Unclear value logic
Use cases are discussed as AI initiatives, not as measurable changes to cost, risk, capacity, speed or revenue.
AI Strategy Co advisory / mAIGO™ pathway where required
AI Mandate ROI & Evidence Sprint For Enterprise AI Accountability.
A fixed-scope diagnostic for enterprise and regulated-market leaders who need to connect AI ambition, executive sponsorship and project activity to clear ROI logic, evidence requirements, control ownership and stop / scale / redesign decisions.
ROI discipline
Evidence gaps
AI use-case portfolio
Decision gates
Governance triggers
Executive accountability
The problem
Enterprise AI activity can grow faster than measurable value.
New AI leadership creates executive accountability and budget focus. The next question is whether the organisation has enough discipline to decide which AI work deserves funding, controls, evidence and scale.
Weak baselines
Teams cannot show what improved because the starting point, owner and target metric were not defined early.
Control gaps
Risk, legal, procurement and assurance needs appear late, slowing momentum or increasing delivery risk.
No decision gate
Pilots keep running because there is no clear stop, scale, redesign or hold decision model.
Core thesis: the next constraint for enterprise AI is not activity. It is proving which use cases deserve funding,
which controls are required, who owns the economic outcome, and what evidence supports a stop, scale or redesign decision.
Sprint method
A short diagnostic that turns AI ambition into accountable decisions.
The sprint produces a practical executive readout, not generic AI strategy slideware. It is designed to identify value, evidence and control gaps before the AI cost base grows faster than measurable business impact.
1
Mandate intake
- Executive AI mandate
- Active initiatives
- Business outcomes
- Ownership model
2
Use-case map
- Portfolio view
- Priority use cases
- Owner mapping
- Dependency scan
3
ROI lens
- Cost-to-serve
- Risk outcomes
- Decision speed
- Capacity release
4
Evidence scan
- Control gaps
- Approval artefacts
- Monitoring needs
- Assurance triggers
5
Decision gates
- Stop
- Scale
- Redesign
- Hold
Deliverables
What the sprint produces.
The output is a decision-ready artefact pack for senior stakeholders, with clear separation between advisory, governance evidence work and any later implementation shaping.
| Component | What is included | Commercial intent |
|---|---|---|
| Mandate and portfolio intake | Capture AI mandate, active initiatives, business outcomes, executive expectations and current ownership model. | Turn broad AI ambition into a structured portfolio view. |
| ROI and value-driver model | Map use cases against cost-to-serve, risk outcome, decision speed, capacity release, revenue protection or customer experience. | Shift the conversation from AI activity to business economics. |
| Evidence and control gap scan | Identify what evidence, approvals, control ownership, monitoring or assurance artefacts are missing before scaling. | Create the bridge into mAIGO™ only when required. |
| Decision gate design | Define stop, scale, redesign or hold criteria for priority AI use cases. | Help leaders avoid growing the AI cost base without decision discipline. |
| Executive readout | Provide a concise artefact pack with findings, priorities, gaps, recommended next moves and follow-on options. | Produce a senior-stakeholder output that can convert into the next engagement. |
Best-fit buyers
Built for enterprise AI leaders who need accountability before scale.
The sprint is strongest when the organisation has visible AI activity, executive interest and funding pressure, but inconsistent value evidence, decision gates or governance artefacts.
AI, data and digital executives
Chief AI Officers, Chief Data Officers, Chief Digital Officers and technology executives shaping enterprise AI portfolios.
Transformation leaders
Executives responsible for turning AI ambition into measurable operating, financial or customer outcomes.
Risk and governance leaders
Teams asking what evidence, controls, approval artefacts and monitoring are needed before AI initiatives scale.
Consulting partners
Partners who need a sharper strategy-to-execution diagnostic before delivery teams build the wrong things faster.
Regulated enterprises
Banks, insurers, public-sector adjacent organisations and large corporates balancing innovation with assurance.
Pure experimentation teams
Less suitable where there is no senior mandate, no funding question and no need for evidence or accountability.
What this sprint includes
- ✓Stakeholder discovery and structured intake
- ✓Use-case and mandate mapping
- ✓ROI and value-driver assessment
- ✓Evidence and control gap scan
- ✓Prioritisation and decision-gate model
- ✓Executive summary and recommended next actions
What this sprint does not include
- ×Building or implementing AI systems
- ×Software licence resale or platform procurement
- ×Detailed legal advice or audit opinion
- ×Full enterprise AI operating model redesign
- ×Unlimited stakeholder workshops
- ×Deep technical architecture unless separately scoped
Commercial shape
Three practical ways to use the sprint.
Keep the entry point fixed-scope. Expand only when the diagnostic shows a clear governance, advisory or solution-shaping need.
Lite diagnostic
Best for one business unit, small AI portfolio or early-stage AI mandate.
- Targeted discovery
- Artefact review
- Executive readout
- Priority recommendations
Core sprint
Best default when multiple AI use cases need ROI discipline, evidence and decision gates.
- Structured interviews
- Use-case mapping
- ROI and value-driver lens
- Evidence and control gap scan
Partner-led version
For consulting, transformation or delivery partners needing a clearer pre-delivery diagnostic.
- Protected method
- Controlled partner model
- No silent IP transfer
- Follow-on scope separation
Boundary rule: this is an AI Strategy Co advisory front door. mAIGO™ is introduced only when evidence,
approval, DDQ, audit, assurance or regulator-aligned support is explicit. AI Strategy Co Solutions is introduced only if
a separate solution-design or implementation need is confirmed.
Use this when AI ambition needs stronger investment discipline.
Start with a short discussion to confirm the mandate, active AI portfolio, evidence pressure and whether this should be handled as advisory, mAIGO™ governance support, or later solution-shaping work.