The Makers Manifesto - And What It Means For Product Operations

On the 18th March I got an email asking me to be part of the making of the next manifesto for product development. On the 7th May I was on a plane to London, to take part in a workshop alongside other product leaders and practitioners and ask some very uncomfortable questions: what will stay the same, what will become radically different, and what will need guidance, now more than ever?

Today, I am proud to present the Makers Manifesto to you. Brought to you by 44 practitioners from across product, tech, design, and innovation (many of whom you’ll recognise) and built for a future where humans work alongside machines to bring better products to market.

I was the only Product Operations contributor in the room. And I’m here to tell you exactly what the Manifesto means for how your organisation operates in the future.

https://makersmanifesto.org

If you haven’t read it yet, go do so. It is a serious, grounded piece of work. It does not tell you to move fast and break things. It does not promise AI will solve everything. Actually, it asks more of us than that: are we building things worth building? Are we staying close to the people we serve? Are we making context explicit so that the humans and agents in our organisations can make good decisions? Are humans actually signing the work?

These are good questions. Important questions. But here is the thing nobody in the room said out loud, and that I want to say now: knowing the principles and being able to implement them are completely different problems. And the gap between them is a Product Operations problem.

Why Most Organisations Will Change Nothing

I imagine that the Makers Manifesto will be read by a lot of people in the upcoming weeks.

Senior leaders will nod along. Product practitioners will share it on LinkedIn. Someone will add it to an onboarding doc, or maybe even a job description. And then, in the vast majority of companies, nothing will structurally change.

Because organisations do not fail at knowing what good looks like. They fail at creating the right conditions for it.

I have spent my career working inside and alongside product organisations of various shapes and sizes - from scaleups to enterprises and regulated businesses - and the pattern is consistent: The strategy exists. The principles are agreed on. Everyone’s on the same page.

But take one look at the operating model underneath and you realise that it either does not support the strategy, or it actively works against it.

AI does not change that pattern. If anything, it accelerates it.

What The Manifesto Actually Asks Of Your Operating Model

Look closely at what the manifesto’s values demand in practice.

Purpose over possibility requires your organisation to make real choices about what is worth building and what is not. That demands a strategy that is clear enough to actually guide decisions, and a prioritisation process that reflects it. Without it, trade-offs cannot be articulated.

Value realised over effort spent requires you to know what value looks like, measure it honestly, and have feedback loops fast enough to act on. That is a data and tooling question as much as a maturity question. (I’ve previously written about data maturity in teams - you can read it here)

Learning loops over launch plans requires the operational rhythm to support continuous discovery, not just the theoretical ambition for it. Knowing you should stay close to your users and actually building the cadences, the infrastructure, and the team habits to do it consistently are two very different things.

Human accountability over full automation - perhaps the most important value in an agentic world - requires you to know exactly where human oversight lives in your process, who owns it, and how it is enforced. That does not happen by default. It has to be designed.

None of this is impossible. We aren’t talking about pipedreams or leaps of faith. All of this is 100% an operational challenge. And none of it gets done without someone in the organisation whose job it is to make the operating model work in your favour.

The Principle I Kept Coming Back To In London

Of course, I have contributed to and co-signed all sixteen principles. But there’s one principle that’s dearer to me than most: make context explicit.

Good judgement requires clear intent: strategic direction, trade-offs, constraints. Without this, what we optimise for will drift.

In a world where agents are increasingly executing work inside your product organisation, this principle is not about good practice. It is about whether your agentic workforce can function at all. I cannot stress this enough.

Agents do not (cannot!) work around dysfunction the way humans do. A human PM can navigate an unclear strategy through experience, intuition, and the political capital they have built over years. An agent cannot. It will optimise for whatever context it has been given. And if that context is incomplete, inconsistent, or out of date, it will confidently execute in the wrong direction. Now think about not one agent, but an army of agents - and extrapolate the real damage to your reputation and bottom line.

This is the thing I kept coming back to in that workshop. The companies that will genuinely benefit from agentic AI are not the ones with the most sophisticated models or the largest AI budgets. They are the ones with the cleanest operating models. The ones where strategic intent is explicit and accessible. Where decisions, trade-offs, and constraints live somewhere that both humans and machines can find them. Where ‘good judgement’ becomes machine-readable.

That is a Product Operations problem - and it is one most organisations are not even close to solving.

With Simonetta Batteiger and Matt LeMay in London

What An AI-Native Operating Model Requires

I want to get specific here, because vague calls to “go get your foundations in place” are not useful.

An operating model that can support human and agentic workforces needs to do several things that most organisations currently do not do well:

1. It needs a single, accessible source of strategic truth - and by that I do not mean a strategy deck that was updated eighteen months ago and lives in a folder nobody checks. I’m talking about a living articulation of where the organisation is going, why, and what trade-offs it has decided to make in pursuit of that goal. This is the context that feeds every decision, human or machine.

2. It needs integrated tooling that connects the parts of the process that currently live in silos - discovery, prioritisation, delivery, measurement - and makes them seamless. Agents cannot operate effectively across disconnected systems. Neither can humans, if we are being honest, but they cope. Agents do not cope.

3. It needs explicit human checkpoints. Not because any automation is bad, but because the manifesto is right: you can delegate decisions, you cannot delegate accountability. Someone has to own the consequences of what gets built and released. That ownership has to be baked into the process explicitly, not assumed.

4. And it needs a continuous feedback loop between what the organisation is doing and what is actually happening in the market, with the customers, and in the product. The manifesto calls this staying close to the people you serve. In operational terms, it means building the rhythms and infrastructure for insight to flow back into decisions quickly enough to matter.

Honestly, none of this is new, and that’s the real kicker. Just like the nascence of Product Operations as a separate discipline didn’t suddenly invent new tasks, the nascence of human/machine Product teams doesn’t suddenly mean we are upending absolutely everything.

These parameters are not AI-era inventions. They are the things that good Product Operations has always been about. But what AI changes is the cost of not having them. Because a half-functional operating model that humans could navigate through goodwill and tribal knowledge will simply not support a workforce that includes agents.

Possibly for the first time ever, the dysfunctions we’ve always had to work around become a real financial threat.

Who This Matters To, And What To Do About It

If you are a CPO, a VP of Product, or a founder leading a scaling organisation: the Makers Manifesto is asking something of your operating model that your operating model may not currently be able to deliver. I say this with a lot of compassion - but this will be your biggest downfall if not handled early.

If you are an Operations or Enablement leader who has been handed the job of making the AI Transformation happen in your organisation, remember: you are not just implementing tools. You are redesigning how your organisation thinks, decides, and executes, and you need to make it far more explicit than ever before. That is a bigger job than most people around you realise.

The manifesto gives us the what. But your operating models are the how. And the how is where this gets hard, and where most organisations will fall behind while publicly claiming to be ahead.

I’m the only Product Operations voice on the Manifesto roster. And I think it reflects where the industry still is: clear on the principles, not yet clear on the infrastructure that makes them real.

That infrastructure will not build itself. And the longer it takes to start, the more expensive it’ll get.