The Enterprise AI Strategy Playbook

Act as a senior enterprise AI strategy consultant.

From the attached org chart, technical architecture documentation,
IT audit summaries and data strategy overview, identify:

1. The concrete bottlenecks that would block an AI deployment
2. Security and governance risks
3. Organizational silos
4. Missing foundations

End with an executive summary of readiness gaps, ranked by severity.

Act as a senior enterprise process engineer.

Compare the attached official SOPs against the interview transcripts
from frontline employees. Produce four sections:

1. The Delta: where the real-world workflow deviates from the documented SOP
2. The Shadow IT: every workaround, spreadsheet or unauthorized tool
3. The Time-Wasters: the top 5 manual data entry or reconciliation tasks
4. The Automation Candidates: each rated 1–10 on technical feasibility

Act as a pragmatic enterprise AI operating partner.

Ruthlessly prioritize the attached list of use cases by time-to-value,
technical feasibility and strategic alignment. Sort every idea into
one of four buckets:

1. The Sandbox: high impact but technically impossible with current data
2. The Distractions: low impact, high effort (eliminate immediately)
3. The Incremental Wins: low effort, medium impact (build momentum)
4. The Lighthouse Pilots: high impact, highly feasible, aligned with strategy

For the top two lighthouse pilots, justify with the exact business
metrics that will move.

Act as a neutral enterprise architect with no commercial interest.

For the attached use case, produce:

1. The Commodity Trap: the components that should absolutely be bought, not built
2. The IP Advantage: what represents a unique competitive edge and must remain owned code
3. The 3-Year Total Cost of Ownership, for both building and buying
4. The Verdict: a hard recommendation: build, buy or hybrid

Act as a skeptical CFO.

From the baseline costs of the manual workflow and the estimated
project costs, build a conservative business case:

1. Hard Cost Savings: the exact dollar amount saved through reduced manual hours
2. Implementation Costs: development, API usage, compute, integration
3. The Change Management Tax: add 30% to budget and timeline for training, adoption resistance and workflow redesign
4. Payback Period in months
5. The CFO Defense: the 3 most aggressive questions and their data-backed answers

Act as an operating model designer.

From the org chart, the mapped workflow steps and the expected
AI capabilities, produce:

1. Role Evolution: which roles change and how daily tasks shift from execution to validation and supervision
2. New Roles Required: net-new hiring or training needs
3. The Handoff Map: every exact touchpoint where AI passes to a human and vice versa
4. The Human-in-the-Loop Protocol: escalation paths for edge cases and failures

Act as a senior data architect.

From the database schemas, API documentation, sample payloads
and the data requirements of the target workflow, produce:

1. Missing Data Vectors: exactly what data the AI requires that is not available or accessible
2. Structure Deficits: where data is unstructured but must be structured for reliable processing
3. Latency Risks: sources whose retrieval delay would break real-time requirements
4. The Remediation Plan: the top 3 data engineering tasks to complete before production

Act as an AI governance and cybersecurity lead.

From the proposed workflow, the system architecture and the data
sensitivity classifications, produce:

1. Threat Vectors: the concrete ways this implementation could be compromised
2. Compliance Exposure: potential violations of Quebec's Law 25, GDPR, CCPA or industry-specific rules
3. The Hallucination Blast Radius: if the AI fails silently at step 3, what is the downstream impact
4. Governance Controls: guardrails, access controls and logging requirements

Act as a financial analyst.

From the baseline manual costs, the estimated AI costs and savings,
and the provided assumptions, produce:

1. Financial Projections: NPV, internal rate of return and the exact payback period in months
2. Three Scenarios: conservative, base and aggressive
3. Hard vs. Soft ROI: separate direct savings from softer gains
4. The CFO Pushback: the 3 most likely objections and their answers

Act as a technology delivery director.

From the deliverables of the previous modules, the team's current
capacity and the known dependencies, produce:

1. The Sprint Zero Definition: the exact technical and organizational prerequisites for the first two weeks, before any code
2. The 12-Week Pilot Plan: broken into two-week sprints with deliverables and success criteria
3. Critical Path Dependencies: the bottlenecks that block the entire project if they slip
4. Resource Allocation: exactly who is needed at each phase