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Why the C-Suite Needs to Know About Neuro-Symbolic AI · C-Suite Perspectives

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Ivan Pollard: Welcome to C-Suite Perspectives, a signature
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series by The Conference Board.
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I'm Ivan Pollard, leader of the Marketing & Communications Center
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at The Conference Board and the guest host of this podcast episode.
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By admission, I'm also an AI nerd, and today, we're going to discuss
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neuro-symbolic AI: what it is, how it works, and what we can do with it.
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And I'm delighted to have Jeff Schumacher, growth platforms
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leader at EY-Parthenon, join me.
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Welcome, Jeff.
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Jeff Schumacher: Thank you, my friend.
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Ivan Pollard: Great to have you here.
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And we'll go straight into it.
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So for listeners who are new to this facet of AI, how would you define
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what neuro-symbolic AI is in layman's terms, and how does that advance what
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we can already do with generative?
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Jeff Schumacher: The simple statement we go around with is, neuro-symbolic
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AI is a business growth engine that turns data into value, right?
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If you think about it in AI terms, if you take corporate value and
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you branch it into two simple MECE branches—mutually exclusive, collectively
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exhaustive—one branch is productivity.
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That's where generative AI lives and agentic.
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And then the other branch is growth, and that's where neuro-symbolic lives.
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If I can drill that for you a little bit, it's the company's commercial
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model, how they make money.
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If I drill that a little bit, it's how they price something, how they
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forecast, how they promote, how they launch a new product, new service, how
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they buy something, divest something.
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That's where neuro-symbolic plays.
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Ivan Pollard: OK.
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And so it's more of the generative AI produces things and predicts things.
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The neuro-symbolic helps us make decisions around those strategies.
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Jeff Schumacher: Correct.
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Yeah.
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You might say generative AI learns, neuro-symbolic reasons.
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Ivan Pollard: Ah, very good.
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So the power of learning with generative AI and neural networks.
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The power of reasoning or the ability to reason with symbolic AI.
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And those two things coming together sounds interesting.
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So if I get it right, then, what you are saying is neuro-symbolic AI understands.
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It's not just guessing at what comes next.
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It actually reasons.
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So it's kind of got causation, not just correlation.
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Can you explain that difference and how, if that's true, if it can
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see causation, how's that changing the way executives will view its
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use in developing strategies?
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Jeff Schumacher: You're getting it exactly right.
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So most of growth, when you're thinking about growth you're thinking about
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you're going to correlate the growth.
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You're going to, if you're a consultant, you'll do a two by two.
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If you're an investor, you'll look at a comparable.
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If you're a corporate, you might do a survey, right?
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You're getting to correlations of growth.
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But it's not necessarily causal.
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So, I live in Los Angeles, right?
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And so in the summer, consumption of ice cream goes up, but so do shark bites.
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They're correlated, but they're not causal.
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Now my 7-year-old daughter who's getting ready for second grade here,
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she's heard that story so many times.
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She's convinced that she goes down to the ocean with her mint chip ice cream.
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She's going to get eaten by a shark.
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So there is some 7-year-old logic to that.
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But neuro-symbolic gives you those causal relationships.
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The neuro side is the unification of data to understand behavior.
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The symbolic side is the rules.
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So Ivan, it's almost like you're getting a historian and a
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mathematician coming together, right?
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That's what you're getting with neuro-symbolic.
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And that causality for executives, and this is a business play, not an IT play.
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This is more of a lens than how you look at the market,
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but it gives CEOs certainty.
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And certainty is, gives you confidence.
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And then you can act on it.
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You can get behind it, you can invest behind it.
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And that's what we're finding with the executives that we're
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working with neuro-symbolic.
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Ivan Pollard: That's interesting.
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I love that story, Jeff, about your 7-year-old daughter.
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Thank her for it cause our members will learn from her.
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But essentially, what you're saying is generative AI might spot that
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correlation in the data and tell me that I should make carcharodon-flavored ice
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cream, and that would be a big hit.
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But what you're saying, the symbolic side then comes in and starts
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looking at the reasoning and whether or not that would actually work.
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You just used a word that's very unusual in the world of AI, or has
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been for the last two years: certainty.
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So why does the symbolic side of neuro-symbolic bring certainty?
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Jeff Schumacher: One of the main reasons in what we find—you know, I invented
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the platform that we use, that's built on neuro-symbolic, is Growth Protocol.
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That's the company I founded four years ago.
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It took us a couple of years to figure this out.
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If you look at the analysts, they'll tell you neuro-symbolic is two to five
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years away, so we're talking about the only enterprise application in there.
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Generative AI.
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It's a learning platform, and it's natural language, right?
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So it gives you the answer when you ask the question.
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Now whether or not that answer is right is another question.
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And the reason why that is difficult, if you ask a gen AI platform a
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question, if you ask that same question five times, you'll get
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slightly five different answers.
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That causes uncertainty.
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The other problem is you can't audit it.
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You don't know where it comes from.
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Neuro-symbolic is just the opposite.
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It is fully auditable.
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You can see where it's coming from.
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You can go all the way to audit, and if you ask it the question five
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times, the same question, you will get exactly the same answer five times.
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Now, the other part of that is if it does not have the data, it will
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not compute it, it'll come up null.
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That aspect, it's very binary and where we're deploying it, we have it in,
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call it 40-plus situations now on the market and 50 more onboarding, if the
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data isn't there, and there's nothing to reason with, there's nothing to do.
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And that's an important point.
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Ivan Pollard: So that's interesting.
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We talked about growth and growth protocols.
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So business goals, commercial strategies, work processes, those sorts of things.
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How is neuro-symbolic AI best suited to drive growth through the
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expression of strategy and process?
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Jeff Schumacher: It's another interesting question.
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So if I put my consulting hat on, and we've been at EY now for a few months.
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In that world.
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I'll do a two by two for you.
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So where does neuro-symbolic best play?
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Where something is high complexity and high impact.
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So go on your X-axis complexity, go on your Y-axis impact, and that top,
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that's where you want neuro-symbolic.
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And it deals with complexity.
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And I can give you an example.
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So we're working with a manufacturer, manufacturers construction
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equipment and such, right?
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And the power of neuro-symbolic is the ability to unify a lot of data.
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So our software unifies currently 110 million data sources.
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So where we applied it initially was to unify all the messy parts of the internet
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to give us a foundational aspect there.
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So we unify all the financial information that's public.
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We unify all of the news that's global and public.
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We unify all the scientific, the research, global and public.
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We unify all the e-commerce information, global and public.
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We unify even the social.
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So that goes into what we call a workflow.
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And in this equipment manufacturer, what they were looking at is
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they finance a hundred percent of all the products they sell.
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And they think about construction and mining and that type of thing.
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And in there, because they finance it all, they have all the financing
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information—they have the credit rating of the customer, they have
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how much the product is worth, how long the financial terms are.
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And if you think about it, Ivan, if you finance something, what do you have to do?
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You have to insure it.
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So the question became, could we build a neuro-symbolic insurance platform?
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And the answer is yes, because what neuro-symbolic allows you to
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do is now say, OK, I can take the telemetrics data off the machine.
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I can take the financing data.
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I can take OSHA data because I can take—in neuro-symbolic, you
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can take both structured and unstructured—so I can take OSHA data in.
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And therefore, now as an insurer, I can understand what price, how long,
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under what conditions, what's the repair versus replace versus finance cost?
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What creates loss?
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What prevents loss?
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All of those are reasoning questions that goes into this model and then
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allows them to underwrite better than what an insurance company could do.
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And so therefore, you create an entirely new revenue stream for this company.
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You create an entirely new margin profile for this company because it
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has a far significant better margin profile, and therefore you change
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the EBITDA profile of the company.
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Therefore, you change the multiple of the company, and therefore you change
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the market capitalization of the company.
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And these are the power of neuro-symbolic.
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And the other advantages, Ivan, if you think about it, my background
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is computer science, MIS, right?
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So, most people think in years in technology.
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We're talking in weeks that this gets put together and then months in deployment.
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So the ability for executives and CEOs to truly affect their top
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line and bottom line in a near term basis is significant for them.
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And we're finding that to be quite exciting as we show this
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to CEOs that we bring it to.
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Ivan Pollard: Now, if we were talking in the 1970s, we'd have had the same
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discussion about how to generate reliable information about insurance,
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for instance, for this machines operator.
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And we would have a huge group of people doing it.
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They would never be able to crunch 110 million data sets.
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They probably would still make good reasoning assumptions using the logic
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that they've got from all of their mathematical and probabilistic methods,
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but also the data that they've got.
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What you're saying is this is doing better than any of they could ever
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do it, faster than they can do it.
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We could argue whether or not it's cheaper than they can do it.
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But essentially, we're going to get the brilliance of everybody put into this
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neuro-symbolic machine, and then when you ask it the right questions and build
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it the right way, it can almost answer anything that drives commercial growth.
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Is that correct or am I getting off piece?
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Jeff Schumacher: I think it would drive anything that
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requires a reasoning or risk.
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But if you think about it, so this is another question, Ivan.
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So now let me go put my mathematician hat on.
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So in most executives that we see, most people think of
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growth, let's say it that way.
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Most people think of growth as an arithmetic.
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It is the sum of the parts.
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Therefore, different types of growth are completely different.
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Like whether you're pricing something or you're entering a market or
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you're creating a new product.
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Those are very different arithmetics, very different sums, different parts
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that go into making that equation.
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Neuro-symbolic says no, they're all the same.
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They're all risk.
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And so pricing is a risk.
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Underwriting is a risk.
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Entering a new market is a risk.
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So it studies the motion, and it learns from the motion.
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And that motion, if you go back to your math classes, that's calculus.
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Calculus is the study of motion.
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Therefore, at Growth Protocol and what EY and Growth Platforms that
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absorbs the software, we build one reasoning model, and we just
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configure to different questions that you're asking, and that becomes
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quite powerful in what you're doing.
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And then if you give me new product, new service, new market, new capability,
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new process, or just re-engineered process, if you give me all those words
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and you allow me to call them workflows, that's how we see it, as workflows.
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But then those workflows are always on.
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That insurance example that I talked about is always on.
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That workflow is always on.
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So what does that mean, Ivan?
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It means it's always learning.
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So I don't have to, if one of the attributes change, it's
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going to learn from that.
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I don't have to go back and rerun a regression or redo the whole thing as you
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did back in the '70s, or we were talking about in the '70s, that you would have to
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get all the machine learning guys together and get it redone and redo the regression.
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This is why pricing has to be redone when you do regression and you replan
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it on planes, on the factor charts.
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The point, the value of neuro-symbolic is that it learns.
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The value of neuro-symbolic is, it's always on.
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The value of neuro-symbolic is it gives you certainty.
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And the value of neuro-symbolic is it treats risk and reasoning
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questions all the same.
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So you're building one model.
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What does that mean?
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You can build an entire growth operating system for a company.
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And for an executive, they don't need 19 different solutions.
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They can have one reasoning solution that becomes their growth OS.
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Ivan Pollard: So always on, Jeff, always learning and always connected.
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Like you said, the idea that we had silos of different units managing those
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vectors of growth that will now disappear.
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So when you're talking about this with neuro-symbolic, and as you say,
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it's starting to come pretty fast.
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DeepMind have already got it working with AlphaGeometry and a few other things.
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You've got it working with Growth Protocol and EY-Parthenon.
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But who is responsible inside a commercial organization?
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Who's the person that's advocating for the implications and the
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implementation of neuro-symbolic AI?
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Jeff Schumacher: It's the business that is responsible for it.
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And what we're finding, Ivan, is when we started with this back in April, when EY
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did the acquihire of the services team from Growth Protocol and I came over and
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we founded Growth Platforms and EY, we said, "Hey, we built a business plan.
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And I can tell you currently, every executive we've shown it to, we planned
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it to be like, hey, we'd show it to 10 executives, and three would take it.
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But currently every single executive we've ever shown it to is, take it.
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It's 100%."
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Because what they're getting to Ivan is, it's almost like they understand
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that they're playing "Moneyball" here.
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And they're going to see value in the market others don't see.
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And they're going to say, others are doing this arithmetic of an equation when
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they should be looking at it as calculus.
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And it's all the same with just different configurations.
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And therefore, if you think about "Moneyball," in the movie,
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everybody's out there buying players.
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You're buying wins, and you're buying wins by getting runs, and you're
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buying runs by getting on base.
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So the arithmetic in baseball was on base percentage, or the calculus
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in baseball was on base percentage.
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Same goes here.
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Ivan Pollard: Brilliant way of putting it.
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So you've been pitching to Brad Pitt and others.
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I like that.
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It's also evidence that you are right, that it doesn't matter what
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executive to you pitch it to in any commercial organization, whether
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they're the head of HR or the head of marketing, or the chief revenue
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officer or the chief strategy officer.
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It binds them all together.
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It goes there.
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OK, so we're going to take a short break, and we'll be right back with more of
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my conversation with Jeff Schumacher.
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And we're going to be fascinated to explore where this is going next and
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what the implications are for business.
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Welcome back to C-Suite Perspectives.
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I'm your host, Ivan Pollard, leader of The Conference Board's Marketing
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& Communications Center, and I'm joined by Jeff Schumacher, Growth
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Platforms leader at EY-Parthenon.
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So Jeff, can you share a few examples when neuro-symbolic AI has already
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shown tangible business results, whether it's new revenue streams or operational
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efficiency, or faster decision cycles?
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Give us a real example.
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Jeff Schumacher: Yeah, Ivan, and I think there's multiple, so we could
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spend the entire call on different ones, but I'll give you another one.
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Remember, so we're talking about it's a reasoning model, and when neuro-symbolic
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looks at something, they don't care if you're forecasting, entering a new
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market, buying something, divesting something, pricing something, or
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creating a new product or service.
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So it's basically the commercial model.
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But let's take one in pricing.
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Pricing is always an immediate impact, right?
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So you can get to it.
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And it's been around for eons.
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So most people, when they price, and again here we're talking about, we
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will go into the retail channel here.
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When you price, you run a regression, and you try to figure
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out where the elasticities are and what the right price is.
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But pricing can get extremely complex in the reasoning of
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all the different attributes.
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And what happens is most of the time, you take a more simple version of that
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so that you can get to a price, and you know that there's a lot more out there.
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But you just don't have the resources, the time, or the energy to go after it.
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So in this case, we looked at price in a channel.
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Again, this is in the HVAC space.
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So think about an air conditioning unit, right?
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So you got to stay cool.
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And so what's interesting about that is, so when you do price, you first do, you
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got to figure out, am I going for share?
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Am I going for margin?
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Am I going for growth or profitability?
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And what happens there is, by market, you might have a different answer.
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So now that regression tree grows, right?
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What you're doing grows.
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And then you have to define market.
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Is market Pittsburgh?
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Is market Pennsylvania?
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Is market Eastern United States?
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Is the market the United States?
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Is market another country?
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So now you have that complexity.
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Then, OK, now you overlay competitor.
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And when you overlay competitor, what happens is it's rarely
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Coke versus Pepsi, right?
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The products don't line up.
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It's more like Coke versus Nestea or something like that.
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So you have just differences.
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And so now you have to account for those differences between products.
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And then you say, "OK, now if I'm in Pittsburgh, I want ease
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of use in Minneapolis. I want feature functionality." So now I
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got to account for all of that.
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And then you add time of year.
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In the summer in New York, you're probably, if you're in a five
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story walkup, you're like, "Hey, speed of implementation is pretty
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important." Whereas in, say, Seattle, you might be more price sensitive.
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So now you have to do all of those reasoning -related questions.
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And that's where regression, when you typically see it, people just
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leave all of that on the table.
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So when we were able to plug all of that into a neuro-symbolic system
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and basically build a pricing workflow in a $50 million EBIT category, we
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found $8 million in a matter of weeks.
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So that's the speed to which you can do.
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And now, if one of those attributes change, like feature functionality versus
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ease of use, the model will learn and just adjust so you don't have to redo
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everything, and therefore it's always on.
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And so once you have that ontology for the company—what is their language and
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how they think about it, and then the knowledge graph of all the attributes and
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things that you're doing—once that's set up, you can just keep clipping through.
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So for me, I built one of the largest incubators in the world previously,.
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So I've founded hundreds of companies.
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What happens there is, you hack a company or hack an idea and
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then you have a long build once you think you got the idea right.
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Here, once you have the ontology and knowledge graphs, the follow on is fast.
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You could keep adding and adding and adding.
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So when you say speed to market and certainty and expandability,
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these are the things that executives get pretty excited about.
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Ivan Pollard: That's cool.
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Can it go the other way as well?
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It can go from Philadelphia to Pennsylvania and then beyond,
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but can it go from Pennsylvania to Philadelphia to Pollard?
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Jeff Schumacher: Yes.
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It can go from Philadelphia, I don't know Philadelphia very well,
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but it can go from Philadelphia to Allentown to downtown store.
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Ivan Pollard: Can it get to me, though?
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Can it get to the individual price that it knows I'm willing to pay?
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Jeff Schumacher: Exactly.
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Yeah.
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That—
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Ivan Pollard: That's cool.
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Jeff Schumacher: Willingness to pay is a key factor, and a whole bunch of factors
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go into what is your willingness to pay.
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Ivan Pollard: Yeah, that's super cool.
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In the middle of that, Jeff, as you were talking you talked
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about the model doesn't care.
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It's just doing the reasoning based on the data and all the inputs
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that are coming in at the moment.
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But we often hear about the guardrails, so sure, it's doing the reasoning with the
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symbolic bit, but it's pulling the data.
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Are there ethics still applied to neuro-symbolic in the same way that we've
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argued about them for generative AI.
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Jeff Schumacher: What I can tell you, Ivan, is what we're finding from like
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in the regulatory environment, the auditability regulators really like.
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But the auditability also, you can show—we do a significant amount of
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work in insurance, because who needs auditability more than insurance, and
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who needs certainty and causalities more than insurance companies—is that
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we can show there's no bias in the data.
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And that is important, that you don't have the human biases
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that you otherwise might have.
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And you can prove that you don't have that.
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So from an auditability side and a regulatory side, this I think is quite
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significant, versus what a gen AI and what, which, what I think you're
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hearing in the market is, the ability to prove how you got to that answer
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and prove there's no bias in there.
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Ivan Pollard: Yeah, cause I like that.
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So even if there was a little bias in the data you're using, there's
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absolutely no bias in the logic.
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So the logic removes any bias in the data.
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Jeff Schumacher: And even more powerful, if the regulatory body changes or
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they want more restriction, that is just a constraint that goes into the
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model, and then it's always on again.
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Ivan Pollard: Start with the logic.
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As we're thinking about this, you touched on this about the
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different executives, that you've got 100% hit rates at the moment.
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It doesn't matter what executive they are, they're interested.
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But from a leadership perspective, what sort of organizational cultural shifts
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are needed to make neuro-symbolic more of their operation effectiveness at play?
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What shifts need to happen for the organization or the culture?
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Jeff Schumacher: I think it's, neuro-symbolic is targeted at growth
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or a reasoning related question.
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But I can give you a simple example.
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So we were at a retailer, and we were showing them two
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categories that were performing.
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They were contributing equally the same in a revenue and margin profile.
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But one is, you were looking at the data and how it we were
23:43
representing cause we were unifying.
23:44
Remember the neuro side of the data getting unified.
23:47
One you could see was in a headwind and predicted to stay that way, so that
23:52
merchant was doing all of these tactics to maintain his revenue and margin profile.
23:58
The other was in a tailwind and so basically, high tide raising all boats.
24:04
So the CFO was looking at the data, he goes, or looking at the output
24:09
of the model, and he was saying, they're contributing the same
24:13
revenue and margin profile, therefore we're paying them the same, but
24:18
should we be paying them the same?
24:20
And we're like, absolutely not.
24:23
Because one is doing a lot more to get that.
24:26
That revenue shouldn't be treated equal.
24:28
So Ivan, to your question, the implications of understanding that
24:33
this becomes your growth OS is you're going to have operational implications.
24:39
You're going to have frontline implications.
24:41
You're going to have strategic implications.
24:44
So where this is owned, almost always, it is championed by the
24:48
CEO, and then different quarterbacks have different components of this.
24:54
But it relies on the business adopting it and understanding that it's going
24:59
to change the metrics that they have, and what gets measured, gets managed.
25:04
And then IT is the enablement of that function, but not
25:08
the leader of that function.
25:10
So that's I think a shift.
25:12
A lot of AI, generative AI solutions tend to reside over in IT.
25:18
Neuro-symbolic almost always resides over at the business.
25:22
Ivan Pollard: Yeah.
25:23
Smart.
25:24
Looking ahead, just to wrap this up.
25:26
When you think about forward looking companies, what do you
25:29
think they'll be doing with neuro-symbolic AI by, let's say, 2030?
25:33
What might be the lasting strategic effect on their market cap and their
25:37
commercialization business models?
25:40
Jeff Schumacher: I think it favors first movers.
25:42
So those that adopt quickly will gain a significant share over the others.
25:47
I think those that understand that it can go through their value chain and
25:52
change the way that they operate so the incentive structures will change and such.
25:57
And then even then, I see, IT is an incredibly important
26:01
aspect for organizations, but it has been largely centralized.
26:07
And that was done, I think, because of Y2K and hey , I have to
26:11
get behind all of that and get a centralized look at my value chain.
26:14
Neuro-symbolic causalities, you want to get as close to the revenue as you can,
26:19
and therefore, I think you're going to see IT begin to federate as neuro-symbolic
26:25
becomes more ubiquitous in the market.
26:28
And if you ask me where I see it today versus 2030, neuro-symbolic
26:33
is where gen AI was in 2019.
26:35
That's where neuro-symbolic is today.
26:37
So if you take that forward, that is just about the timing of your 2030 aspect, I
26:42
think you're going to see IT federate.
26:44
I think you're going to see it'll be in that word cloud, one of
26:47
the biggest words in the market.
26:49
I think you'll see that it's going to bolt on to gen AI to help it improve
26:54
the outcomes that gen AI has, which you're seeing some organizations do.
26:59
But it will become fairly ubiquitous.
27:01
And right now, I think it'll favor significantly—as we did talk
27:05
about "Moneyball"—in 2002, Oakland A's won more games than anybody
27:09
else in the American League.
27:11
That's case in point of favoring first movers.
27:14
Ivan Pollard: Brilliant.
27:15
Thank you so much, Jeff, for sharing all of this.
27:17
If we had to summarize it, we've got the learning power of the neural
27:21
networks matching to the reasoning ability of the symbolic knowledge.
27:26
And that's going to change business, the way we do business,
27:30
and even what business is.
27:32
We should probably just comment that it's also going to apply to things
27:35
like autonomous robotics and scientific discovery, and this ability to bring
27:40
reasoning together with learning.
27:41
Always on, always effective, a little bit more auditable, and a lot less bias.
27:47
But let's just leave everybody thinking about that.
27:50
This is coming, this is going to come fast.
27:52
And those who get there first will be the ones who win in the long term.
27:56
Jeff Schumacher, thank you very much.
27:58
Keep going with what you are doing with evangelizing for the power of this next
28:04
step in AI to help businesses do better.
28:07
Thank you very much.
28:08
This has been your C-Suite Perspectives.
28:11
I'm Ivan Pollard, and this series is brought to you by The Conference Board.
28:15
Tune into the next one.
28:16
Thank you.