Overview

Thursday, 10:30 AM. Your operations leader presents an AI-generated implementation plan for moving your invoice processing system to automation. The plan looks comprehensive: 12 weeks, phased rollout across three departments, clear milestones, 60-person-days estimated. Leadership approves it. Week 4 into implementation, your team discovers the plan didn't account for your company's year-end close process. During close weeks (months 11 and 12 of the fiscal year), your finance team is unavailable for testing and training. They're deep in close procedures. The plan scheduled deployment during Q4 close. Timeline now extends to 16 weeks, not 12. The error wasn't in execution, the team executed exactly as planned. The error was in the plan itself: it assumed finance would be available when they're actually committed. Your project now costs $50K more in overtime and consulting fees, plus delays other departmental automation projects scheduled for early Q1. This is the error-catching challenge: AI generates plans that look coherent but contain hidden flaws discoverable only through systematic checking before execution starts.

Part 1: AI Error Patterns and Why They Occur

AI-generated plans don't make random errors. They make patterned errors, and learning these patterns is how you build effective detection frameworks. The patterns exist because of specific knowledge gaps: the AI doesn't have access to your organizational constraints, historical project learnings, resource calendars, or vendor-specific knowledge. Your job isn't to blame the AI for not knowing these things. It's to systematically inject this knowledge into the error-checking process. Five error patterns appear repeatedly. Understanding each helps you design targeted checks to catch errors before execution.

Missing or underestimated dependencies is the most common pattern. The AI designs an implementation approach without fully mapping what other systems, processes, or approvals it depends on. Real example: An AI-generated system migration plan proposes moving your customer database to a new platform over 12 weeks. The plan outlines data extraction, transformation, testing, and cutover. What the plan misses: the data extraction depends on IT infrastructure upgrades that haven't been funded yet (IT says 8 more weeks to complete). The plan assumes your data validation process will be complete; actually, validation testing won't start for 6 weeks. The plan assumes sign-off from your Chief Data Officer; actually, that person is on sabbatical until week 14. None of these are flaws in the AI's logic. They're dependencies the AI didn't know about. Another example: A procurement process design assumes you can implement new vendor vetting procedures immediately. It misses that your procurement team is fully allocated supporting an acquisition for 8 weeks. The plan doesn't account for this resource constraint on an unrelated project.

Unrealistic timelines is the second pattern. The AI estimates task durations based on theoretical best case or average case, without accounting for your organizational reality. The AI might estimate "install and configure new servers: 2 days." In theory, that's possible. In your organization, with your security review process, your network team's involvement, vendor coordination delays, and your typical rework rate, it actually takes 6 days. The AI estimates "staff training on new system: 1 day." In ideal conditions with perfect documentation, sure. In reality, your staff needs 3-4 days to become proficient because your documentation is unclear, questions take time to answer, and people need practice with the live system. These aren't random errors. They're predictable underestimates of how long tasks actually take in your context. A third example: The AI estimates "budget approval for new equipment: 1 week." Your actual approval process involves submitting a request Monday, getting reviewed Thursday (3 days without response), going to committee Friday, reconvening Monday with additional questions, and final approval the following Wednesday. That's 3 weeks, not 1 week. The AI doesn't know your approval process intimately.

Overlooked constraints is the third pattern. The AI proposes a solution that violates an organizational constraint it wasn't informed about. The plan says "migrate database during business hours." It misses that your organization is a 24x7 financial trading floor and there are no "off-peak" hours. The plan says "source components from a new supplier." It misses that your industry requires all safety-critical components come from a pre-approved vendor list (regulatory requirement). The plan says "reroute all customer traffic through the backup infrastructure." It misses that your backup systems are already running at 80% capacity and can't handle the full production load. These constraints exist, but the AI doesn't know about them because they weren't explicitly stated in the information provided to the AI. They're obvious to anyone who's worked in your organization for six months, but they're invisible to the AI.

Undercounted resource requirements is the fourth pattern. The AI estimates resource needs accurately in isolation but misses dependencies on other resources. The plan says "1 database administrator for the migration." True, one DBA could technically run the migration. But your DBA also has production support responsibilities (databases are running, issues come up during migration). So you really need 1.5 or 2 DBAs. The plan says "3 developers for code integration." Three developers is correct for the actual development work. But developers need QA support during testing (another 1-2 people), they need architecture review from your senior architect (0.5 person), and they need deployment support from your operations team (another 1 person). So the real resource requirement is closer to 6 people. The AI made accurate sub-estimates but didn't account for resource split-allocation and supporting functions.

Ignored organizational constraints is the fifth pattern. Organizational factors that affect execution aren't technical constraints. They're business calendar and governance factors. Example: The plan proposes spending $400K on new equipment in Q3. It misses that your organization has a Q3 spending freeze due to budget reallocation concerns (a finance-level decision, not an operations decision). The plan schedules key personnel availability in August. It misses that your organization gives people three consecutive weeks off in August (corporate policy). The plan proposes rolling out changes without formal approvals. It misses that your governance requires steering committee approval every two weeks before major changes proceed, adding 2-4 weeks to the timeline. These aren't project management oversights. They're organizational culture and policy factors the AI doesn't automatically know.

Why These Errors Matter: Each error pattern causes project failures: missing dependencies cause downstream cascades (one task gets blocked, which blocks the next task, which blocks the third task); unrealistic timelines cause schedule misses and cost overruns; overlooked constraints cause rework (you implement something that violates policy and have to redo it); undercounted resource requirements cause burnout and quality issues (staff are overallocated and make mistakes); ignored organizational constraints cause timeline shock (three weeks of planned work gets delayed two months due to a spending freeze you didn't anticipate). The errors are all preventable if you systematically check the plan against organizational knowledge before execution starts.

Part 2: Error Detection Framework for Implementation Plans

For any major implementation plan or operational change generated by AI, use this five-check error detection framework. These checks are most effective when performed by someone who knows your organization's constraints, not just the technical domain.

Check 1: Dependency Mapping. For each major task or milestone in the plan, explicitly answer: "What must be complete before this task can start? What systems must be ready? What approvals must be obtained? What external parties must be engaged?" Create a dependency map visually (draw boxes and arrows, or use simple text lists). Compare the plan's stated dependencies against your knowledge of how things actually work. Example: The plan says "Data validation testing begins Week 3." Your dependency question: "Does validation testing depend on anything?" Answer: "Yes. IT infrastructure must be provisioned first, validation procedures must be documented, and the validation team must be trained." Follow-up: "Are IT infrastructure provisioning and validation procedure documentation scheduled to complete before Week 3?" Check the plan. If not, you've found a gap. Fix the plan before execution. Another example: The plan says "Cutover happens Week 10." Dependency question: "What must be complete before cutover?" Answer: "Data migration successful, testing passed, user training complete, rollback procedures documented and tested, support team trained, executive approval granted." Check the plan against each dependency. If any dependency isn't scheduled to complete before Week 10, the timeline is wrong.

Check 2: Timeline Stress Testing. For each major task with a duration estimate (e.g., "Database migration: 5 days"), ask: "What could go wrong that would delay this by 50%?" This stress test reveals whether the estimate includes contingency for real-world friction. Example: The plan estimates "Install and configure new servers: 2 days." Stress test: "What could cause this to take 3 days instead?" Possible answers: vendor shipping delay (you need servers by Friday; vendor ship Monday instead of Thursday, add 1 day), security review discovers configuration issues (needs rework, add 1 day), network team is unavailable for 2 days (they're supporting the data center migration, add 2 days). Revised estimate: 4-5 days, not 2 days. Another example: "Staff training: 1 day." Stress test: "What extends training?" Possible answers: system documentation is unclear (people have questions), you only cover 80% of use cases in one day and need a second day, support team needs to be trained too, some people are slower to adopt and need one-on-one coaching. Revised estimate: 3-4 days, not 1 day. The question isn't whether worst-case happens every time. It's whether the estimate includes realistic contingency. Most AI-generated timelines don't.

Check 3: Constraint Validation. Map the plan against every constraint you know about. Create a constraint checklist: Operational windows (when can you take systems down? What times are off-limits?), resource availability (when are key people unavailable? vacations, other projects, conference travel?), budget constraints (spending freezes, capital budget cycles?), policy constraints (approval processes, governance requirements?), vendor constraints (can you get what you need when you need it? supplier lead times?). Go through the plan task-by-task and verify against constraints. Example: The plan schedules database migration Thursday 2-6 PM. Constraint check: "Can we take the database down Thursday 2-6 PM?" Answer: "No, Thursday 2-6 PM is peak trading hours." Revise the plan to schedule migration Thursday 10 PM to Sunday 2 AM (off-peak). Another example: Plan schedules vendor selection in May. Constraint check: "Can we spend money in May?" Answer: "No, finance freeze is April 15 through June 30 due to budget reallocation." Revise the plan to schedule vendor selection after June 30 or to secure budget commitment before April 15.

Check 4: Resource Capacity Verification. For each role required in the plan, verify actual availability. The plan says "1 database administrator 20 hours per week for 12 weeks." Your verification: "Do we have a database administrator? Yes. Is that person available 20 hours per week for 12 weeks?" Answer: "No, our DBA has production support responsibilities (30 hours per week) and can spare 10 hours weekly maximum." Plan is overcommitted. Revision: Either hire a contractor DBA, extend the timeline to reduce weekly hours required, or descope work to fit available capacity. Another example: Plan says "3 developers for code integration." Verification: "Do we have 3 developers available?" Answer: "We have 3 developers, but one is allocated to bug fixes (40% of time), one is supporting an acquisition (25% of time), leaving only 1 full-time equivalent plus partial time from the other two." Plan needs adjustment, either increase timeline or add developers.

Check 5: Organizational Context Validation. Before approving the plan, answer these questions: Are there organizational spending constraints (freezes, budget cycles) that affect the plan? Are there personnel events (vacation blackouts, acquisition-related absences) that conflict? Are there governance requirements (approval cadence, steering committee reviews) that add time? Are there seasonal operations factors (year-end close, peak season, maintenance windows) that conflict? Is there executive leadership change or distraction that might slow approvals? This is the "organizational knowledge" check. Example: The plan proposes rolling out changes in Q4. Organizational context check: "In Q4, our finance team is doing year-end close (September-November). Can we do major testing and training when finance is in close?" Answer: "No." Revision: Move rollout to Q1 when finance has capacity. Another example: Plan proposes vendor selection starting June 1st. Organizational context: "Who's the executive sponsor for this? That person is on sabbatical June-September." Revision: Delay to September when sponsor returns, or assign decision-making authority to a delegate.

Pro Tip for Busy Operations Teams: Create an "Organizational Context Checklist" once and use it for every AI-generated plan. The checklist should cover: spending freezes/budget cycles, vacation blackouts, seasonal operations factors, key personnel unavailability, governance meeting schedules, vendor lead times for critical supplies, system maintenance windows, and known process constraints. This checklist becomes your institutional knowledge repository. When reviewing AI plans, just run them against the checklist instead of debating each one ad-hoc. Takes 15 minutes per plan once you have the checklist.

Part 3: Error Detection for Analyses and Recommendations

Plans aren't the only AI outputs that need error checking. Analyses and recommendations (vendor performance analysis, process design alternatives, resource allocation models) also contain patterns of errors. The errors are different from planning errors but equally systematic.

Incomplete data analysis is the first error pattern in analyses. The AI analyzes available data but misses relevant data that would change the conclusion. Example: An AI analyzes vendor performance data and recommends switching to a new, lower-cost vendor based on price and delivery performance. The analysis misses that your current vendor relationship includes dedicated account support (24/7 response time for critical issues) that the new vendor doesn't provide. When you switch, support quality degrades and you lose $200K in productivity. The analysis was technically correct given the data; it just didn't have the full picture. Another example: An AI recommends a process redesign that increases throughput by 30%. The analysis misses that the redesign violates a constraint you have. Your business requirement is "zero customer-facing defects" and the redesign trades quality for speed in ways you didn't communicate to the AI. When you implement, defect rate increases and customers are unhappy. Detection for incomplete data analysis: Ask, "What data did the AI use? What data is missing that might change the conclusion?" Before accepting a vendor recommendation, get the full vendor comparison including support SLAs, historical performance trends, and any relationship factors. Before accepting a process redesign, validate the redesign against all constraints (quality, capacity, compliance, customer experience).

Biased or cherry-picked analysis is the second error pattern. The AI analyzes information in a way that supports a predetermined conclusion (if it had one) or oversimplifies a complex situation. Example: An AI recommends keeping an existing system instead of replacing it. The analysis emphasizes the high replacement cost and low ROI. The analysis undersells the fact that the existing system is held together with custom code and is brittle (high maintenance cost, frequent failures). When you keep the system, maintenance costs balloon and you wish you'd replaced it. The analysis wasn't wrong; it just weighted factors in a way that supported one conclusion. Detection: Ask, "What's the alternative recommendation to this one? What would analysis supporting that alternative look like?" For the vendor example, ask the AI to also present the case for switching vendors and the case for renegotiating with current vendor. Compare the three analyses. If one obviously has more favorable framing, investigate whether data is missing.

Overgeneralization from limited examples is the third error pattern. The AI makes a broad recommendation based on a small sample. Example: An AI recommends a staffing model based on data from your three largest locations. The analysis misses that your smaller, newer locations have different staffing dynamics (people are more generalized, technology adoption is slower, team cohesion is higher). When you apply the model organization-wide, it doesn't work in smaller locations and you have to undo it. The AI's data was limited; it didn't know to ask for location-type variation. Detection: Ask, "Is this recommendation based on organization-wide data or a subset? If subset, how representative is it?" Before implementing a recommendation, validate whether it works for different scenarios (different geographies, different business units, different product lines).

Naive or theoretical assumptions are the fourth error pattern. The AI makes assumptions that are theoretically sound but don't match your organizational reality. Example: An AI recommends implementing a new procurement system and assumes "staff will adopt the new system within 2 weeks of training." Your organizational reality is that staff are averse to change and adoption usually takes 6-8 weeks, with many reverting to old methods for several months. When implementation happens, adoption is slow and you have to extend support. The assumption wasn't crazy; it just didn't account for organizational culture. Another example: An AI recommends consolidating vendors from 12 to 5 to reduce complexity. The assumption is that fewer vendors means lower costs. Your organizational reality is that your largest vendor abuses their position with 30% price increases once competitors are eliminated. Detection: When you see recommendations with strong assumptions, ask, "Is this based on how we actually operate, or theoretical best practice?" Force assumptions to be made explicit, then challenge them with organizational knowledge.

False precision is the fifth error pattern. The AI generates a recommendation with high numerical precision (e.g., "reduce staffing by exactly 8.3 people") implying certainty when the analysis is fundamentally uncertain. Example: An AI analyzes historical hiring data and recommends "you need exactly 12.7 full-time equivalents for customer service based on historical volume and handling time analysis." The analysis is precise but false. There's inherent variability, peak seasons require flexibility, and you can't hire 0.7 of a person. The recommendation should be "12-14 FTE depending on season" not "12.7 FTE." Detection: When you see precise numbers, ask, "What's the confidence range? What assumptions underlie this number? How sensitive is this number to changes in key assumptions?" A good analysis will give you ranges and uncertainty bands, not false precision.

Part 4: Implementing Error Detection as Standard Practice

Error detection only works if it's systematic. Build it into your process. When an AI-generated plan or analysis comes to you, don't approve it immediately. Run it through your error detection checks. Assign someone (usually a subject matter expert who knows your organization) to spend 1-2 hours checking the plan or analysis. That investment prevents bad launches. Document what you find. Share findings with the AI team or the group that ordered the analysis. This creates feedback loops that improve future outputs. Over time, as the AI learns your organizational patterns, the number of errors decreases, and your error-checking time reduces.

Error Detection Framework for Plans

For implementation plans and project schedules, use this error detection framework:

Check 1: Dependency Mapping

For each task in the plan, ask: "What must be complete before this task can start?" Map dependencies visually.

Example: Task "Database Migration" has dependencies: (1) Data validation complete, (2) Backup systems tested, (3) Rollback procedures documented, (4) Downtime window approved.

Error pattern caught: Missing dependencies (AI forgot to mention data validation must happen first).

Check 2: Timeline Stress-Testing

For each major task, ask: "What could delay this by 50%?" Build contingency into estimates.

Example: AI estimates "Setup new equipment: 5 days." You ask: "What if vendor delays shipping? What if setup requires rework? What if network team is unavailable?" Revised estimate: 8-10 days.

Error pattern caught: Unrealistic timelines (AI underestimated practical duration).

Check 3: Constraint Validation

Map the plan against known constraints: operational windows, resource availability, policy requirements, vendor SLAs.

Example: Plan schedules work 3 AM-6 AM (your trading floor is active 7 AM-10 PM). That's fine. Plan schedules vendor setup during Q3 when finance freeze is in place. That's not fine.

Error pattern caught: Overlooked constraints (AI didn't know about trading floor hours or finance freeze).

Check 4: Resource Capacity Verification

For each role required in the plan, verify: (1) Do you have someone in that role? (2) Are they actually available during the timeline? (3) Can they do the work while maintaining other responsibilities?

Example: Plan requires "database administrator 20 hours/week for 12 weeks." You have 1 DBA. But the DBA has production support responsibilities requiring 30 hours/week. You're overallocated by 50%.

Error pattern caught: Undercounted resource needs (plan didn't account for split allocation across projects).

Check 5: Organizational Context Validation

Verify the plan against known organizational factors: spending freezes, approval processes, personnel availability, policy requirements.

Example: Plan proposes spending $400K on new equipment in Q3. Your organization has a Q3 capital spending freeze due to budget reallocation. Plan violates this organizational constraint.

Error pattern caught: Ignored organizational constraints (AI didn't know about the Q3 freeze).

What to Do Monday Morning

• Review your most recent AI-generated plan or analysis. Run it through the five error detection checks in Part 2 (dependency mapping, timeline stress testing, constraint validation, resource capacity verification, organizational context validation). Document any gaps you find. This becomes your baseline for what "good error detection" looks like.
  - Create an "Organizational Context Checklist" specific to your operation. It should document: spending freezes and budget cycles (when can you spend money?), personnel availability constraints (vacation blackouts, key person unavailability windows), seasonal operations factors (year-end close, peak seasons, maintenance windows), governance requirements (approval processes, steering committee meeting schedules), system constraints (maintenance windows, capacity limits), and vendor lead times for critical supplies. Make this a living document (update quarterly).
  - Assign error-checking responsibility for future AI outputs. Decide: Who will review AI-generated plans before they're approved for execution? Ideally someone who understands organizational constraints, not just the technical domain. That person should spend 1-2 hours per major plan running it through error detection checks and documenting findings.
  - Build error detection into your approval process. Before any major plan or analysis is approved, add a step: "AI output error check complete? Findings documented and addressed?" This creates accountability for checking, not just for generating.
  - Create a feedback loop back to AI owners. When error detection finds gaps (missing dependencies, unrealistic timelines, overlooked constraints), document them and share with whoever ordered the AI analysis. Over time, this feedback improves AI outputs. Prompt patterns like "always include resource availability checks" and "always validate against our business calendar" help the AI learn your organizational patterns.

Key Takeaways

• AI-generated plans contain predictable, patterned errors: missing dependencies, unrealistic timelines, overlooked constraints, undercounted resources, and ignored organizational factors. These aren't AI limitations. They're knowledge gaps about your specific organization.
  - Implement the five-check error detection framework for every major plan: dependency mapping (what must happen first?), timeline stress testing (what could delay this 50%?), constraint validation (does this violate any constraints?), resource capacity verification (are the people actually available?), organizational context validation (does this account for our business calendar and policies?).
  - Assign error checking to someone who understands your organization, not just the technical domain. This person should spend 1-2 hours per major plan. That investment prevents bad launches worth thousands in rework.
  - Create and maintain an "Organizational Context Checklist" documenting your spending cycles, personnel availability, seasonal factors, governance requirements, and other constraints. Use it to validate every AI plan before execution.
  - For analyses and recommendations, check for incomplete data analysis (what data is missing?), biased analysis (is alternative analysis supported?), overgeneralization (is this based on representative data?), naive assumptions (does this match our organizational reality?), and false precision (should this be a range, not a precise number?).
  - Error detection creates feedback loops. When you find errors and share them with whoever ordered the AI work, they learn. Over time, AI outputs improve as it learns your organizational patterns.
  - Timeline estimates in AI plans are almost always optimistic. Add 30-50% contingency to most estimates, or stress-test each major task explicitly. Don't trust "best case" timelines without challenge.
  - Constraint validation is often missed. Before approving a plan, explicitly validate against: operational windows, personnel availability, spending authority, governance approvals, policy constraints, and seasonal factors.
  - Resource capacity is frequently undercounted. For every role required in a plan, verify: Do you have someone in that role? Are they actually available or are they partially allocated elsewhere? Who backs up that role if they're unavailable? Can they do project work while maintaining operational responsibilities?
  - Build error detection into your approval workflow. Make "AI output reviewed and validated" a required step before approval, just like "budget approved" or "legal reviewed."

Error Detection Framework for Analyses

For analytical outputs (root cause analysis, cost-benefit analysis, vendor assessments), use this framework:

Check 1: Factual Verification

Verify key facts cited in the analysis. Does the analysis claim "supplier has a 2-week lead time"? Verify this against your current supplier agreements or recent orders.

Error pattern caught: Hallucinated or outdated facts (AI confidently stated something that's not actually true).

Check 2: Assumption Audit

List all assumptions in the analysis. For each, ask: "Is this assumption reasonable? Is it documented anywhere?"

Example: Cost-benefit analysis assumes "labor productivity increases 15% from process redesign." Is this assumption based on past redesign results, or is it a guess? If it's a guess, is 15% reasonable or optimistic?

Error pattern caught: Unjustified or overly optimistic assumptions (analysis rests on weak assumptions).

Check 3: Comparable Data Reality Check

For quantitative claims, compare them to comparable situations you know about. Does the analysis claim process redesign will reduce cycle time from 14 days to 6 days? That's 57% reduction. Have you achieved similar reductions in comparable redesigns? If past redesigns achieved 20-30% gains, the 57% estimate is likely too optimistic.

Error pattern caught: Unrealistic projections (analysis over-estimates benefits or under-estimates costs).

Check 4: Alternative Explanation Audit

If the analysis proposes a root cause, ask: "What else could explain these observations?" Does the root cause have to be what the analysis proposes, or are alternatives plausible?

Example: Root cause analysis concludes "defect rate increase is due to supplier quality degradation." But alternative explanations exist: new operator error, calibration drift in test equipment, environmental factors. The analysis should acknowledge these alternatives.

Error pattern caught: Incomplete root cause analysis (analysis jumps to conclusion without ruling out alternatives).

Check 5: Recommendation-Conclusion Linkage**

Verify that the recommended action actually addresses the identified problem. If the root cause is "supplier quality," the recommendation should be supplier management, not internal process changes.

Error pattern caught: Misaligned recommendations (analysis identifies Problem A but recommends solution for Problem B).

Before AI vs. With AI: Error Discovery Timing

Before AI: A human created an implementation plan. Errors were discovered during execution (week 3 or 4 into a 12-week project). By the time errors were discovered, the project was already off track. Rework and replanning took 2-3 weeks.

With AI + Error Detection Frameworks: An AI generates a plan. You apply error detection frameworks before execution. Errors are discovered during review (same day or next day). You request AI revision with specific error feedback. Revised plan is reviewed and approved. Execution proceeds with error-free plan. No rework during implementation.

The speed difference is dramatic because errors caught before execution don't cause execution failure.

Workflow: Multi-Layer Error Detection for a Vendor Assessment

An AI has generated a vendor assessment recommending a new supplier. Here's how error detection works:

Layer 1: Factual Verification

The analysis claims the vendor has "ISO 9001 certification" and "3-day lead time from overseas facility." You verify: Check their cert online (passes). Call them about lead time (they say 5-7 days, not 3). Error detected: Lead time claim is wrong.

Layer 2: Assumption Audit

The analysis assumes "transportation cost will be $200/unit." You ask: Based on what? Is this based on quotes? On estimates? You discover it's based on a generic estimate, not actual quotes. Error flag: This assumption needs verification.

Layer 3: Comparable Reality Check

The analysis projects "10% cost savings vs. current supplier." You compare to past vendor switches. Previous switches achieved 5-8% savings. The 10% projection is optimistic. You note this is a risk to the analysis.

Layer 4: Alternative Explanation**

The analysis concludes "vendor is a good fit for commodity sourcing." You ask: Are there alternative assessments? Could someone argue this vendor is risky despite low cost? You note the analysis could acknowledge tradeoffs more explicitly.

Layer 5: Recommendation Linkage**

The analysis recommends "trial order of 1,000 units." You ask: Does this address the concerns you identified? Yes, it does. It validates lead time and quality without full commitment.

Error Summary: Factual error (lead time), assumption risk (cost estimate), projection optimism (savings estimate). The recommendation (trial order) is sound but should be framed as "verify lead time before full commitment."

You send the analysis back to AI with specific feedback. Revised analysis corrects facts, acknowledges assumptions, notes the cost savings as estimated not guaranteed, and frames recommendation as verification-gated.

Real Schema: Error Detection Log

```json
{
"error_detection_log": {
"ai_output_id": "PLAN-MIGRATION-001",
"output_type": "implementation_plan",
"review_date": "2026-04-09",
"errors_detected": [
{
"error_id": "ERR-001",
"error_pattern": "Missing Dependency",
"description": "Database migration task assumes data validation is complete; no predecessor task for validation",
"severity": "High",
"impact": "If validation not complete, migration will fail",
"remediation": "Add data_validation task as predecessor to migration; add 2-week buffer"
},
{
"error_id": "ERR-002",
"error_pattern": "Unrealistic Timeline",
"description": "Equipment setup estimated at 2 days; comparable past projects took 5-7 days",
"severity": "Medium",
"impact": "Timeline will slip; schedule risk",
"remediation": "Revise equipment setup to 6 days; add vendor delay buffer"
},
{
"error_id": "ERR-003",
"error_pattern": "Overlooked Constraint",
"description": "Plan schedules migration during trading hours; violates 24x7 trading floor constraint",
"severity": "Critical",
"impact": "Migration cannot proceed as scheduled; operational disruption",
"remediation": "Reschedule migration to 10 PM - 4 AM window; coordinate with trading ops"
}
],
"errors_fixed": 3,
"final_status": "Plan resubmitted with corrections; ready for approval"
}
}
```

Building Your Error Detection Library

Over time, you'll see patterns in AI errors specific to your organization. Build a library tracking these patterns so you can improve your processes and prompts:

For each error type you discover, document:

• What's the pattern? Missing dependency, unrealistic timeline, overlooked constraint? Be specific.
  - When does it happen? In what types of plans or analyses? In what domains (IT vs. procurement vs. process design)? What conditions trigger this error?
  - How do you detect it? What question do you ask? What comparison do you make? What warning signs indicate this error?
  - How do you fix it? What prompt modification prevents it? What template change? What additional context prevents the error?
  - Frequency: How often does this error occur? 5% of plans? 50%? Track frequency so you prioritize fixing the most common errors.

Example entry in your library:

"Error Pattern: Underestimated task duration for IT infrastructure configuration tasks. When: Happens in 60% of IT-related implementation plans; does not happen in process design or vendor selection. Detection: Compare AI estimate to historical duration of similar tasks; typically AI underestimates by 40-60%. For example: AI estimates 'Configure new servers: 2 days' when historical data shows similar server configs take 5-7 days. Fix: Revise vendor selection prompts to include context: 'Account for typical task inflation on IT infrastructure tasks. Server configuration typically takes 1.5-2x initial estimates due to vendor delays, network coordination, security review delays. Include appropriate contingency.' Frequency: 60% of IT plans contain this error; fixing the prompt could prevent ~90% of these errors."

Your error detection library becomes: (1) feedback for improving your prompts (address the most common error patterns), (2) training material for new reviewers ("here are the errors to watch for in IT plans"), (3) institutional knowledge about AI blind spots specific to your operation.

Systematic Error Detection Process

Don't wait until errors are found accidentally during implementation. Build systematic error detection into your review process:

Before Implementation, Run This Checklist:**

• Dependency Check: List all tasks. For each task, ask "What must be complete before this task starts?" Map dependencies visually. Identify any missing dependencies.
  - Timeline Stress Test: For each major task, ask "What could cause this to take 50% longer?" List risks. Add contingency. Compare AI's estimate to contingency-adjusted estimate. Large gaps suggest underestimation.
  - Constraint Validation: List operational constraints (trading floor hours, Q3 spending freeze, equipment capacity, etc.). Scan the plan: Does the plan violate any of these constraints? Flag violations.
  - Resource Reality Check: For each resource required, verify: Do we have this resource? Are they actually available in the timeline? Can they do this while maintaining other responsibilities? Scan for impossible allocations.
  - Organizational Context: Run the plan against known organizational factors. Any conflicts with strategic priorities? Conflicts with past decisions? Any assumptions that contradict what you know about your organization?

This checklist is mechanical but valuable. Run it before moving to implementation. Errors caught here prevent project failures.

Integrating Error Detection into Quality Gates

Error detection should happen at a quality gate, not at implementation. Here's how to structure it:

Quality Gate: Pre-Implementation Error Detection

After AI generates a plan, before implementation approval, run error detection. If errors are found: (1) Document the error, (2) Return plan to AI with specific feedback ("Dependency on data validation is missing; add 2-week data validation phase before migration"), (3) AI revises plan, (4) Re-run error detection on revised plan, (5) Verify errors are corrected. Proceed to implementation only after this gate passes.

Time investment: 1-2 hours for error detection. Prevention value: Prevents 2-4 weeks of rework if the same errors are caught during implementation instead.

Monday Morning to Takeaways

Monday Morning Scenario: An AI-generated vendor assessment lands on your desk. Instead of approving it, you apply error detection frameworks. Factual verification reveals the lead time claim is outdated (vendor's capability changed). Assumption audit reveals cost savings estimate is based on generic benchmarks, not quotes. Comparable reality check shows the savings projection is 25% more optimistic than past vendor switches. You send the analysis back with specific feedback. AI revises it, correcting facts, acknowledging assumptions, and noting savings as estimates pending quotes. The revised analysis is now credible and actionable, where the original analysis had hidden flaws that would have surfaced later.

Key Takeaways:

• AI makes patterned errors: missing dependencies, unrealistic timelines, overlooked constraints, undercounted resources, ignored organizational context.
  - For plans: Use dependency mapping, timeline stress-testing, constraint validation, resource verification, organizational context checks.
  - For analyses: Use factual verification, assumption audit, comparable reality check, alternative explanation review, recommendation linkage.
  - Error detection frameworks are your way of catching flaws before execution.
  - Build an error detection library over time. Learn the patterns specific to your organization and improve your prompts and reviews accordingly.
  - Errors caught during review take minutes to fix. Errors caught during execution take days to rework.

Frequently Asked Questions

Q: How long should error detection take?

A: Depends on plan complexity. Simple plans (5-10 tasks): 30-45 minutes. Complex plans (30+ tasks, many dependencies): 2-3 hours. It's faster than catching the same errors during execution.

Q: Who should do error detection?

A: Domain expert familiar with your operational constraints, vendor landscape, and past project experience. For IT plans, your IT director. For supply chain, your procurement manager. For process design, your operations manager.

Q: What if I find errors in every plan?

A: That's useful learning. It means: (1) Your prompts need refinement to give AI more context, or (2) Your reviewers are being very thorough, or (3) Both. Track which types of errors are most common. Improve your prompts to prevent the most common errors.

Q: Can I automate error detection?

A: Partially. Structural checks (all dependencies documented? all resources listed?) can be automated. Judgment checks (are assumptions reasonable? is the timeline realistic?) require human expertise. Mix automated checks for efficiency and human checks for judgment.