Adoption, Not Hardware: Voya Financial Shows AI Value Comes From Training, Not Tools

[00:00:02] 11,000 people at an insurance company sat through five hours of training. 98% of them now use the tools 24 times a week each. Nothing about the technology changed. Somebody just taught them. This is the Business of Tech. I'm Dave Sobel. Three companies ship the same idea in three different sizes and none of them called it what it is.

[00:00:28] We'll start with Broadcom. At its Explore conference in Singapore, the company announced VMware Private AI Cloud, a platform for running inference and agentic applications on hardware the customer owns, instead of sending that work out to a public AI service. That is Broadcom describing its own product, so take the framing for what it is.

[00:00:49] But note what they say it is for. They name three cost drivers the thing exists to address, and the third one is token economics. Then perplexity, which went the other direction, down instead of up.

[00:01:04] Working with NVIDIA, the company released something called Portable Computer, a full AI agent running on the machine in front of you, escalating to the cloud only when it decides it has to, on a DGX Spark desktop or on any NVIDIA card with 24GB of memory. The pitch, and these are perplexity's own benchmarks, is zero token cost. The demonstration was a folder of tax documents, read locally, with no cloud usage at all.

[00:01:33] Then Apple. Two new desktop chips, the M6 in the Mac Mini and the M5 Ultra in the Mac Studio, with 50% more memory bandwidth than the part it replaces. Apple says Mac revenue grew 29% year over year to $10.4 billion. But the useful voices on this one are not Apple's.

[00:01:57] Ranjit Atwal, a senior director analyst at Gartner, told CIO Dive the chips were built to enable future AI workloads, agents specifically, and then made a point about the money. The budget for what he calls an infrastructure PC does not come from where desktop budgets normally come from. It comes from the infrastructure side of the house. Techopedia's read on the same launch is about the shape of the thing.

[00:02:25] A desktop with no lid to close and no battery to drain that simply stays powered, which is exactly what an agent running continuously needs and a laptop is not. And Brendan Burke at the Futurum Group put the whole thing as an open question. Enterprises want frontier models running on hardware they control, he said. The test is whether local inference at this scale actually pulls workloads off rented GPUs and onto the desk.

[00:02:55] So hold that word. Weather. Because here is where the work is right now. NVIDIA closed its second quarter at $96.2 billion, up 106% from a year ago at 75% margins. $89 billion of that is data center silicon, up 117% from a year ago. That is the hardware sold to the people who rent GPUs out, and it's never sold faster. Three companies building for hardware the customer owns.

[00:03:24] One company being paid enormously for hardware that's rented. Which of these is the leading indicator depends on a number that has nothing to do with either of them. If you're listening to this and haven't hit follow yet, on Apple Podcasts search Business of Tech. It takes five seconds and you'll get the next episode automatically. You know the LogMeIn name, but when did you last look at what it does for MSPs today?

[00:03:52] Beyond the remote access it's known for, LogMeIn Resolve is a full MSP platform. And the partner side is built for the channel. A dedicated customer success manager, direct tier 2 support, and an actual voice in where the product goes next. If LogMeIn has been sitting in the I already know them category in your head, it's worth a fresh look. Start at LogMeIn.com slash MSPGrowth

[00:04:22] The reason the hardware is arriving ahead of the workload is that almost nobody has turned the workload on. Look at what adoption actually means when somebody measures it carefully. Alexander Bick and his colleagues run something called the Real-Time Population Survey, and the register carried the latest results. Generative AI has now reached roughly 80% of occupations. Four out of five detailed occupations show adoption above 20%.

[00:04:50] That's the number everybody quotes, and it sounds like saturation. And here's the number nobody quotes. Only one occupation in six is above 70%. And measured at the level of individual tasks, the actual unit of work, only 2.8% show adoption above half. 2.8%. And not one single task in the survey clears 70%. Not one.

[00:05:16] As of May, 62% of American adults use generative AI at all, and 45% use it at work. So the picture is enormously wide and almost entirely surface. Nearly everyone has touched it. Nearly nobody has moved their work onto it. And the natural reading of that is patience. Adoption curves fill in. Wait three years, and the depth arrives by itself. That reading is wrong, and there's a company that shows you why.

[00:05:46] Voya Financial has about 11,000 employees. They built a five-hour AI literacy course. Five hours. With input from more than a thousand of their own people, and put the workforce through it. Nearly 100% certified. Co-pilot adoption hit 98%. And here's the figure that matters. Those employees now average 24 prompts each every week.

[00:06:12] And about 300 of them have gone on to build their own agents. And Voya's Chief Technology and Operations Officer, Santosh Keshivan, does not credit the tooling for any of it. He credits how the leadership talked to people about it. Nothing about the technology changed at Voya. Same models, same licenses. What changed is that somebody decided to teach 11,000 people.

[00:06:37] And a company that was generating almost nothing is now running north of a quarter of a million prompts a week. That is the mechanism. Consumption is not a property of the model. It's a property of how many people were taught what to do with it. It does not accumulate on its own. Somebody has to cause it. Which tells you exactly what today's price signal is measuring.

[00:07:03] CIO Dive reported this summer that enterprises have entered what they called the cautious era. The phase of using AI for everything gave way to consumption pricing and bills that got somebody's attention. Gardner still expects spending on AI models and platforms to climb 63% to $64 billion. The cost pressure is real. It is being felt. And it is producing precisely the response you would expect. Buy the hardware. Own the compute. Stop renting.

[00:07:31] But set the two numbers beside each other. The bills driving that response were generated by 2.8% of tasks. The hardware answer is not wrong. It's early. And it's being sized against a bill that reflects almost none of the work. Early is a different problem than wrong. And it is a harder one to act on. What that leaves you holding is a timing problem.

[00:07:59] And timing problems are the ones this industry has historically lost. Gardner put dates on it. And Redmond's channel partner carried the numbers. Their argument starts with licensing and pricing changes in virtualization. Which have handed enterprises a reason to reconsider the platform underneath everything. You cannot move the virtualization layer without opening the storage question. So the two now get evaluated together. Gardner calls it a major displacement window. And into that window comes the AI peaks.

[00:08:29] Consumption-based storage as a service, they project, goes from a quarter of on-premises storage spend at the start of the this year to half of it by 2029. Purpose-built storage tiers for large-scale inference go from under 10% of enterprises today to 70% by 2029. Notice what that actually describes. The on-premises infrastructure conversation is going to reopen at your clients regardless. For hypervisor reasons, not AI reasons.

[00:08:58] And the AI hardware question rides in through a door that was already going to swing. On a horizon of about three years. So the obvious move is to get ready now. Build the practice, take the training, be standing there when it opens. That is the move I would not make. Three years is a long time to carry a practice you cannot yet sell on a timeline that is not yours to set. So here's the choice. And three product launches have already told you which way they prefer you went.

[00:09:28] Do not build the on-premises AI practice this year. Build the adoption practice. Go to the clients who bought the licenses 18 months ago and never taught anybody how to use them. Run the five-hour version of what Voya ran and charge for it. That is revenue in this fiscal year. And it is the only work that moves a client towards the day the hardware math actually changes.

[00:09:51] Or build what the launches are pointing at, carry the cost of being ready and wait on a crossover whose timing is set by adoption you decided not to influence. And there is a practical problem sitting underneath that choice that nobody mentions. Here's a reality every MSP knows. Native Microsoft 365 security leaves gaps. Those gaps land on your desk. Proofpoint 365 Total Protection closes them.

[00:10:19] It is an MSP-first platform that unifies Microsoft 365 security backup and compliance into one integrated console. High security efficacy, less operational friction, and multi-tenant management that scales as you grow. Protect clients against modern threats and stop stitching point tools together. Build on Hornet security, now part of Proofpoint. See it at Proofpoint-Total-Protection.com

[00:10:49] Why do we care? Because the adoption practice needs a person your shop probably does not have. Everybody on your bench can configure the tenant and set the policies. Almost nobody on it can stand in a room with 40 accountants and change how they work on a Tuesday. That's a teaching skill, not a technical one. The cheapest way to find out whether you can sell this is to run the 5-hour course on your own staff first and watch who turns out to be good at delivering it.

[00:11:19] So what to consider? Find the teacher you already employ and time them. The person who can deliver this is almost certainly already on your payroll, and it's probably not your most senior engineer. Look for whoever the rest of the team goes to with questions or whoever handles client onboarding without anyone complaining. Run the internal version first and put a clock on it.

[00:11:42] If it takes them 8 hours of preparation to deliver 5 hours of material, you've learned what your margin looks like before you ever put it in front of a client. Start with a client whose licenses are already paid for. Do not open this with someone who has to buy something first. Open it with a client who bought Copilot or the equivalent 18 months ago, and whose usage you already quietly suspect is close to nothing.

[00:12:07] The money is spent, the internal argument about whether to do AI is over, and the only missing ingredient is somebody teaching people what to do on a Tuesday morning. That is the shortest distance between a proposal and a delivered result anywhere in your catalog this year. Write down what you are not doing and calendar the revisit. Restrain only works when it's an actual decision instead of a drift.

[00:12:32] So put on-premises AI infrastructure practice on a specific quarter with the two or three conditions that would change the answer written next to it. A client asking unprompted. A hypervisor migration landing on the books. Somebody complaining about a token bill. Without that, the vendors will reopen this conversation with you every quarter until you eventually say yes on their timing rather than on yours. And picture the provider who starts this in October.

[00:13:00] A year from now, they have run the five-hour course at four different clients. They know which two people on their team can actually teach and which cannot. They've watched four companies go from almost nothing to something real. And they know what it took each time. That's a business that learned something specific about itself in a year, which is more than most of us can say about the last one.

[00:13:24] If this trend continues, by 2029, when that displacement window is fully open at the average client, the providers who spent the intervening years inside those accounts doing the adoption work will be the only ones whose infrastructure numbers come from somewhere. And everyone else will be quoting the identical figure off the identical calculator. This is the business of tech. Want to go deeper than the news?

[00:13:52] The Small Biz Thoughts community is where MSP owners and operators work on the business, not just in it. Member meetings, a deep resource library, and courses through IT Service Provider University. Everything you need to run the practice you actually want. Join us at smallbizthoughts.org. Interested in advertising? Head to mspradio.com slash engage.

[00:14:17] The Business of Tech is written and produced by me, Dave Sobel, under ethics guidelines posted at businessof.tech. Thanks for listening. I'll see you on the next episode. Part of the MSP Radio Network.