For New Disruptors

Disruption Now® exists to make emerging technology more human, accessible, and actionable. This week I’ve been reflecting on why some of the smartest teams—across Fortune 500s, startups, and agencies—get unreliable results from the same AI models others use to build billion-dollar efficiencies.

A few months ago, during an office-hours session, a senior leader from a global company shared his frustration: “We’re feeding the model everything, and it’s giving us polished nonsense.” The team had invested weeks in building AI workflows, yet their results were off, inconsistent, or outright wrong. The room went quiet when I told them the truth—it wasn’t the AI that was broken. It was their process.

AI doesn’t replace thinking. It rewards structure. The difference between a weak output and a useful one isn’t model power—it’s discipline. Let’s unpack the six failure patterns that cause even elite teams to stumble, and the habits that separate chaos from consistency.

1. The Projection Trap: Overestimating AI Capabilities

Imagine a product team under pressure. Leadership wants a migration update by the end of the day. Someone types, “Summarize the migration progress,” hits enter, and breathes a sigh of relief when a crisp paragraph appears. But when the report lands on the executive desk, it’s missing every milestone that mattered.

The AI didn’t fail. The humans did.

The Pattern

Teams assume the AI understands intent. They think it knows the audience, tone, and goal. But AI doesn’t infer meaning—it mirrors the structure of your input.

The Real Cost

Executives make decisions based on incomplete summaries. Developers automate flawed assumptions. Everyone works harder fixing avoidable mistakes.

The Fix: Schema-First Prompting

Start with structure before substance. You are a product analyst writing a weekly migration update.

Output format:

1. Title (<=10 words)

2. Audience (Executive or Engineering)

3. Summary (3 sentences)

4. Risks (2–3 bullets)

5. Next Steps (3 bullets)

When you hand the AI a map, it stops guessing and starts executing. Schema-first prompting is like giving a chef a recipe instead of saying, “Make dinner.”

2. The Revision Loop: The Endless Rewrite

Next came a consulting team. They had spent weeks fine-tuning an investor deck. When the lead asked ChatGPT to “polish the intro,” the model rewrote the entire presentation—tone, flow, and facts included. Half the team thought it sounded better. The other half panicked.

The Pattern

AI overcorrects small edits because it lacks surgical precision.

The Fix: Surgical Edits Only

Anchor the AI to the specific text you want changed. Only edit this line:
“The migration will be complete by the end of the week.”
Change 'end of week' to 'Friday, Nov 8.' Return only that line.

When you give specific instructions, you stop the AI from rewriting what works. Think of it like editing film—you cut one frame, not the whole reel.

3. The Planning Illusion: One-Pass Thinking

In another case, a marketing team asked for “a retention strategy.” The AI produced five generic bullets—predictable, shallow, and useless. The issue wasn’t intelligence—it was missing a process for reasoning.

The Pattern

AI tends to compress complex problems into a single, fast pass. It summarizes instead of analyzing.

The Fix: Stage the Work

Force step-by-step reasoning. Stage 1: Identify churn factors.
Stage 2: Rank by impact.
Stage 3: Suggest experiments for top three.
Do Stage 1 only, then stop.

Each stage becomes a checkpoint where you can review, refine, or redirect. It’s like coaching an intern—you don’t want them skipping straight to the final report.

4. The Confidence Illusion: Fluent but False

A communications executive once asked the model for the latest workforce data and received a beautiful, confident answer—complete with citations. None of them existed. It looked credible enough to end up in a report sent to 30,000 employees.

The Pattern

Fluency disguises fabrication. The AI’s confidence seduces people into skipping verification.

The Fix: Verification by Design

Force accountability into the prompt. Output each claim with:
- Statement
- Confidence (High / Medium / Low)
- Source or “Verification Needed”

Verification schemas turn the model from a storyteller into an analyst. If the model can’t cite it, you shouldn’t trust it.

5. The Drift Problem: Inconsistent Outputs

A financial firm built a tagging workflow for classifying clients. Same data, same prompt, but the AI changed its labels every time. “Enterprise” one day, “SMB” the next. The automation broke before it began.

The Pattern

Inconsistent results stem from randomness, ambiguity, and too much freedom.

The Fix: Constraint and Control

Constrain everything that can vary. Temperature: 0
Categories: [SMB, Mid-Market, Enterprise]
Never create new categories.

When you reduce ambiguity, consistency follows. You don’t need a smarter AI. You need tighter rules.

6. The Cognitive Bandwidth Trap: Too Much Context

One research group thought more data would help. They pasted an entire 20-page report into ChatGPT, expecting perfect summaries. Instead, the AI blended unrelated sections, made up conclusions, and even quoted footnotes that weren’t there.

The Pattern

More context often makes outputs worse, not better. The model loses focus and drifts into irrelevant material.

The Fix: Clean Context Loading

Treat context like a diet—quality over quantity. Use only these two pages from a 20-page report to summarize key risks.

By curating what matters, you train the model to think clearly. Good context is clean context.

My Disruptive Take

Every organization I’ve worked with wants AI to make them faster. The irony is, the fastest path is discipline. The teams that slow down, define structure, and clarify intent end up moving further and more reliably than the ones racing ahead without rules.

AI isn’t magic—it’s multiplication. It amplifies your structure, your clarity, and your mess. If you feed it chaos, it scales chaos. If you feed it a design, it scales the design.

The model doesn’t need to get smarter. We do.

🔧 The Team Playbook

The most effective organizations don’t treat prompting as creativity. They treat it as process engineering.

This December, Disruption Now® launches the AI Mastery Cohort, a hands-on program for leaders ready to move from improvisation to system design.

You’ll learn how to:

• Build schema-first workflows.

• Create verification loops that catch hallucinations before clients see them.

• Design reasoning pipelines for research, writing, and analysis.

• Apply field-tested methods used inside enterprise teams.

Schedule a Call to learn about Free Training for Ohio businesses

MidwestCon 2026 at the 1819 Innovation Hub & Digital Futures Building

Disruption Now® Podcast

This Week LATEST EPISODE Upgrading Government Tech: Startup Thinking for Public Service, we’re joined by Pavan Parikh, Hamilton County Clerk of Courts, who’s bringing a fresh, startup-minded approach to modernizing government. Pavan breaks down how agility, innovation, and data-driven thinking can transform public service from the inside out.

Keep Disrupting, My Friends.

Rob Richardson – Founder, Disruption Now® & Chief Curator of MidwestCon