AI readiness is now a standard domain in technology due diligence for any software business. For most software companies, AI is either a current feature, a near-term roadmap commitment, or a differentiator in the investment narrative. Buyers and their advisers will assess it, and the quality of the answer materially affects how the business is valued and perceived.
What AI readiness assessment covers
AI readiness in technology due diligence is not a single yes/no question. It covers:
Product integration Is AI embedded in the product as a genuine value driver, or added superficially to the narrative? Buyers want to understand what AI actually does in the product: which user problems it solves, how it differentiates from non-AI alternatives, and what the evidence of value is (user adoption, retention, pricing power).
Model infrastructure How is AI capability delivered? The main models are:
- Proprietary models: trained or fine-tuned by the business on its own data. Higher cost and complexity, with potential for significant competitive differentiation if well-executed.
- Fine-tuned open-source models: lower cost and greater flexibility, but still require data and ML engineering capability.
- API-based (third-party models): fastest to ship, though cost, dependency, and competitive risk need to be understood.
Each model has different cost structures, dependency risks, and competitive implications. Buyers want to understand which applies, why, and what the implications are for the investment thesis.
Data readiness AI is only as good as the data underpinning it. Buyers are specifically looking at:
- Data quality and governance: is data well-managed, clean, and well-labelled?
- Training and retrieval data: does the business have proprietary data that confers competitive advantage?
- Data strategy: is there a clear plan for how data assets will support AI capability over time?
AI governance and risk controls This is the area that has moved fastest in the past 12 months. Buyers are now asking:
- How are AI outputs validated and monitored?
- What is the process for identifying and addressing model errors or bias?
- Is there an AI policy, and does it address regulatory requirements?
- What are the liability exposures from AI outputs, particularly in regulated sectors?
Cost and scalability of AI infrastructure AI inference is expensive, and costs can scale non-linearly with usage. Buyers want to understand the unit economics of AI features and whether the cost model is sustainable at the growth rates in the business plan.
What “good” looks like
A business that has thought carefully about AI will be able to answer the following questions clearly:
- What do AI features do in the product, and what is the evidence that they create value?
- What is the model infrastructure, and what are the cost, dependency, and risk implications?
- What proprietary data assets does the business have, and how are they maintained?
- How are AI outputs monitored and validated?
- What is the AI governance framework, and how does it address regulatory requirements?
- What will AI infrastructure cost at 2x, 5x, 10x current usage?
A business that cannot answer these questions clearly, or that answers them with vague assurances, will attract scrutiny.
What “bad” looks like
Common AI readiness issues found in technology due diligence:
- AI in the narrative, absent in the product: AI described as a key differentiator but with limited actual product implementation. Often visible from a demo and a review of release history.
- Undifferentiated API usage: “AI-powered” features that are thin wrappers around commodity APIs, with no proprietary data or capability. These are easy to replicate and hard to defend.
- No governance framework: AI features deployed without monitoring, validation, or policy. This is a liability exposure, particularly in regulated sectors.
- Unsustainable cost structure: AI inference costs that are manageable at current scale but break the unit economics at projected growth rates.
- Data quality debt: AI features dependent on poorly managed or inconsistent data, with no clear plan to address it.
Why AI assessment matters for deal outcomes
For businesses where AI is a material part of the investment thesis (a differentiator, a growth driver, a source of defensibility), the quality of the AI assessment directly affects valuation. Buyers who cannot validate the AI story will either discount for the uncertainty or apply a more conservative set of assumptions to the business plan.
A clear, evidence-based AI readiness assessment, included in the Sell-Side Technology Report, allows buyers to take the AI thesis seriously, and price accordingly.
Proof Edge has hands-on experience building AI products and embedding AI strategy at scale. We assess AI capability as a first-class domain of every engagement, not as an optional module added to a generalist framework.