As I type, Microsoft Copilot suggests ways to continue, restructure, or even rewrite this very sentence. On the surface, it feels like a small thing, no more remarkable than Gmail finishing an email or Google predicting a search—but small things can have outsize influence.
Just as the steady drip of water on rock can carve out new channel over time, so predictive text has already reshaped how we write. Research from Harvard has shown that predictive text systems do not just make texting easier—they change the content of those texts, reducing lexical diversity and making our writing more predictable.
This flattening effect is beginning to extend beyond language. Filmmakers have been worried for some time now about the rise of “algorithm movies”—movies whose form and content are dictated by what recommendation algorithms tell companies about viewer preferences, instead of by the creative imagination of writers and directors. And if executives aren’t careful, we can soon expect the emergence of “algorithm business”—strategy, operations, and culture flattened out by the rise of LLMs and the race to adopt AI.
AI Models as Consensus Machines
Large language models have become the invisible architects of business strategy. For an increasing number of executives, these AI systems have become default first advisers, strategists, and thought partners. And, as we have already seen with language and movies, this kind of progression can measurably narrow the range of ideas available to us.
Social media is the canary in the coal mine here. Anyone with a LinkedIn account knows that posts from different individuals often sound very similar and that the same ideas are often recirculated again and again. Taken in isolation, this could be seen as a feature of the homogenizing effect of social media algorithms. But the phenomenon is not localized to posts that might be driven by the demands of a recommendation algorithm. Pitches are beginning to sound identical, marketing copy is becoming strangely generic, and if the process continues unchecked, we can expect that the internal documents, analyses, and company strategies will begin to mirror those found in other businesses. In the longer term, we could even see company cultures lose their distinctiveness as differentiating factors begin to blur together.
Smarter Alone, Narrower Together
Generative AI can massively boost performance and productivity. A recent meta-study found, for example, that humans working with AI were significantly more creative than humans working alone.
However, as both that study and a paper in Nature show, while using LLMs improves the average creativity of an individual, it reduces the collective creative diversity of groups. Individuals find their access to new ideas boosted but collectively we end up tapping in to a narrower range of ideas.
The result is that AI’s promise of supercharged innovation may actually narrow the frontiers of possibility.
Competitive Convergence
Almost 20 years ago, Michael Porter introduced the idea of “competitive convergence”. Briefly put, this is a phenomenon that sees companies beginning to resemble their competitors. They chase the same customers in the same ways, their strategies and pricing models become indistinguishable, their operational processes and supply chains end up looking identical.
This process traps companies into a race toward the middle, where distinctiveness disappears and profits are squeezed. With AI, businesses risk falling victim to an accelerated and intensified version of this process: a Great AI Convergence in which operational playbooks, strategic vision, and culture become increasingly generic as organizations increasingly drink from the same conceptual fountain.
AI can optimize efficiency, but it can’t capture the human fingerprints that make a company truly distinctive. Your organization’s war stories, hard-won lessons, contrarian beliefs, and cultural quirks don’t live in any training set. They live in memory, practice, and identity.
And when strategy, messaging, or culture is outsourced to AI, there is a real danger that those differentiating elements will vanish. The risk is that companies will end up losing the authentic, uncommon, and sometimes counterintuitive features that are the vehicle for their uniqueness—the things that makes them them.
The Three Pillars of Business Homogenization
Business homogenization can be broken down into three pillars.
1. Strategic Convergence: When Every Plan Looks the Same
Your competitor asks Claude to analyze market opportunities. You ask ChatGPT. What’s the result?
Well, the effect is subtle rather than dramatic. Because the same models are shaping the same executives, the outputs don’t collapse into outright uniformity so much as drift toward a narrow band of acceptable options. What looks like independent strategic judgment is often just a remix of the same patterns and playbooks. And so, over time, the strategic choices companies make lose their texture and edge.
2. Operational Convergence: The Automation of Averageness
Companies are already acting on the huge potential that AI has in the realm of operations. For example, Shopify and Duolingo now require employees to use AI as the default starting point for all tasks, and one of the major reasons for this is the prospect of the efficiency gains that AI can deliver.
It is absolutely right that companies use AI to transform operations. But when every company uses similar AI tools for operations, we can expect a drift toward similar processes. Customer service chatbots might converge on the optimal patterns for customer interactions, for example—and in this convergence lies both danger and opportunity.
The opportunity is optimized efficiency. The danger is that companies lose what differentiates them and drives their unique value proposition. It is essential that leaders recognize this danger so they can begin to think intentionally about authenticity as a potential edge in operations. For instance, it might be worth sacrificing a small level of customer handling speed for a chatbot that delivers quirky and engaging responses that reflect the company’s authentic culture and character.
3. Cultural Convergence: When Companies Lose Their Souls
Perhaps the most insidious risk is cultural convergence. When AI drafts your company communications, writes your value statements, and shapes your employee handbooks, it imports the average corporate culture encoded in its training data. The quirks, the specific language, the unique ways of thinking that define your organization—all get smoothed into statistical averages.
Over time, the effect will not only dilute external brand perception but also diminish the sense of belonging employees feel. When people can no longer recognize their company’s voice in its own communications, engagement erodes in ways that spreadsheets won’t immediately capture.
From Artificial Intelligence to Authentic Intelligence
If AI accelerates sameness, then competitive advantage comes from protecting and amplifying what makes you different. Here’s how:
- Audit your uniqueness
Identify the knowledge, stories, and perspectives your company holds that no AI model can access. What do you know that others don’t? - Create proprietary datasets
Feed AI your unique data—customer insights, field notes, experiments, failures—instead of relying on the generic pool of information available to everyone. - Establish “AI-free zones”
Deliberately protect areas where human judgment and lived experience matter most—strategy off-sites, cultural rituals, moments of customer intimacy. - Adversarial prompting
Don’t just ask AI for answers. Ask it for the contrarian view, the blind spot, the uncomfortable perspective.
Authentic Intelligence
In a world in which every company has access to the same artificial intelligence, the real competitive advantage isn’t having AI—it’s having something AI can’t replicate. And that can only come from authentic intelligence: the messy, contradictory, beautifully human insights that no model can generate.
AI is the price of admission. Authenticity is how you win.