AI is making your teams faster. So why aren't your customers seeing the benefits?
A short, adaptive diagnostic (8 to 13 questions) that traces how AI-accelerated work moves from idea to customer, and shows where the extra capacity AI creates is being absorbed along the way.
An adaptive AI delivery diagnostic, not a maturity score
AI can help teams write code much faster, but when funding decisions, reviews, cross-team dependencies, testing, or release processes still move at their old pace, that AI-enabled capacity gets absorbed in queues instead of reaching customers. The diagnostic starts with four questions about your role and organization, then branches based on each answer so you only see questions relevant to where AI speed gets stuck.
A value-stream read, tailored to your role
Your 7-stage delivery flow
A visual map from deciding what to fund and build through learning from outcomes, showing where AI-accelerated work flows well, where friction builds up, and your main problem.
What your answers show
Plain-language findings on why AI hasn't sped up end-to-end delivery yet or which stage is absorbing the extra capacity AI creates, plus secondary delays and what is already working.
Where to start
Concrete, prioritized first actions matched to your primary bottleneck, whether that means moving reviews earlier, reducing hand-offs, or separating deploy from release.
Role and scale context
Guidance calibrated to the decisions you own (strategy, delivery, architecture, tech lead, or product) and your organization's size, emailed to you after a quick one-time code check.
See the shape of the diagnostic result
Unlike a maturity survey that gives you an arbitrary percentage like 62/100, the diagnostic maps your end-to-end delivery flow and isolates the primary constraint. Select a sample scenario below to preview how the readout adapts. Your actual result is generated from 8 to 13 branching questions.
Reviews
AI has increased what you can build, but this part of your delivery system is holding the extra work back before it reaches customers.
- Deciding what to fund and buildNot assessed
- ReviewsMain problemSignificant friction
- DependenciesSome friction
- Building with AIWorking well
- TestingWorking well
- Deploying and releasingNot assessed
- Learning from outcomesSome friction
- Reviews happen late, after a lot of work is done, so building faster only makes the queue longer.
- Several groups review the same change, creating an approval bottleneck.
- Waiting on other teams also causes delay.
- Measure how long changes wait for each review, and start with the longest wait.
- Move reviews earlier, before most of the work is built.
From an architecture and delivery point of view: combine overlapping reviews so each change is reviewed once by the right people.
The diagnostic shows where , we help with what next
Finding the constraint is step one. Xodiac works with financial services and technology leaders to map the real value stream end to end, redesign the workflow, governance, or dependency absorbing your AI-enabled capacity, and prove the improvement against a clear baseline.
Ready to see what's absorbing
the speed AI creates?
Eight to thirteen adaptive questions. About 4 minutes. A clear view of where AI-enabled capacity is getting stuck in your delivery flow, and where to start.