How Businesses can Scale Practical AI Products in a Post-COVID world with Matthew O’Kane


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Matthew O’Kane leads Cognizant’s AI & Analytics practice across Europe.  His team helps clients modernize their data and transform their business using AI. Matthew brings close to two decades of experience in data and analytics, gained across the financial service industry and consulting. Prior to joining Cognizant, he led analytics practices at Accenture, EY and Detica. 

Over this period, he has delivered multiple large-scale AI/machine learning implementations, helped clients transition analytics and data to the cloud and collaborated with MIT on new prescriptive machine learning algorithms.  Matthew is passionate about the potential for AI and analytics to transform clients’ businesses across functional areas and the customer experience.

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Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:14) – I finished a math and stats degree. Got interested in statistics, joined banking and realized there was tons of data I could play around with and apply predictive models to. But almost 20 years ago, I never realized how important AI in Analytics will become as it is today. I joined Cognizant a year and a half ago to really drive what the next level is around analytics and AI, how clients are really scaling AI across the companies and it’s a big engineering effort now. Hence why we’ve got a big team of people who do all the things you need to get started on around AI.

(04:03) –  I still go back to the underlying machine learning algorithms that have been around for a long time. Some of the models have gotten more sophisticated and computing power has come along and cloud computing power has come along too, to help us actually power these more and more.

(06:04) –  We’re obviously going to enter a large recession. The type of AI and the type of work you can do within the ISP will change dramatically. Things like revenue generating opportunities for AI are going to be less on the priority list for at least the next year, and it’s probably going to be more on cost reduction

(07:39) – If we say it’s moving from revenue generating opportunities to cost optimization opportunities, most organizations are gonna see a big shift towards automation, around AI, and we’ve seen a lot of clients are working at the moment looking to apply AI in new areas they probably hadn’t thought about. Automation and the fact that automation means less jobs in a recession and it takes away human effort, we have to square up for what is going to be the reality of the moment.

(09:39) –I don’t think privacy is going to go away. It still seems to be top of priority, we’re just trying to solve privacy problems by Webex and by remote working and by email rather than face-to-face but it’s still a big issue and coming out of this if you’re going to apply more data and AI to your business, the privacy aspect goes up and is always going to be top of the agenda.

(11:03) – There are still fairly distinct areas where humans are good and certain tasks where machine learning is good at a task, so it’s really about taking another look at every process you have and re-imagining it within this new digital AI world. This is certainly a crisis that has created significant demand in some areas and a drop in demand in other areas. That’s how it’s going to play out going forward so we need to be shifting humans to the right areas.

(12:41) – Typically if you send an engineer out to solve a problem they’re not the expert; there’s only about five experts in the entire company. But by taking some of the knowledge from those five experts and turn them into some models you can infuse the insight and the knowledge from the five SMEs into the day-to-day work that the engineers are doing and they can use augmented reality to actually see something. 

(14:39) –  It allows a human to essentially take what’s in their brain and turn it into a model, it allows your experts in the organization, your best claims handler, your best salesperson, your best engineers to take what they have and their understanding and turn them into a set of rules. This is called data programming and these rules can then be turned into a neural network model. AI is very good at processing all the massive data, but it doesn’t have the intuition that’s held inside of an expert’s hat.

(17:38) – It turns around to the ethical AI Space as well as the fact that if the research you’re doing and what you’re developing isn’t open and people can’t go in to get help and look at it and look at your code and understand how it works. What my team does is take the complex research and a client problem and try to fit the two together and that’s usually the hardest thing to do, getting something that impacts clients business.

(19:20) – It’s not just about algorithms and code. We have to convince the executives in our company to change their business or some new deep learning could do to the actual outcomes.

(20:31) – The UK government has been doing a lot of research on AI, they’ve used that to develop a set of ethical AI pieces, a good set of standards. Now we’re working with the UK government infusing ethical AI into every single machine learning model or project that they run. 

(23:36) – From the data scientist all the way through to the product engineer if the business where we’re actually applying the AI is making different decisions, that responsibility has gone all the way through the organization. 

(25:33) – Data is always biased if you look at that data without realizing COVID etc was happening. There’s always something behind data and there’s something generating that data.

(27:35) – A lot of execs in companies, people that are budget holders can control where AI is used and how they can accelerate and improve business results.

(28:39) – A lot of companies have worked out how to operate remotely, and that’s a very good time to open up about ideas, about how you could be scaling AI in the organization, how you can really get going and change things so now is the time to have that conversation.

(29:47) – It’s important getting the right data platform before you can do AI.  A lot of clients that are going back and saying we need to solve our data, modernize our data, create the right governance model around it usually move on to the cloud. That’s what most clients are doing, enabling it and then really scaling AI.

(31:37) – They’ve really got to reduce costs, reduced errors, all these things that are dragging their business down, if we can really help in that area we can really speed up growth in the local companies.