AI did not make Shraddhaben R Gajjar a better engineering leader by writing more production code. In her August 31, 2026 LeadDev essay, it helped her turn incomplete reasoning into an ADR draft, prepare sharper design-review questions, and expose assumptions while a team could still challenge them.
That is a useful leadership pattern, not proof that AI universally improves engineering performance. The productive role is a critical thinking aid: let a model produce a rough structure, then test every consequential claim against implementation, documentation, metrics, or the people who own the system.
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The leadership benefit is clearer reasoning, not automatic speed
Engineering leaders spend much of their time making decisions that are only partly technical. They have to explain why a choice is being made, state its constraints, compare alternatives, anticipate failure, and give other engineers enough visibility to disagree before a decision becomes expensive to reverse.
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This distinction matters. Generated prose can sound complete while hiding an untested premise. As Gajjar puts it, “Do not treat generated analysis as evidence.” A leader still owns the decision, the evidence standard, and the consequences.
A repeatable AI workflow for architecture decisions
An AI-assisted workflow is safest when the model handles structure and questions while engineers supply facts and judgment. The following sequence turns a vague problem into a reviewable decision without treating the output as authoritative.
1. State the decision and the constraints
Begin with a plain-language description of the decision. Include the outcome you need, the systems affected, deadlines, reliability or security requirements, regional or regulatory boundaries, and constraints such as compatibility or migration cost. If a fact is uncertain, label it as an assumption rather than silently presenting it as true.
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2. Ask for an ADR-shaped draft
Give the model the headings you use in an architecture decision record (ADR): context, decision, alternatives, consequences, risks, and open questions. Ask it to place each supplied fact under the appropriate heading and to mark missing information explicitly. This creates a review surface for your own thinking.
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For example, an initial draft might say that a replicated identity store is available in a new region. That statement should remain provisional until someone checks the deployment configuration, replication status, and traffic-routing rules. The draft is a map of what to verify, not verification itself.
3. Use the draft to find assumptions that could fail
Prompt for hidden assumptions and failure paths. Ask questions such as:
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- Which dependency could be unavailable, stale, or only partially deployed?
- What happens if traffic arrives before state is synchronized?
- Which rollback action is safe if the failure occurs during a migration?
- What observation would disprove the proposed design?
Classify each result as a fact to verify, a risk to mitigate, or a question for the system owner. This prevents a model’s plausible-sounding additions from becoming accidental requirements.
4. Convert assumptions into review questions
Design reviews improve when questions are specific enough to answer. Replace “Is this reliable?” with “What happens when the primary region is healthy but replicated identity data is incomplete?” Replace “Can we roll back?” with “Which configuration and data changes can be reversed independently, and how long does each reversal take?”
Ask the model to generate questions, then remove duplicates and questions that do not affect the decision. The leader’s work is selecting the questions that expose consequential uncertainty.
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5. Verify every consequential claim
Check implementation details in the repository or deployment configuration, compare behavior with authoritative documentation, inspect relevant metrics, and speak with the engineer who owns the subsystem. Record the source and date of each verified fact in the ADR. If a claim cannot be checked, keep it as an explicit uncertainty and avoid presenting it as a basis for approval.
6. Record what changed after review
An ADR should show how review feedback altered the decision. Add rejected alternatives, newly discovered constraints, and safeguards introduced during discussion. This turns an AI-assisted draft into durable organizational knowledge rather than a polished but unexplained conclusion.
How the approach changes a design review
AI can improve preparation, but it should not replace the meeting’s social and technical functions. The team still needs to test the design against real ownership boundaries, operational experience, and failure conditions.
Before the meeting
- Provide the model with the decision statement and known constraints, not confidential data that your policies prohibit sharing.
- Ask for a one-page summary, alternatives, assumptions, and failure scenarios.
- Compare the generated list with the implementation and remove claims that cannot be supported.
- Mark unresolved items as questions for named owners.
During the meeting
- Start with the decision and success criteria, then review assumptions before debating implementation details.
- Use the prepared questions to probe state transitions, dependency failures, observability, security boundaries, and rollback.
- Invite the person closest to each subsystem to confirm or correct the model’s description.
- Capture disagreements and evidence requests in the ADR instead of resolving them through authority or fluency.
After the meeting
- Update the decision record with verified facts and accepted mitigations.
- Create owners and due dates for checks that must happen before launch.
- Add monitoring or validation that will reveal whether a critical assumption remains true in production.
The multi-region example: make a hidden dependency a gate
Gajjar describes a multi-region rollout in which the team needed to know whether traffic could reach a running region before replicated identity data was complete. The risk was not simply that replication might be slow; traffic could arrive while the region lacked the state required to authenticate or serve users correctly.
Her team responded by adding explicit state validation before enabling traffic. The rollout could proceed only when the required synchronization condition was met. They also turned partial synchronization into a repeatable review concern rather than a one-off warning in a single project.
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This example illustrates a practical leadership move: convert an assumption into an enforceable gate. The model can help phrase the scenario and enumerate failure modes, but only the team can confirm the actual routing behavior, define the valid state, implement the check, and choose the response when validation fails. The operational account is Gajjar’s experience; it is not an independently audited experiment.
Does AI make developers faster?
Evidence on speed is mixed and highly dependent on the people, tasks, tools, and measurement method. Leadership decisions should therefore separate perceived acceleration from observed task outcomes.
| Evidence | What was measured | Result | Important qualification |
|---|---|---|---|
| METR randomized trial, July 10, 2025 | 16 experienced developers working on 246 issues in large open-source repositories, with AI allowed versus not allowed | Tasks took 19% longer on average when AI was allowed; participants later estimated that AI had made them about 20% faster | The result applies to this sample, these tasks, and early-2025 tools. METR warned against generalizing it to most software work or other domains. |
| METR experiment update, February 24, 2026 | Later experiment estimates for original and newly recruited developers | Estimated speedup was -18% for the subset of original developers (confidence interval -38% to +9%) and -4% for newly recruited developers (confidence interval -15% to +9%) | METR said selection effects and time-measurement problems made the estimates unreliable and the evidence weak for the size of any increase. |
| Stack Overflow 2025 Developer Survey | 12,823 respondents answering about AI-agent effects | About 70% reported reduced time on particular development tasks, 69% reported increased productivity, and 17% reported improved team collaboration | These are self-reported survey responses, not causal estimates and not evidence that every developer or team benefits. |
METR’s own limitation is especially important: “We do not claim that our developers or repositories represent a majority or plurality of software development work.” A leader can use these studies to calibrate confidence, not to settle whether AI is valuable in a particular organization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure leadership value instead of trusting a speed impression
No validated universal framework establishes that AI improves engineering leadership. You can, however, make the expected benefits testable by tracking the quality of decisions and team learning alongside delivery outcomes.
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Visibility of reasoning
Check whether ADRs state the decision, constraints, alternatives, and unresolved assumptions before implementation begins. A practical signal is the proportion of major decisions with those fields completed before approval. Review the records for substance, not merely for filled-in headings.
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Exposure of high-consequence assumptions
Count assumptions that became explicit review questions, tests, rollout gates, or monitoring alerts. Also record escaped assumptions: conditions discovered only during an incident, rollback, or late-stage migration. The goal is not to maximize the number of risks listed; it is to surface the risks that could materially change the decision.
Verification quality
For consequential claims, record whether the team checked implementation, documentation, metrics, or an owner’s confirmation. Sample completed ADRs and ask reviewers whether they can trace key statements to an authoritative source. A fluent paragraph without a check should not receive credit.
Team learning and collaboration
Look for evidence that more people can understand and challenge a decision: useful review comments from outside the author’s immediate group, fewer repeated questions about the same dependency, and clearer ownership of follow-up work. The Stack Overflow survey’s 17% collaboration figure is a perception measure, not a target or causal benchmark.
Actual task and review outcomes
Track cycle time, rework, escaped defects, rollback frequency, incident severity, and review changes for comparable classes of work. Compare periods or teams carefully, because staffing, project difficulty, and tool changes can confound the result. Pair these outcomes with qualitative review of a sample of decisions; throughput alone cannot show whether reasoning improved.
Guardrails that keep AI useful
- Protect sensitive information. Follow your organization’s rules for source code, credentials, customer data, and incident details before sending context to a model.
- Require human ownership. Name the engineer or leader responsible for each decision; the model is not an approver.
- Separate generation from verification. Keep the draft stage distinct from evidence collection so a plausible sentence does not become a “known” fact through repetition.
- Test for omission. Ask different prompts for failure modes, alternatives, and rollback; one generated list is not exhaustive.
- Watch for false consensus. A polished summary can discourage disagreement. Invite reviewers to challenge the assumptions explicitly and preserve dissent in the record.
- Review the tool’s changing behavior. Models, context windows, integrations, and data policies change. Recheck your process when the tool or its permissions change.
A practical starting plan for an engineering team
- Select one decision type. Start with ADRs for service boundaries, migrations, or failover changes where assumptions and rollback matter.
- Create a fixed prompt and template. Require context, constraints, alternatives, assumptions, failure modes, verification steps, and open questions.
- Run a short pilot. Use the workflow on several decisions while keeping normal review and approval processes unchanged.
- Sample the records. Have senior engineers judge whether the drafts exposed useful questions and whether factual claims were verified.
- Compare outcomes. Examine review rework, escaped assumptions, rollout incidents, and time to reach a decision, while noting project differences.
- Adjust or stop. Keep the workflow only where it improves evidence quality or team understanding without creating unacceptable privacy, security, or review overhead.
Used this way, AI is a forcing function for explicit reasoning. It can help a leader see what has not yet been explained, but the organization improves only when people verify the gaps, debate the trade-offs, and encode the resulting safeguards in systems and decisions.
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