Data & AI Strategy and Governance
Where to invest, what it should return, and who is accountable for scaling it.
One use case, two decisionsIllustrative
A service assistant for account questions
Investment decision
Fund the pilot
Governance decision
Approved, with conditions
Start where you are
Strategy Should we do this?
Ideas made comparable, then ranked as a funding decision.
A business case from a measured baseline, then proof the value showed up.
Maturity scored against your systems, not your answers.
Governance Can we do this?
A path with a named owner, a tier and a record.
Proportionate review, with a defensible fast lane.
Traceable data, reproducible decisions, a trigger for re-review.
Not sure where you stand? Two free self-assessments, six to seven minutes each, no email required.
The decision system
Is it worth doing? Can we responsibly do it? Every initiative needs both.
Strategy asks Should we do this?
A business result with a baseline, not a technology.
Value against total cost, including the change it takes to get there.
The data, platform, people and process the use case depends on.
A ranked portfolio, sequenced into a roadmap.
Governance asks Can we, and who is accountable?
Named, tiered, and owned by someone.
Evidence that the data and the system can be relied on.
A role, at each tier, that owns the outcome.
The decision, its evidence and its conditions, where oversight can find them.
We build both columns. Then we prove them on a real decision.
Work through one decision with usStrategy
“We need an AI chatbot” is not a use case. “Lower the cost of routine service contacts without losing first-contact resolution” is close to one.
Data, architecture, talent, process, governance and adoption, scored on evidence. One question: what can we realistically do next?
Problem, user, outcome, baseline, data, owner, dependencies, risk and success measure, so ideas can be compared.
The business case says what an investment should return. Value realization checks that it did. Cost includes remediation, integration, change and run cost, not only the model.
Value, strategic fit, readiness, time to value, dependencies, cost and risk, weighed as a funding decision.
Outcomes tied to use cases, capability gaps, platform dependencies, governance and funding gates.
Direct, through data and AI-enabled products. Indirect, through pricing, retention, cost to serve and faster decisions.
Portfolio boardIllustrative
| Finalist | Value | Readiness | Decision |
|---|---|---|---|
| Service assistant | High | Medium | Fund |
| Contract review | High | High | Fund |
| Demand forecasting | Medium | Low | Test |
| Dynamic pricing | High | Low | Defer |
Governance
No value without adoption. No adoption without trust. No trust without evidence and accountability.
A named role at every tier
Tests, sources and review notes, on file
Named, tiered, with conditions
A trigger for reconsideration
Built for executive and board oversightEnterprise risk, not only regulatoryTrust you can evidence
The review path that produces that record
Delivered as The review path, with owners
Delivered as Review criteria and risk tiers
Delivered as The decision log
Delivered as Decision roles and the policy
Good governance lets you take more intelligent risk, faster. Low-risk work gets a fast lane; scrutiny goes where the consequences warrant it.
Proof
A value decision · Financial services
15–25%
of documents were actually nonstandard, and they are now the only ones that need a reviewer. A global financial information and ratings provider had its contract terms checked against the approved clause library first.
As reported by the client.
Read the case studyA trust decision · Life sciences
Auditable
An AI assistant for a life-sciences data and communications provider that answers only from approved labels and inserts, with audience rules enforced on every response.
Controls built into the system, not written beside it.
Read the case studyWhat we put in place
For both data and AI.
Built on NIST AI RMF and ISO/IEC 42001 where they apply, sized for your organization.
How to engage
Need to decide where to invest?
Two weeks that replace assumptions with evidence. Fixed fee agreed in writing first.
Need the strategy and the system around it? · The main engagement
The target state, the value model, the roadmap and the governance that lets it proceed, built with the people who have to run them.
Know what you want, but cannot approve or scale it?
Two to four weeks to a working review path, proven on one real use case. Fixed fee agreed in writing first.
After the Sprint
Already running? Governance in operation keeps the review cadence and the policy current as obligations change. Ask about ongoing governance
How we work. Every engagement follows our Data IQ™ methodology, from diagnosis through implementation and handover. See how we work
Before you commit
From first principles: the baseline today, the intervention, its total cost, the adoption and process change it needs, the measurable outcome, and the value realized. Total cost covers data remediation, integration, workflow redesign, infrastructure, change management, oversight and run cost, not only the model. Return can be revenue, margin, cycle time, capacity, customer outcomes, risk reduction or avoided cost. We keep the business case and value realization separate: one is what the investment should return, the other is whether it did.
First each idea is articulated well enough to compare: the business problem, the user and process, the outcome and its baseline, the data required, an owner, the dependencies, the risk and a success measure. Then the portfolio is ranked on value, strategic fit, readiness, time to value, dependencies, cost, adoption requirement and risk. The result is a funding decision, not a scored spreadsheet.
Business outcomes tied to the use cases that move them, the capability gaps in the way, the data and platform dependencies, the operating-model changes, the governance each stage needs, the funding gates and the delivery sequence. It is what connects strategy to the engineering and AI work that follows.
Against your systems and the people who run them, with sources cited, across data, architecture, technology, talent, process, governance, operating model, adoption and value management. The free online assessments are a self-diagnostic for orientation. An engagement assessment replaces your answers with evidence, and exists to answer one question: what can we realistically do next?
By producing the evidence oversight relies on: what leadership knew, which alternatives and risks were considered, who owned the outcome, what supported the decision, and what would cause it to be revisited. The decision log is the mechanism. We do not offer legal advice; we build the decision structure and the record.
A single heavy review for everything does. Tiered review does the opposite: the low-risk majority takes a fast lane with a short record, and depth goes where a use case touches sensitive data, affects people or acts on its own.
By making it concrete. Can the data be relied on? Can a decision be reproduced and explained? Is the source of the evidence known? Do people know when a human must step in? Is there a trigger for re-review when performance changes? Each becomes a control with an owner.
No. Most policies have the right intent and no decisions in them. We keep the intent and add the criteria, thresholds and named owners, then re-run past requests through it so you see it decide rather than defer.
NIST AI RMF for structure, ISO/IEC 42001 for the management-system shape auditors recognize, and the obligations that actually apply to you: federal and state requirements, sector rules and your contracts. Your own policies are the starting point.
The people who scope it are the people who build it
What to fund, or how to govern it. Thirty minutes with the senior people who would do the work. If a program isn’t the right next step, we’ll say so.
Book time with the people who would do itDeciding what to fund, or how to govern it? Thirty minutes with the people who would do the work.
