AI makes individuals faster.
We turn that into a system.
We help product organizations move beyond the experimental phase and transform AI from isolated chats and agents into a shared way of working — with clear context, decisions, checkpoints, and measurable results.
A consulting & implementation engagement for product organizations
How we work with you
- 01AssessMaturity analysis and planning — setting the points for what comes next
- 02EnableAgentic product management training — from chat to agents
- 03PilotBuilding one product loop on shared context
- 04ScaleTransformation into the AI Product Operating Model — at your pace
AI use spread quickly. The way work gets done did not.
88% use AI in at least one function. A third scale it across the organization. Only 21% have redesigned workflows — the change most closely associated with EBIT impact.
Source: McKinsey, The state of AI 2025: How organizations are rewiring to capture value.
McKinsey tested 25 factors: redesigned ways of working showed the strongest relationship with gen-AI EBIT impact. That is the operating-model work: make decisions, context, gates and learning repeatable across teams.
Do you recognise yourselves?
Scroll on — the next section is the answer
Your path to an
AI operating model.
Four stages lead there, and not everyone starts on the same one. Most organizations are climbing into L2: agents do real work, everyone is personally faster, and none of it compounds. That is the false summit, and it convinces precisely because you had to climb to reach it. Find the stage whose description you recognise — it decides which step is the right one, not how far behind you are.
Pick the level you recognise. What it costs you, and which step moves you off it, appear here.
Four decisions, each one a step up.
We do not sell an agent or a rebuilt piece of software. We help an organization get from one level of agentic maturity to the next. Every step produces a usable result and ends in a decision to continue, adjust or stop.
AI Product Maturity Assessment
You know which level you are on and which constraint a pilot should test.
Map the current product system, surface the key bottleneck, and define the evidence and scope for a pilot.
Applies at any level — it decides where to start, not how far to go.
Open the assessment
Product Team Enablement
The team can work with agents deliberately and evaluate their work against a shared product context.
Train the team to work with agents in a product context, and create the first reusable artifacts, practices and evaluation habits.
This is the foundation, not the destination. Knowing the tool is not yet running an agentic product process with a team.
The enablement is delivered by our vetted training partners at agenticpm.de, held to the same bar as our own work. You book there, and you can still come to us at any point — and if you do not know your constraint yet, start with the assessment.
Operating Model Pilot
Product and engineering work from one shared context instead of separate chats.
Run one complete, bounded product loop in a real context, with explicit goals, gates, ownership and metrics.
A bounded test of the operating model, not a generic implementation project. The evidence it produces decides what happens next.
How it connects to delivery
Rollout & Scale
Agentic work produces reliable, predictable results across teams.
Extend proven practices, services and governance to the teams that need them, so results become repeatable and reliable.
Scale only what the pilot has justified. Repeated capabilities become shared services, and ownership becomes explicit.
What operations require
- L1
- L2False summit
- L3
- L4
The next step depends on what you need to settle.
Either where you stand on the way to an AI operating model, or how that connects to an agentic delivery pipeline.
Maturity model
See where your organization stands, what the false summit costs, and what changes as you move from personal AI to an AI operating model.
Explore the maturity modelTechnical deep dive
Ownership, context, gates and the reference architecture behind the loop.
Open the deep diveOr go straight there: book an assessment
Better product decisions show up in speed, adoption, margin and return.
These studies measure different parts of the business case. Together they show the value at stake when teams choose better bets, ship them sooner and validate the outcome.
higher shareholder returns among companies McKinsey identifies as product operating model leaders; operating margins were also higher.
faster time to market reported for organizations that align product and platform operating models.
of features in Pendo's dataset were rarely or never used — a reminder to validate demand before building.
ideas tested at Microsoft failed to improve their target metric. Experiments show which ideas work before a broad rollout.
of surveyed agentic-AI early adopters reported returns; DORA links the size of the return to the surrounding system.
Sources: McKinsey, The bottom-line benefit of the product operating model · McKinsey, The big product and platform shift · Pendo Feature Adoption Report 2019 · Kohavi et al., Online Experimentation at Microsoft · DORA State of AI-assisted Software Development 2026.
The questions that actually come up.
Product experience across industries.
These organizations have worked with ARISE on product and consulting engagements. The logos represent our broader track record; they are not all AI operating model projects.
Start with a decision map.
In about a week, the assessment maps how decisions are made today, identifies the first constraint and defines the evidence needed for a pilot decision.