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Science Fiction and Fighting Friction: a Conversation with Venkat Chary

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Venkat Chary is currently the CEO of Zenon, a company that delivers AI-enabled automation to financial service, healthcare, and tech-forward companies in both the B2B and B2C categories. He was previously Chief Data Officer at American Express where he began applying artificial intelligence and machine learning to real-world business problems decades ago—in spite of the challenges posed by the hardware of the period!

In a recent Zetta Bytes Live chat with Jocelyn Goldfein, Venkat told listeners that his first exposure to AI and ML came when watching and reading science fiction. “For me it was Star Trek and Isaac Asimov,” he recalled. “That was the future. I wondered if that was ever going to be real, or if we’d see it in our lifetimes.”

Over the course of our conversation, Venkat emphasized the need for founders and entrepreneurs to find ways to work within legacy systems and cultures, acknowledging and smoothing sources of friction rather than insisting on some idealized all-or-nothing technical revolution.

But in some ways his vision is true to the AI in SciFi, in which humans and machines formed seamless teams, without an intervening layer of technologists. “It’s fantastic that we are able to see the beginnings of the transformation; the beginnings of a value-add to society,” he said. “We’re seeing more human-machine augmentation, which is really going to make a difference to the future.”

What follows is an edited summary of Venkat’s chat, which covered Amex’s early adoption of AI and ML and some very pragmatic advice to listeners and then circled back almost to where it began. Star Trek, after all, was fundamentally optimistic; so is Venkat. He even made a few ethical observations that recalled Asimov’s “First Law of Robotics”.

But to match science fiction, we first must fight friction…

“When I joined American Express in 1998, we were on tapes,” Venkat recalled. “That first request would be to load the tapes onto the main frame before I could even extract data or run a model.”

Most people probably think the FAANG companies were the first enterprises to put machine learning into production use. But in spite of the technical challenges, Amex already used ML for real-time fraud detection and real-time credit assessments.

As Venkat explains, the banks didn’t necessarily want to innovate. “If you’re making two, two-and-a-half percent on a transaction, and you’re losing that much on credit and fraud… you’re forced to innovate,” he says. However, those early models were necessarily built on samples of data. “If you tried to process the entire model or build a model on the full sample of data, um, even as recently as 10 years ago, SAS on Teradata, it would just collapse.”

“I remember in 2000, implementing some of the first rules to approve transactions on credit cards that were over the limit or transactions which were delinquent, but using a risk-based methodology,” he told us. At the time, they were logistic or regression models and they weren’t built on a hundred percent of the data. But within 10 years Amex had replaced the rules with models.

Amex’s first use of Big Data was prosaic: personalizing merchant offers for customers. “When you have millions of merchants and hundreds and hundreds of offers and millions of card members, it’s not a problem you can solve with logistic regression,” Venkat recalled. “It’s a problem that can only be solved with collaborative filtering or matrix factorization. Netflix was using it; Google was using it. The question was: How do we bring this to American Express to solve this problem because there’s no other way to solve it?”

Once Venkat’s team had built the infrastructure required to solve Amex’s offer problem, it was then scaled to solve things like fraud and credit risk, and to model customer response and lifetime value. “You’re using a hundred percent of the data now,” he recalls. “And when you go from using a 5% sample for fraud models to a 100% sample—and fraud is not a common occurrence; credit risk is much more common—it significantly improves the accuracy of the model. You’re not only catching more fraudulent transactions but you’re not stopping good spending, which is equally important.”

That notion of not having your card declined—that an algorithm worked invisibly in the background of your life—is, when you think about it, pretty sci-fi in a good way. But as someone who once acquired AI solutions and now provides them, Venkat is uniquely positioned to explain sources of friction that bedevil today’s class of founders and entrepreneurs, many of whom are struggling to close their first sales.

It’s easier to work within legacy cultures than it is to overthrow them

“Often in large enterprises you’re dealing with legacy technology and processes that were built up over very long periods of time,” he said. “There are legacy people and culture, and there’s a strong motivation to deliver results on a quarterly basis—which forces you to build things quickly.”

All those things were factors when Amex struggled to get a merchant recommendation program off the ground. “There are a lot of priorities at a company like Amex; there’s a lot of siloed teams; there’s a lot of decision-makers and there’s not a lot of incentives that a startup would have,” he said by way of explanation, noting that, “It’s beyond just technology. It’s really about culture and people.”

Those challenges create opportunities for start-ups. In the last few years Amex has acquired Resy and Mezi– two recommender platforms that are consumer-facing and provide dining and travel recommendations and reservations. “Now all they have to do is feed their data into those existing platforms and make it work versus trying to build those platforms themselves,” Venkat says.

Mentoring founders and advising start-ups was a big part of Venkat’s job at Amex and it has occupied even more of his time since leaving the bank in 2018. One key question that start-ups need to be able to answer is, why hasn’t the customer you’re targeting built a solution for themselves?

In Venkat’s experience, it usually isn’t because the technology is too hard or that the enterprise doesn’t have the software engineers or the data scientists. It’s usually because the customer lacks one big advantage that the start-up has in spades: the start-up can focus on one thing. But even understanding that advantage isn’t enough unless you can eliminate most or all of the natural friction involved in relationships between start-ups and legacy enterprises.

Large enterprises are focused on their priorities. They trickle down their goals and priorities for every calendar year; every person has their priorities. And then it ladders back up as budgets and planning happen in Q3 for the next year. So trying to get something done in the middle of the year–if it’s not on the priority list, it’s not gonna fly.

People in different departments have different priorities; some are focused on growth, others on customer experience, so what are the real priorities and who is the real decision-maker? Start-ups have to really understand how customer processes work and how things get implemented.

“Convincing those people to open up is very important,” he notes—especially when they’re opening up about the ways they may be constrained by legacy systems or operations.

“Frankly, the implementation issues and buy-in issues are real,” he warns. “People need to syndicate stuff across multiple teams and organizations. And they may not even be able to make the decision on their own. So really be honest with yourself about how much friction there is when it comes to getting your pilot or your product running—whether that’s organizational friction or technological friction, how do you eliminate it?”

Don’t wait for 100% data readiness. Find customers who have enough data ready.

Data readiness—or rather, the lack of it—is a common problem. Despite being a data scientist himself, Venkat’s frustrated with data scientists who constantly push to have 100% of the data before running the models.

“What I would say about data readiness is, you don’t need it,” he says. “It’s not about, do they have all their data ready? It’s about, do they have the data that your startup or your product needs access to in an easy way” You have to figure out which companies have easy access to the data readiness that serves your product.”

Another limiting factor is the current shortage of data scientists. He’s honest about the first hurdle: compensation. People will at most take a small pay cut to work on an interesting project. Beyond that, his secret is not a secret: Network, meet people, build relationships, and stay in touch.

I’m not asking you to fake interest in a person. I think you should have a real interest in these people because you want them to be part of something you’re passionate about. You can’t suddenly email somebody out of the blue or text them or LinkedIn message them out of blue saying, ‘Hey, remember I met you four years ago? What are you up to?’

“But the next stage of evolution is, don’t hire more data engineers and data scientists,” he adds, asking, “Where are your no-code or minimal-code platforms to help you do things faster? Where is the automation?”

Venkat sees AI automation as having a big advantage over the traditional disruption model as defined by Clayton Christensen. “In the past there was always a downside,” he says. “You know, it’s going to make it faster but the decision is actually going to be worse. The one thing with AI and automation is that the decisions are better and the cycle time is lower; it’s not a trade off the way it used to be.”

It’s not as expensive as it used to be either. Enterprises used to ask themselves: Buy, build, or partner? Those decisions are often based on whether or not something’s a core priority or a distraction. “Frankly, if you look at a lot of large enterprises—where they’re investing people and time and effort—it’s not core to their business,” he notes. “So startups and entrepreneurs can help alleviate those problems. [Enterprises] are into that.”

“The other comment I would make is that technology and machine learning are tools,” he said, adding a caution. “Saying you have AI or a data-driven product is very different than transforming them into a product. People care about the business outcome you’re producing, and the value you’re creating for them.”

Value creation is a more enduring pitch, too. “Thirty years ago it was logistic regression on a mainframe; today it’s machine learning in the cloud; tomorrow it’s going to be something else, but we’re still going to be sitting here solving the same business problems. Like, how do I acquire more customers or how do I create more efficiency or how do I service them better?”

An obsession with delivering value may have shaped Venkat’s opinion on the notion of regulating monopolistic juggernauts like Google or Facebook.

If it’s so addictive, you can regulate the hell out of it, but it’s not going to make a difference. When it comes to something like Facebook or Instagram, people are very addicted. They want to be on those platforms. [But] if giving up your privacy gives you enough value, I think you’re okay. If you’re using Google maps and you want to know how to get to wherever you’re going, you are going to give them your location.

He’s unimpressed with some of the opt-in features that seem to exist primarily to make someone, somewhere, feel better. “Who doesn’t click ‘Yes’ when you see that little button that says ‘Accept Cookies’?” he asks.

Not that he’s totally into a free market in which users unconsciously trade personal information for convenience or a momentary dopamine hit. “We are trading off privacy for value. So now the question is, are we valuing some things too much? Or are we undervaluing our privacy until it matters? In the case of social media, maybe it’s mental health; maybe there’s other considerations, which aren’t quite clear. And particularly for individuals who are not capable of making decisions yet if they’re younger, regulation is important. There’s a place for these things; there’s a place for government. But at American Express we used to say, ‘We’re not doing this because the law says we have to do it; we’re not doing this because regulators say we have to; we are holding ourselves to the standard of what our customers expect from us, which should be frankly, a higher standard.’”

Another hot topic on which he has a contrarian opinion is algorithmic bias. “Everybody spends a lot of time on Twitter and other places talking about algorithmic bias and how something is biased or not biased. I’m not saying I’m okay with algorithmic bias, but it can actually be monitored. It can be regulated; it can be improved. How could you ever fix the behavior of a single human loan officer in a small town that was rejecting mortgage applications for underserved people?”

He has a point. We’ve been trying to eradicate bias in humans for eons, without success.

Having read this far, you won’t be surprised to learn that Venkat’s work at Zenon has reaffirmed both his short-term pragmatism and long-term optimism. “We’re building solutions,” he says. “Manual solutions are a waste of time, but in the large enterprise space, there’s a lot of problems that can’t be fully automated yet—maybe in five years or 10 years, there will be product solutions; maybe one of you on the call will be offering them. But right now we’re helping them build this mix, which is human-augmented automation.”

“The last thought I’d leave you with regarding AI is a definition of AI based on it doing what humans can do,” he says. “For me, it’s not about AI doing what humans can do. It’s about going beyond that. We’ve brute-forced our way into a semblance of AI by pushing more data, pushing more compute. If it’s a game of chess, the first way was figuring out all the permutations and combinations; now it’s adversarial again, fine. But I think we’re really reaching the phase of AI where elegance will really drive superiority. And the brilliance of the solution will be in design thinking. And so one of the things that we are focused on as a company is to bring more design thinking. That’s Zenon very quickly, but I think our premise applies generally; it’s not about one company, it’s about, what is the future for all of us?”

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