Which process should we point AI at first?
While 95% of GenAI pilots yield no measurable return, 74% of leaders report positive ROI when formally tracking broad metrics. The difference lies in how organizations evaluate initiatives, as AI usage and complexity scale costs proportionally.
The question
We have budget for one serious AI initiative and several candidate processes. How should we choose which to attack first — by cost, by volume, by how much expert judgment is trapped in a few people, or by how measurably the outcome can be proven — and what should disqualify a process from being the first candidate?
Counsel's position
Prioritize a high-volume, expert-judgment-intensive process with clearly measurable outcomes to demonstrate AI's strategic value.
Verdict
The verdict: Prioritize a high-volume, expert-judgment-intensive process with clearly measurable outcomes to demonstrate AI's strategic value.
How the criteria decide
3 of 5 criteria resolved on cited evidence. 2 had none either way.
| Criterion | Favours | Evidence |
|---|---|---|
| cost reduction potential | By cost | AI usage and complexity scale costs proportionally As usage scales, so does cost. As use case complexity scales, so does cost. AI scorecards can evaluate ideas across four feasibility dimensions The scorecard assesses hard factors (such as projected cost savings and outlay), soft factors (including customer and employee satisfaction), feasibility (by technology, data, and governance), and time to value. |
| process volume | By volume | AI usage and complexity scale costs proportionally As usage scales, so does cost. As use case complexity scales, so does cost. |
| reliance on expert judgment | No evidence either way | |
| measurability of outcome | By measurable outcome | 95% of organizations see no measurable return from GenAI pilots 95% of organizations seeing no measurable return, and 5% of integrated pilots extracting millions in value. Artificial Intelligence on Medium AI scorecards can evaluate ideas across four feasibility dimensions The scorecard assesses hard factors (such as projected cost savings and outlay), soft factors (including customer and employee satisfaction), feasibility (by technology, data, and governance), and time to value. 74% of leaders report positive ROI from GenAI 72% formally measure gen AI ROI and 74% report positive ROI |
| disqualification criteria | No evidence either way |
AI usage and complexity scale costs proportionally
AI workloads have a unique cost structure where expenses scale directly with usage and complexity, requiring a strict evaluation of unit economics.
95% of organizations see no measurable return from GenAI pilots
Enterprise GenAI pilots fail to generate measurable returns when they are approved without rigorous operational design or clear accountability.
AI scorecards can evaluate ideas across four feasibility dimensions
Organizations are using weighted scorecards to systematically assess AI use cases by projected costs, satisfaction, feasibility, and time to value.
74% of leaders report positive ROI from GenAI
Business leaders are moving past initial experimentation to demand accountability, with a strong majority reporting positive returns when they formally track broad ROI metrics.
Only 18% of coding token spend translates into shipped products
Unchecked AI usage and agentic loops are driving exponential cost increases without delivering proportional business impact, particularly in knowledge work.
Read another verdict
- Put one person in charge of AI — or is a Head of AI premature for us?
- Buy a tool for this process, or build around our own knowledge?
- Centralize AI strategy under CEO or distribute ownership?
- Adopt new AI ROI tools or refine existing methods?
- Invest in pre-build costing or post-deployment ROI tracking?
- Our documents are a mess. Clean them up before AI, or after?
- How do we measure the return on an AI workflow — and what baseline is honest?
- Our best people's know-how isn't written down — can AI even use it?