Two-week AI Technical Readiness Initiative
Rapid assessment across Preside's four-foundation framework (data quality and access, infrastructure and tooling, governance and policy, team capability), structured to map to NIST AI RMF's four functions (Govern, Map, Measure, Manage). Each foundation scored, gaps named, and the shortest path to production AI mapped out. One week if you arrive with current documentation and quick stakeholder access; two weeks otherwise.
Who this is for
A defensible AI readiness scorecard in one to two weeks.
CIO / CTO
The board is asking when AI gets deployed and you need a defensible answer with the gating items named.
Chief Data Officer / Data Lead
You are being asked to support an AI rollout and want the data-readiness side scoped honestly.
CEO
You want a third-party read on whether the AI investment thesis holds up before committing budget.
CISO
AI usage is happening informally and you want the readiness picture before formal deployment lands.
Scope
What this initiative delivers, and what it does not.
Scope is fixed at signing. Items tagged TOP are available inside the broader Technology Operating Partner retainer; the initiative alone does not include them. Items tagged with an outside source require a separate specialty engagement.
In scope
- Preside four-foundation assessment: data quality and access, infrastructure and tooling, governance and policy, team capability, mapped to NIST AI RMF's four functions (Govern, Map, Measure, Manage)
- Readiness scorecard per foundation with named gaps
- Shadow AI signal (existing informal usage detected via tenant scans where access is granted)
- Prioritized roadmap to first production AI use case, with gating items called out
- Reference to NIST AI RMF (authoritative) and vendor-published AI adoption guidance from Microsoft (Microsoft 365 Copilot rollout) and AWS (Cloud Adoption Framework, AI/ML perspective) where applicable
Out of scope
- Building, fine-tuning, or deploying any AI modelAI build firm
- Drafting AI governance policySee AI Security & Compliance Initiative
- AI tool selection or vendor evaluationTOP
- Adversarial testing or prompt-injection validationSpecialty firm
- Use-case ideation workshops with business unitsTOP
Inputs
What we need from you
Provided at kickoff. Missing inputs delay the initiative; they do not change scope.
- Read-only access to Microsoft 365 / Azure tenant for AI-usage signal
- Existing data architecture or data catalog if any
- Any existing AI policy or governance documentation
- Two to three interviews (CIO or CTO, data or analytics lead, optionally CISO)
Timeline
Week by week
Daily visibility throughout. Mid-initiative check confirms direction before the deliverable lands.
Week 1
Scans, interviews, scoring
Scope locked, tenant access provisioned. Existing AI usage signal pulled, data and infra documentation reviewed. Two to three structured interviews. Four foundations scored.
Week 2 (if required)
Roadmap, walkthrough, handoff
Roadmap drafted, scorecard and handoff packaged with walkthrough. One-week completion is realistic when documentation is current and interviews land on the same calendar week.
Output
What you walk away with
- Written AI readiness scorecard across four foundations
- Shadow AI signal report from tenant scans
- Prioritized roadmap to first production use case with gating items
- Walkthrough call
Honest framing
What this initiative is not
A one to two week initiative produces a directional readiness picture, not a deep technical implementation plan. Findings are based on documentation, tenant scans, and a small interview set; broader organizational diagnosis requires longer engagement. We do not build, deploy, or train AI models, and we do not draft governance policy in this initiative (see the AI Security and Compliance Initiative for that scope).
If you are a portfolio company
How the work calibrates to the PE-backed seat.
Companies inside a PE portfolio operate against constraints generalist enterprise framing does not cover. Each of these shapes how the Initiative is scoped and sequenced.
- Board reporting cycle. Output is sized to land before the next quarterly board read, not the company's annual planning calendar.
- Exit window math. Decisions made 12 to 24 months ahead of exit show up at the bid. Where applicable, findings are tagged for the exit-window timeline they affect.
- Add-on integration tempo. Findings that pertain to acquisition integrations are surfaced separately so the deal team can either price them in or sequence the integration around them.
- Cost discipline by hold position. Recommendations are calibrated to where the portco sits in the hold cycle. A company in early hold has different cost flex than one 12 months from exit.
Related
Initiatives that pair with this one
FAQ
Questions buyers ask first
Why do most AI pilots fail to reach production?
Recent IDC and Lenovo research found that 88 percent of AI proofs of concept never reach production. MIT NANDA's 2025 research found that 95 percent of generative AI pilots in their sample generated no measurable ROI. The two studies measure different things, but the pattern is the same: the root cause is usually data readiness rather than model quality. The data sits in the wrong systems, lacks lineage, or fails the quality bar the use case actually requires. The AI Readiness Initiative scores data across five dimensions: quality, governance, architecture, discoverability, and compliance. The output is a short list of use cases that can ship and a longer list that needs data work first.
What does an AI readiness assessment cover?
Six dimensions cover most published frameworks. Leadership and strategy alignment. Data readiness. Technology infrastructure. Governance and ethics. Organizational capability. Use-case prioritization. Preside runs the assessment in four weeks with executive interviews, data sampling, infrastructure review, and a scored output the board can read. The deliverable is a ranked use-case list with go, go-with-fixes, and not-yet categories.
How do we know if our data is ready for AI?
Five dimensions answer it. Quality, the data is accurate and complete enough for the use case. Governance, somebody owns each data set and access is controlled. Architecture, the data is in systems that can serve the model at the latency the use case needs. Discoverability, the model can find it without a treasure hunt. Compliance, the data can be used for the purpose without violating privacy or contract terms. Preside scores each dimension and ties the score to specific use cases.
For your role
Where this initiative fits into the wider Preside view
For CEOs →
The CEO question behind every AI pilot: will this be one of the 5 percent that produces ROI, or one of the 95 that does not.
For Boards and Audit Committees →
What the board needs to see before approving the AI budget: NIST AI RMF posture, governance, data readiness scored.
For CIOs and Heads of IT →
Where data readiness, governance, and use-case prioritization meet, and how the assessment surfaces what is shippable now.
Inside the broader program
When the initiative becomes the standing engagement
This Initiative is a one-time fixed-price engagement. The Technology Operating Partner relationship continues the work on a quarterly cadence at one of four Program tiers: the dashboard gets re-run, the savings get re-baselined, the architecture gets re-mapped, and the board gets the same format every meeting. Most clients begin with an Initiative like this one and decide on the tier after the deliverable lands.
Ready to scope this
From AI hype to a board-ready scorecard. One to two weeks.
One email. Brief description of the situation. We respond within one business day with initiative confirmation or a recommendation of a better fit.
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