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Aloha,

Just got back from weeks of traveling to various events and meetings with clients and partners. The conclusion is simple: We are at the end of the beginning of the AI revolution.

People finally realize they and their teams need "AI strategy" - actual guidance on what to do, not just more tools.

We knew this was coming, so we've been cooking cool updates to the AI Maturity Index that let you go from insights to action. AI Growth Guides - personalized strategies that meet you where you are in your AI journey and show you exactly what to do next.

But first, I want to share the research findings I presented last week at South Summit in Madrid - and WOW, what an INCREDIBLE event! HUGE kudos to Camila, Christopher, Emma, João, Mercedes, María Camila from the SS team for creating such an amazing experience. The connections, the people, those "crème de la crème" side events - everything was absolutely perfect!

Plus, the universe decided to help with my South Summit ambassador duties and got me featured in Spanish Business Insider by accident while I was there.

So… here's what blew that amazing Madrid crowd away...

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The Performance Cliff: What 240,000 AI Adoption Data Points Revealed About Time Savings

The top 1% of AI performers save 31 hours per week. Average users save just 6 hours - a staggering 5.1x performance gap.

After analyzing 240,000 AI Adoption data points, we discovered that AI performance isn't distributed on a gentle curve - it's a cliff. While most users achieve modest time savings, elite performers operate in a completely different league, saving nearly a full workweek every week. This dramatic gap correlates with specific usage patterns and behaviors that create compound advantages. Understanding what drives this performance cliff offers insights into how AI mastery actually develops and scales.

Contextual Framework

When we launched the AI Maturity Index, we expected to find a normal distribution of performance - some people slightly better, others slightly worse, most clustered around average. Instead, we found a cliff.

The bottom 50% of users save 6 hours per week using AI.

The top 1% save 31 hours per week.

This isn't a gradual improvement - it's a 5.1x jump that suggests fundamentally different approaches to AI integration. Behind this performance cliff, we found specific patterns: elite performers are 8x more likely to use AI multiple times daily (94% vs 12%), and they exhibit distinct behavioral clusters that separate them from occasional users.

The Performance Cliff Revealed

The 5.1x difference in time savings reflects a broader pattern across all performance metrics. This isn't just about working faster - it's about operating in a completely different performance category.

AI Maturity Index scores show the same cliff pattern.

Top performers average 79/100 while bottom performers score 26/100—a 3.1x skill gap that mirrors their outcome differences.

The cliff extends across all four dimensions we measure: productivity (2.4x gap), collaboration (4.3x gap), creativity (3.7x gap), and decision-making (13.6x gap).

Behind these dramatic outcome differences, we found specific behavioral patterns. Elite performers are 8x more likely to use AI multiple times daily (94% vs 12%), but more importantly, they use AI differently when they do engage with it.

The Behaviors That Cluster with High Performance

Our analysis revealed five behaviors that strongly correlate with both high performance and frequent usage:

Decision simulation appears in approximately 95% of top performers. They routinely run scenarios through AI before making choices, making "What if I take approach A vs B?" a standard thinking pattern.

Personal prompt libraries emerge among 52% of high performers. Rather than starting fresh each time, they build and refine collections of effective prompts over time.

Multi-tool workflows characterize 37% of elite users who operate four or more specialized AI tools daily. They've moved beyond single-tool usage to create integrated systems spanning research, creation, and automation.

Meeting action plans represent a practical application for 41% of top performers who systematically convert meeting notes into structured next steps using AI.

End-to-end automation encompasses 25% of total work hours for the highest performers, who have systematically identified and automated repetitive processes.

Whether these behaviors drive performance or result from it remains unclear, but their clustering with high scores suggests they're part of a coherent approach to AI integration.

The Individual vs Institutional Pattern

Why Solo Practitioners Show Higher Performance Rates

Perhaps our most intriguing finding: solopreneurs achieve top performance 10.2% of the time compared to just 3.5% at companies with 10,000+ employees—a 3x difference in elite performance rates.

This pattern suggests that AI mastery may develop more readily in environments with fewer constraints. Individual practitioners can experiment with AI multiple times daily without approval processes, IT restrictions, or bureaucratic delays. Whether this freedom enables higher performance or whether high-performing individuals gravitate toward unconstrained environments isn't clear from our data.

One respondent with an 84/100 AIMI score who saves 14 hours weekly illustrates this systematic approach:

The language—"various AI tools," "different work tasks," "automate business processes"—suggests systematic thinking about AI integration rather than ad hoc usage. This correlates with both high performance and frequent daily engagement.

Industry Variation in Performance Patterns

The correlation between performance and usage frequency varies across industries. Education and training professionals show the highest rates of top performance at 11.6%, followed by marketing and advertising at 9.2%. Information technology shows lower rates at 4.9%, and healthcare lowest at 2.9%.

These variations suggest that AI performance patterns may relate more to experimentation opportunities than technical background. Industries leading in performance are often those where practitioners have flexibility to integrate AI into daily workflows.

From Discovery to Action: AI Growth Guides

Discovering these usage patterns created a new challenge: how do people systematically test and adopt the behaviors that correlate with high performance?

When users completed their AIMI assessment, they consistently asked the same question: "Now what?" Knowing their score and seeing the benchmarks was interesting, but they wanted specific, actionable guidance for closing the performance gap.

This led us to develop AI Growth Guides—personalized roadmaps that help individuals systematically experiment with high-performer usage patterns. Rather than generic advice, the system analyzes your specific AIMI results, compares them to peer benchmarks, and creates tailored recommendations for testing the behaviors that correlate with top performance.

The Growth Guides address four key areas where usage patterns diverge: integration frequency (helping you build AI into daily workflows), tool orchestration (systematic approaches to multi-tool usage), automation identification (finding processes worth systematizing), and measurement frameworks (tracking what actually moves your performance metrics).

While we can't guarantee that adopting high-performer patterns will improve your results, the Growth Guides provide a structured way to test these correlations in your specific context. The goal isn't to assume frequency causes mastery, but to systematically experiment with the behaviors that cluster with top performance.

The Widening Performance Divide

Our data reveals more than just current performance differences - it shows a widening divide between those who achieve breakthrough AI results and those who don't. The 5.1x performance cliff we've identified likely represents the early stages of an even broader gap.

Whether this cliff results from usage driving performance, performance enabling usage, or both reinforcing each other, the practical result is clear: a small group of AI users are achieving dramatically superior outcomes. As AI capabilities expand and integration becomes easier, this performance advantage may compound further.

The question isn't whether frequency causes mastery or mastery enables frequency. It's whether you'll systematically experiment with the usage patterns that correlate with top performance while there's still time to close the gap. The AI Growth Guides provide a structured way to run that experiment.

Further Exploration

• Take the Free AIMI Assessment: Discover your usage patterns and performance correlations with our 15-minute conversational evaluation

• Get Your AI Growth Guide: Receive a personalized roadmap based on your AIMI results and peer benchmarking data. Vote in the poll to learn more - scroll up.

Until next time,

Iwo