Two lenses, one honest picture.
A few weeks with your leadership and your platform teams. We come back with a baseline that names what is real, what is experimental, and what is slipping through, ready to govern the next 90 days.
Book the assessmentDecision-oriented, not boil-the-ocean
01 / Why this first
Get the honest read before the build.
Most AI and integration roadmaps stall on the same hidden questions. Where does the data actually live, and is it ready? Which AI tools are people already using, and which ones is the team learning about for the first time when something leaks? Whose decision is it, and what does scaling responsibly mean here?
The assessment is the answer to those questions. It is light by design, decision-oriented rather than boil-the-ocean, and structured to produce a grounded read of the current state in weeks, not months. The goal is to reduce uncertainty early so the next investment is a choice rather than a guess.
The output is a baseline. It feeds the broader delivery work that follows, whether that is an AI Acceleration engagement or a Data Foundation build, and it is calibrated to be useful to a CIO and to a delivery lead in the same conversation.
02 / The AI lens
The AI you have, including the shadow AI.
Two perspectives, one read. We look at AI usage from the leadership angle and from the platform angle in parallel, so the picture covers both the bets that are being placed and the workloads that are actually running.
Perspective / 01
Business and leadership
How AI is creating value today, where the team is reaching for tools without telling anyone, and what would let leadership scale with confidence. The conversation sits with CxOs, business-unit leaders, and product owners.
- Current usage and intent
- Shadow AI awareness
- Governance comfort and gaps
- What would unlock confidence
Perspective / 02
Technical and platform
The actual tools, model providers, workloads, and access patterns, including the ones nobody filed a ticket for. The conversation sits with IT, architects, and data and AI platform leads.
- Approved platforms vs the rest
- Model providers and sprawl
- Workload types and integration
- Policy, logging, and audit
03 / The integration lens
Where the data lives, and how it moves.
A factual baseline of the data estate and the integration platform that connects it. The aim is to know whether the foundation can carry an AI workload before the AI workload finds out for itself.
Layer / 01
Data domains and storage
Core domains, the systems of record, and where they actually sit. Operational databases, SaaS, warehouse, lake, mainframe, all of it. We track ownership including the places where two teams think the answer is theirs.
- Domains and ownership
- Storage and platforms
- Data accessibility and freshness
- Real-time vs batch
Layer / 02
Integration capabilities and governance
Patterns, platforms, and the controls around them. What is in place, what is improvising, and the constraints to design around so the next architecture decision is not the first time we hear about them.
- Patterns, tooling, and reuse
- Authentication and authorization
- Observability and monitoring
- Constraints and red flags
04 / What you walk away with
A decision-ready baseline.
The deliverables are calibrated for the next 90 days of delivery. Not a roadmap deck, not a maturity score, not a six-month enterprise architecture review.
- 01
Current-state read of AI capability and usage
What is in production, what is in pilot, what is on laptops, and which model providers are behind any of it. Inventory included.
- 02
Shadow AI summary, surfaced without blame
The informal usage that already exists, framed so it can be brought into governance rather than driven further underground.
- 03
Map of data domains and systems of record
Customer, Product, Asset, Transaction, Contract, Location. Ownership and overlap included, because the disputed domains are usually the load-bearing ones.
- 04
Integration and governance baseline
Tooling, patterns, observability, and the controls around AI and integration. Both the AI side and the platform side, in one picture.
- 05
Executive summary and next steps
Written for the people who decide the next investment, not for the people who write the tickets. For example: which AI surface to bring into governance first, or which integration seam to harden before an agent workload hits it. This is the input to the next 90 days.
05 / How it works
From kickoff to baseline, in weeks.
A short, focused engagement designed to land the read without disrupting the work it is meant to inform.
- 01
Scope
A short kickoff with the sponsor and the integration and AI leads. Access, stakeholders, and constraints get locked in the first session.
- 02
Interview
Targeted conversations with leadership, business, and platform teams. Themes get surfaced, not transcribed. Shadow AI gets named, not blamed.
- 03
Walk
The data domains and integration platform get walked the way they actually move. Real systems, real owners, real exceptions.
- 04
Read out
The two lenses meet in a joint readout with the sponsor. The baseline is the artifact, the prioritized next steps are the close.
What it feeds
The baseline is a starting point. We hand it off to the right delivery, on the right cadence.
Want the honest read first?
Tell us the shape of the question: where AI is starting to land, where the data and integration foundation feels uncertain, and what the next 90 days could look like. We come back with the right scope for the assessment so the baseline is in your hands fast.
