Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

AiThority Interview Series With Jayant Lakshmikanthan, CEO and Founder at CLARA analytics

Interview with Jayant Lakshmikanthan, CEO and Founder, CLARA analytics_cue card

The way we interact with machines will fundamentally change. The common theme across all of this is really that technology is becoming more and more invisible. It is doing more than ever before, but it won’t get in the way.

Know My Company

Tell us about your interaction with AI and other intelligent technologies that you apply in your daily life.

As much as I am coming to rely on various AI-based technologies to help my own life run smoother, I’m a bit obsessed with applying AI to the commercial insurance industry in order to drive down costs. So, I am hyper-focused on making AI come to life in the context of claims operations.

To do this, my team and I are leveraging the underlying technology from easy-to-use services like Google Assistant and Amazon Alexa and using that technology to solve very specific problems. We believe that by doing so, we can bring down the claims cost structure considerably and significantly improve claim outcomes.

How did you start in this space? What galvanized you to start CLARA analytics?

CLARA analytics was carved out of a company called LeanTaaS, which was applying data science and machine learning to the healthcare industry. I served in multiple capacities there after my time at Novartis, so it was a natural progression.

In terms of what we saw that necessitated the need to start CLARA and ultimately spin it out as an independent company, it was the desire to dive deep into an area that could desperately benefit from AI and machine learning in terms of cost and productivity. We found a great match between hammer and nail, with the hammer being machine learning and the nail being claim operations.

How do you differentiate CLARA from other AI-as-a-Service providers?

It’s our singular focus and how we apply the underlying technologies — machine learning concepts, the ability to handle unstructured data, natural language, voice, images, etc. — to create compelling solutions that drive efficiency and cost reduction in commercial insurance. We stand out in this regard.

How do you see the raging trend of including ‘AI in everything’ impacting businesses?

There are three ways that I see AI truly impacting business as well as life at large:

By removing routine or mundane tasks from people’s plates, we can increase automation and open new opportunities to leverage human intelligence where it matters most.

AI can provide highly valuable augmentation tools that help people make much better, more informed decisions which take all sorts of relevant data into account — data that could never be considered previously.

Because, AI-based solutions can handle massive amounts of structured and unstructured data — and can learn on their own — users gain very nuanced levels of information, which they can then apply to personalize offerings. This will ultimately enable new and exciting business models to emerge.

What are the biggest challenges and opportunities for AI companies in dealing with inflating technology prices?

The biggest challenge I see is honing in on where to concentrate. Pick one battle you want to fight, go all out, and don’t spread yourself too thin. You must execute at a very high level in order to succeed. If you try to take on too much, your core product or service suffers, and it won’t justify its cost.

In terms of opportunities, there are many. To capitalize on any, however, companies must demonstrate a high ROI. Look at where there is a burning need and address it. Get people comfortable with what you offer, and then add more to it.

Take Google as an example. Its focus, in the beginning, was to serve information well — better than anyone else could. By conducting very accurate searches, it earned the right to serve ads and, over time, added several more lines of business that were offshoots of its core functionality. As part of your company’s journey, concentrate intently on where you can create the most value and make the biggest impact first. Then expand once you earn the right.

How should young technology professionals train themselves to work better with AI and virtual assistants?

First, get in the right mindset. AI will clearly improve our quality of life down the road, so it is best to embrace the technology. It’s not a threat but rather an extension of the technology journey, which is constantly evolving. Remember, we had to learn how to use the internet; we had to learn how to use social media. Similarly, there is a need to understand how AI works and what it can do.

It is essential to get to know the basics behind AI and how it comes to life, regardless of whether someone has a technical bent or not. AI is a little different than the technologies that came before it in that it has the ability to handle natural forms of data like free text, voice, and image processing as well as the ability for the machine to learn on its own. But if someone spends two or three months diving into a Udacity program, for example, they will be well situated. It is easy to reach a certain level of depth that will serve them well in the future.

And it bears saying that to move the needle forward with AI-based technologies, professionals should think in terms of creative problem solving; consider where there are pain points and how AI can be applied to address them. Those who do can thrive in this new world.

Tell us how you deliver AI claims tools for Workers’ Comp insurers? What bigger role could AI play in insurtech?

In terms of CLARA specifically, we apply AI and machine learning to claims operations in order to reduce costs and achieve better outcomes. We want to empower any claims team out there by delivering smart, easy-to-use tools that provide instant access to information that enables them to do their jobs better.

CLARA currently offers three products, CLARA providers, CLARA claims, and CLARA litigation, each devoted to a different aspect of the process. In delivering these products, we promise customers that no matter what their cost structure, workflow or scale, we will bring the power of AI to them in a way that makes sense, fits operationally and quantifies value.

Regarding the role of AI in insurtech, there are multiple aspects in the insurance flow and value chain where AI will be important. Aside from claims operations, underwriting stands out. There are companies emerging in this area that are using AI to serve small customers better. They can target things differently and create pricing in a much more nuanced way using AI. Customer interactions are also an area that can benefit from AI-based technologies. Connections with customers can be made much richer.

How do you consume information on AI/ML and related topics to build your opinion?

Honestly, I rely on AI day in and day out, which shapes my opinion. AI is everywhere. Search engines are really massive AI engines. When I talk to Alexa, I am using AI. Even my calendar today uses AI to schedule my meetings. What I see is that I can access all sorts of information orders of magnitude faster and more efficiently. That’s on the practical side.

I also find that conferences are excellent places to get a quick view of what’s emerging. If I can’t make it to an event, I’ll read the recaps online to get a good sense of what was discussed. Analyst coverage on AI is also useful. These reports and events help me to identify companies doing interesting things, and I’ll go to their websites to read about how they are approaching a problem.

What makes understanding AI so hard when it comes to actually deploying them?

Fundamentally, if you take AI as a model or algorithm and leave it at that, it can be very difficult to understand. It’s not a rules-based thing. AI looks at patterns, relationships and various factors to come up with an answer, which people may struggle to explain.

Additionally, AI works both mathematically and operationally. It’s the operations piece that many people fail to consider. Yes, AI can provide you with this great, rich data, but is it the information you need? Are you looking at the right pieces to solve an issue?

What are you going to do with what you learn? How does it fit into your claims process? These questions matter, and without answers, AI deployment will be both a struggle and a disappointment.

How does an end-to-end solution with data capturing of online behavior help a company better compete with the likes of Amazon on Google search?

It’s about building a solution that is optimized to solve one problem. The deeper you go toward solving it, the more valuable the solution becomes. Google and Amazon have done a terrific job of creating a platform you can use for this, but they are not really focused on building solutions that strategically solve an isolated problem.

What companies like CLARA are doing is looking at the different pieces of the puzzle and putting them together within the context of a particular problem. This type of deep solution typically addresses a company’s needs better than a more generic one.

Which is harder — choosing the AI solutions or working with them?

I tend to look at a situation and say “here is where we could use AI to make this better.” Choosing which technology to apply and how is part of solving that problem. This is often very difficult. It’s not as though you are choosing between vendors so much as deciding what you want to accomplish and the best way to achieve it. But then you have to actually work with what you select.

On the whole, choosing takes less time, but working with AI-based technologies requires more effort. There is a constant cycle of launching, measuring and iterating over and over again. Ease of use when it comes to AI-based products takes some patience. Think about when the iPhone launched. It brought so many technologies into one nice format, but it took a while for people to learn how to really use its different features and to even understand what an app was. Its interface has always been simple, but to uncover its true capabilities took time. Similarly, we have to build fluency for AI.

How potent is the human-machine intelligence for businesses and society?  Who owns machine learning results?

AI by itself is not much use; AI coupled with human intelligence is what will drive massive change. Consider the process: The machine says something; the human reacts to it; the human makes a judgement, acts on it and adds to it; the machine learns from it and modifies what it does the next time; and the cycle keeps going. It may seem like something new, but we’ve been doing this all along.

Think about electric cars; and soon there will come a time when we are completely comfortable with self-driving cars. The cases AI is applied to are much greater, and both machines and humans are reacting faster. The pace of movement on all fronts will only continue to increase.

Now, who owns the results is a very complicated question. It depends a lot on circumstance, partnerships and the model in place. There will be a lot of dialogue over who owns the back-and-forth with end users. It depends a lot on the situation.

Where do you see AI/machine learning and other smart technologies heading beyond 2020?

The way we interact with machines will fundamentally change. The common theme across all of this is really that technology is becoming more and more invisible. It is doing more than ever before, but it won’t get in the way.

Also, the interaction between humans is going to continue to be a rich source of satisfaction for people, and technology can help us facilitate better personal interactions. This could be through Augmented Reality that can project someone’s image in front of us as we talk or something not yet discovered.

The good, bad and ugly about AI that you have heard or predict?

The Good:

AI is opening up many more opportunities to improve processes that will ultimately make life better for society at large.

The Bad:

There is a constant perception problem that AI is going to take over, that it’s a threat, and it’s because we are still in the early stages. So many people feel uncertain about how this is going to go, but we have to just keep our heads down and focus on adding value. We just have to fight our way through it to ensure that AI achieves its very positive potential.

The Ugly:

There is always the chance that someone will use AI maliciously. There is always the fear that someone could weaponize AI in different ways. The other way this could turn ugly has to do with the sheer volume of information collected. The companies that own the largest collections of data could gain a pretty unfair advantage in the marketplace. This could turn into an extreme monopoly.

We must guard against this, but regulation will play a role.

What is your opinion on “weaponization of AI”?

It’s like using any technology for harm. It’s really about keeping checks and balances in place to prevent this from happening. We need to have deep discussions about how to use AI appropriately and go use case by use case. We may need to develop some frameworks for an application.

The Crystal Gaze

What AI startups and labs are you keenly following?

CLARA analytics … just kidding. I follow a whole bunch. I like companies that take things we do on a regular basis and give them a huge boost. x.ai is one of my favorites. It is consistently getting better and is a tremendous help to me for simple things, such as answering my email.

Lemonade is another that I like because it is making such bold moves to disrupt an industry. Additionally, OpenAI is a group that is very useful in terms of building tools for the industry at large in an unbiased way.

What technologies within AI and computing are you interested in?

Fundamentally, any technology that makes AI invisible. I’m very interested in natural language processing as well as image processing. When applied to commercial insurance, for example, I see value in being able to take a photo of my broken leg, which happened at work. That photo essentially becomes how I file my workers’ comp claim. But in order to do that, there first needs to be advanced image processing in place and all of the steps behind it.

Integrating voice with other kinds of structured data will also be very important. I believe you should be able to ask your system for answers. For example, just ask the application “what is the status of my claim?” or “which doctor should I go to?”

Augmented reality is another area that is very exciting. I love Magic Leap; it is very disruptive — releasing an AI assistant that you can see, that has a face, that has expressions, and you can interact with it.

As a tech leader, what industries do you think would be fastest to adopting AI/ML with smooth efficiency?

Any industry that has been underserved by technology so far, industries that have just grown incrementally over the years. Insurance is one. There is a lot going on in personal financial management. There are a whole bunch of HR processes as well that can be absolutely transformed. Law enforcement is another area. Basically, any sector that has rote processes in place could benefit from AI.

What’s your smartest work-related shortcut or productivity hack?

Don’t stop a task until you finish — and AI-based technology can help you finish tasks faster. Utilize tools to help you knock something out before moving on to the next thing.

Tag the one person in the industry whose answers to these questions you would love to read.

I don’t know that I can choose one, but I will narrow it to two for very different reasons. The first is Reed Hastings because I have been impressed by the clarity of his thoughts on AI, technology and the evolution of society. I would also like to listen to what Barack Obama has to say because I think he strikes a good balance of understanding overall macro trends from an economic perspective as well as a political standpoint, and he is well-versed in technology.

Thank you, Jayant! That was fun and hope to see you back on AiThority soon.

Jayant Lakshmikanthan is the CEO and founder of CLARA analytics where his focus is on using the power of scalable, cutting-edge data science to transform the claims operations process dramatically by shaving off every day/hour/minute in the course of getting claims back on track. Jayant has architected more than 30 scalable analytic applications and products and holds multiple patents. ok and Twitter.

CLARA analytics drives change in the commercial insurance markets with easy-to-use artificial intelligence (AI)-based solutions that dramatically reduce claims costs by anticipating the needs of claimants and helping align the best resources to meet those needs. Leading examples of our solutions include CLARA providers, an award-winning provider scoring engine that helps rapidly connect injured workers with top-performing doctors, and CLARA claims – an early warning system that helps frontline claims teams efficiently manage claims, reduce escalations and understand the drivers of complexity. Our customers include a broad spectrum from the top 25 insurance carriers to small, self-insured organizations.

Leave A Reply

Your email address will not be published.