Artificially Intelligent, Actually Useful.

Potential versus Reality

We began by highlighting a fundamental contradiction that we're still seeing today.

Research from MIT showed that properly matched human-AI teams could boost productivity by 60%. At the same time, industry benchmarks from Salesforce revealed that AI agents were failing nearly half the time on basic business tasks, with only a 58% success rate.

McDonald's hiring platform exposed 64 million records due to basic failures like using a default password. Apple research exposed critical reasoning flaws in billion-dollar models that couldn't solve simple logic puzzles.

Critical Reality: AI is a powerful magnifier, but without human oversight, it simply scales existing errors rather than creating new capabilities.

The Human Fluency Gap

We discovered the biggest barrier to success isn't technical. It's a learning gap.

MIT research confirmed that half of all AI performance gains come from user prompting skills, not just model upgrades. Despite the hype surrounding GPT-5, a staggering 95% of enterprise AI pilots were failing to move the needle on revenue.

Competitive Advantage: The competitive edge is shifting from those who have the best tools to those who build the most AI-fluent teams capable of extracting value from existing capabilities.

From Generalists to Specialists

We explored how the "one-size-fits-all" era may be coming to an end.

Large Reasoning Models emerged for math and strategy. Mixture of Experts architectures only activate the specific "expert models" needed for each request to save on costs. Individual productivity tools like Claude Projects and Gemini Gems became mainstream, allowing users to build custom assistants that remember specific business contexts.

Strategic Evolution: Instead of using a Swiss Army knife for every job, organizations are moving toward specialized tools matched to specific problems, improving both efficiency and outcomes.

Agentic Revolution

We discovered AI browsers, like Comet and Dia, are beginning to reimagine the web not as pages to read, but as environments where autonomous agents could schedule meetings and make purchases for us.

While some debated whether we were in a bubble, breakthroughs in medical imaging provided tangible value, such as a system detecting pancreatic cancer at a 1mm stage.

Fundamental Shift: The focus is moving from chatbots that answer questions to agents that take action, fundamentally changing how organizations interact with digital systems.

The Power of Systems

We learned that while ad-hoc AI use might give you a 30% gain, building a team of AI visionaries, operators, and implementers could unlock productivity gains up to 3,000%.

However, other limits are becoming clearer. Advanced AI models only achieved a 40% success rate when asked to control a robot to fetch butter in physical space.

Implementation Reality: Sustainable success with AI requires building the connective tissue between human intent and automated execution through systematic processes, not isolated experiments.

Foundation First Principle

The sixth and final lesson reinforces our earliest observation that AI is a magnifier: it scales whatever process already exists. If your Standard Operating Procedures are messy, AI will simply amplify the confusion. And understanding what AI is, and isn't, is an imperative in leveraging AI for good.

At the NeurIPS conference, 26,000 experts admitted we still lack the tools to truly understand how frontier models work. Beyond that mystery, we explored a fundamental truth: AI functions through correlation rather than true comprehension. It may think you drive a school bus because you like the color yellow, not because that makes sense, but because those words appear together in training data.

Design Imperative: Reliable automation requires intentional design. Organizations must architect systems that combine deterministic logic with AI interpretation based on task requirements.