Accelerating AI Diffusion
What it takes for technology to permeate into society
In a previous post, I argued why diffusion might be the key drag for AI to generate notable economic value.
Diffusion, in this context, is defined as the ability and time it takes for AI to permeate through institutions and companies.
For businesses small and large, and of course institutions, this takes much longer than for the individual or series A startup.
Compliance, security, contracting, procurement, provisioning: all those are valid arguments that do drag the adoption of new technology in the enterprise. (Surprisingly, those are tremendously accelerated when a top leader in the organization says so.)
However, I argued that the core reasons are much more intrinsic to human behavior. Specifically three: Awareness, Focus, and Incentives.
“The primary constraints for organizations are no longer model performance or tooling, but rather organizational readiness.” - OpenAI, The State of enterprise AI (2025)
This post lays out the strategies and tactics to make AI diffuse faster and better through your organization, to create meaningful productivity gains as agentic systems and human-agent teams mature.
Forcing awareness
In the post, I defended the thesis that awareness, about the capabilities of top models and tools, is the key brake on its actual permeation into business processes, operations and customer experience.
If you take that at face value then generating that awareness throughout the company and, especially to leadership, should be the obvious point of attack.
If you don’t want to believe it, take a look at the recent OpenAI’s State of Enterprise AI report. Even among active ChatGPT Enterprise users, 19% had never used the data analysis feature, 14% had never used reasoning, and 12% had never tried search (what?).
And I am not even discussing usage, I am talking about real awareness. The kind that makes someone feel the agentic and reasoning capabilities, applied to their own reality.
So how do you generate awareness within your organization?
I would argue that to really assimilate the capabilities of a new technology, you have to go through two phases. First you see it, then you feel it.
The see part should be the easiest, but it’s many times wrongly assumed and overlooked.
Showcasing the highlights
The quickest way to get a leadership team interested, like really interested in AI capabilities, is to bring them the highlights.
Foundation models reached a tipping point somewhere around November 2025 and onwards. The things they can accomplish (and finish) now, particularly in agentic workflows, have nothing to do with the ones they were previously able to do.
But most leaders and managers haven’t seen how.
They need to be exposed explicitly to a show on how far these models, wrapped in agentic architectures, can go.
I don’t think that, at this stage, that should be tailored to your industry. In fact, depending on your sector, it might be counterproductive.
I would take a use case that organically has gotten ahead in advanced agentic loops and make it the highlight. Creating a branded and semi-complex web app in a few minutes is a good example, since coding agents are the lighthouse of progress.
Assuming you have management attributions, organizational influence or just want to make yourself an AI evangelist within your company, you can do these DIY (get some cool demos from enthusiasts online and show them) or get your fav consultant to do it for you.
Making highlights real
Once the seeing step is done, the org needs to feel the tech.
I would do it in two ways:
First, give the leadership team sandboxed access to a frontier agentic tool. The easiest is to provide them with Codex or Claude Code through the desktop apps (terminals and IDEs tend to scare people off) and provide broad guidance on how they can accomplish things, just talking naturally to the thing.
Second, tailor some use cases to the specific industry and verticals within the company’s area of existence. This is particularly effective if you present a demo of the different workflows and what users (internal or clients) will see through them. Again, you can do this yourself, carve out a small squad (of humans or robots) to do this for a couple of weeks or get a consultant.
By the way, although I do think that the first barrier to break is the awareness within the leadership teams, the whole business will definitely benefit from seeing and feeling these. So also take the chance to present it in a company-wide meeting if possible.
Democratizing experimentation
Don’t be cheap.
Give everyone in your team access to, at least one, foundation model. I’m talking basically about the latest Opus, GPT or Gemini Pro. This should be the baseline.
You shall also provide them with access to coding agents and agentic building platforms. Think Claude Code, Codex, Cursor, Replit or MS Copilot Studio. Claude Cowork may work too.
Complement this with links to some basic how-to guides and a generous token budget. Ivan Zhao, the CEO of Notion, and Jensen Huang, CEO of Nvidia, believe that you should, in fact, give them unlimited token budget.
I do agree with them.
Costs are an obvious concern for all companies but, to be honest, the AI momentum does not get infused in everyone at the same time. Most people in your organization probably won’t use their fair share of tokens (this should be tracked, though).
With this, incentivize and let the team surprise you.
On one hand, the people who live with them day to day are the ones that really know their own workflows, processes, and projects. They are the best positioned to translate those into effective AI-powered systems that automate and/or improve them.
On the other hand, you will probably (for sure) have some hidden talent within your company. This might be the avenue for them to shine. And for you to get some awareness again about their brilliance.
The creation of useful AI systems is, by nature, much more bottom-up than top-down.
Host Demo Days, Hackathons, Economic Prizes, revenue sharing schemes, internal venture structures. Pick your mechanism. But do incentivize creation.
Ask for actual demos and the basic technical architecture. Do not settle for slides. Building is thinking.
Governing production
Depending on how regulated your industry is and how critical your business processes, you will need to be much more careful about putting things in production than others, particularly if those workflows touch clients or users directly.
In any case, my strong view is that you need an AI office that governs demand, prioritization and implementation.
Think of it as a project management office for AI initiatives: it owns the agentic tech stack, defines architectural standards, and decides what gets promoted from prototype to production.
Depending on your size, this can be a part-time team member or a full-fledged team. But definitely, companies need a solid and evolving AI and agentic tech stack, as well as consistency in how things are architected and deployed.
Once those great ideas by the team members across the organization have crystallized and have been prioritized, this office should be the one that works on making them production-ready.
Creating space
The most innovative companies in history share a counterintuitive conviction: productivity requires protected unproductivity. This is even more true with AI.
You need to force space into your team members’ schedule for them to experiment with the AI tools you so generously gave them.
From 3M’s 1948 “15% rule” that produced the Post-it Note, to Shopify’s 2025 mandate that employees prove AI can’t do a task before requesting headcount, the most successful enterprises have always carved out structured time, space, and programs for experimentation.
Google’s 20% time is probably the most famous corporate experimentation policy in history and the products attributed to it are genuinely remarkable (Google News, AdSense, Talk, Autocomplete...).
While percentage-based time policies defined one era of innovation, the hackathon model (concentrated bursts of creative autonomy) became equally influential. Companies like Microsoft, Atlassian or Meta have made extensive and successful use of it.
“These one-day bursts of autonomy allow people to work on anything they want, provided they show what they’ve created to their colleagues 24 hours later.” - Daniel Pink, Drive (2009).
That constraint (show what you’ve built) is exactly what separates productive experimentation from aimless tinkering.
Academic evidence strongly supports structured experimentation time.
The playbook of innovation is being rewritten at speed as companies move from encouraging to requiring AI experimentation.
Three tactics stand out for businesses specifically seeking to accelerate AI diffusion:
Dedicated AI experimentation time (Duolingo’s F-r-AI-days, Shopify’s mandated monthly AI reviews) ensures AI learning is protected from regular work demands.
AI sandboxes and governed experimentation environments (Okta, Syngenta, AWS Innovation Sandbox) let employees experiment safely without disrupting production systems.
Visible leadership participation (Tobias Lütke of Shopify coding with AI agents, Brian Armstrong of Coinbase hosting Saturday catch-up sessions, Nicolai Tangen of Norges Bank Investment Management “running around like a maniac”) signals that AI experimentation is a strategic priority, not a passing initiative.
Whether it’s “15% time” or Building Fridays, pick your flavor to make room for experimentation.
Being clear
Resistance is natural in the short term but really unproductive in the long one.
If you are somehow exposed to AI, particularly in the enterprise, you would have heard a lot of buzz around the change management required for AI to actually permeate and generate value.
I think at least 80% of that change management people talk about derives from clarity or lack thereof with the organization members.
My honest opinion: If you have a clear strategy around how AI might make the company more competitive, be clear about it.
For some businesses, it might be an infinite augmentation of productivity (those CEOs mentioned above may fire you for not using your tokens).
For others, it might be creating new markets, new service lines, serving more customers, selling to different customers.
And for others, it might be downsizing and cutting costs to remain competitive. This last category is the most complex to deal with and the most sensitive, but I still argue that being clear is the best path forward.
If you plan to reskill your best team members to become a sort of orchestrator of agents, what do you really gain not being clear about it in the interim?
I get that some motivation downfall might occur in the short term, but you might be missing out on great future talent by not being clear about your expectations and not treating your team members as adults.
I know this is the hardest one. But clarity compounds. Ambiguity doesn’t.