TLDR: AI has made prototypes, POCs and MVPs cheap — a plausible demo in days. What has not gotten cheaper is everything after: reliability, adoption, governance, cost-to-serve. Teams that fund projects now get more demos. Teams that fund products — with stable ownership and health metrics — get more compounding. In the AI age, transformation compounds when ownership compounds.
We kept delivering projects successfully and still living with the same problems.
Every initiative had a launch date, a steering deck, and a handoff that nobody loved. Teams moved on. The systems they changed kept aging, drifting, creating new friction. At first I thought we had a delivery problem. It took a painful handoff to see we had an ownership problem. That shift changed more than many reorganizations — and in the AI age, it matters more.
It is not just my experience. Marty Cagan has made this distinction for years — product teams are given problems to solve and held accountable for outcomes, not handed projects to finish. Harvard Business Review put it bluntly on the cover this March: temporary teams can build a system, but you need permanent teams to live with it.
The Handoff That Would Not End
The project closed on time. On budget. Green dashboard. Two weeks later the same friction was back — same integration debt, same support questions, same adoption gap, just under a new title. The problem stayed alive while the team was gone.
I have used project completion as a false proxy for impact myself. It feels good to ship. It is worse to watch the same problem return because no one was left to carry it.
I told that team: we did not have a delivery problem. We had an ownership problem. We rewarded completion while the business needed continuity. When we started asking "who improves this next quarter?" instead of "when does it ship?", the tension changed.
Today that question is louder. AI has collapsed the cost of the first version — prototype to plausible MVP in days. What has not gotten cheaper is everything after. If no team owns what happens post-demo, the prototype just becomes a faster way to create unowned debt.
Why Projects Crack — Especially Now
Projects create motion, not continuity. Most transformation problems are not solved at launch. They are solved when a team keeps improving the capability after launch.
In project mode, continuity breaks at the handoff. Ownership blurs. You see it in repeated incidents and the quiet integration tax no project owns.
We mapped a few value streams and named what needed to stay owned. Immediate pattern: internal platforms, core systems, customer capabilities suffered most as temporary projects. They needed roadmaps and health metrics, not milestones.
That matches Team Topologies' default for value flow — a stream-aligned team that is long-lived and owns the outcome end to end. Stable teams build shared context you cannot recreate by reshuffling. One 2023 review found becoming a high-performing team takes longer than picking up new skills — why stable teams beat dynamic task forces in stable domains (Wiley, 2023).
AI makes that brittleness more expensive. Team Topologies puts it this way for the AI age (Aug 2026): every extra AI-generated line is an extra line to test, maintain and secure. Flooding production with unverified AI output grows the attack surface that a temporary team has already left behind.
What We Changed Instead of Reorganizing
We started with ownership, not a reorg.
Named owners with real authority. Not a title on a slide. Clear decision rights and a sustained business partnership. Without that, you have re-labeled a project team. Cagan is blunt: empowered teams must be trusted to figure out how once leadership is clear on which problem (SVPG, July 2024).
Kept teams stable longer. We stopped reshuffling every quarter. Context stuck. Morale improved when teams owned something coherent. Team Topologies says it plainly: "I'll take a team that's worked together for years over rock stars any day" (Key Concepts). DORA backs it: high performers stay loosely coupled to test and deploy independently without hand-offs (Loosely Coupled Teams).
Planned around outcomes. Quarterly planning anchored on health, adoption, reliability — not just on-time, on-budget. Tech debt became part of product health, not a side battle. McKinsey frames it the same: tie funding to measurable goals and treat tech debt as a managed tax (McKinsey Dec 2023 via Planview Sep 2024).
Made health visible, simply. Adoption, reliability, cost-to-serve, backlog balance. Fewer dashboards. When health is visible, funding gets honest.
Let teams improve what they operate. No hard wall between "run" and "change." HBR found the winners did exactly that — permanent cross-functional teams that keep investing based on user feedback long after launch (HBR Mar–Apr 2026).
Made AI part of stewardship. Prototypes and agent workflows now pass the same product checks as any increment — evals, cost bounds, rollback — defined by the owning team, not the demo author. That is the stewardship model Team Topologies calls essential for AI: every workflow governed by a long-lived team (Stewardship for AI Age, Aug 2026).
What Compounded
Projects end. Products accumulate — outcomes, debt, risk, learning. Stable teams compound because context persists.
A team that shipped less in a quarter but cut repeat incidents and lifted adoption created more durable value than one that hit a launch date and handed off fragility. Launch is not the finish line. It is the start of stewardship.
Projects are good at finishing work. Products are better at carrying responsibility.
McKinsey's 2023–24 study of 400+ companies makes the business case: top-quartile product model maturity correlated with 60% higher returns to shareholders and 16% higher operating margins, plus 38% higher customer engagement (McKinsey).
In the AI age the gap widens. When a POC takes a week, the value is not the demo. It is who carries the model, prompt, evals, cost and incident after the demo. Projects get more demos. Products get more compounding.
How AI Tilts It Both Ways
Positive — AI makes the product model affordable. A small stable team can generate a prototype in days and test earlier. That is exactly what product thinking wants — outcomes over outputs, continuous investment — and why HBR's permanent teams pulled ahead in 2026 (HBR). AI shortens the discovery loop; it does not replace it.
Negative — AI makes the project trap cheaper to fall into. When the first version costs near zero, it is tempting to fund another pilot as a project and never fund the product work that makes it trustworthy — evals, data boundaries, cost controls. Unowned AI prototypes become shelfware or, worse, production code that expands the attack surface (Team Topologies AI Age). DORA's hand-off data predicts what breaks next: waiting on shared environments.
Same power, opposite compounding. Ownership matters more now, not less.
What Still Trips Us
Funding. Planning models still reset quarterly around scope. McKinsey flags funding and tech-debt as the top maturity gaps and suggests releasing funding on performance objectives instead of annual pots. I wish we had involved Finance earlier.
Incentives and AI. Saying "products" while rewarding milestone theater ignores health metrics. Without a product home, AI pilots become demos without owners. I would have made tech-debt and AI eval budgets part of the first planning cycle, not an afterthought.
Boundaries and operational work. Without clear boundaries, product ownership is just more meetings. Platform health rarely looks like transformation, so it gets devalued. The Wiley review is useful here — stable teams win in stable, predictable domains small enough to own, which is exactly internal platforms (Wiley 2023). I would have revisited org design sooner and published fewer, clearer health metrics from the start.
Our clearest before/after: stable ownership cut repeat incidents within two quarters; reshuffled teams kept re-paying the same integration cost. Seeing that delta once ended the vocabulary debate.
If You Are Considering the Shift
You do not need a new reorg. Pick one value stream where pain — and AI prototypes — already pile up. Give it a home for two quarters. Name an owner with real authority, keep the team stable, plan around one outcome users feel, require every AI POC to pass the same product checks: evals pass, cost bounded, rollback tested.
If it compounds, you will feel it in fewer surprises, not more slides. Start with one pilot team, as Cagan recommends, while the rest stays on project governance (SVPG pilot teams).
What About You? Let's Compare Notes
Are we wielding AI like a sword — to ship more, faster — or like stewardship — to own more, longer? The same power that lets a stable product team compound value lets a temporary project team compound debt. The tool did not change. The ownership did.
I am genuinely curious how this lands where you are — I read every comment and I learn more from these threads than any framework deck:
1. What should never be run as a temporary project in your org? For us it was the internal platform — every project handoff just re-created the support queue. What is yours? Integration layer? Data platform? Customer workflow? Drop the one you would protect first.
2. Where do handoffs already create repeat pain — and where have AI prototypes quietly added to it? If you have a shelf of AI demos that never got an owner, what did that cost you in rework or trust?
3. What blocks persistent ownership most right now — funding, org design, or mindset? If you could convert one value stream tomorrow — especially one with an AI POC already floating around — where would you start and what outcome metric would you hold it to?
Share the real friction, not the theory. If you have a handoff story that still stings, or a stable-team win that compounded, I want to hear it. The comments on pieces like this end up better than the article itself — let's keep that tradition going. What do you think?
If you found this useful, tell me which section you would challenge — I will reply to the first 20 comments with what we tried and where we were wrong.
Further Reading — All 2023–2026, AI-Relevant
- Product Model in Traditional IT — SVPG, July 2024
- Why Digital Product Model Beats Project — HBR Mar–Apr 2026
- Team Topologies — Organizing for Fast Flow + Fowler overview, July 2023
- Stewardship of Value Flow — Team Topologies for AI Age, Aug 2026
- Dynamic vs Stable Team — Wiley 2023
- Loosely Coupled Teams — DORA 2024
- Bottom-Line Benefit of Product Operating Model — McKinsey Dec 2023 via Planview Sep 2024
Top comments (6)
Good Post! I wanna discuss further about experience and collaboration.
Best
Thanks, Lee — glad it landed. Happy to compare notes: which part maps closest to what you're seeing, the fund-projects trap or the AI POC with no owner? Give me a bit of your context and I'll dig in.
Thanks for asking. The area that resonates most with my experience is the AI POC without clear ownership.
I’ve seen many AI initiatives succeed technically but struggle operationally because the ownership model, success criteria, and production path are not defined early enough. A POC can demonstrate that a model works, but moving from a demo to a reliable product requires clear responsibility across data quality, evaluation, infrastructure, security, and business outcomes.
In my experience, the biggest gaps usually appear in three areas:
No defined business success metrics
A POC may measure model accuracy, but production adoption depends on metrics like workflow efficiency, user adoption, response quality, cost, latency, and measurable business impact.
Unclear ownership after the prototype phase
The question changes from "Can AI do this?" to "Who owns the system when it fails, needs monitoring, or requires continuous improvement?" Without an owner, the project often becomes an experiment rather than a product.
Missing production engineering discipline
For AI systems, I usually introduce evaluation pipelines, version tracking for prompts/models/embeddings, observability, feedback loops, and human-in-the-loop processes where appropriate.
A pattern I’ve found effective is treating AI POCs like early product releases rather than isolated experiments: define the user problem, establish measurable outcomes, assign ownership, and design the path from prototype to production from the beginning.
I’d be interested to hear more about the specific situation you’re seeing - whether the challenge is mainly technical delivery, stakeholder alignment, or the transition from POC to production.
Best
Lee — great read, and you put your finger on the exact failure mode the post is about. Your three gaps sharpen it.
The honest version of "the right way" is boring: don't treat the AI POC as a demo — treat it as an early product release with a named owner, a measurable outcome, and a production path defined before the demo wows anyone.
Your three gaps map directly to things I've shipped and paid for in production:
Thanks for the detailed perspective. I strongly agree with the point that the hardest part of AI adoption is rarely the model itself — it is the operational system built around it.
From what I have seen, the most difficult area is usually the POC → production handoff, because it is where technical uncertainty meets organizational reality.
During the prototype stage, teams can move quickly because the constraints are limited: a small dataset, manual validation, a few users, and flexible expectations. But production introduces completely different requirements:
One pattern I have found effective is designing the production path during the POC itself.
For example:
For agentic systems especially, I think the industry is shifting from "Can the agent complete the task?" to "Can we operate the agent reliably at scale?" That requires stronger controls around permissions, tool execution, cost limits, observability, and human escalation paths.
The interesting challenge is that AI systems behave more like evolving products than traditional software features. They need continuous evaluation and governance because the behavior surface area changes with data, models, prompts, and integrations.
In my experience, the strongest teams are not the ones that build the fastest demo - they are the ones that design the feedback loop and ownership model early enough that the demo can actually become a dependable product.
I would say the biggest challenge I have seen is the POC → production transition, especially when technical teams have proven feasibility but the organization has not yet defined ownership, success metrics, and operational responsibility.
I look forward to working on projects together in the future.
Best
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