Playing With AI Is Work: The 3-Tier Framework for Organisational AI Adoption

Right now, AI adoption in most companies looks like this: a few keen people on the team have ChatGPT or Claude open in a browser tab. They use it to draft emails, summarise documents, brainstorm ideas, maybe write some code. It works. It's useful. And everyone thinks that's what "using AI" means.

It's not.

What most companies call AI adoption is actually just individual experimentation. And while that experimentation is essential - it's the foundation everything else gets built on - it's also where 90% of companies stop. They never progress beyond scattered, individual usage into something that actually transforms how the business operates.

The reason? Most companies try to skip straight to integrating AI into their core product. They hire an AI engineer, launch a chatbot, or announce an "AI strategy" to the board. But they've missed two critical layers underneath.

AI adoption is not a switch you flip. It's a staircase you climb. And if you try to skip steps, you fall.

Tier 1 is where everyone starts, and it's necessary. This is people on your team using AI tools on their own - ChatGPT for drafting, Claude for research, Midjourney for images, whatever the tool is.

At this stage, AI is a personal productivity boost. Each person gets maybe 20-30% faster at certain tasks. That's genuinely valuable. But it's also limited.

Why Tier 1 isn't enough:

• Every person is starting from scratch with every prompt. No memory, no context, no accumulated knowledge.
• The AI gives generic outputs because it doesn't know your business, your voice, or your standards.
• There's no consistency across the team. One person's AI-drafted email sounds completely different from another's.
• Knowledge stays trapped in individual conversations that disappear when the chat window closes.

Tier 1 is like giving everyone on the team a calculator but never connecting them to a spreadsheet. Useful? Yes. Transformative? Not yet.

The most important thing at Tier 1 is making experimentation feel safe. At the beginning, it will feel slow. It feels like you're doubling your work and spending more time doing things the long way. But I can absolutely guarantee you that those gains catch up real quick once you get into a flow with AI. The key is giving people permission - and time - to experiment.

One practical thing that works: put prize money on the table. Make AI adoption fun and competitive. Have the team create the funniest AI-generated image each week. Run a challenge for the best AI-assisted workflow. It sounds silly, but this is how you build muscle memory. And it has to come top-down from leadership to incentivise the team and make it happen.

This is where things get interesting, and where most companies have never been.

Tier 2 is when your AI stops being a generic tool and becomes a personalised assistant. It knows your voice, your preferences, your history, your way of working. It's not just responding to what you ask - it's responding as if it already knows you.

What does Tier 2 actually look like? In my own workflow, my AI has access to every idea I've ever had, every quote I've saved, every podcast and YouTube video I've consumed, all my emails and my tone of voice, my personal notes, the full context on all the meetings I've had and everything I said in them. It's wild how much it knows about me.

The result? When I ask it to draft a LinkedIn post, it doesn't give me generic corporate content. It writes like me - with my vocabulary, my rhythm, my opinions. When I ask it to prepare for a coaching session, it already knows the founder's history, what we discussed last time, and what we said we'd follow up on.

This is the difference between a tool and a teammate.

How do you build Tier 2? You create what I call a "brain" - a knowledge base that your AI can reference. This could be:

• A set of markdown files with your writing samples, frameworks, and preferences
• Your meeting notes and transcripts fed into a system the AI can search
• Your email history and communication patterns
• A voice and style guide that captures how you actually talk

The biggest mistake people make moving from Tier 1 to Tier 2 is over-engineering it. They try to build some elaborate system on day one. Don't do that. Start with a single document that describes how you write. Then add your last ten emails. Then your meeting notes. Build it up gradually.

You want the team to get comfortable first so they get more and more excited, rather than having to push AI down onto people. If it feels forced, it won't stick. If it feels like a superpower, people will pull it into their workflow themselves.

Tier 3 is the holy grail, and honestly, almost no one is there yet. I haven't seen a single company fully operating at this level. It's still early days.

But here's what it looks like in theory - and in the early experiments I'm seeing:

Tier 3 is when AI has access to your entire company's knowledge, not just one person's. Your investment criteria, your portfolio data, your customer history, your internal processes, your team's collective expertise - all of it accessible to AI across the organisation.

Imagine a VC fund at Tier 3:

• A new LP asks a question about your fund's APAC exposure. Instead of someone spending two hours pulling data, AI instantly generates the answer from your portfolio database, formatted in your fund's style, with the right compliance language.
• Your IC memo gets drafted from the meeting transcript, cross-referenced against your investment thesis and comparable deals you've done.
• DDQ responses - those 300+ question documents from super funds - get auto-drafted from your existing materials, with a human doing the final review.

This is not about replacing people. It's about removing the repetitive cognitive labour that keeps smart people from doing their highest-value work.

To build Tier 3, you need:

• Centralised, structured data - your company knowledge in formats AI can actually use
• Clear governance - who can access what, what's confidential, what needs human review
• Integration points - AI connected to your actual systems (CRM, portfolio tools, document management)
• A team that's already comfortable at Tier 2 - you can't skip the personal context layer and jump straight to organisational context

Here's why this framework matters: the tiers don't add - they multiply.

At Tier 1, you get maybe 1.3x productivity on individual tasks. Useful, but incremental.

At Tier 2, that same person is now 3-5x more effective because the AI isn't starting from zero. Every interaction builds on the last. Your personal context layer means the AI's output quality goes up dramatically, which means less editing, less back-and-forth, less "that's not quite right."

At Tier 3, the entire organisation benefits from everyone's accumulated context. One person's meeting notes become another person's preparation material. The founder's investment thesis informs the analyst's research. The ops team's processes feed into the investor relations team's reporting.

Each tier compounds the value of the tier below it. This is why companies that skip Tier 2 and try to jump straight to Tier 3 fail. Without the personal context layer, the organisational layer has nothing to build on. You end up with a fancy system that nobody uses because it doesn't feel personal or useful.

Think of it like building a house. Tier 1 is learning to use the tools. Tier 2 is building yourself a solid workshop. Tier 3 is building the house. You wouldn't try to build the house without first knowing how to use a hammer and having a workshop to build components in.

Your Monday morning action: make AI experimentation an official part of the work week. Not a side project. Not "when you have time." Actual, protected time. Put budget behind it - even a small prize for the best AI-assisted workflow each week makes a difference.

Specific moves: - Pick one repetitive task per team member and challenge them to AI-assist it this week - Share wins in your team meeting - "here's what I used AI for this week" becomes a standing agenda item - Give people permission to be bad at it. The first attempts will be clunky. That's the point.

Start small. One document. Your writing style, your common phrases, your preferences. Feed it to your AI tool and see how the outputs change.

Specific moves: - Create a personal "brain" document - start with 500 words about how you work and communicate - Connect your meeting notes so AI can reference past conversations - Build templates that use your personal context - email templates that sound like you, not like a robot

This is where you need leadership buy-in and some infrastructure.

Specific moves: - Audit your company's knowledge: what's documented, what's tribal, what's scattered across people's heads? - Start with one high-value, high-repetition workflow (LP queries, onboarding docs, investor updates) - Build the governance layer before the tech layer - decide what AI can access and what needs human review - Invest in data hygiene - AI is only as good as the data it has access to

Most companies try to get AI into their core product first. That's the wrong starting point. You really want to get AI into supporting the team - internal workflows, ops, communication - first. Build confidence. Build muscle memory. Get people excited. Then, once the whole team is fluent, you integrate it deeper into your product with a team that actually understands what's possible.

The popular "best practice" of launching an AI feature in your product before your team is AI-fluent is like a restaurant putting a new dish on the menu before anyone in the kitchen has learned to cook it.