For PE/VC Funds & Operating Partners
AI initiatives are running across your portfolio with no clear owner of outcomes. That is not a technology problem. It is an accountability problem.
Buyers are asking about AI capability in diligence. Operating partners need a credible answer before the process starts, not during it. We assess AI readiness across your portfolio and deliver diligence-ready output: documented capability, governance evidence, and a prioritized value creation roadmap aligned to your hold period.
Adaptive Alchemy helps PE/VC funds and operating partners close the Accountability Gap — the risk that AI spend across portfolio companies produces no measurable exit value. We run The Signal Review, a structured AI readiness assessment that scores each portfolio company across six dimensions and delivers a board-ready verdict in 20 working days. The output is a fund-level AI readiness dashboard, per-company investment priorities, and diligence-ready documentation that connects AI capability directly to hold-period value creation.
The Accountability Gap is a portfolio-wide risk.
Operating partners see these patterns across the portfolio:
“Our portfolio companies are running scattered AI experiments with no coordination and no measurable outcomes.”
— No coordination across the portfolio
“Buyers are asking about AI capabilities in diligence. We don't have a compelling answer.”
— Nothing compelling when buyers ask about AI
“We mandated AI in the 100-day plan. Six months later, nothing has shipped.”
— Six months in and nothing shipped
“Each company is hiring their own AI team. It's expensive and inconsistent.”
— Every company building the same capability separately
What we assess across your portfolio.
A structured evaluation across the dimensions that institutional investors and acquirers care about.
AI Strategy Clarity
Does leadership have a coherent AI vision tied to business outcomes? Documented strategy, executive sponsor, board alignment.
Data Infrastructure
Is data accessible, clean, and governed? Data warehouse quality, access controls, governance policies.
Use Case Pipeline
Are there identified, prioritized AI use cases with business cases? Scored pipeline, POC results, production deployments.
Technology Stack
Is the tech stack AI-ready? Cloud adoption, API coverage, legacy debt ratio, integration capability.
Governance & Risk
Are there controls around AI usage, bias, and privacy? AI policy, model monitoring, audit trails.
Operational Integration
Is AI embedded in workflows or siloed as experiments? Production deployments, workflow integration, business metric impact.
What this actually produces.
Operational outcomes that show in the numbers
AI-powered operations that reduce cost per transaction, automate back-office functions, and let companies scale without proportional headcount growth.
A stronger position at exit
AI-capable companies attract better valuations. We build the documentation and evidence that strategic buyers and next-round investors want to see, before they ask for it.
Consistency across the portfolio
A repeatable assessment and implementation process that can be applied across multiple companies, with faster results and lower cost each time.
No diligence surprises
Architecture documentation, governance evidence, and a clear AI roadmap ready before due diligence begins, so the process accelerates rather than stalls.
How we work with funds.
Fund-level engagement
Portfolio-wide AI readiness assessment
We assess AI maturity across your portfolio using a structured framework, then prioritize where AI investment will drive the highest return. The operating partner gets a dashboard view of AI readiness across all companies, with specific recommendations per company.
Company-level engagement
Embedded AI strategy and execution
For priority companies, we embed as fractional product and technology leadership. We co-design the AI strategy, build the product and technology roadmap, and execute alongside the team. Hands-on delivery, not a report that sits on a shelf.
Transaction support
Technical due diligence and exit preparation
Whether you are acquiring or exiting, we prepare the technology organization for institutional scrutiny. Architecture documentation, scalability validation, governance evidence, and a compelling technology narrative for buyers.
We have been on both sides of the diligence table.
We have built and scaled product and technology organizations at companies that went through institutional diligence (as operators, not consultants). We know what acquirers and next-round investors scrutinize because we have prepared companies for those conversations and sat across the table from the people asking the questions.
We build governance that actually works at the pace of a growth-stage company — not compliance frameworks designed for enterprises with dedicated risk teams.
Frequently Asked Questions
- What is an AI readiness assessment for portfolio companies?
- An AI readiness assessment evaluates each portfolio company across six dimensions: AI strategy clarity, data infrastructure, use case pipeline, technology stack, governance and risk, and operational integration. The result is a scored maturity profile per company and a prioritized investment plan showing where AI will drive the highest return on the fund's value creation thesis.
- How do PE/VC funds measure AI-enabled value creation?
- AI-enabled value creation is measured through EBITDA improvement from operational efficiency gains, multiple expansion at exit driven by a credible AI narrative, and portfolio-wide cost reduction through repeatable implementation playbooks. Effective measurement ties each AI initiative to a specific financial metric, not vanity metrics like number of models deployed.
- How long does a portfolio-wide AI readiness assessment take?
- A portfolio-wide AI readiness assessment typically takes 4 to 8 weeks depending on the number of companies. Each company assessment takes approximately one week of engagement. The deliverable is a fund-level dashboard with per-company scores, recommendations, and a prioritized investment roadmap aligned to your value creation plan.
- Should AI readiness be part of the 100-day plan for new acquisitions?
- Yes. Technology due diligence at acquisition should include an AI readiness baseline as a standard component, treated with the same rigor as financial and legal review. The 100-day plan should then include a focused AI assessment, identification of two or three high-confidence quick wins, and an 18-month AI roadmap aligned to the hold period. Companies that establish AI strategy in the first quarter of the hold period build measurably more exit value than those who start late — buyers pay for documented capability, not plans to build it. One structural note: internal assessments consistently overestimate AI readiness. An external assessment produces a baseline that stands up to diligence scrutiny because it was designed with that standard in mind from the start.
- What is The Signal Review and how does it apply to PE/VC portfolio companies?
- The Signal Review is a structured AI readiness assessment that evaluates a company across six dimensions — AI strategy clarity, data infrastructure, use case pipeline, technology stack, governance and risk, and operational integration — and delivers a scored, board-ready verdict in 20 working days. For PE/VC funds, The Signal Review runs at the portfolio-company level: each assessment produces a per-company maturity score, a prioritized investment roadmap aligned to your value creation thesis, and diligence-ready documentation that can be handed directly to a buyer or next-round investor. At the fund level, results aggregate into an AI readiness dashboard across the portfolio, giving operating partners a consistent baseline to compare companies, target co-investment in shared capabilities, and prioritize where embedded execution support creates the most exit value.
- How do you help portfolio companies avoid failed AI experiments?
- Most failed AI initiatives stem from poor use case selection, not technology limitations. We apply a structured prioritization framework that evaluates each use case against data availability, business impact, technical feasibility, and organizational readiness. This eliminates science projects and focuses investment on initiatives with a clear path to production and measurable business outcomes.
Let's assess your portfolio's AI readiness.
Book a 30-minute conversation with a senior strategist. We will discuss your portfolio, your value creation thesis, and where AI fits.
Schedule a Portfolio Review