Evolutionary Leaders Stories – CapGemini & Chata.AI

BottomLine’s Founder, Lisa Genovese, is fascinated by companies that are working on the cutting edge of technology. Most recently, she sat down with Kelly Cherniwchan, Director of Marketing and Customer Success with Chata.AI as well as Chris Rampley, Director of Strategy and Operations, Applied Insights Foundry, at Capgemini to discuss their work in machine learning and A.I. and how it’s going to change the standards for businesses like yours.

We Are CapGemini

We Are Chata.AI

What does your company do?

Kelly Cherniwchan, Chata.AI

We are humanizing how people interact with data. Data is stored in databases, but one of the challenging pieces is getting access to that data. We allow people to ask for things they want to know using their own words and natural language, and our AI interprets that, then determines how to grab that data and present it to the client. The human doesn’t have to worry about what the underlying data structure really is.

Chris Rampley, Capgemini

Capgemini is a global consulting firm commonly referred to as a S.I. Systems Integrations Partner. We do everything from application development to digital strategy and transformation to organizational trade management. We compete with the Deloittes and PWCs of the world with  230,000 employees worldwide. Specifically my job is with the Applied Insights Foundry, where we differentiate ourselves through our value propositions and the solutions that we have for specific business problems. What that means is, I’m not the guy that’s going to tell you that our ability to integrate your SAP ERP into your data and analytics engine is going to be better because it’s Capgemini. I’m going to be the guy that comes and says, “If you have a specific problem around this, I have a solution around that.”

What is the difference between machine learning and artificial intelligence?

K.C.  People use the terms interchangeably, which is unfortunate. Artificial Intelligence is a system that’s been trained. It takes input and produces a result, but the key differentiating factor is that the system can take in information that it has never seen before and infer correctly what the output should be. Machine learning is more of the tools and the systems you use to train an artificial intelligence. It’s about training the system to do what it needs to do.

Artificial intelligence is machines replicating a human task. Machine learning is a part of artificial intelligence. It’s how the machine gets to that point of taking over that specific task. While the concept can be technical, think about it as machine learning is a part of A.I. and the end goal of A.I. is the replication of a human task.

C.R. When I think about artificial intelligence, it is a computer recognizing that an action needs to take place without a human having to be there. A lot of times A.I. is used for situational monitoring. Let’s use fast food as an example. If you have a fast food restaurant, they do a lot of business in the drive-through, and all of a sudden, their drive-through starts to get backed up. If we get to where we have a three or four car queue, it might send an alert to your branch manager saying, “Hey, the situation in your drive-thru is turning yellow.” That’s A.I. When we start talking about machine learning, that’s when we start talking about the machine being able to develop context between two data points on its own and knowing when to send an alert. The machine starts to learn what deviation looks like among the correlations.

How is AI and machine learning impacting your team?

K.C. AI and machine learning is at the core of what we do. It’s what a lot of the people in our company do as a hobby as well! We use our system internally and makes us more efficient. We’re looking at adopting other (A.I.) systems, however we find ourselves wondering if some of these products just sound fancy, or are they actually achieving the end objective? And that’s the challenge in this space, where even we have made the mistake of blasting out the cool buzzwords where at the end of the day, how is it useful, and how does it make life better?

C.R. It’s forcing a cultural shift where our value proposition is centred on business impact and opportunity for our clients, by either solving a very specific problem, opening a very specific door, or driving a specific KPI, but it’s not about the ability to deliver “whatever” on “whatever” platform moving forward. That’s becoming so much more commoditized, that there’s not that much value in it. There’s so much information available on Google that if you want to go the least expensive route and give your employees time, they can find the answers to pretty much any problem they’re trying to solve, just by using tools that are available over the internet.

What is the biggest trend fuelling change?

K.C.  The natural language processing space and the computer vision space are the two biggest areas at the moment. Computer vision is the ability to generate false images, so not only identifying things like faces, or biometrics technology, but it’s a trend where you can use these things called Generative Adversarial Networks to build fake images. You’re seeing these (Deep Fakes) pop up all the time now on the internet. It’s a big trend right now because the research is moving very fast but there’s all of these ethical questions coming up.  On the natural language processing side, a big trend is taking a whole bunch of un-processed data or text, and then using it to create an analysis. An example would be a restaurant owner using something like this to measure the tone of online reviews for their business.

C.R.  I think that the biggest change has been the industrial adoption of these concepts (like Internet of Things) and then the multiplication across different specialties so these things are now embedded traits within other specialized service lines, not their own individual thing. If I go back 5 years, concepts like IoT were so unique that you had specialized practices setting up. Now it’s like talking about what mobile was back in the early 2000’s. It’s all commerce. The customer doesn’t care if it’s a brick and mortar experience or digital.

What’s next for your company or industry?

K.C. Something that’s not quite so exciting, but still very necessary, is the handling of .CSV files. That’s the common export from a database system, and there’s literally thousands of different software applications people use in and out of the ecosystem. They want the ability to get out of the spreadsheet world and analyze all that data. So we’re working a system that allows you to upload a .CSV, and then immediately you can start asking questions. It’s not exciting, but it’s hugely practical and it works everywhere from a small business right up to the huge enterprise.

C.R. As a society we are moving into value-based engagement. People don’t go to Starbucks because it’s the cheapest coffee. They go because they find value in the Starbucks experience, which is a combination of ease of interaction, quality of product, and everything involved in the purchase.

What we are seeing now is people wanting to buy from more people, knowing that they are buying from the most experienced person in that field, and then go to their neighbor who is an expert in an adjacent field. I don’t need one person who’s pretty good at everything, I’d rather deal with someone who is an expert at one thing. You see it when companies acquire startups because the startup gives them something for free, then they pilot something for 6 months and they acquire them, even though they may be able to develop a similar product on their own for far less. Once you prove the value is there, the market will reward you for that value. That’s the biggest thing the AI industry is going to have to compete against moving forward.