Is Your AI Project Stuck in PowerPoint Hell?

(While you're perfecting slides, your competitors are perfecting AI)

"Just one more slide deck," Sarah promised her team. "We need to get the governance framework visualization perfect."

The conference room groaned collectively. They had spent the last three months creating what was arguably the most beautiful AI implementation strategy deck in corporate history. The animations were flawless. The risk matrices were color-coded to perfection. They even had a 3D visualization of their AI architecture.

There was just one tiny problem...

While they were perfecting slide transitions, their competitors were perfecting actual AI.

As the newly appointed Chief AI Officer at TechForward (not its real name - protecting the PowerPoint enthusiasts here!), Sarah was determined to have the most comprehensive AI strategy ever documented. And documented it was - across 147 slides, 12 appendices, and what one team member called "the never-ending story of phase 2.4.b."

Six months later, their competitors had launched three AI features while TechForward's meticulously planned chatbot existed only in high-resolution mockups and perfectly formatted project charts.

A Tale of Two Companies

Let me tell you about two approaches to AI implementation.

TechForward (Team PowerPoint)

- 147 strategy slides

- 47 stakeholder alignment presentations

- 12 risk assessment frameworks

- 1 very thick project charter

- 0 live features

QuickLearn (Team Just Do It)

- 5-slide project brief

- 2-week pilot sprints

- Real customer feedback

- Rapid iterations

- 3 live features and counting

The Plot Twist

Here is where it gets interesting. QuickLearn's first AI feature wasn't perfect. It made mistakes. Some customers complained.

But something magical happened: They learned more in two weeks of real usage than TechForward learned in six months of planning.

The "Aha" Moment

The breakthrough came during a joint conference where both companies presented their AI journeys. Sarah (TechForward's CAIO) and Mike (QuickLearn's AI lead) had a conversation that changed everything:

Sarah: "But how do you handle the risks?"

Mike: "We manage them in small doses. What is riskier - a small feature used by 100 people, or a huge system launching to everyone at once?"

That's when the lightbulb went on.

The Momentum Playbook

Here is how QuickLearn made momentum work (without breaking things).

The "Small Bets" Strategy

• Choose narrow, specific use cases
• Start with internal users first
• Limit initial access to friendly users
• Set clear expectations about the "learning phase"

The Safety Net Approach

• Build guardrails before features
• Create clear fallback processes
• Have human backup ready
• Monitor everything obsessively

The Learning Loop

• Launch small
• Gather real feedback
• Fix fast
• Repeat

Real Examples of "Good Enough"

This is how they approached their releases.

First Version -

• Basic email summarization for internal team
• Limited to non-critical emails
• Human review required
• Only certain departments

Second Version -

• Added sentiment analysis
• Expanded to more departments
• Reduced human review
• Added priority flagging

Third Version -

• Full email automation for certain types
• Smart routing based on content
• Minimal human review
• Available company-wide

Each version took 2-3 weeks, not 6 months.

The Reality Check Framework

Before each launch, they asked these questions.

1. What's the worst that could happen?
2. How quickly can we fix it?
3. How many users would it affect?
4. What's our rollback plan?
5. What are we hoping to learn?

Key Principles for Leaders

Here are some suggestions to the executives and leaders.

For AI Execs and Leads

• Start with "lighthouse" projects
• Build cross-functional rapid response teams
• Create clear escalation paths
• Measure learning, not just performance

For Business Execs and Leads

• Foster a "learn fast" culture
• Celebrate quick wins
• Support controlled experiments
• Focus on progress over perfection

For Risk Officers

• Define "acceptable learning risk"
• Create tiered testing frameworks
• Build progressive rollout plans
• Focus on containment over prevention

The Results

After 6 months, this was the progress made.

TechForward

• Perfect plans
• No live features
• No real feedback
• Declining team morale
• Mounting pressure from board

QuickLearn

• 3 live features
• Rich customer feedback
• Engaged team
• Clear direction for next steps
• Board buying in for more investment

Your Action Plan

Here is a suggested action plan you can adopt.

This Week

• Identify one small, low-risk AI use case
• Find 10 friendly users
• Set up basic monitoring
• Create a simple feedback loop

Next Month

• Launch your first "good enough" feature
• Gather real user feedback
• Make improvements weekly
• Document learnings

Next Quarter

• Scale successful features
• Add complexity gradually
• Expand user base thoughtfully
• Share learnings across teams

I am curious

• What is your smallest possible AI experiment?
• What is stopping you from launching it next week?
• How could you make it even smaller?

Drop your thoughts in the comments - especially if you have been caught in the perfection trap before!

#GenerativeAI #Innovation #Leadership #CIO #CEO #CTO #CMO #CDO #CAIO #AIStrategy

All opinions are my own and not those of my employer.