Red-to-Blue AI Opportunity Gap
Prioritize AI investments by comparing possible automation with actual deployment.
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 5
- Confidence
- 94%
The Red-to-Blue AI Opportunity Gap compares two quantities: how much work AI could theoretically cover and how much is actually being performed with AI. The distance between those states highlights underused capability and provides a way to prioritize transformation opportunities. Leaders first assess potential at the workflow level, then measure present deployment and rank the resulting gaps. They temper the ranking with implementation cost, inference economics, risk, and organizational readiness. The framework shifts attention away from benchmark gains and toward the practical work required to turn existing model capability into production outcomes. Its output is a prioritized portfolio of workflows where closing the adoption gap could generate valuable and feasible change.
Origin
Extracted from Marketing Against The Grain's interpretation of an Anthropic chart comparing theoretical and observed AI coverage across industries.
Core principles
- 01Potential coverage and actual adoption are separate measures.
- 02The largest gap can indicate the largest transformation opportunity.
- 03Model capability is useful only when integrated into workflows.
- 04Economic and implementation costs must temper theoretical potential.
How to run it
- 1
Map Theoretical Coverage
Estimate which portions of each workflow current AI systems could perform or materially assist. Separate technically plausible coverage from proven production performance.
Pro tip Assess tasks within roles rather than assuming an entire occupation is equally automatable.
Watch out Theoretical coverage is not a forecast of when adoption or job displacement will occur.
- 2
Measure Observed Coverage
Document where AI is currently deployed and how much real work it completes. Distinguish regular production use from experiments and occasional prompting.
Pro tip Use workflow telemetry, employee interviews, and process observations to validate actual usage.
Watch out Tool licenses and self-reported adoption do not prove that work has changed.
- 3
Quantify the Gap
Compare theoretical and observed coverage for every workflow. Rank the largest gaps as candidate opportunities rather than automatic priorities.
Pro tip Express the gap in both work coverage and potential business value.
Watch out A large gap can reflect difficult integration or unacceptable risk rather than neglected opportunity.
- 4
Apply Feasibility Filters
Evaluate model sufficiency, inference cost, data access, integration complexity, quality requirements, and organizational readiness. Remove opportunities whose economics or risks are currently unfavorable.
Pro tip Model costs without assuming permanently subsidized pricing.
Watch out Ignoring full production costs can make an attractive gap economically misleading.
- 5
Prioritize Transformation
Select opportunities that combine a meaningful gap, sufficient model capability, feasible economics, and strategic value. Feed those priorities into workflow redesign and measured pilots.
Pro tip Start with a bounded workflow that can demonstrate business value without requiring enterprise-wide change.
Watch out Do not interpret a high-priority gap as permission to automate without human and risk controls.
In the wild
A finance leader estimates that AI could assist heavily with invoice classification, variance commentary, forecasting research, and policy lookup. Current production use is limited to occasional drafting. After ranking the gaps, the team finds invoice classification valuable but integration-heavy, while variance commentary has accessible data, lower risk, and measurable time savings. It prioritizes the latter for a controlled pilot.
→ The company invests in the most feasible high-value gap rather than the most dramatic theoretical automation claim.
Common mistakes
Treating Blue as an Immediate Forecast
Theoretical coverage describes possibility, not when or whether an organization will achieve it.
Ignoring the Cost to Close the Gap
Compute, inference, integration, data preparation, and organizational change can make theoretical coverage expensive.
Chasing Benchmark Leaders
Marginally stronger models may not improve results when workflow integration is the real bottleneck.
Is it for you?
Best for
It is best for leaders allocating AI transformation effort across teams, functions, or workflows with uneven adoption.
Not ideal for
It is not ideal when theoretical coverage, current deployment, costs, or operational constraints cannot be estimated credibly.
From the transcript
“So what it's showing you is theoretical AI coverage, which means how much of that industry could be theoretically automated with AI.”
“And the red is the observed AI coverage, just how much AI is being deployed within this industry and how much of that work it…”
“Well, one, it shows you that the single biggest opportunity on this chart is actually the gap between the red and the blue.”
From the episode
This One Chart Exposes Why Most Companies Are Failing At AI