easily integrated into existing applications as consumable APIs, can reduce time to value.
Question 2: “Where is the value from Artificial Intelligence/Machine Learning?”
As we look across client environments, most clients are very comfortable with structured data that is within their firewall – things like transaction systems, customer records, and even predictive models. Many analysts estimate that 80% of data created today is unstructured, which requires clients to expand their current perspectives in 2 dimensions:
Structured to unstructured, such as documents, transcripts, social media, weather, images, IoT and sensor data, and news
Within the firewall to outside the firewall, including public data, licensed private data, and new types/sources being created every day
From a value perspective, the enticing aspect of AI and machine learning is connecting these structured and unstructured data types within and outside these walls. This enables richer, more, and unexpected insights, new business processes, and improved workflows at dramatically reduced cost levels.
As AI and machine learning mature, three use cases patterns have emerged around customer care, human capital management and the rethinking/reimagining of processes thanks to insights gained from unstructured data and natural language capabilities.
We’ll expand on these use cases and how we are applying AI/ML for our clients in our next blog post.
With most innovative technologies, at some point in the solution evolution there’s a tipping point from “this is cool” to “there are real business outcomes to gain.” We are now seeing that shift with artificial intelligence (AI) and machine learning (ML), especially in three major use case patterns:
1. Customer care
When it comes to customer care, many companies face a similar set of challenges: Customer service representatives (CSRs) typically need to view multiple windows on multiple screens to find answers to a caller’s questions. This results in calls taking a long time to handle, frustrated customers, lower CSR productivity and overall poor customer satisfaction.
AI and ML can help change these outcomes. One insurance customer implemented AI and ML to transform its contact center, which responds to approximately 5 million voice calls per year. It also introduced an intelligent voice agent to respond directly to 50-70% of all customer inquiries. The power of AI and ML extended and enhanced the customer experience and the productivity of CSRs, who are now able to deliver more accurate and consistent answers, at lower cost and greater speed. As a result, we have also seen reduced attrition, as the CSRs are happier in their roles and require less training time to become self-sufficient. Overall, by using AI and ML, the contact center is able to provide a high level of customer service, enhanced customer satisfaction and more efficient engagement.
In another example, a customer service center handling 60M service requests per year is implementing AI “smart chat” to transform customer service, drive better quality, improve consistency and lower costs.
The transformation of an operation as complex as a customer call center is a multi-step journey:
Organizing and visualizing the data required to support typical workflows is a critical early step and can include authenticating/validating the caller, providing easy access to the data from different back end systems, initiating transactions and closing out the call.
A second step adds natural language processing (NLP) to allow the CSR to ask more complex questions. NLP uses a trained ML model to determine the most likely answers from a collection of large, unstructured documents.
A third step includes self-service for external callers, using voice and natural language to respond directly to client inquiries without requiring interaction with a CSR.
While the steps themselves remain the same, the sequencing may vary to align to the specific outcomes the organization desires.
2. Human capital management
By applying cognitive capabilities to human resources, companies are able to transform their human capital management (HCM) processes.
One valuable use case improves the employee experience by leveraging intelligent natural language conversation services to integrate data in operational HR data systems, such as Workday, and in knowledge management systems that explain policies, procedures and guidelines. Benefits include improved employee engagement, higher productivity and lower costs.
Other potential HCM use cases that are ripe for AI and ML include recruiting, selection and on-boarding of candidates. AI and ML models are already being used to help analyze and match job descriptions and candidate resumes, and to recommend potential jobs for existing employees.
3. Process rethinking/reimagination
There are also multiple examples of leveraging AI and ML for rethinking and reimagining processes.
In one example, an insurance company reduced claim settlement time and costs by incorporating AI and ML to improve First Notice of Loss (FNOL) initiation and data collection, as well as claim classification/assignment processes.
Companies can improve the speed and accuracy of FNOL by enabling claimant self-service using natural language voice/chat to initiate a claim. They can integrate external data sources such as weather and employ a fraud scoring model to facilitate claim classification and corresponding assignment to the correct processing alternative. Processing alternatives can include:
Straight-through processing without human involvement
Sending appropriate claims to the legal department for potential early intervention to reduce lawsuits
Rejecting claims because the deductible is higher than the claim
Assigning claims to a human analyst/adjustor
Predictive modeling to triage injured worker claims has been developed and applied to help determine “return to work” outcomes, along with typical treatment plans for various types of injuries (NOT replacing doctors) and estimated treatment costs. This technique optimizes case manager resource assignment and helps injured workers get back to work, improving service quality and lowering costs.
Applying an ML model to extract information from unstructured documents and forms helps to provide insights from previously unanalyzed sources. This capability works by accelerating processing of information buried in those large unstructured documents and drives new insights, lowers costs and improves processing efficiency.
In a fourth example, Anteelo is automating the underwriting process for home inspection to lower costs and improve scalability and overall quality. This use case applies visual recognition to highlight details in home inspection photos and use those details to consult the underwriting guidelines and then augment the underwriting decision.
As you can see from these use cases, the ability to improve the customer experience and gain operational efficiencies through AI and ML are real and very achievable. What other use cases can you think of for your industry? I’d love to hear your thoughts!
To demonstrate the value proposition for Artificial Intelligence(AI) and Machine Learning (ML), we recommend concentrating on unlocking insights from vast amounts of unstructured data to help augment human intelligence, create process efficiencies and lower operational costs. We have also found that the best results tend to come from a business-led/IT supported model.
Four key questions are central to scoping out an initial minimal viable product (MVP) for a valid use case.
Which persona are you trying to assist – external or internal? The answer is not a value judgement, but rather a focus that impacts some of the design considerations and decisions. As an example, in a contact center, the focus for the customer service rep is productivity – quickly giving the rep as much information as possible. This usually means fewer prompts, fewer stops, one- or two-word questions, and fewer and more dense answers. Contrast that with the external customer, where we tend to focus on a warm and engaging experience that incrementally guides the customer to the answers they need. This typically means more prompts, more questions, a wider variety of natural language to ask the questions, and more and shorter (more digestible) answers.
How are you are trying to helpthat persona? This aspect typically helps us hone in on part of an initial business process to understand how we are going transform — whether by adding more data for consideration, augmenting analysis, and/or automating the execution of steps.
Where does the data come from to support this initiative? This is often the most challenging aspect for several reasons: There may be more questions than current answers, the current answers may be inconsistent or conflicting, or the current answers exist, but may not easily understood or comprehensible. All of these situations tend to require focus on curating, modifying, and/or creating content in order to get the right answers.
What is the value? Value can be determined most successfully when we can be as specific as possible. Very often, benefits can be described in terms of metrics. From a best practice perspective, having specific metrics-based objectives makes it clearer for business and IT to determine the value. Examples include: a) higher customer satisfaction, measured by Net Promotor Scores or revenue increases, b) lower costs, shown by reduced handling times or increased processing volumes with the same staff and c) better employee engagement, indicated by reduced attrition and training time.
To further the dialogue as you consider progressing towards transforming your business by leveraging artificial intelligence, here are some additional questions to guide your thinking:
Are you focused on transformation and innovation?
For what initiatives are you considering AI/ML support?
What business outcomes are you driving?
What is your current level of experience with AI and ML?
Have you identified any use cases or do you need some assistance in defining them?
The key to evolving your approach from MVPs to multiple initiatives developed and deployed in production at scale, is to effectively balance faster time-to-value and major transformation outcomes. This balance demonstrates business results in smaller time increments and sets the foundation for flexible and continuous transformation and innovation.
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