AI-versus-Human Task Allocation
Assign each task by comparing AI quality, human quality, and economics.
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 5
- Confidence
- 98%
This allocation model compares AI with a capable human on each specific task, classifying the system as worse, equivalent, or better. The classification is then combined with resource economics. A worse-than-human system may still deserve the work when the organization would never allocate scarce human capacity to it. At equivalent quality, cost, throughput, and near-unlimited replication favor automation. When AI is better, it becomes the preferred executor, subject to appropriate controls. The output is not a blanket automation strategy but a task-by-task operating map showing where AI should execute, assist, or defer to people. Because capabilities and data quality change, the map should be revisited regularly.
Origin
Kieran Flanagan developed the three-bucket comparison while mapping where AI should operate within a modern go-to-market system on Marketing Against The Grain.
Core principles
- 01Judge AI at the task level, not as one general capability.
- 02Inferior AI can still create value when the task would otherwise remain undone.
- 03Equivalent quality makes resource economics and scalability decisive.
- 04Reserve human effort for tasks where it produces a meaningful advantage.
How to run it
- 1
Define the task
Describe one bounded service or activity and the quality standard it must meet. Avoid evaluating an entire role as a single unit.
Pro tip Use the smallest task that can be measured independently.
Watch out Broad labels such as “sales” hide major differences between subtasks.
- 2
Compare performance
Classify the AI as worse than, equivalent to, or better than a competent human for that task.
Pro tip Test with representative inputs rather than relying on a polished demo.
Watch out Performance may change materially with better source data.
- 3
Apply the resource test
If AI is worse, ask whether a human would actually be assigned the work. If not, imperfect automated execution may still beat non-execution.
Pro tip Include opportunity cost when valuing human time.
Watch out Do not use resource scarcity to excuse unacceptable legal, ethical, or safety risks.
- 4
Compare unit economics
For equivalent performance, compare cost, throughput, response time, and the number of parallel tasks each option can handle.
Pro tip Calculate marginal cost at the volume you expect to reach.
Watch out Include review, integration, and failure-handling costs.
- 5
Assign and revisit
Choose AI execution, human execution, or a hybrid workflow, then repeat the analysis as capabilities and conditions change.
Pro tip Record the evidence behind each classification.
Watch out A decision based on today's model should not become permanent policy.
In the wild
A sales team finds that AI drafts are roughly equivalent to ordinary rep-written emails, while AI categorizes large volumes of customer data better than individual reps. It lets AI analyze data and prepare drafts while reps concentrate on conversations and closing deals.
→ Reps spend less time on repetitive preparation without surrendering the higher-value sales interaction.
Common mistakes
Automating whole roles
Evaluating an entire job obscures the individual tasks where AI and humans have different relative strengths.
Treating quality as the only variable
A slightly inferior system can still be valuable when the alternative is leaving worthwhile work undone.
Ignoring changing capabilities
A static allocation map becomes obsolete as models, tools, and available data improve.
Is it for you?
Best for
Teams prioritizing AI opportunities across a go-to-market or operational workflow.
Not ideal for
High-risk tasks where quality differences cannot be offset by cost or scale.
From the transcript
“There's three buckets, right?”
“Which is like worse than a human or better than a human, right?”
“Then there's certain things AI is equivalent to a human, and it's just unit economics, you know, better resource, you deconomics, it's infinite amount of…”
From the episode
How To Master Sales Prospecting With Ai In 2024