The era of experimentation is over, with Arvind Krishna, CEO of IBM

Have conviction. The technology is useful. I don't think there's any doubts left on that. Have conviction and figure out that's the art of the leader to figure out the two or three areas where AI could be a huge unlock and put enough resource and enough investment behind those.

Sarah, it's a pleasure to be here speaking with both you and Andrew.

In most new technologies there is an era of experimentation. People are trying out because they don't fully trust it. They don't fully believe in it. They're not quite sure whether it's really transformative or not. I think that that is passed for AI. There is no question that it is going to be incredibly productive in a number of areas. Now perhaps the mistake some people make is that it's going to be incredibly productive in every single area. That's probably not quite true. I advise all our clients, please don't do a hundred experiments. Pick three, four, five things which you can scale like crazy and go put the correct investment, the correct teams and the correct technology behind that to go get that benefit. You know, customer experience, customer service, coding, I think we can say that these have been proven out. I don't think there's much of a debate on that. The one where I have a lot of faith and belief is on enterprise operations or what people might call the back office or how an enterprise functions and I actually believe it's ready for that also.

Let's say a reasonable enterprise is willing to spend a couple of hundred million dollars a year, which is not a trivial amount of money. But if you now spread it across a hundred experiments, you probably have three to five people in each experiment. You got a couple of million dollars of total spend. And then you don't have the ability on how to scale it because you've not thought through all of it. If you instead put a hundred people on one of those things and you put 15 or 20 million of spend and you thought about scaling it, which one is going to be more successful? And it is shocking to me that most people are in the first bucket, not the second.

So before I get to the complete answer of IBM let's talk about that AI journey. So we clearly saw that AI could make a difference. I think IBM Watson winning Jeopardy was a huge proof point to the world that the mixture of compute and algorithmic techniques in this case deep learning can be applied and can be applied to the real world because you had to understand language, you had to look up answers, you had to understand written text and understanding not just language in a very clinical way but idiomatic spoken language, analogies, innuendo, all of that is part of Jeopardy proved that. So now what was the mistake? The huge mistake we made was we believed now how do you unlock value or how do you monetize that? And we took the approach that if we construct monolithic vertical applications in certain use cases then you can derive a lot of value and that can come back to us the corporation. That has never been the first four to five years of any new technology. We should have decomposed it and come out with piece parts that let people gain trust, gain knowledge, get their own expertise. But instead, we took the monolithic approach and that was a terrible idea. That is sort of with 2020 hindsight. And we're going to avoid making that mistake again. The second mistake we made is that not only did we do monoliths, we then took a monolith in an area where we didn't know very much, which was the area of health. So is AI applied to health going to be incredibly productive? I really do believe that. However, who is going to derive that value? So we certainly deal with payers, we deal with insurance companies, but we don't deal much with doctors. So in some sense, we didn't know the actual client. Second, we didn't know the regulator. And three, we don't have a lot of medical staff that is inside IBM. It's easy again in hindsight to say that but you should always try to at least get only one of those areas wrong. So we had known the doctor or we had known the regulator it might have played out differently. So those are my two observations. The second one I think is that this moment of LLM is really fundamentally different. It's really about much more I'll say brute force compute than it is about throwing hundreds of really smart people at a problem. So that is a really big inflection point that is going on right now.

Look, I think it's not a surprise to our audience here for me to say platforms tend to survive and win, not so much just individual components or individual products. And I think IBM should be thought about as having four platforms. One, the mainframe is still going strong even though it's been 60 years and it runs a lot of the world's transaction workloads. Ones where there is, I'll call it large vertical. There used to be a phrase used but it's kind of fallen out of vogue scale up versus scale out systems and I think mainframe is the survivor of the scale up systems. The second platform is our hybrid cloud platform. People can think of maybe Red Hat as the anchor products with OpenShift in there. The third is what we're doing around AI and the fourth we are investing in but it's probably still a couple of years away from mainstream is quantum.

So as opposed to monoliths, we want to open it up. So people get into this debate of which LLM and I kind of say it's going to be an and world. I actually believe people will use multiple LLMs for various reasons. Could be technical, could be political, could be diversity. They will also use open weight models. I've held the belief for five years, but in the last 9 to 10 months, it's come true in some sense. As you go across these, you're then going to also need all of the guardrails. People are going to worry about how do I optimize cost, but also how do I maintain a full audit trail? How do you do eval? How do you do harnesses? How do you really capture the value? And people talk about hallucination. Look, any statistical technique is always going to be somewhat imprecise. But if we can get it into the 95, 96% accuracy, we probably beating humans. I think we just have this innate desire that machines have to be perfect and humans can be imperfect. But if you're trying to do cognitive tasks, then maybe up in the 90s is effective enough for many things, maybe not all.

Two separate statements in there Andrew that maybe the media sort of coupled together which should not be. My two percent statement was of the software products that IBM has on the market how many could be easily replaced by AI including agents or LLMs and I said 2% of the products we have in terms of revenue mass could be replaced by AI I think pretty easily so those are products that are more of an interaction style than those that have deep data or transactional or moving data around which I don't think can be easily replaced by AI or just agents. That was the first statement. The second absolutely I actually would agree with Anthropic. I think LLMs are great at writing code. LLMs could be great at taking hundreds of thousands of lines of code and replacing one language with another. After all, those are in some sense deterministic routines. So I actually agree with that. What I disagreed with is that it's going to be a concern for the mainframe. People don't use the mainframe because of language lock-ins. It runs a bunch of languages. The mainframe gets used when people need to get a certain massive volume of tasks done. Like if I think about credit card authorization, approving 15 or 20 billion transactions an hour. Think about the tokens you would take to do that. I would say in that case the mainframe is probably a hundred times more cost effective. That's why I think the mainframe survives not because of a language lock-in.

So I'll always begin from it's got to be in chase of a business goal. So let's for a moment imagine that you're trying to replace and I've heard you talk about this Sarah before like you're going to go across an end-to-end process. For example, let's take quote to cash. Somebody's prospecting a customer. Somebody's getting a price. Somebody is entering that order. You got to make sure that there is inventory. You got to make sure there's distribution. You got to make sure you can invoice to go collect it. Think of that whole end-to-end process. This is probably touching a half dozen or a dozen different systems. Today, how do we do it? We organize the company into silos. Here is sales, here is distribution, here is finance, here is legal, etc. And then we do it within each step. And there's a lot of human touch offs and handoffs. And that's what you could call friction or cost inside an enterprise. Well, agents can go deal with all these systems and actually deal with multiple of them. So that then opens up the question, how do you organize and how do you make sure your data is clean enough? Opening that up and solving that problem for an enterprise I think has incredible value because that opens up how you can scale the business, how you can begin to grow revenue, how you can make your customer happier because you're doing things faster on their behalf and that is where I think the massive unlock is without a whole lot of risk coming into the picture.

Absolutely. Look, we started down this journey about four years ago in early 23. I'll take a simple example from HR. We began by saying can people interact with an agent as opposed to all of the underlying systems whether it's directories or whether it's your HCM human capital management system etc. And I use a simple example employee verification for the sake of getting a mortgage. What would happen in the past? You would go to your manager and say, 'Get me a letter.' The manager had no desire to write the letter. They would ask their HR partner who had no desire to write the letter. They would call up some HR back office who knew nothing about the employee who would go look up three or four different systems, write the letter, get it out. I think we counted up, it was 17 different human touch points, probably maybe an hour of work in total, and then the letter would go out. So, we asked a simple question. If you're on our network, we know exactly who you are. Otherwise, you're not on our network. If that's the case, why can't you ask an agent and you don't even tell the agent who you are? You just say, 'I need an EVL, employee verification letter.' All the agent should ask you is which email or which physical address. It can look up the system on your behalf. It can then produce that letter and what used to be 30 minutes of human work is now you spending 15 seconds and 30 seconds later the thing is done.