AI Monitor 2026
What should your product team change now?
2 min readPublished Published by Teklens
Your product team should pick one workflow where AI is meant to create a concrete benefit and check it along the six dimensions: clarify the context, find the biggest lever, delete unnecessary work, simplify the rest, record the best playbook and only then accelerate and automate. The study shows that acceptance is there and adaptability is missing. This page is our interpretation, not a result of the study.
Contents · Short answer
Analysis
The starting point: acceptance is not the bottleneck
Three findings carry this recommendation. Openness to change is the highest score in the study at 3.94. Adapting structures and processes is the lowest at 2.47. And adaptability separates advanced from lagging organisations most sharply. Anyone still working on acceptance is working at the wrong end.[Probst 2026, Appendix B]
Our answer is an improvement process that determines which work is necessary before anything gets accelerated. It starts with context, because a product team can only decide what may be dropped when customer feedback, decisions, Jira and code fit together. The Product Brain, a shared memory for people and agents, keeps that context current; the AI Product Playbook is the method a team uses it with.
From dimension to action
Pick the dimension where your team is most likely stuck. The guide shows the work, the owner, an observable result and a review question. It is an interpretation, not a result of the study.
Study finding
Backing 3.56, resources 3.11, vision 3.09: engagement is there, means and plan are missing.
Practical work
Define one AI initiative with a fixed budget, one accountable person and a result in product language.
Owner
CPO or Head of Product
Observable result
One documented decision with goal, budget and approval that the team can find without asking.
Review question
Was the budget renegotiated in the last three months instead of derived from results?
Study finding
Openness 3.94, talent 3.11, collaboration 3.08: willingness is there, capability lags.
Practical work
Run one Activity from task to approval with the people who will own it later; record what was learned as context for the next Activity.
Owner
Product lead with engineering
Observable result
The second Activity needs fewer handovers than the first, and the team can say why.
Review question
Which question did someone have to answer a second time at the last handover?
Study finding
Architecture 3.22, infrastructure 2.70, integration 2.66: prototypes get built, operation is the bottleneck.
Practical work
For one AI feature, write down the path from prototype to operation: data flow, monitoring, ownership; reproducible environments before the next automation.
Owner
Engineering lead
Observable result
The feature runs for one iteration without manual intervention, and results flow back into the core systems.
Review question
Which integration is maintained by hand today?
Study finding
Access 3.05, governance 2.73, quality 2.61: adoption runs ahead of data maturity.
Practical work
Name the sources of an Activity (tickets, decisions, specs, code), assign one person to keep them current and check the sources before automating.
Owner
Product owner
Observable result
A list of stale specs and open decisions that gets shorter every week.
Review question
Which source did the team's last wrong assumption rest on?
Study finding
Policy 2.67, ecosystem 2.66, EU AI Act 2.63: a systemic gap across all profiles.
Practical work
Record the team's operating constraints for AI: which data to which model, where it runs, who may automate what; make human approval visible in every workflow.
Owner
CTO with legal or compliance
Observable result
A one-page document that every new Activity cites instead of asking the questions again.
Review question
Which decision did the team last delay because a rule was unclear?
Study finding
Adapting structures 2.47, the lowest score in the study and the sharpest divide between the profiles.
Practical work
Change the workflow after every initiative: delete one step, simplify one, reassign one approval; record the result as a playbook.
Owner
Product lead
Observable result
A playbook that has demonstrably changed since the last initiative.
Review question
What changed in roles or workflows after the last rollout?
An application model for software teams
Discover → Define → Build → Operate → Discover is the loop a product team works in. It is not the research model; the six dimensions describe preconditions, the loop describes work.
- Discover
Evaluate customer feedback, competitors and conversations; collect signals.
- Define
Shared refinement with current knowledge; the approved spec attaches to the story.
- Build
Coding with a freely chosen agent; hand over context, bring results back.
- Operate
Connect decisions with results; evaluate support and operations; test the next hypotheses.
Seven steps before anything gets automated
- 1 Clarify the context
- 2 Find the biggest lever
- 3 Delete unnecessary work
- 4 Simplify the workflow
- 5 Record the best playbook
- 6 Accelerate
- 7 Automate
Discuss the biggest lever
30 minutes with a founder. You tell us where your product team suffers most. We tell you honestly whether the AI Product Playbook helps.
How a team gets started
- Pick one workflow that costs coordination work today, say from customer feedback to an approved spec.
- Clarify the goal in one sentence and name who decides and who approves.
- List the steps and delete what does not contribute to the result. Simplify what remains.
- Record the approach as a playbook, run it once and note what improved.
- Only now: which steps can be accelerated, which automated, with which approval?
Scope & limits
- The study examines organisations across sectors; the transfer to software product teams is our interpretation.
- The study measures neither the effect of Teklens nor time savings, returns or legal compliance. We make no such claims here.
- The guide sorts measures by dimension. Which comes first is decided by your context, not by the order of the table.
Questions & answers
Where should we start?
With one workflow, not with a dimension. Check it along the six dimensions and start where the team loses the most time searching, reconstructing and updating.
Do we need Teklens for this?
No. The seven steps work with any tool. Teklens keeps the context together so a team can repeat the steps without rebuilding it every time.
How long does it take?
That depends on the workflow. A first pass with a clearly bounded workflow is possible within a few weeks; we make no 90-day promise.
Sources
- Probst 2026, Appendix B · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Appendix B – Mean, standard deviation, median, minimum, maximum and item-rest correlation of all 19 items. Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Maturity scale 1–5 · Limits: Self-assessment, exploratory, not representative.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.
- Probst 2026, Table 5 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Table 5 and Figure 7 – Dimension scores by profile (Section 4.4.2). Sample: n = 64; profiles of n = 12, 27 and 25 · Scale: Maturity scale 1–5 · Limits: Profiles from K-means on the same six dimensions; differences between profiles are large by construction.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.
- Probst 2026, Section 5.1 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Section 5.1 – Interpretation of key findings. Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Maturity scale 1–5 · Limits: Interpretation by the thesis; no causal claim.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.