WHITE PAPER

THE PRACTICAL APPROACH TO AI

Ahson Pai Global Head, Transformation Consulting

lookdeeper@exlservice.com

April 25, 2019

Written by

Narrowing, Nurturing and Maximizing Results

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For years, AI has been the muse of the entertainment industry, depicted as a super-intelligent entity that can effortlessly power a spaceship (HAL in 2001: A Space Odyssey), anticipate criminal actions (PreCog in Minority Report) and conquer human chess masters (Deep Blue); all while exhibiting the vocabulary and communication skills of a Rhodes Scholar.

This exaggerated portrayal has fueled the belief that AI has near-magical powers when applied to business. That it can step in, take on 50 different functions, and handle them all, flawlessly, from day one.

This belief is pure science fiction.

The reality is, Artificial Intelligence is not a prodigy with a super-human intellect. It is a machine-derived form of human intelligence.

Although AI does have the capability to transform organizations; like humans, it has to be taught how to perform the desired functions, starting with the basics and iteratively expanding the scope of its acquired knowledge over time.

Consider how you work with a new employee fresh out of college. You train her on some basic job functions, assign a mentor to coach her and answer questions, and, then, as her expertise on those basics increases, add more duties to her job. No matter how bright she is, if you simply put her in a room, and expect her to take on all of your

accounting functions with no context, training or help, she will certainly fail.

The same is true with AI. If you apply the technology to a wide berth of end-to-end tasks from day one, it will fail to deliver the desired results.

A better approach is employing what we call “Narrow AI.” The premise is, instead of trying to teach AI to do 100 things well, you narrow the scope to a very specific set of well-defined tasks within a domain, build upon these, and augment the process with human judgment.

When executed well, this combination of artificial and human intelligence working in tandem produces more value for the company than either entity could ever produce on its own.

The Fine Art of Thinking Small

The fundamental principle of Narrow AI is laser-sharp, strategic focus. Organizations zero-in on a specific function that would bring the most business value, and train AI to perform that function. Essentially, you apply the technology where it can accomplish one thing, very well, and build from there.

For example, instead of reading every medical record to interpret every medical diagnosis, the machine reads medical records to identify patients at risk for cardiac arrest.

Artificial Intelligence (AI) is one of the most critical components of digital transformation — but also, one of the most misunderstood.

THE PRACTICAL APPROACH TO AI Narrowing, Nurturing and Maximizing Results

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Instead of reading every incoming email, it only reads and acts upon the ones involving travel reservations.

Instead of reviewing every claim in detail, the machine reviews claims to look for specific characteristics associated with fraud.

The idea is to start small, and, as AI gains proficiency, train it to take on additional, more complex functions that build upon its base knowledge—just as a new employee begins to take on additional responsibilities as his or her proficiency levels increase.

The Critical Elements of AI Training: Intent, Validate and Action

In addition to determining where you’re focusing the technology, the most important factor in a successful Narrow AI initiative is training. Machines can only perform as well as they are taught.

HOW AI LEARNS

In many ways, AI and human beings learn much the same way—by being fed information, reinforcement of that information, practice and repetition. While a human relies on intellect to process data, AI relies on machine learning to absorb and identify patterns in the information it receives. For example, if AI is going to be applied to a narrow invoice receipt function, it is fed a number of sample sets, so it can be trained to distinguish invoices associated with Vendor X from those associated with Vendor Y.

Deep learning, which is a subset of machine learning, enables the machine to train itself through repeated practice. As it repeats the same task, it continually refines its own process. It’s AI version of what a human would call “learning from experience.”

More advanced AI functions employ cognitive interaction capabilities with natural language understanding, which

enables the machine to discern what it needs to do, based on what it reads or what it hears, and respond back in context.

Yet, even with its vast capacity to learn, what AI learns is completely dependent on the breadth and quality of the information it receives.

WHAT IT NEEDS TO KNOW

In Narrow AI, the machine has to learn a lot about a specific function in a narrow domain, so it can master the function it has been given. That learning model falls into three categories: Intent, Validate, and Action. Let’s explore each one in detail.

Intent involves understanding the context of the information that AI has to discern in order to do its job.

If the goal is to train AI to read all incoming emails and handle those related to travel bookings, it has to understand the context. So, it has to be taught the vocabulary used in that domain, like “booking,” “reservations,” “flight status,” “hotel,” and “fare,” so, it can identify the emails related to travel, and understand the intent of the request. Does someone want to make a reservation or cancel one? Do they want to know their flight status, or do they want to change their travel date?

Instead of teaching it every word in the English language, the goal is to enable the machine to develop a narrow set of vocabulary pertaining to its specific focus. Any email that doesn’t fit within those parameters is funneled to a human to handle.

Validate ensures that what the machine discerns (for example, someone wants to cancel a reservation) is actually true. In a voice application, the machine might ask, “Are you calling about your existing reservation?” In email, it might recognize a form that’s used for cancellations to confirm the requested action.

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organizations can quickly realize the benefits.

For example, consider the case of an insurance company that wants to reduce customer service costs by utilizing AI. The first step is looking at the previous patterns of calls to determine how to eliminate those calls. For example, AI can be used to proactively reach out and send status information, which contributes to a significant number of calls to customer service centers. Next, it can identify the best channel in which to engage.

Instead of applying AI to phone calls immediately, it makes more sense to start with digital interaction through chat or

a text-based web interface. The machine learning behind text interactions is much more predictable and manageable than the nuances of voice. Written conversations are relatively consistent, whereas voice involves inflections, accents and the use of filler words, like “um” and “uh,” all of which can impact AI’s ability to infer intent.

Next, the company has to determine what kinds of chat inquiries AI will handle.

The best approach is to start using AI for simple chat inquiries, like the top frequently asked questions, addressing changes or payment receipt confirmation. Then, expand its use to moderate complexity tasks, like handling changes to coverage limits for home and auto policies.

From here, the chat capabilities might be expanded to a more complex application, like customers who are surrendering their policies, supported by human agents who could answer questions regarding tax implications of the change and other, more complex inquiries.

To validate the name of the person making the request, the machine might pull information from the customer master database using a reservation number, a traveler number or, in the case of a call, a phone number.

Essentially, the machine is verifying who the person making the request is, what they want to do, and, in this example, if they actually have a reservation to cancel. It does this by co-mingling data and information from the email or call with other sources, like the customer master database and airline records.

Validate also helps reduce errors that could occur in understanding intent. The intent may be to cancel a flight, but the email may not have the actual date of the flight included. Validating the reservation number helps the machine understand the intent better, without having to ask the user for that information.

Then, the machine can perform the requested Action, cancelling the reservation.

Before the machine can ACT upon the request, it needs to verify for sure the action it needs to undertake. The intent module provides the context, the validate module ensures that it has all of the information it needs, so it can finally perform the requested ACTION correctly. If the ACTION module cannot process the request, it will request additional information or augment the process with human intervention.

Developing a Well-Planned AI Strategy

The whole strategy behind Narrow AI is focusing efforts where the organization can get the greatest business value. By starting small, aiming the solution in the right places, and adding complexity incrementally as it matures,

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If this continues on, the machine could actually be taught to discriminate and respond differently to certain types of customers. So, how does a company combat, and thwart, this problem?

“ Although the romantic notion of AI will continue to be fodder for fiction writers everywhere, the fact is this: AI is not a magic bullet or a standalone strategy”

Step one is acknowledging that any time human inference is involved in a process, there is the potential for bias. So, organizations should put processes in place to ensure they’re using comprehensive data sets that represent all genders, nationalities and ages—and represent them fairly. They should also set up self-governance procedures to ensure human inference applied to algorithms isn’t skewed to an unsupported belief or prejudice. The deep learning and self-learning models for AI are validated and tested for bias.

Finally, it’s important to educate staff members so they fully understand the issue, the ramifications and what they can do to ensure objectivity.

PRIVACY CONCERNS

AI is fueled on large volumes of data. The more information that’s fed into the solution, the more it learns, and the more it refines the algorithms it uses to do its job. As such, its propensity is to learn everything and anything it can about the data sets it is given.

That continual consumption of real-time information has raised some privacy concerns around how that data is disseminated and used. The challenge is striking a balance between facilitating the continual data consumption AI requires to make intelligent decisions and the fundamental privacy rights of the subjects of that data.

The fact that organizations are required to get individuals’

This very calculated approach also enables the company to better manage the change internally. For example, company leaders can ease agents into working with virtual counterparts, and set up a model where digital and human agents work in a complementary manner.

Companies need to prepare themselves for the changed environment. This includes how to compensate and coach supervisors who previously managed an all-human workforce on how to effectively manage a blended workforce of humans and digital workers.

Overcoming the Potential Stumbling Blocks: Bias and Privacy Concerns

The capacity and speed at which AI can learn today has never been greater. However, the vast capabilities of AI can be derailed by two potential issues: bias and privacy concerns.

BIAS

In the AI structure, the machine learns based on the information, cases and inference provided by human beings.

The challenge is, the information and interpretations that are shared by these human workers could be subject to bias.

For example, the data set for customer care training could innacurately portray people from a particular demographic or those supporting a particular ideology in a certain way, triggering the machine to identify a pattern among these particular groups. Or the data could exclude certain groups entirely.

Consciously or not, individuals working in tandem with AI could inflect their own beliefs on what the machine ultimately learns, based on their viewpoints, and the way they handle the exceptions that are funneled to them.

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For example, a financial institution that applies Narrow AI to pre-assess credit risk could, in time, utilize that same engine to provide analytic capabilities around fraud , waste and compliance.

A healthcare organization that uses Narrow AI to identify patients with a predisposition to a particular disease can eventually study those demographics on a macro level to create new treatment protocols.

As the original use case matures, the same technology can be applied to multiple aspects of the business using the same data—and leveraging everything the machine has already learned.

Much like the new employee acquires expertise that expands the value he or she brings to the company, with the right training and mentoring, Narrow AI can do the same thing. The more AI is used, the better it gets. So, it’s a non-depletable resource that continually increases its value.

The Practical Approach to AI is the One that Delivers Tangible Business Results

While the romantic notion of AI will continue to be fodder for fiction writers everywhere, the fact is this: AI is not a magic bullet or a standalone strategy. It’s connected to a broader digital transformation strategy involving the right combination of robotics, automation and supporting technologies.

Although Narrow AI is focused on specific, targeted functions, it is orchestrated as part of a larger, outcome-based transformation, with each component building upon the next to produce tangible business results.

Organizations that take this approach are the ones that will realize the most value and long-term impact.

permission to use their data, and to give them the opportunity to opt-out of having their data collected, compounds the challenge. If too many data sources stop feeding into the machine, the input will be skewed, and the resulting inferences will be inaccurate.

The use of Narrow AI actually mitigates these privacy issues because it requires less information than broader applications of AI.

For example, if Narrow AI is being used to automate airline reservations, all it needs is the date of travel, the destination and the customer’s passenger name record number to handle the request. Narrow AI applied to address changes for insurance customers can identify the caller by the phone number, and only has to collect the new address information.

As regulatory requirements continue to tighten, companies taking this more practical approach to AI, with very specific, targeted use cases, will be less impacted then those that take the all-encompassing, blanket approach that acquires more information than required to feed future data mining exercises.

Realizing the Macro Benefits of a Mature AI Function

Although Narrow AI gets its strength by focusing at the micro-level, investing in this technology does have long-term macro benefits.

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