As we head into 2H26 budgeting and 2027 planning, the question is whether the company we are building today is one our next owner will actually want to inherit, or one that still requires a painful post-close transformation. AI is not just a productivity topic; it is a valuation multiple issue.
Edison's 2026 Growth Index, published this spring, surfaced a counterintuitive finding that frames this conversation. Across the participating portfolio companies and 66 C-suite and board respondents, AI adoption on a standalone basis showed essentially no correlation with financial performance. The most sophisticated AI adopters are not consistently outperforming those using less—at least not yet. What does predict financial performance is what the Index calls Healthy Urgency: a composite of Efficiency, Sustainability, and Alignment.
The implication is direct: AI amplifies conditions already in place. Aligned, urgent organizations use AI to compound their execution advantage. Misaligned organizations use AI to compound their execution debt. The same Cursor license produces materially different financial outcomes depending on whether Healthy Urgency exists before the deployment. For a CFO heading into 2H26 planning, this is the difference between an AI line item that lifts NRR by two hundred basis points and one that becomes a permanent operating cost with no measurable return.
In a recent talk at Y Combinator, General Partner Tom Blomfield argued that the traditional org chart (pyramids of managers managing managers) is a Roman Legion model: built for moving information up and instructions down through layers of humans. That structure made sense when information was scarce, and decisions had to be aggregated. It does not make sense when a non-human worker can read every Slack message, every customer ticket, and every line of revenue data, then act on it in real-time.
Copilots, Blomfield argues, are the wrong mental model, as they are the equivalent of a faster typewriter. The right model is a recursive self-improving loop: a system that senses an external signal, executes a corrective decision through tools and policies, and learns from the outcome to improve next time.
The Growth Index's three AI adoption archetypes map cleanly to this distinction. Systemic1 adopters (43%) treat AI as an operating model change, with ELT ownership and measurement tied to outcomes. Functional2 adopters (50%) deploy AI inside individual functions without cross-functional coordination. Incipient3 adopters (7%) acknowledge AI as important but have no formal strategy. Only the Systemic cohort clusters in the highest Healthy Urgency quadrant.
This conversation belongs in the CEO seat, with the CFO close beside. The CEO owns the vision; the CTO builds the systems. The CFO's contribution is the one most directly tied to valuation—deciding which workflows are worth converting, in what order, against which P&L line. The Growth Index identifies the spend-to-performance lift question (whether increased spend produces measurable results within two quarters) as a finance insight; it is the single survey behavior most predictive of both GRR and NRR. Three reasons make the finance seat especially relevant:
The Growth Index's financial composite weightings tell the same story from the buyer's side: NRR contributes 30% of the score, GRR another 25%. Retention economics carry more than half the weight of what buyers are pricing, and the survey behaviors that predict retention (spend efficiency and proactive customer health) are the same behaviors that define Healthy Urgency.
A growth-stage SaaS company doing $40M ARR at 20% growth and 70% gross margin today might trade at 6x revenue. A peer at the same revenue and growth, but with 40% fewer FTEs per dollar of revenue, a higher Healthy Urgency profile, and a library of self-improving loops in production, might trade at 9x-11x. The gap is not about being "AI-enabled." Every company will claim that. The gap is about whether a buyer, on day one post-close, inherits a company that improves itself while it sleeps, or one that requires a 24-month transformation program to get there.
Your next owner will not willingly choose to put themselves through the hard work of transformation. Those willing to put the work in will see their multiples re-rate; conversely, if you are not building that company, an attractive exit opportunity is likely off the table.
Software has become ephemeral; context is the durable asset. The competitive moat is the curated, AI-legible body of work that defines a company's decisions, policies, edge cases, and operating logic—the recursive foundation layer. Companies not building it will be at a competitive disadvantage. The early wins are loops that augment the people running today's process.
As CFOs and operating teams move into 2H26 and 4Q26 planning, the question is how deeply in the organization these changes should be embedded. In Part 2, we will walk through a practical framework for ranking which loops belong on the 2H26 build list and where the execution gap actually lives.
1Systemic adoption is defined as ELT-owned AI workstreams with defined measurement frameworks. AI is an operating model change, not a tool rollout.
2Functional adoption is defined as meaningful AI adoption in specific functions. Cross-functional strategy still in development or measurement still shared.
3Incipient adoption is defined as early or reactive AI usage. No formal strategy in evidence from survey responses.
According to our 2026 Growth Index, AI adoption alone showed little correlation with overall financial performance. The companies generating the strongest results were not necessarily those using the most AI, but those operating with high levels of Healthy Urgency—defined by efficiency, sustainability, and organizational alignment. AI amplifies existing operating strengths or weaknesses rather than creating them. Download the 2026 Growth Index to learn more.
AI is increasingly becoming a capital allocation and valuation issue, not just a technology initiative. CFOs help determine which workflows generate measurable returns, how AI investments affect operating leverage, and whether AI initiatives improve key metrics such as Net Revenue Retention (NRR) and Gross Revenue Retention (GRR). These factors can directly influence enterprise value and future exit outcomes.
Investors and acquirers are increasingly evaluating whether a company has built an AI-enabled operating model or simply adopted AI tools. Organizations that embed AI into repeatable, measurable workflows may achieve stronger retention, greater operating leverage, and higher productivity. As a result, AI readiness can contribute to valuation multiple expansion when paired with strong business fundamentals and disciplined execution.