AI Projects

Traditional project: define requirements, someone builds them, review against requirements. Predictable relationship between input and output.

AI project: the same input can produce wildly different output. A task that takes 30 minutes one day might take 5 hours the next because the AI went in a wrong direction that looked right.

Speed is uneven. AI produces first drafts in minutes. But review and iteration can take as long as traditional approaches. Total time: 40-60% less, not 90% less.

Quality is variable. Same prompt, different results. Build review cycles into your plan as required steps, not afterthoughts.

Review is a new skill. You're evaluating output you didn't create and may not fully understand how it was generated.

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Look at your current project plan. Where does it assume linear progress? Those are the spots where AI will create the most variance. Mark them. They need buffer.

The biggest mistake: treating AI as a faster version of what you already do. Better approach: scope around what changes for the end user.

Five questions. What is the final deliverable? Be specific. Not "an AI tool" but "a form where clients enter their situation and get a personalized recommendation in 30 seconds."

Who reviews the output? Every AI output needs review. By whom? With what expertise?

What does good enough look like? AI output is 75-90% there. Where on that spectrum is acceptable?

What happens when it fails? AI will produce something unusable sometimes. What's the fallback?

The scope split: separate "AI generates" from "humans refine." Most projects are 60-70% AI, 30-40% human. Budget for both.

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Take your next AI project. Answer all five scoping questions in writing. If you can't answer one clearly, that's where trouble will hit.

Track A: fast and focused. One goal, short timeline, minimal infrastructure. Prototypes, proofs of concept, campaign assets. Days to weeks. Priority: speed and learning.

Track B: built for growth. Multiple goals, longer timeline, real infrastructure. Customer-facing products, systems used daily. Weeks to months. Priority: reliability and sustainability.

The decision is not about quality. Both produce good work. It's about what happens after launch.

Ask one question: "If this works, what happens next?" Learn and move on: Track A. Roll it out broadly: Track B.

The most common mistake: a Track A prototype that succeeds and gets expected to scale without a Track B rebuild. Every temporary solution that becomes permanent without proper rebuilding will cause pain.

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Classify every AI project on your roadmap as Track A or Track B. For any unclear ones, ask 'what happens if this succeeds?' Adjust your approach accordingly.

AI output has a specific failure pattern: fluent, structured, professional-looking, and potentially wrong in hard-to-catch ways.

Five review points. Accuracy: every fact, statistic, specific detail needs verification. AI generates plausible numbers that may not be real.

Relevance: does this actually answer the brief? AI excels at impressive work that doesn't address the actual question.

Voice: does it sound right? AI defaults to slightly formal, generic, too polished.

Gaps: what's missing? AI produces what you asked for, rarely what you should have asked for.

Overconfidence: where are opinions presented as facts? AI doesn't hedge naturally.

Give specific feedback: not "this isn't right" but "section 2 overstates our position, add the caveat that we're entering this market."

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Run the last AI output your team produced through the five points. Time it (usually 5-10 minutes per page). Did you catch something you missed before?

The fear: AI replaces people. The reality: AI changes the work. 4 hours writing a report becomes 1 hour directing AI and reviewing. The other 3 hours shift to higher-value work.

Team structure. The AI lead: one person who goes deep on tools, sets up Projects, tests approaches. 20-30% of their time. Pick the most curious person.

The reviewers: everyone else uses AI and reviews output using the five-point framework.

The quality standard: define what 'good enough' looks like for each output type before introducing AI.

Introducing AI without panic: start with augmentation ("AI writes the draft, you make it yours"). Share wins publicly. Give permission to fail. Don't force adoption.

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Identify your most AI-curious team member. Give them 2-3 hours per week to find ways AI helps your team. That's your AI lead.

The hype: AI makes everything instant. The reality: AI makes the first draft instant. Everything around it takes time.

Realistic breakdown. Setup and scoping: 20% of total time. AI generation: 10% (the fast part). Review and iteration: 40% (the big one). Human refinement: 20%. Testing and shipping: 10%.

Total saving vs traditional: 30-50% for Track A, 20-40% for Track B. Significant, but not the 90% demos suggest.

Budget: AI tools cost $20-50/month per person. The real cost is human time, which shifts but doesn't disappear. Budget for review. Budget for iteration.

When to pay for help: if Track B and stakes are real, experienced help costs less than learning on production work.

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Apply this breakdown to your next AI project. Does your plan allocate 40% for review and iteration? Most first plans don't. Adjust before you start.

Layer 1: self-review. The person who used AI does the first check. Five-point framework. Catches 80%. Takes 5-10 minutes.

Layer 2: peer review. For important deliverables, a colleague reviews the final piece. They don't need to know AI was involved.

Layer 3: spot checking. You randomly review 20% weekly. Not to catch mistakes. To calibrate. Is quality trending up or down?

Layer 4: escalation. Define triggers for your personal review. Client-facing enterprise work. Financial or legal content. High-stakes situations. Everything else flows through layers 1-3.

The mindset: quality control for AI is about systems, not supervision. Design a process, don't check every piece.

Watch for output quality trending down over time. Usually means the team is skipping iteration, not an AI problem.

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Define your four layers. Who self-reviews, when peer review is needed, how you spot-check, what triggers your involvement. Share with the team. That's your quality system.

AI is wrong when: stakes are too high for variable quality (regulatory, legal, medical). The task requires genuine creativity. Trust is the product and AI use would erode it. The team isn't ready. The cost of failure exceeds the speed benefit.

AI is right when: volume is high and quality tolerance is reasonable. Speed matters and review exists. The task is organizing, drafting, or analyzing. The team can review effectively. Failure is manageable.

The wisest thing you can say in a meeting: "That's not the right use case for AI. Here's what would work better." That's leadership, not resistance.

Not every problem is an AI problem. The best leaders know the difference.

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Audit your AI initiatives. For each one: is AI the right tool? If the answer is 'we're using AI because we want to,' that's the one to reconsider.