Transforming M&A with Acquisition Intelligence
Redesigning acquisition underwriting with reusable AI skills that transform unstructured deal rooms into standardized, evidence-backed investment insights.
The Challenge
Acquisition teams evaluated every opportunity using highly variable financial statements, rent rolls, leases, market reports, and supporting documents.
Critical information was buried across hundreds of pages, requiring analysts to manually normalize data before meaningful underwriting could begin.
Each seller packaged information differently, making it difficult to compare opportunities consistently and increasing the risk that important issues would be discovered too late to influence pricing.
As acquisition activity increased, manual analysis became the limiting factor on how many opportunities the team could evaluate.
Approach
Redesigned the acquisition underwriting workflow around a library of reusable AI skills rather than a single AI solution.
Identified Acquisition Intelligence as the highest-impact workflow for an initial Proof of Value.
Built specialized Claude Skills for financial statement mapping, rent roll analysis, market intelligence, external risk assessment, and investment summary generation.
Integrated MCPs to provide document extraction, deterministic calculations, market research, and external data enrichment.
Designed editable reference files, business rules, and self-learning mappings so the workflow continuously improves without requiring software development.
Maintained human review and approval throughout every material investment decision.
What Was Delivered
Financial statements automatically mapped into standardized underwriting models.
Rent rolls normalized despite inconsistent seller formats.
Market intelligence and external risk analysis incorporated into every acquisition review.
Evidence-backed seller questions generated automatically from gaps, inconsistencies, and anomalies.
Executive-ready investment summaries produced with full traceability to supporting documents.
Self-learning mapping library improves accuracy over time as analysts validate outputs.
Validated against live acquisition opportunities using real operational data.
Across sample acquisition opportunities, the generated underwriting models closely matched Sienna’s existing models. Remaining variance primarily reflected different capitalization rate assumptions or spreadsheet formula errors identified and corrected by the AI workflow.
Business Impact
Significantly reduced manual effort required to prepare acquisition opportunities for underwriting.
Enabled acquisition teams to evaluate more opportunities without proportionally increasing effort.
Improved consistency across highly variable seller documentation.
Surfaced pricing risks, inconsistencies, and missing information earlier in the acquisition process.
Shifted analyst time from document preparation toward investment judgment.
Established a repeatable AI-powered workflow that continues improving as new acquisitions are evaluated.
AI Workflow Redesign • Claude Skills • MCP Integrations • Human-in-the-loop Review
Frequently asked questions
- How can AI improve acquisition due diligence?
- AI accelerates the most time-consuming parts of acquisition underwriting by organizing unstructured deal rooms, normalizing financial information, identifying inconsistencies, researching external market conditions, and preparing evidence-backed investment summaries. Analysts remain responsible for valuation decisions and final recommendations.
- What is an AI workflow?
- An AI workflow combines multiple reusable AI skills into a repeatable business process. Rather than relying on a single prompt or chatbot, each skill performs a specific task, such as financial mapping, rent roll analysis, or market research, creating a governed process that supports faster and more consistent decision making.
- How long does an AI Proof of Value take?
- A typical Proof of Value engagement runs 4-6 weeks - long enough to validate against real operational data, short enough to prove value before committing to a full build. The PoV produces a working system tested against live scenarios, not a prototype or mockup.
- Can AI be used safely in regulated industries?
- Yes, when designed with governance from the start. Human-in-the-loop validation, audit trails, and enterprise-grade security controls are built into the workflow - not added after the fact. AI handles analysis and synthesis; humans retain final decision authority.
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