Your Team's Biased. The Algorithm's Biased. That's the Brilliance. Here's How to Turn Competing Biases Into Collective Genius.

A couple of weeks ago, my team was stuck. We were building an executive dashboard for a CEO I was meeting for the first time, and after three feedback rounds, something still felt wrong in my gut.

We have worked together for years. They have built executive frameworks with me before. Every rational signal told me to trust them. Yet the dashboard still felt like a project status tracker, ticking boxes off a list, rather than clearly articulating a "path to green" at the strategic level the CEO needed.

We were going in circles. I was not seeing what I thought I was explaining. They were getting frustrated. I was getting frustrated. Was I being too picky?

Your team is biased. You are biased. Sometimes those biases align and magic happens. Sometimes they collide and you get stuck. Desperate for speed, I did something that felt slightly ridiculous: I asked ChatGPT for counsel. My prompt read something like: "I trust my team. They are talented and experienced, they have nailed it before. Yet this time it is not coming together as I would like, and I fear the CEO might get frustrated."

ChatGPT offered not just valid reasons why my gut was right, but an actual framework for my team to follow. Did I hide it? Of course not. I copied and pasted the rationale and instructions into our email trail. Within minutes, we had what we needed. Frustration gone. Happy client.

We should have started with a joint prompt. We would have saved days.

The New Collective Intelligence

This is what I mean by collective intelligence in 2026. It is no longer just humans in a room finishing each other's sentences. It is humans and machines, each bringing different biases, different pattern recognition, different blindspots.

The brilliance is not in choosing which bias to trust. The brilliance is in integrating them.

As we explored in Part 8, 2026 rewards clarity about which lane you are in. The same is true for how you work. Some teams will resist AI entirely, trusting only human judgment. Others will over-index on AI outputs, losing their intuitive edge. The teams that will thrive are those who understand that both human and machine biases are valuable precisely because they are different.

Remember from Part 1, we are terrible at prediction, but excellent at sensing patterns. AI excels at finding patterns in vast datasets, but it cannot sense when those patterns are about to shift. The edge comes from partnership: human judgment establishing intent and emotional direction, while AI explores a broader field of possibilities than any team could reach alone. 

When AI Expands Human Experience

This partnership is already visible in the wild. Spotify's "algotorial" playlists combine machine intelligence with editorial intuition to create experiences that feel both personalised and emotionally coherent. The mood behind playlists like "Beast Mode" or "Songs to Sing in the Shower" sounds simple, yet is nearly impossible to encode explicitly through an algorithm. You just know it when you hear it.

That is why Spotify gets human editors to gather a wide pool of potential tracks from which algorithms select and sequence songs in a way that is personalised for each listener. The human gives the playlist its soul. The algorithm gives it reach.

My colleague Omar Bakhshi used a similar principle with his reading app Luna. Here, children answer simple prompts about characters and settings, and AI turns those seeds into illustrated storybooks. The creative spark remains human, originating from a parent and child, while AI expands the imaginative territory they can explore together.

This is the clearest model for the future: technology extending our humanity.

How to Actually Do This

To collaborate with AI effectively without losing your intuitive edge, here is what is working for us:

Use AI to test hunches, not generate them. Intuition forms the hypothesis while AI accelerates validation. When AI becomes the source of ideas rather than the amplifier, teams lose originality unless you are willing to really challenge the output. 

Set the question before querying AI. Decide what you are testing before you see the output. This keeps you in control and prevents you from drifting toward confirmation bias.

Override when the output contradicts instinct. Disagreement is a valuable signal. Seize on the gaps between intuition and algorithm. This is where valuable learning happens.

Treat AI as a fast processor, not a wise counsellor. AI thinks faster but without context. It is human judgment that gives it relevance. Use AI to accelerate your thinking, never to replace it.

Why This Matters More Than Ever

The coming years will only increase the importance of human intuition because judgment becomes the quality-control layer that keeps AI honest, relevant and humane.

We can see this in three emerging signals:

AI hallucinations make human judgment more valuable

We have seen the rise of AI hallucinations: polished, confidently incorrect outputs such as Deloitte's $440,000 report for the Australian government, for which they were obliged to offer a partial refund, or the New York lawyer who submitted a brief of fabricated precedents.

As incidents like this become more common, leaders will need to rely on a felt sense of when something is not right, even if the answer looks authoritative. Paradoxically, using AI effectively means knowing when not to use it. Here intuition becomes the safeguard that notices subtle inconsistencies before they become embarrassing missteps. 

Personalisation walks the line between helpful and creepy

Machine learning can personalise anything. Whether it should depends on human intuition. The same algorithm can feel either helpful or intrusive depending on context.

A retailer recommending products based on browsing history may feel supportive. Another predicting life events may feel voyeuristic, despite using the same underlying technology. The difference is subjective, a human judgment as to whether the experience feels like service or surveillance. AI cannot sense where that line is, but humans can.

Vibe coding democratises creation but not judgment

AI now allows people without technical backgrounds to create products simply by describing what they want. This is extraordinary. Teams can prototype 50 ideas in the time it once took to test five.

This abundance quickly becomes overwhelming, intensifying the need for intuition. The future belongs to leaders who can sense which early signals matter and which experiments deserve to ship.

Expanding the Room 

Taken together, these signals point to a future in which humans and machines collaborate closely, but where humans remain the originators and final interpreters of value. AI may expand what is possible, but it is human intuition that decides what is worthwhile.

Creativity, at its most alive, is relational. Think of a jazz quartet improvising together, creatives finishing each other's ideas in a brainstorm, a sports team moving as one. These moments are the outward manifestation of a group intelligence that transforms individuals into a collective greater than themselves.

In 2026, AI becomes the 12th player on the football field.

Not replacing anyone. Not diminishing the team. But bringing a different perspective, a different processing speed, a different set of pattern recognition capabilities that make the whole team stronger.

Most leaders are trained to listen for explicit signals: performance dips, survey results, customer feedback. Exceptional leaders listen for the subtle and recurring impressions that emerge when multiple trusted people independently express the same unease or the same spark of excitement.

When repeated intuitions land across a group, that is not randomness. It is atmosphere. It is the room speaking. Shared intuition becomes a strategic asset when leaders treat it not as proof, but as direction. Not the final answer, but the early outline of one.

What Comes Next

Next week, in our final instalment, we will bring all of this together into a mini toolkit designed to extend your intuition into a new kind of creative intelligence.

Until then, keep listening to your teams, even when the signal seems weak. Include the machines in that listening. Those subtle reverberations, whether human or algorithmic, always tell you more than you think.

Ultimately, our work only carries significance because humans respond to it and find meaning in it. AI can help us move faster and dream bigger, but it cannot replace the collective intelligence through which humans understand one another and the world in which they live.

The choices ahead are many, and they are consequential. Your team's biased. The algorithm's biased. Stop trying to eliminate bias and start using it. The magic is in the collision, not the consensus. So ask yourself: are you expanding the room or guarding the door?