AI Co-Creation Models: Five Ways to Build AI Together

Co-creation means building an AI solution together with the people who will use it, buy it or support it, instead of handing over requirements and waiting for a finished product. There is more than one way to do it, and the right model depends on who holds the knowledge, who carries the risk and who will own the result. This guide describes five common AI co-creation models, when each fits and the governance each needs. For what co-creation means and why it matters, see co-creation and the future of AI innovation; for examples, see case studies in AI co-creation.

Five AI co-creation models

Model	How it works	Best for	Watch out for
Joint delivery team	Client domain experts and partner engineers work as one team on a shared backlog	Custom solutions where domain knowledge sits with the client	Clear decision rights and time commitment from client experts
User co-design	End users shape the product through workshops, prototypes and testing in real work	Tools that change daily workflows	Representative users, not only enthusiasts
Co-development partnership	Two organizations build a product together and share costs, rights or revenue	New products that need both parties' data, expertise or market access	IP ownership, data rights and exit terms agreed up front
Internal business and tech squad	A business unit and an internal AI team share goals and staff	Organizations with in-house AI capability	Shared metrics and funding so priorities do not drift
Open innovation	Challenges, hackathons or startup programs invite outside ideas against a defined problem	Exploring many options quickly	A path from winning idea to production

How to choose a model

• Where is the knowledge? If the domain expertise is yours and the AI skills are not, a joint delivery team fits.
• Who will use it? If adoption depends on frontline staff, add user co-design to any model.
• Who will own it? If both parties want to sell or reuse the result, you need a co-development agreement.
• How certain is the problem? For open questions, start with open innovation or a short discovery; for clear problems, go straight to a delivery team.

Governance every model needs

• A shared charter: the problem, success metrics, scope and time frame.
• Roles and decision rights: who decides on priorities, design, data use and release.
• Data and IP terms: what data is shared, how it is protected and who owns code, models and outputs.
• Cadence: regular demos and reviews with users and sponsors.
• Responsible AI checks: testing for accuracy, bias and security before release, using a framework such as the NIST AI RMF.

Running a co-creation engagement

1. Discovery: agree the problem, users, data and metrics.
2. Prototype: build something users can try within weeks, not months.
3. Pilot: test in real work against a baseline.
4. Scale: harden for production, train users and hand over ownership.

The benefits and risks of this way of working are covered in benefits and risks of AI co-creation. To see how we co-create AI solutions with clients, visit our AI implementation support page.

Conclusion

AI co-creation can take five main forms: joint delivery teams, user co-design, co-development partnerships, internal squads and open innovation. Choose based on where the knowledge sits, who will use and own the result and how clear the problem is. Every model needs a shared charter, clear decision rights, data and IP terms, a regular cadence and responsible AI checks.

If you want to co-create an AI solution, book a consultation with Ekipa's AI engineers.

Frequently asked questions

What is an AI co-creation model?

A way of building AI together with users, clients or partners, with shared work, decisions and sometimes ownership.

What are the main types of co-creation?

Joint delivery teams, user co-design, co-development partnerships, internal business and tech squads, and open innovation.

Who owns the IP in a co-creation project?

It depends on the agreement. Settle ownership of code, models, data and outputs before work starts.

How long does a co-creation project take?

A prototype can be ready in weeks; a production system usually takes several months, depending on scope and data.

Is co-creation more expensive than outsourcing?

It needs more of your team's time, but often reduces rework and improves adoption.