#53 - We Are Partners in CrAIme.
Why Internal AI Products Only Succeed When We Build Them Together
#beyondAI
When we begin building an AI product inside a company, especially one aimed at solving real problems for real teams, it’s easy to assume that everyone is already aligned - after all, they brought the idea, or they nodded to ours. But what I’ve learned again and again is that even when everyone’s nodding in the same direction, their understanding of what happens next can be worlds apart.
Some teams believe that their job is done once they’ve shared the idea. Others believe that saying yes opens the door to unlimited feature requests, as if the AI product team were now an internal agency. Both perspectives are understandable, and both will quietly derail an AI product if we don’t correct course early.
The truth is, getting from idea to a mature, usable product is not a handoff. It’s a journey. A long, effortful one. And the moment someone says “yes,” the real work begins. First, we have to understand the problem deeply enough to run a Quick Assessment. If it’s promising, we move into a Detailed Assessment, which means checking data availability, defining success, shaping the first version, estimating costs, and aligning across different teams and layers of the organization. Even then, we’re just getting started.
Once we begin building, we step into an entirely different reality: turning intent into experience, building something that works in practice, with all its edge cases, user behaviors, and change management needs. And then, even after the product goes live, the journey continues. Every new feature idea, every new user group, every seemingly small request must go through some form of structured evaluation. AI products are never really finished. They evolve. And with each evolution, complexity grows. That’s why it’s so important to make expectations transparent early on.
This isn’t about bureaucracy. It’s about clarity. If we don’t name the expectations, we leave too much room for misunderstanding. The product team becomes reactive. Stakeholders become frustrated. Strategic focus slips. But if we do make things transparent, if we show what this journey really involves, and who needs to be involved at each stage, we create shared ownership. We give everyone a map, not just a promise.
The Reality of the Internal AI Product Journey
Building an AI product is not a single event. It’s not an app you build in a hackathon and hand over. In practice, AI product development has many gates, each requiring decisions, commitment, and careful attention.
We start with Problem Discovery, where we try to understand if the problem really needs an AI solution - or if it’s a process, policy, or visibility issue in disguise.
Then comes the Quick Assessment, where we decide whether it’s worth exploring further based on impact potential, data availability, and feasibility.
If it’s still promising, we enter the Detailed Assessment, where we estimate the cost of development and ownership, build a lightweight prototype, and align with legal, security, and governance partners.
And if the product idea passes all this, we enter the 0→1 phase. The first real attempt to deliver value through a working product.
But even when the product is live, it’s not over. Every new request, every additional user group, every iteration - it all requires us to revisit what we’ve built and make sure it still holds.
AI products are not static. They are living solutions that grow with the business, and so the people involved must grow with them. Which brings us to a conversation we want to have with every team we build for.
A Letter to Our Stakeholders
We Are Partners in Crime, Which Turned Out to Be AI
Dear Colleagues,
When we agreed to explore an AI solution together, we didn’t just start a project. We entered a kind of partnership - not the ceremonial kind, but the kind that unfolds through real work, shared problem-solving, and the occasional chaos that comes with doing something new inside a big company. It turns out, our “crime” is AI. But the story we’re writing together is really about something more human: solving meaningful problems, with all the constraints and complexity that come with working in the real world.
So before we go any further, we’d like to be clear with you - not just about what to expect from us, but also what we’ll need from you. Because AI product development isn’t a service. It’s a collaboration. And the success of what we’re building depends on how well we stay aligned.
What You Can Expect From Us
A structured approach. We won’t throw tech at your problem. We’ll take it seriously, understand it deeply, and explore whether AI is the right fit before we commit to building.
Clear steps. From Quick Assessment to Detailed Assessment, and later into development, we’ll guide the process and make sure you always know where we are and what’s coming next.
A real product mindset. If we agree to build something, it won’t be a one-off experiment. We aim to build something usable, valuable, and maintainable.
Challenge and honesty. If your expectations are too high, or the timeline unrealistic, or if we believe the solution you have in mind won’t work, we’ll tell you. Respectfully, but clearly. We owe you that.
A team that listens. We won’t disappear into our backlog. We’ll involve you in the right moments, bring you into design and testing, and keep your voice part of the journey.
What We’ll Expect From You
Active participation. The discovery phase needs your time, your business logic, your data, and your real use cases. We can’t invent those on our own.
Commitment beyond the idea. Saying yes to an idea is just the start. We’ll need you in refinement, decision-making, and especially during the early product iterations.
Openness to the unknown. Not everything will go as planned. We’ll need space to experiment, sometimes fail fast, and adapt. That’s part of the process, not a sign of failure.
Shared prioritization. Once live, your team might come with new ideas, changes, or user groups. That’s good. But each one needs to be assessed, prioritized, and resourced. We’ll need your help to make smart decisions.
Executive sponsorship. If you’re not the decision-maker, we’ll need your leadership to help align with those who are. This kind of product journey works best when it’s visible and supported from the top.
This letter isn’t a contract, and it’s not a checklist either. It’s an invitation to approach this like a joint venture.
We don’t build AI products for you. We build them with you.
And if we do this right, the result won’t just be another internal tool. It will be a real capability that solves something meaningful in your world, and keeps delivering long after we’ve shipped the first version.
Let’s stay close. Let’s be honest with each other. Let’s keep learning as we go. And if we hit some bumps along the way, good. That’s how we know we’re doing something that’s worth it.
Product Maturity: What It Really Means
One of the most important things we’ve learned in our AI product work is this: just because an AI product ambition is approved, it doesn’t mean we don’t need you anymore. And just because something is live doesn’t mean it’s done. If we don’t set the right expectations around what it means to grow an AI product - not just build one - we end up with solutions that look complete on the surface, but break down when used in practice, or stall the moment new requests emerge.
Product maturity doesn’t mean feature-rich. It means: the solution is proven, adopted, stable, and trusted by the business and users. It means we’ve not only built the solution but also put the processes in place to operate it, evolve it, and support it responsibly.
That kind of maturity only comes with time, usage, learning, and adjustment. Not just from the AI team, but from everyone involved.
Why New Requests Aren’t Free
It’s completely natural that once something useful is live, more ideas emerge. And in most cases, we welcome those. But it’s important to be clear: every new request is a new mini-product. Even if it seems like “just another button” or “can we add one more user group,” each of those changes needs discovery, prioritization, testing, and enablement. And often, they carry downstream implications for governance, user support, and compliance.
We apply the same mindset to new requests that we used at the beginning:
What’s the problem we’re solving?
What user behavior do we expect to change?
What is the estimated value?
What is the cost of ownership?
If we don’t ask those again, we risk building features that slow us down more than they help. Or worse - that break something we’ve already made work.
The Total Cost of Ownership (TCO)
There’s one more thing we’d like to be honest about. Building an AI product can be relatively fast. Owning one is where the real cost shows up.
Many internal AI projects seem cheap in the beginning. A couple of developers, a use case, maybe a pre-trained model. But the true costs arrive later:
Integrating it into real workflows
Handling errors and exceptions
Training and re-training users
Monitoring for AI quality
Meeting security and compliance standards
Supporting expansion to more teams
That’s why we assess so much before building. We’re not trying to block innovation. We’re trying to protect our focus and prioritize what’s worth owning.
Roles Across Phases: Who’s Involved and How
To make this journey work, we can’t walk alone. Here’s a rough sketch of how collaboration shifts across phases - and what’s typically needed from you as our business partners and domain experts along the way:
1. Problem Discovery
Our role: Facilitate the discovery process, frame the context, and start mapping how value could be created.
Your role: Clarify the problem, walk us through the current process, and help us understand where the real pain points are.
Your time investment: Around 3–4 hours spread over one week.
2. Quick Assessment
Our role: Evaluate the technical feasibility and early fit for AI, based on data, problem framing, and business priority.
Your role: Share your expectations, validate that the problem is worth solving, and connect us to the right domain or technical experts.
Your time investment: 2–3 hours, focused and compact.
3. Detailed Assessment
Our role: Architect the solution approach, run early prototypes, and estimate effort, value, and risks.
Your role: Provide access to relevant data, co-shape the use case logic, and help us define what success looks like.
Your time investment: 6–10 hours, typically spread across two weeks.
4. 0→1 AI Product Development
Our role: Build the product, test and fix iteratively, and refine based on feedback.
Your role: Be part of ongoing validation, join feedback rounds, test early versions, and help with first-user onboarding.
Your time investment: Around 2 hours per week, usually for 6–8 weeks.
5. Go-Live and After
Our role: Ensure the product is stable, monitor its performance, and maintain functionality.
Your role: Support adoption by onboarding users, helping interpret early feedback, and guiding change in your team or department.
Your time investment: About 1 hour per week, depending on the scale of rollout.
6. New Requests
Our role: Assess technical feasibility, estimate effort, and evaluate alignment with the product roadmap and total cost of ownership.
Your role: Support in reframing the need, explain the user or business problem, help validate the potential impact, and be ready to prioritize.
Your time investment: Case by case - usually at least 2–3 hours per new request.
An Invitation
AI product development isn’t just about building smart solutions. It’s about building smart relationships - with each other, with the problem, and with the organization we’re trying to serve. That takes effort. It takes structure. And above all, it takes clarity. We’ve seen what happens when clarity is missing. And we’ve also seen the power of a shared journey, when roles are defined, expectations are aligned, and everyone involved understands what it really takes to bring something new into the world and make it stick.
If you’re ready for that kind of partnership, not just for the launch, but for the lifecycle, then we’re ready to build with you.
JBK 🕊️