Part 1 of "Owning Your Company's Terminal Value" makes the case that AI is becoming a valuation issue and that the CFO has a natural seat at the table, alongside the CEO and CTO.
Our 2026 Growth Index's central finding is that Healthy Urgency, not AI sophistication, predicts financial performance.
These perspectives raise an important question: Which workflows carry the greatest operational burden, because they are frequent, slow, or costly when wrong?
Every workflow in a company carries a measurable operational burden. The size of that burden, and therefore the value of converting the workflow into a recursive, self-improving loop, scales with three variables. The variables are multiplicative, not additive: when all three are high, the negative effects compound or explode.
Operational Burden ∝ V × T × E
Transaction Volume (V) — How often the workflow runs. A process performed 10 times per month is tolerable manually; one performed 10,000 times a day is existential. Frequency is what makes even small inefficiencies compound and what makes recursive loops worth building, because every cycle's learning re-enters the system.
Cycle Time (T) — Latency from trigger to completion. Cycle time converts directly into working capital drag, DSO, blocked downstream work, and customer wait. T is the variable most traceable to the cash conversion cycle, and the one most visible to the board.
Error Cost (E) — Expected downside from a single mistake. Not just rework — compliance exposure, fraud, churn, restatement risk, reputational damage. E is reliably nonlinear: one error may cost $50, $5,000, or $50 million. A workflow with low V and low T can still justify a major investment when E is extreme.
High V × T workloads are candidates for full automation coupled with sampling-based human audit. Extreme-E workloads — even at low V and T — are not automation candidates in the same sense; they are agent-assisted, human-gated loops where the human is the control. Both are recursive loops; they differ only in who closes them.
Building these loops will be difficult to sustain. Most companies will start them, but far fewer will get them into production at the quality bar your next owner will reward. Our 2026 Growth Index frames the distinction precisely: 87% of C-suite respondents say they are using AI to drive efficiency, but AI adoption alone barely correlates with financial performance. The cohort actually converting AI investment into retention and revenue lift is the same cohort that scores highest on Healthy Urgency — the Systemic adopters who treat AI as an operating model change.
Two things will separate the cohorts that succeed:
Without those, loops degrade quickly, and the company ends up where it started... only with a larger AI line on the cost side. Aligned, urgent organizations are using AI to compound their execution advantage. Alignment should precede the agents.
As CFOs and operating teams build the 2H26 and 4Q26 plan, four questions can anchor the conversation:
A final question, drawn directly from the Growth Index, may be the most important to ask first: If we ran the priority alignment exercise tomorrow — asking each leader independently for the company's top three priorities — what would the match rate be? If it is below 80%, fix alignment before deploying any major new AI initiatives. Otherwise, the investment will amplify a misalignment problem rather than solve an execution problem.
The companies whose 2027 valuation looks different from their 2026 valuation will be the ones doing this work. The path runs through Healthy Urgency, then through prioritized loops, then through the measurement discipline that turns AI from an expense line into multiple expansion.
CFOs should prioritize AI investments based on business impact. A practical approach is to evaluate workflows using Transaction Volume (V), Cycle Time (T), and Error Cost (E) (V × T × E) to identify where AI can reduce costs, improve execution, and create measurable enterprise value. The most effective AI investments are tied to financial outcomes such as cash flow, customer retention, and operating efficiency.
Many AI initiatives fail because organizations automate inefficient or misaligned processes instead of fixing the underlying operating model. Edison's 2026 Growth Index found that AI adoption alone has little relationship with financial performance. Companies that generate stronger results first establish organizational alignment and Healthy Urgency, then use AI to accelerate execution around shared priorities.
The best candidates for AI automation are workflows with the greatest operational burden—those that occur frequently, take a long time to complete, or have a high cost when errors occur. Evaluating processes through the lens of Transaction Volume, Cycle Time, and Error Cost (V × T ×E) helps leaders identify where AI can deliver the greatest operational and financial return while preserving human oversight for high-risk decisions.