{"id":50153,"date":"2026-06-18T08:39:00","date_gmt":"2026-06-18T06:39:00","guid":{"rendered":"https:\/\/cinode.com\/?p=50153"},"modified":"2026-06-18T08:39:03","modified_gmt":"2026-06-18T06:39:03","slug":"the-best-ai-is-built-when-great-minds-dont-think-alike","status":"publish","type":"post","link":"https:\/\/cinode.com\/en\/blog\/consulting\/the-best-ai-is-built-when-great-minds-dont-think-alike\/","title":{"rendered":"The best AI is built when great minds don’t think alike"},"content":{"rendered":"\n

If one wanted to summarize the articles\u00a0we ‘ve\u00a0written about our DEI strategy over the last years, it would be this: “We are continuing to make progress, but we will never be finished.” The more\u00a0we’ve\u00a0investigated\u00a0where AI development is heading, the more this feels like an understatement.\u00a0<\/strong><\/p>\n\n\n\n

Roughly 70 nationalities work under the Netlight umbrella, and around 40% of our employees are women. Neither number is incidental. But how do those numbers hold when you look at professionals focused on data and AI specifically? Yet take gender alone: according to the Stanford AI Index 2026, the share of women among AI authors and inventors has been essentially flat since 2010; across 21 countries tracked, no country comes close to parity. Fifteen years of AI becoming one of the most consequential technologies in human history, and the gender balance of the people building it hasn’t moved.  <\/p>\n\n\n\n

When good numbers aren’t enough<\/h2>\n\n\n\n

Even by market standards, our numbers may look better, but we are still struggling to find female talent in data and AI. And even when we do hire diverse talent, that doesn’t automatically translate into diverse teams on the ground. A delivery team where one person with a different background is outnumbered ten to one has placed a disproportionate burden on a single person, not built a diverse team.<\/p>\n\n\n\n

One of our consultants was the only woman on a team of roughly a dozen, the only person with her background. She pointed out blind spots, asked uncomfortable questions, proposed solutions. Her points were dismissed. Only to be adopted months later after costly mistake that were identified in the \u201cmandatory\u201d validation phase. Whether that happened because she was seen as an outsider is impossible to prove. What is clear is the outcome: a costly delay, a preventable mistake, and a solution that was always in the room.<\/p>\n\n\n\n

Diversity as a competitive advantage<\/h2>\n\n\n\n

However, focusing only on the risks diverse team help to mitigate is missing the bigger story. The real value is not that diverse teams prevent bad decisions. It is that they enable better ones. The most successful AI solutions are rarely the result of better technology alone. They are the result of better decisions, shaped by teams that challenge assumptions, bring different perspectives, and understand a broader range of users and real-world situations. Diversity is a competitive advantage because it helps teams make better decisions, build better AI, and ultimately achieve better outcomes.<\/p>\n\n\n\n

In consulting, that matters. Clients approach us to go beyond risk reduction and assessment. They come to us to make better decisions, build better products and create better outcomes.  The quality of those outcomes is directly influenced by the perspectives involved in shaping them. No single consultancy can hire its way out of an industry-wide pipeline problem. But every consultancy can decide whether they treat it as someone else’s to solve, or as something that shows up in every client delivery.<\/p>\n\n\n\n

The industry blind spot in AI quality<\/h2>\n\n\n\n

AI systems inherit the blind spots of their creators. A homogeneous team building a hiring algorithm won’t see that their model favors candidates from specific gender, geographies or schools, because their network looks the same. A team that’s never navigated healthcare as a non-English speaker won’t anticipate how a clinical decision support system fails with patient communication gaps. A group that’s never experienced disability won’t think to test accessibility in a customer service chatbot.<\/p>\n\n\n\n

Getting this right has the opposite effect. Teams that build with DEI as a design requirement don’t just avoid those costs, but they deliver AI that has a greater impact and value. They ask different questions. They challenge assumptions earlier. They identify edge cases before they become failures. They build solutions that work for a broader range of users and real-world situations. In other words, diversity is not only a safeguard against poor outcomes but a driver of better outcomes. And that’s a competitive advantage.<\/p>\n\n\n\n

Many organisations still talk about diversity primarily as an ethical initiative. We increasingly see it as a quality issue. The question is not only who gets represented. The question is whether the team has the breadth of perspectives required to make the best possible decisions. You can put structured reviews in, and they will surface some of what the team missed. But you can’t review away the missing pieces that come from absent perspectives: different identities, genders, ethnicities, neurodiversity, socioeconomic backgrounds, career paths.<\/p>\n\n\n\n

You also can’t review away that no one said “Wait, what about\u2026?” in the first place. As consultants, we should be the ones bringing those perspectives to the table. Research on AI development teams describes this function as a “bias buffer”; diverse teams distribute the responsibility for catching assumptions across the whole team, rather than concentrating it in a single review stage (de Souza Santos et al., 2025). That buffer either exists in the team’s composition, or it doesn’t exist at all.<\/p>\n\n\n\n

In our experience, the best solutions are the ones that get pushed back on, pulled apart, and rebuilt, not the ones that came easiest. If you have a model that’s 95% accurate on the segment that looks like your training data, but only 70% accurate on a minority segment, that model is not fit for purpose. That gap is what clients inherit.<\/p>\n\n\n\n

Why this matters now<\/strong><\/h2>\n\n\n\n

And that is ultimately why this conversation matters. AI quality is not measured by how well a system performs under ideal conditions. It is measured by how well it performs in the complexity of the real world. The teams most likely to succeed in that environment are rarely the ones where everyone thinks alike.<\/p>\n\n\n\n

We started this article the way we start most conversations about DEI at Netlight: by admitting what we haven’t solved yet. That’s a truth we agreed not to ignore. Meanwhile, AI is no longer an abstraction. It shapes who gets hired, who gets a loan, who gets flagged as a risk, who gets seen by a doctor’s diagnostic tool, and who doesn’t. The cost of getting it wrong is compliance fines, lawsuits, customers who quietly leave, and harm that tends to fall hardest on the people who were already least protected.<\/p>\n\n\n\n

But focusing only on the cost of failure understates the opportunity. Organisations that consistently build trustworthy, robust, and relevant AI systems will create a significant competitive advantage in the years ahead.<\/p>\n\n\n\n

So: if you were delivering an AI system with your clients today, which blind spots would the team have? What wouldn’t you see? Who needs to be in the room before the project starts, not after the model ships?<\/p>\n\n\n\n

The question isn’t whether DEI belongs in AI development. The question is whether organisations can afford to build AI without the diversity of perspectives that improve decision-making, strengthen outcomes and create better products. It’s whether you treat it as a design question that needs answering before the model ships, not after.<\/p>\n\n\n\n

We are continuing to make progress. But we will never be finished. In the age of AI, that’s no longer just an internal aspiration. Every system we help build is a small argument about who gets to be included. We’d rather make that argument well.  Because that is not just a DEI ambition, it is a business advantage.<\/p>\n","protected":false},"excerpt":{"rendered":"

If one wanted to summarize the articles\u00a0we ‘ve\u00a0written about our DEI strategy over the last years, it would be this: “We are continuing to make progress, but we will never be finished.” The more\u00a0we’ve\u00a0investigated\u00a0where AI development is heading, the more this feels like an understatement.\u00a0 Roughly 70 nationalities work under the Netlight umbrella, and around 40% of our […]<\/p>\n","protected":false},"author":260,"featured_media":50154,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[73,1],"tags":[],"class_list":["post-50153","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-consulting","category-okategoriserad"],"acf":[],"yoast_head":"\nThe best AI is built when great minds don't think alike - Cinode<\/title>\n<meta name=\"description\" content=\"Discover key insights on The best AI is built when great minds don't think alike from the leading consultancy blog.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cinode.com\/en\/blog\/consulting\/the-best-ai-is-built-when-great-minds-dont-think-alike\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The best AI is built when great minds don't think alike\" \/>\n<meta property=\"og:description\" content=\"Discover key insights on The best AI is built when great minds don't think alike from the leading consultancy blog.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/cinode.com\/en\/blog\/consulting\/the-best-ai-is-built-when-great-minds-dont-think-alike\/\" \/>\n<meta property=\"og:site_name\" content=\"Cinode\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/Cinode\/\" \/>\n<meta property=\"article:published_time\" content=\"2026-06-18T06:39:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-06-18T06:39:03+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/cinode.com\/wp-content\/uploads\/2026\/06\/Netlight_1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"2400\" \/>\n\t<meta property=\"og:image:height\" content=\"1600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Guestbloggers - 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