INBOX INSIGHTS: Activity is Not Adoption, Enterprise AI Part 6 (2026-06-24)

INBOX INSIGHTS: Activity is Not Adoption, Enterprise AI Part 6 (2026-06-24) :: View in browser

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Activity Is Not Adoption.

Real adoption is something a different team can copy.

You haven’t been adopting AI. You’ve been using AI.

I say that to leaders who tell me their AI adoption is up 40% or 60% or 90%, and who can’t understand why nothing is actually shipping. The dashboard is real. The usage is real. The thing the dashboard is measuring is just not what they think it is.

Use is when somebody opens the tool and types a prompt. Adoption is when a workflow runs the same way across people, produces the same quality of output, and could be handed to a different team that could run it without coaching. Most organizations have a lot of the first thing and very little of the second.

So here’s what’s happening underneath. The company bought a tool. The tool got rolled out. People started using it. Marketing uses it one way. Sales uses it a different way. Customer service uses it a third way. Inside marketing, three different people use it three different ways. There’s no shared definition of what “good” looks like for any specific job. There’s no documented workflow anybody could copy. There’s no version of any task you could hand to a new hire and say “do it this way.”

What looks like adoption is actually sprawl. The license utilization is up because everyone has the tool. The compounding is zero because none of it is shared.

Adoption is a Process question, not a Platform question. The tool is the easy part. What makes a workflow adopted is that the work is the same shape every time a different person does it. That’s what makes it copyable. That’s what makes it scalable. That’s what lets it survive somebody going on leave.

If the only thing your team can hand off is access to the tool, you haven’t adopted anything. You’ve purchased licenses.

The leaders looking at the activity dashboard aren’t being naive. The dashboard is the only data they have. The vendor sells the dashboard with the platform. The IT team installs it. The activity numbers go up over time because the company keeps deploying more seats. Everyone agrees the line is going in the right direction. Nobody is asking whether what’s being measured is the thing that matters.

The framework, applied to Process (the upstream version)

Here’s the thing. The 5P Framework by Trust Insights™ keeps doing the work, one trap at a time.

Purpose, People, Process, Platform, Performance. The Pilot Purgatory piece named Platform as the trap of commitment avoidance. The Before You Scale piece named skipping over People. The Stop Restarting piece named letting Platform set the pace for everything else. And last week’s Later Never Comes piece named the trap of letting Process and People decay after you’ve built them.

This piece is upstream of that one. The trap here is thinking you have a Process to maintain when what you actually have is twenty people improvising on the same tool. You can’t maintain something that doesn’t exist. (If last week resonated and you went looking for the AI workflow to refresh, this piece is for you if you couldn’t find one.)

Process is the part that gets quietly dropped because it’s unglamorous. Defining a workflow, documenting the steps, standardizing what “good” looks like, training people on it, keeping the documentation current. None of that is exciting. None of that gets a vendor demo. None of that is what anybody wanted to talk about when they wrote the AI strategy.

It’s also the part that turns usage into adoption. Without Process, you can have everyone in the company using the tool every day and have nothing scalable to show for it. With Process, one team’s good workflow becomes the next team’s starting point. The work compounds, instead of restarting in a different shape every time a different person sits down.

Why this is worse right now than ever

Two reasons.

First, the tools are good enough now that a smart person can produce useful output with no defined process at all. That’s the trap of capability. The output is fine, so nobody pushes for the workflow to be standardized. Each person figures it out. Each person’s version is slightly different. Nobody notices, because every version produces a usable result.

Second, organizations are scaling AI usage faster than they’re scaling AI process maturity. The company that had three people using Copilot last year has three hundred this year. The number of license deployments went up by a hundred. The number of documented, repeatable, copyable AI workflows went from zero to zero. The gap is widening, and the dashboard is celebrating it.

Where do you keep your SOPs?

When Chris first hired me at the agency a million years ago, one of the first questions I asked the team was where they kept their SOPs. They looked at me like I had three heads.

So I tried again. Where do you keep the instructions for creating reports, for starters?

“We don’t have that written down.”

That was the whole problem in one sentence. Everyone was doing the same work, in their own way. The work got done. The output was useful. But the process didn’t exist. Some people were done quickly. Others took twice as long. We were an agency that billed hourly. This needed to be resolved.

So the first thing we did as a team was draft all of our SOPs. (A standard operating procedure, in case the acronym scared you, is just a set of reusable instructions.) Once we had agreement on what a good report looked like and how to produce it, everyone on the team started using the same SOPs for the same tasks.

What happened wasn’t a miracle. It was pragmatic. The team grew from 3 people to 11. The profit margin climbed to north of 40%. We became the second highest revenue generating team in the agency. All because we started doing the same work the same way.

Repeatable process isn’t unique to AI. It’s just discipline. And imagine what discipline like that does when you point it at AI.

Your next move

This week. Same shape as the last four.

Pick the AI workflow that the most people across the most teams already use the tool for. The most common task. Customer email drafts, meeting summaries, weekly reports, RFP responses, whatever it is for your organization.

Watch three different people do it. Three different teams if you can. Don’t coach them. Don’t intervene. Just watch. Then write down the best version of what you saw. Step by step. Inputs, output, quality bar, how long it should take, what done looks like.

Hand that document to a fourth person on a fourth team. See if they can run it without you in the room. If they can, you have your first standardized AI workflow. That’s adoption. Document it, share it, and pick the next workflow to standardize.

If the fourth person can’t run it, the workflow isn’t ready, regardless of what the dashboard says about how many people are using the tool. That’s also useful information. Go back, find what’s unclear, fix the document, and try again.

The moral of the story

I’ll keep saying this one too. The dashboard isn’t lying about what it’s measuring. It’s just not measuring adoption. Adoption is a workflow somebody else can run.

If your AI program can’t produce a single documented, copyable workflow that a new hire could pick up next month, you don’t have an AI program. You have a tool deployment.

Pick the workflow. Standardize it. Make it copyable.

Which workflow are you about to standardize?

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the release of Microsoft Copilot Cowork and its hidden financial implications for your business. You’ll learn how to calculate potential costs by categorizing your daily tasks into light, medium, and heavy workloads. You’ll discover how to apply the 5P framework to prevent runaway AI spending in your organization. You’ll identify specific strategies to optimize your workflows by separating planning from execution. You’ll explore how command-line tools can help you maintain efficiency without burning through expensive credits.

Watch/listen to this episode of In-Ear Insights here »

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In this week’s Data Diaries, we close last week’s templated-versus-human thread and turn to where the work runs — the power grid, the GPU supply chain, and the export-control regime that decide whether your AI program has a home.

Here’s the shift. The Stanford AI Index, citing Epoch AI, reports hardware unit cost falls roughly 30% per year and energy efficiency improves roughly 40% per year, yet absolute AI energy consumption keeps climbing because model scale outpaces both curves. U.S. substation lead times now run 40 months to five-plus years, up from 24 to 30 months before 2020. Power, not Graphics Processing Units (GPUs), now binds on-premises AI.

Industry reports estimate NVIDIA holds 60 to 70 percent of Taiwan Semiconductor Manufacturing Company (TSMC) Chip-on-Wafer-on-Substrate, “L” variant (CoWoS-L) packaging capacity through 2027; any disruption to TSMC’s Taiwan operations would halt global AI hardware production for 12 to 24 months. The U.S. Bureau of Industry and Security (BIS) tightened export controls in October 2023 and across 2024, added the broader “Diffusion” framework, and the H20 chip restriction followed. Sovereign AI programs now run in the UAE, Saudi Arabia, India, the EU, the UK (the October 2025 AI Growth Lab consultation), and Japan — each hedges against U.S. technology dependency.

So what does this mean for your business? Enterprise teams now carry a Scope 2 and Scope 3 disclosure problem — electricity for hardware lands in Scope 2, while embodied carbon and cloud-provider energy land in Scope 3. Cloud inference prices have moved from roughly $5 per million tokens to $15 per million tokens, and if you lack another pivot, you live with that number. Single-source TSMC dependency sits under every cloud you rent and every chip you buy, so vendor concentration risk now reads as geopolitical risk.

If you sit purely in the cloud and own no inference capability, the vendor owns your exit, your data, and your cost line. Right? Anthropic, OpenAI, or Google can lift prices on a Tuesday and your only response is to write the bigger check. That’s the trap, and here’s the move that flips the math.

Stand up an inference hub inside your facility — hardware under your control, running small open-weight models, serving every agent you operate. Put up a multi-tenant server like vLLM, connect every agent to it, run it until capacity, then buy another one. Most agents do not need frontier models; templated agent work runs guilt-free on local hardware and only costs electricity. This is no different than client/server architectures of the 90s or on-prem internet of the 2000s.

The financial case writes itself. The hub lands on your CapEx budget, not OpEx, so finance depreciates it like any other server asset. It’s CapEx, it’s depreciable, and it’s yours. At enterprise scale with billion-dollar IT budgets in play, the math on an inference hub is not hard — most enterprises find $50,000 in the sofa cushions in the lobby, even at department level.

Four moves to make this quarter:

Everyone, SMB through enterprise: tag AI workloads in your cloud cost console this week. Financial Operations (FinOps) savings show up before Environmental, Social, and Governance (ESG) ever asks for the data.
Mid-market and enterprise procurement: verify the Export Control Classification Number (ECCN) on every AI hardware purchase, and on any cloud service you extend to non-U.S. subsidiaries.
Enterprise infrastructure and Chief AI Officers (CAIOs): confirm power availability with your utility before any on-premises build, hold 9 to 12 months of committed compute across multiple providers, and default to the smallest model that meets your quality bar.
Healthcare, financial-services, and public-sector teams: treat sovereign AI as a contract requirement; data residency and model residency now turn mandatory across regulated sectors.

Next week we close the series, turning all of this into a vendor due-diligence checklist your team can run on a Tuesday afternoon.

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

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