Your AI should argue back: Why the winners will build teams, not prompts

From oracle to teammate

Daniel no longer uses AI as a single system he asks and receives answers from. He connects multiple tools and lets them play different roles. One agent acts like an innovator, pushing bold ideas and possibilities. Another behaves like a nearly paranoid CFO trying to destroy them. A third might work as an analyst or synthesizer. The goal is not comfort. The goal is better thinking through resistance.

That is why this perspective matters far beyond students. For leaders, professionals, and organizations, the AI shift is not only about writing faster or summarizing better. It is about building custom thinking systems, custom decision environments, and custom experimentation loops. Once technology becomes a team instead of an assistant, the speed, quality, and shape of idea development all begin to change.

“I deliberately try to create creative friction. That is often where the best insights emerge.”

AI diversity is becoming a strategic advantage

Daniel points to a form of diversity that will become increasingly important: AI diversity. If you bind yourself to one agent, one model, or one platform, you risk more than vendor dependence. You also risk narrowing your thinking. The same style, the same weaknesses, the same blind spots, the same tone.

That is why he combines multiple tools and setups. Not because it looks technically sophisticated on its own, but because it creates more tension between perspectives. Without that diversity, AI easily becomes a machine for reproducing what already exists. With it, AI can become a space for stress-testing ideas, discovering new options, and surfacing weaknesses before they become expensive in reality.

From years to weeks

One of Daniel's most concrete observations is that AI is compressing the distance between idea and execution. Things that once required large teams, significant capital, and several years of development can increasingly happen in weeks. That does not mean everything becomes easy. It means many more people can become producers of solutions instead of mere consumers of finished products.

He uses AI actively to build custom tools for research and leadership. That makes it possible to analyze material from new angles, test hypotheses faster, and understand more clearly what shapes action, direction, and decision-making. This shift from user to builder is one of the most important consequences of the AI moment. And it does not apply only to technologists. It applies to anyone working with problems that can be broken down, analyzed, visualized, or recombined in new ways.

This is as much a leadership shift as a technology shift

Daniel's PhD research in leadership at BI suggests that leadership happens to a large extent through stories. That is one reason storytelling becomes even more important in an AI age. If you want to mobilize people around an idea, it is not enough to be right. You have to paint a future others actually want to help create.

That makes AI especially interesting for leaders. Not just as an efficiency tool, but as a sparring partner in shaping strategies, testing arguments, and clarifying future direction. The leader who uses AI only to produce more content faster may gain speed. The leader who uses AI to challenge assumptions, narratives, and decisions gains something far more valuable: better direction.

Experimentation is no longer optional

Daniel believes many people still approach AI from the wrong starting point. Conversations often begin with risk, fear, and control. Those are important discussions, but when they become the only entry point, they also weaken our ability to imagine what the technology can actually be used for. That locks organizations into a defensive mindset precisely when the opportunity space is expanding fastest.

His advice is therefore as relevant for leaders as it is for younger people: start experimenting. Do not wait for someone to tell you exactly what to do. This is not a distant research conversation. It is a practical shift in how work, problem solving, and leadership will increasingly happen. And it is a shift that can be explored right now.

Five principles for building better AI agents

• Combine several AI tools instead of locking yourself into one system.
• Give agents clear roles so they contribute genuinely different perspectives.
• Create friction on purpose instead of asking mainly for confirmation.
• Be concrete in your instructions: goals, rules, boundaries, and what not to do.
• Start with your own needs and simple tools, then build outward.

The opportunity space is much bigger than an education debate

Even though parts of this advice are useful for students, Daniel's real point is broader. The AI shift does not concern only those about to enter the labor market. It concerns everyone already inside it. Across healthcare, energy, aquaculture, food production, and many other fields, he sees how technology can relieve pressure, simulate solutions, improve resource use, and create new forms of competitiveness.

That is why he insists imagination is becoming a strategic resource. The opportunity space is enormous, and in many cases it is imagination that sets the boundary. Not the technology alone.