Eric Gitter on Unlocking AI Potential: Overcoming Challenges and Driving Adoption

Hey everybody. Welcome back to Ingram Micro One, and we're talking about AI and innovation with my good buddy. Eric, how you doing buddy?

Nice to meet you, Mike. All Right. Welcome to the show.

I guess a lot of folks are talking about ai, it was almost like ubiquitous here in terms of conversation, but we're still barely scratching the surface. So where do you see kind of like the potential and the immediate opportunity for partners? Yeah, I think, uh, it's, it's a great point, and that's always where we see the start, right?

It's this, uh, I think in the US you say drink your own champagne. There are also other ways to say this. I prefer the champagne one.

Um, now what we're seeing in the, in the partner base, um, is twofold adoption. You see adoption of, uh, you can say the standardized, the productivity related AI projects that are, um, kind of, um, um, generative in nature, right? So that's your, uh, meeting assistance.

It's also customer service, uh, related. A lot of that. So you see that internal adoption because all of them are resource constrained.

Like there are very few partners here that are like, Hey, we have more people than we know what to do with Usually not the case, especially not in in current, currently a market where the IT adoption, uh, and, and the speed of change is so fast that you want to, you can, you can barely capture all the opportunity that's there, right? So it's a good problem to have. I'm not saying there aren't tough environments, but that's a good opportunity or a good problem to have.

So how are they dealing with it? One, they're quickly adopting some of those, um, kind of almost off the shelf, uh, AI solutions to focus on efficiency and productivity. That's stream one that's straightforward.

It is basically the fastest way for them to create AI fluency for every employee of an MSP of Avar of ai, right? Because if you use a daily, you create fluency, but then the real projects that, that they're, um, that they're doing and that they're drinking their own champagne internally are the first places where they're going into business processes and they're going ag agentic essentially, and saying, okay, what's the first type of process where we have, and I use the word, I call it non-human value add, which might be a bit contentious, but what I mean with that is you have a process that requires action taking that isn't always deterministic, but the result of it is right or wrong. Once it's right, there is no quality to it.

Once you've done it right, it can't be good or better. It's just right. That's the starting point.

And that's where we're seeing lots of partners focus and say, okay, what do we have? It's not about replacing humans that we can essentially introduce agen because we wanna get to the point where we're not just talking about generative ai, we're talking about AI that's taking action to give the, the, um, customer superpower. So that's the adoption we're seeing.

What we're not seeing so much at scale are like the, you know, model, fine tuning, deep kind of model training. We have partners that do this, but that's not the broad spectrum because it's, it's a long, uh, it's a long way to scale ROI if you're a, a smaller partner and a big investment to do this, and ultimately you have many other places that will get you, uh, an outcome and fluency much more quickly. And at scale, How do I navigate this challenge?

A lot of the business processes are to use what the AI folks will call deterministic, right? They're supposed to be done the same way every time. AI is probabilistic and rarely does the same thing the same way twice.

How do I kind of meld those two things together to get to something interesting? I Think there, there again, two, uh, two ways in which to control this one, um, is, uh, you gotta have someone that controls model drifting in, in the broadest sense, right? The, the difference, uh, in the deterministic programming, which is input a, output B, that we've created those decision trees, right?

Even in customer service and support, when you were talking to bots, that's essentially a, a very large decision tree. Um, you're going to a, a place where depending on, let's take gen AI as an example and like a service, a bot or something that's answering questions, helping with decisions. It's not just that it's deterministic, it's also learning through reinforcement.

So if it starts getting asked questions that weren't the initial expected ones, its answers over time will also start changing. So a simpler way to think of this is like, I, I always say like, I send my kid to school. My kid has like his circle of friends, uh, in his class and I kind of know the behavior, right?

It's not gonna be perfectly deterministic, but I know who he is around what they talk about, what they like doing. Now imagine all of a sudden his behavior starts changing, probably something in the environment changed, been exposed to a new group of friends, whatever, that they're having different conversations, they're doing different things, behavior changes without anthropomorphizing, and that's the same thing happening with those more probabilistic and drifting, uh, AI models. So step one is make sure that you have someone looking that is kind of regrounding less about hallucination, right?

2, um, in this, and that's actually I think, a trend that isn't just solving an immediate challenge, but that will be around in the long run is human in the loop. That's the straight of banks. You wanna ensure that you have a human in the loop.

Not so much to say this is right or wrong, but really to ensure consistent improvement of quality of what those systems are doing. And yeah, on the offhand also making sure that doesn't go off the deep end basically. So human in the loop will be around for a long time.

I think a lot of partners are looking for something easy to get started with. Yeah. And you've seen some use cases out there that, um, might be easier to replicate than others.

So, you know, what's your best advice to partners about, you know, here's something you can go do on a dime and it'll be, and it'll work out well. Yeah. And you'll get that muscle memory You're talking about.

Absolutely. So because we encounter this a lot, uh, at Ingram, we've created, um, basically a program, uh, to help those partners confidently sell, deploy and service AI solutions at scale and ideally in a repeatable way. We call it advantage enable ai.

And it is what, what our partners can, uh, go and seek on the advantage platform. It is aided by all our local expert teams. So they are being hand held.

Why? Right? The starting point is you need to understand as an MSP, as an a var, where do I stand today relative to ai?

Even if you've already built an AI practice, which in our experience has been, there's always a gap because it's changing so fast. So what's my business understanding, my technical understanding, what's my service capability that I have? And not to forget almost the most important things, what is the current ask slash need of my customers?

So example, I'm an MSSP, but all my partners, all my customers are talking about is using AI for productivity, right? So I have a choice pivot and start also doing productivity related, uh, technology business, hard force, the security conversation into the productivity conversation, which is valid, you should consider it, but it's going to prolong any implementation and that dime will turn into multiples very quickly or wait until the first thing is implemented, something relative to to productivity and then fast follow. Okay?

So once that's done, the key thing is repeatable use cases focused on business outcome on select industries, because that's a language that every partner today speaks without an AI training can, can open up the opportunities. So what we decided to say, we focus on only three business outcomes, which is yes, right now it's for ai, but it's the same for any tech. Either you're deploying it as an end customer to be more productive, more with less, more faster, any of these permutations productivity or to create a better experience for your customers, for your employees, for your suppliers, doesn't matter, or that, or you're deploying a technology product to shore up security or governments.

Those are kind of the three only outcomes that are relevant. The reason that's important here, you talked about on a dime. 'cause in the first scenario, productivity, I can turn around a business case in probably three minutes.

Experience gonna be a little bit harder. We've all experienced that, right? Security is not hard, but it is cost avoided versus, you know, immediate benefit created.

Now, once you have this and you map this to an industry, and we do focus on, um, manufacturing, retail, healthcare, finance, and now also public sector, you have the kind of, um, trifecta of I'm trying to solve a productivity focused business problem for somebody in retail using technology from OEMX. Not to mention that we're here today. Then you have a very, very clear path of what you need to do.

And what we're seeing there today is that all the initial lift, as boring as it might sounds, are quick productivity based wins. In a very simple way. If you're using something like a copilot, like I'm really taking that example.

Let's say you pay, I'm gonna use an arbitrary number, 20 bucks for a seed a month. Okay? The first time you've saved yourself an hour of work, which will probably happen on the first day of usage, you're gonna think of that as that ROI 'cause an hour is probably gonna cost you more than 20 bucks.

So that is very easy. But the, the crux is not just sell it, it's making sure that people know how to use it no different to past technologies and adopt it because that creates that tidal wave to move deeper into process, agentic, et cetera. So as kind of standard as it sounds, it's creating those, um, 80% of time, 20% of value projects, first productivity to then go into 20% of knowledge worker time, 80% of value projects.

Next, Ingram is clearly invested heavily in ai. How does the knowledge transfer between you and the partners occur? Because it's not just enough to have something I can go and download and install, I kinda have to know something about this.

So how do you know you guys take all the intellectual capital that you guys have created and kinda share that with the partners? Great question. So let's say in two, maybe three ways.

One is, um, my team's focused on internal Ingram AI fluency. That's not technology specific to ensure that over time every single person that works at Gram Micro can help the partners understand and navigate basically what's happening with ai, not down to the technical level unless you have that role, right? That's the first piece because that's how it scales, right?

We have over 20,000 associates in over 50 countries that are engaging these 160,000 plus partners. And that is the way in which they thoroughly, uh, in which they thoroughly can transfer that knowledge. That's the first one.

The second one is I spoke about advantage enable ai. So that's actually the digital, uh, the digital platform where they can find the assessment in their, their knowledge, the use cases, and then the repeatable, we call them growth tracks where they can build a practice. However, that's digital.

The way we do this is in every country, our local organization adds to it their local engagement. So they'll say, based on who the partner is, the business they're doing with us and the commitment they have to taking this AI transformation series, we're gonna add these workshop components, these certification components, which are human interlock, right? So human in the loop if you will.

So we're doing the same thing, um, at a local level. This, this program is all the automation, scalability, and self-service side of it make it really easy to use. And then the depth of knowledge comes through the transfer of our teams locally being involved from the business technical all the way to the service tribe.

Okay, Last question. So what's your best advice to the partners? What should they be focused on right now?

I think in English you say get stuck in, right? Uh, so, uh, I would say engage with us right now. Come to enable AI and talk to your Ingram counterpart to it.

Understand a where you are today if you don't have a strategy, understand where you are today and where your partners, your customers want you to be and what they want from you. And on that basis, build a roadmap that is very concrete outcome focused and not about the hype side of the technology, but basically understand where you are, build a custom roadmap. And again, the advice is no different than on any other technology.

Focus is what will make you win. If you really understand your customer, that could be their vertical, the geo they operate in, the cultural context they operate in, and then also are able to map the technology to that scenario. You'll win if you just have a great relationship, go golfing, go playing tennis, whatever that might means, your relevance will wane very quickly.

That's been set for a long time, but with technology taking on really more and more proactive pieces, that is what's going to happen. So I'd say get stuck in right now. If you're treat it as hype or treat it as a bubble, you will not be around because nothing has ever moved as fast as this friend.

Hey buddy, thanks for coming. Was an absolute pleasure. Thank You.

Actually, same context is everything. Thank you. Well, that's a wrap for this year's Ingram Micro Conference from us.

And thank you all for watching all these episodes and it's been a great time on our behalf and I wanna thank Ingram Micro for having us, and hopefully we'll see you all next year.