Decide what to build. Then build it differently.
In two days from AI use case to a production-ready decision – on the real backlog, the real architecture and, where cleared, the real code.
Thirty minutes with an operator. Then you get scope and a fixed price.
- CPO and CTO
- Head of Product, Head of Engineering
- Product managers and product owners
- Engineering managers and tech leads
- Cross-functional product teams
The starting point
More generated code is not a product outcome. Without solid problem evidence, AI-ready data, quality gates and an operating plan, AI may only be accelerating the wrong part of the system.
In the AI Monitor, usage sits at 66 out of 100. The organisational groundwork beneath it sits at 50. People moved faster than data, integration and governance.
Dream outcome
The team knows which AI use cases get built now, tested first, deferred or deliberately stopped – and one of them is specified production-ready.
Your first result
Product and tech red-flag map
Within five working days of a complete intake, the team receives a red-flag map with the three biggest uncertainties for the decision sprint.
Triage a use case in two minutes.
Pick an archetype and rate four dimensions. The verdict appears immediately – with the criteria that produced it.
AI Use-Case Decision Snapshot
Basis: 162 valid responses, 106 of them with demographic detail. AI Monitor 2026, published by Teklens with ETH Zurich and the University of St. Gallen. Self-assessments, normalised to 0–100. Pre-release.
What the Decision Lab includes
Every component names the obstacle it removes, the artefact you receive and when it is delivered.
We work on the real backlog and the relevant technical material.
- Obstacle
Signals, decisions and technical reality sit apart
ArtefactA shared baseline from backlog, architecture, data and code context
Deliveredbefore the lab
- Obstacle
Ideas get built before value and feasibility are settled
ArtefactFacilitated assessment by impact, feasibility, evidence and risk
Deliveredday 1
- Obstacle
Use cases can't be steered as a portfolio
Artefact3–5 prioritised use cases marked NOW, EXPERIMENT, LATER or NEVER
Deliveredday 1
- Obstacle
A use case is strategically attractive but not described so it can ship
ArtefactProblem, users, data, architecture, evaluation, failure modes and scope
Deliveredday 2
- Obstacle
Unclear production readiness
ArtefactFive gates for value, data, reliability, security and governance, and operations
Deliveredday 2
- Obstacle
No clear sequence after the lab
ArtefactProjects, owners, kill criteria, metrics and review points
Deliveredwithin five working days of the lab
- Obstacle
Decisions and product evidence get lost
ArtefactA versioned workspace for signals, use cases, architecture, delivery and operations
Deliveredwith the roadmap
Included accelerators
- AI evaluation starter
- Data readiness checklist
- Kill criteria template
- Human-in-the-loop decision guide
Templates created during the lab that your team keeps using afterwards. We don't attach invented individual values to them.
When the evidence holds: Execution Track
6-Week AI Product Shipping Sprint
The Decision Lab plus six weeks of execution on the prioritised work – in small batches, with review and evaluation.
- Execution of 1–2 prioritised pieces of work, where scope and readiness are confirmed
- Small batches with review, tests and evaluation
- Monitoring plus cost and failure handling
- Updated Production Passport
- Handover of the routines to the product team
- Updated Context Workspace
We don't promise «two shipped features» as a blanket commitment. What ships in six weeks depends on scope and technical dependencies – we settle both in Decode before committing to anything.
Proof
Problem proof
- 66 vs 50
AI usage runs ahead of the groundwork beneath it: data, integration, governance and the ability to adapt.
Basis: 162 valid responses, 106 of them with demographic detailLimitation: Self-assessments, normalised to 0–100. Pre-release. - 60%Projection
of AI projects will be abandoned through 2026 for lack of AI-ready data.
Source: Gartner (2025)Basis: Analyst projection through 2026Limitation: A projection, not a measured figure. - −19%
That is how much slower experienced open-source developers were with AI tools – while feeling faster.
Source: METR (2025)Basis: Randomised controlled trial with experienced open-source developers on their own repositoriesLimitation: Small sample, very specific context. Not transferable to every kind of task – the finding about self-perception is the most robust part.
ETH Zurich and the University of St. Gallen are research partners on the AI Monitor. They are not Teklens reference customers.
Mechanism proof
Anonymised example · B2B SaaS, around 40 engineers
| Gate | Criterion | Status | Owner |
|---|---|---|---|
| Value | Metric defined and measured today | Met | PM |
| Data | Coverage, freshness and provenance settled | Open | Data eng |
| Reliability | Evaluation set and error budget in place | Met | Tech lead |
| Security | Data residency, roles and logging settled | Met | CISO |
| Operations | Monitoring, cost and rollback defined | Open | Platform |
Delivery proof
What we have actually delivered: cases, customer quotes or an anonymised before-and-after. Where nothing is cleared yet we show a clearly labelled anonymised example – not an empty placeholder.
We do · You provide
We do
- Product and tech synthesis
- Preparing the use-case triage
- Facilitation and decision artefacts
- Production Passport and roadmap
- Workspace structure
You provide
- The real backlog
- Architecture and data material
- Optionally cleared code access
- Two lab days with the core team
- Named product and engineering owners
Investment
Two options, one scope. The Execution Track sits first because it shows the full extent – the recommended entry is the Decision Lab.
2-Day AI Product Decision Lab
Diagnosis, a shared decision and a concrete execution plan. For teams that need clarity before they commit capital or capacity.
- Role-specific diagnosis
- First-value snapshot
- One to two workshop days
- Decision Pack
- Context Workspace
- 90- or 100-day plan
- First result
- 5 working days after a complete intake
- Full result
- 5 working days after day 2
- Effort on your side
- 2 lab days plus preparing the material
The scope is set; the price is fixed in the scoping call and confirmed in writing. No line items appearing later.
When the agreed inputs are complete, we deliver the defined Decision Pack by the agreed date. If an agreed component is missing, we keep working at no extra fee until the pack is complete.
The team knows which AI use cases get built now, tested first, deferred or deliberately stopped – and one of them is specified production-ready.
Review a use case with an operator6-Week AI Product Shipping Sprint
The Decision Lab plus real execution, reviews and embedding into how the team works.
- Everything in the Decision Lab
- Execution on the real portfolio, backlog or GTM system
- Agreed working assets or agents
- Quality and governance gates
- Review cadence
- Handover and ongoing development in the Context Workspace
- First result
- 5 working days after a complete intake
- Full result
- 5 working days after day 2
- Effort on your side
- 2 lab days plus preparing the material
The scope is set; the price is fixed in the scoping call and confirmed in writing. No line items appearing later.
When the agreed inputs are complete, we deliver the defined Decision Pack by the agreed date. If an agreed component is missing, we keep working at no extra fee until the pack is complete.
The team knows which AI use cases get built now, tested first, deferred or deliberately stopped – and one of them is specified production-ready.
Review a use case with an operatorA decision you can act on – or we keep working.
When the agreed inputs are complete, we deliver the defined Decision Pack by the agreed date. If an agreed component is missing, we keep working at no extra fee until the pack is complete.
We guarantee defined outputs and dates that we control. No revenue, transformation or autonomy guarantee – that depends on your execution, and we can't stand behind it.
- a validated baseline
- prioritised decisions
- reasoned wait and stop decisions
- named owners
- success criteria or metrics
- a roadmap and the next review point
- an updated Context Workspace
- Complete inputs by the agreed date
- The agreed stakeholders take part
- Decisions stay within the agreed scope
- Access and permissions are in place
- No external blocker prevents the work
Fit gate
After Decode and Shape, you decide – not us.
The Execution Track does not start automatically. A reasoned wait or stop is a valid result – and often the most valuable one.
- Amplify
The evidence holds. We start the Execution Track.
- Experiment
A bounded test resolves the open uncertainty before capacity is committed.
- Wait
Fix the groundwork or the input first. With a criterion and a review date, not as a postponement.
- Stop
No sensible business case. Recording that with reasons saves more than any pilot.
Decode → Shape → Amplify → Evolve.
A loop, not a transformation with an end date.
- 01
Decode
· DiscoverUnderstand reality, context and the bottleneck
Where do we actually stand?
- 02
Shape
· DefineSet decisions, priorities and limits
What do we change – and what don't we?
- 03
Amplify
· BuildTurn decisions into real work
How does measurable impact happen?
- 04
Evolve
· OperateEmbed learning, governance and routine
How does the system get better?
The Decision Lab covers Decode and Shape and hands over to Amplify and Evolve. The Execution Track runs the whole loop.
Your company leaves with a system, not a slide deck.
Every lab leaves behind a versioned Context Workspace: evidence, decisions, work, rules and what you learned, in one place. Human-readable, machine-readable for approved agents, exportable.
- Versioned, with traceable sources and assumptions
- An owner per artefact; decisions dated and reasoned
- Human-readable and machine-readable for approved agents
- Exportable – no artificial lock-in
- Permissions and data residency settled, update rhythm defined in Evolve
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Not sure which Lab fits?
Thirty minutes with the founding team. You get a clear recommendation, the right scope – or a reasoned no.