seeking partner

Which explanation makes customers safely delegate to agents?

Test how much explanation, preview, and approval customers need before delegating consequential actions.

ProductAdoption & Retention

Enterprise Software & Cybersecurity

WHAT YOU CAN EXPECT

Evidence protects you in both directions.

Illustrative result · threshold met

+22% · safe delegation

Product managerIllustrative result · threshold met

+22%

safe delegation

A reversible action preview and consequence-focused explanation made customers more willing to delegate useful work while reducing corrective intervention.

What your study would pin down

You get an approval policy by action risk: what customers will delegate, where they need preview or explanation, and which approval steps are pure friction.

Decision

Show consequences at high-risk steps and remove approval friction elsewhere.

Public precedent · not your resultManagement Science

Changing the decision frame removed a measured bias against delegating to AI.

Across multiple studies, participants preferred humans even when AI had been shown to outperform. Under loss framing, that bias disappeared and participants delegated to human and AI assistants at similar rates.

Source: Management Science · 2025 ↗The precedent shows the effect can exist elsewhere. Your study determines whether, where, and how strongly it holds for your customers, workflows, and economics.

Decision

Evidence protects you in both directions.

Product managerThreshold not met · do not scale

What the data may show

Approval burden dominates

If the intervention does not clear the predefined threshold, that is evidence against spending more to build, launch, or scale it in this context.

Decision

Simplify or abandon the pattern.

Product managerThreshold met · value left unused

The other expensive error

A real opportunity can still be left on the table.

If the intervention clears the threshold but the business keeps the current approach, measurable savings, revenue, adoption, or risk reduction may remain unrealized.

Decision

Act only when the measured opportunity is large enough to justify the change.

Source

Public precedent · not your result

Comparable public caseMata v. Avianca legal team

$5,000 in sanctions after lawyers submitted unverified AI-generated citations.

Lawyers submitted nonexistent cases generated by ChatGPT. A federal court imposed $5,000 in sanctions, illustrating why externally consequential actions still need appropriate human verification.

Source: U.S. District Court, S.D.N.Y. ↗Comparable public case — not a claim that this study would have prevented the event.

Related published evidenceHuman–AI delegation study

People missed 3.9% of opportunities to use correct AI suggestions — while also over-relying 1.7% of the time.

Across 387 delegation and 1,440 adoption decisions, human–AI teams outperformed either side alone, but users made both under-reliance and over-reliance errors. Better calibration can recover value in both directions.

Source: arXiv · 2026 ↗External research for context — not a promise that the same effect size will reproduce in your customers.

HYPOTHESIS

A reversible action preview with consequence-focused explanation will increase safe delegation by 15% while reducing intervention errors by 20%.

PRIMARY METRIC

Successfully delegated tasks completed without corrective intervention.

MEANINGFUL THRESHOLD

+15% safe delegation and −20% corrective intervention.

BUSINESS TARGET

Faster enterprise deployment and lower operational support cost.

DECISION RULES

Both thresholds met

Pilot in one controlled workflow.

Delegation rises but errors do not fall

Increase consequence visibility.

Approval burden dominates

Simplify or abandon the pattern.

Business outcomes are research targets, not guarantees. A null or negative result may still create substantial value by preventing investment in an ineffective product, feature, or campaign.

Population

Knowledge workers using an LLM-based agent for multi-step business tasks.

Intervention

A concise plan, consequence preview, and selective approval gates.

Comparator

A generic confirmation request before execution.

Secondary metrics

Time saved · Approval burden · Security-policy compliance

MAKE IT YOUR DECISION

Turn this research question into a decision for your business.

We adapt the population, intervention, thresholds, and economics to your customers. The result may tell you to scale, to stop spending, or to act on an opportunity you are currently leaving unused. Each of those is a useful business decision when the evidence is strong enough.

The goal is not a positive result. The goal is evidence strong enough to change a real decision.

Design this study for your business