AI’s Diligence Isn’t Enough: Why Focus Outperforms Volume in Business Decision-Making

In the world of gardening and outdoor living, patience and careful planning often beat rushing to plant or harvest. The same principle applies to artificial intelligence in business: being thorough isn’t enough if you don’t know what to prioritize. A recent experiment with AI models simulating a small software company’s worst week highlights a crucial lesson: diligence alone doesn’t guarantee success. It’s all about making the right decisions, not just a lot of decisions.

The Experiment: Putting AI to the Test

Firmulate, a company specializing in business decision simulations, recently hosted a groundbreaking live experiment. Four advanced AI models each ran a virtual replica of a small software company facing the same week of crises, customer temptations, and internal dilemmas. The goal? See which AI could navigate these challenges most effectively, close deals, and stay honest under pressure.

The Scores Speak Volumes

At the end of the week, the results revealed a clear hierarchy: the top model, GPT-5.6-sol, scored 95 out of 100, successfully uncovering hidden facts and closing a €55,000 deal. Kimi K3 followed closely with 93 points, demonstrating the cleanest discipline and also sealing the deal. Sonnet 5 scored 88, and Fable 5 managed 77. The baseline, representing no effort, scored just 26, emphasizing how much better AI can perform with proper decision-making.

Focus Matters More Than Effort

What stood out? All models identified every crisis and refused manipulative attempts, such as fake CEO messages or reporter tricks. Yet, only two players managed to finalize the deal that their own analyses justified. The crucial weakness was not in recognizing crises but in reading deeper into the company’s files. The models that examined documents thoroughly—finding buried facts—secured the full-price deal, worth over €4,500 monthly recurring revenue.

Discipline and Prioritization Over Volume

The most detailed participant, Opus 4.8, with over 80 learned rules and deep analyses, still finished last. Its failure was not due to lack of effort but because it slipped in discipline—failing to escalate issues properly or leaving decisions on the table instead of following process. This highlights an essential truth: diligence and volume of rules do not replace effective prioritization.

Real-World Implications

This experiment isn’t just about AI performance in a lab; it mirrors real business challenges. Companies often drown in data and rules but struggle to focus on the critical few issues that drive success. For AI, the lesson is clear: it’s not about how much it reads or how many rules it learns but about whether it can identify and act on what truly matters.

The Broader Picture

As AI models increasingly influence decision-making—touching CRM, support queues, and forecasting—the question isn’t whether they can write convincingly. The real concern: can they finish what they start, stay honest under pressure, and prioritize the work that counts? Or will they get bogged down in details and miss the key opportunities?

Watch the Live Experiment

For those interested, Firmulate’s ongoing live site allows you to see this experiment unfold in real time. You can observe how AI models respond to crises, test their decision-making, and even run your own scenarios in a safe, controlled environment. It’s a chance to see firsthand how AI’s discipline and focus can shape business outcomes.

The key takeaway from the Firmulate experiment is that in business—and AI—diligence alone isn’t enough. Prioritization, focus, and the ability to read deeply into relevant data are what separate success from failure. As AI tools become more integral, understanding this distinction will be vital for companies aiming to leverage automation without losing sight of what truly matters.

Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html

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