Three months ago, I declared AI the great equalizer for small and medium enterprises. Today, I’m not so sure. In fact, I’m worried we might be celebrating prematurely — and that the very technology promising to level the playing field could actually widen the gap between SMEs and their larger competitors.

The September Dream: AI as the Great Democratizer

Back in September, I published “Unlocking SME Innovation: Why AI-Based Problem-Solving is the Great Equalizer,” where I celebrated what seemed like a generational opportunity for SMEs. My argument was straightforward and optimistic: advances in AI, particularly large language models, were dramatically reducing the cost of domain expertise. Tools like ChatGPT, Claude, and Gemini were giving everyone access to knowledge that just years ago was exclusive to large, resource-rich organizations.

The promise was intoxicating. SMEs could now engage in sophisticated scenario planning, competitive analysis, and innovation forecasting — capabilities previously reserved for corporations with dedicated strategy teams. Where SMEs once waited weeks for external consultants or struggled in isolation, AI provided immediate domain expertise, alternative approaches, and consequence analysis before committing resources.

I believed — and still want to believe — that agility can become more valuable than resources, and creative problem-solving can trump bureaucratic processes. But recently, I’ve encountered a paradox that fundamentally challenges this optimistic vision.

The Knowledge Dichotomy: When More Becomes Less

To understand the paradox, we need to distinguish between two types of knowledge: explicit and tacit.

Explicit knowledge is information that can be easily codified, documented, and transferred. It’s the data in your reports, the insights in your dashboards, the processes in your manuals, and the analyses in your presentations. This is precisely what AI excels at generating. LLMs can analyze market trends, produce competitive intelligence, create strategic frameworks, and synthesize information from vast datasets — all at unprecedented speed and minimal cost.

Tacit knowledge, by contrast, is deeply embedded in experience, intuition, and context. It’s the expert judgment honed over years of practice, the creative problem-solving that comes from pattern recognition across multiple situations, the ability to read a room and build relationships, and the organizational culture that shapes how decisions get made.

Here’s where AI turns the tables: by making explicit knowledge abundant, cheap, and universally accessible, AI simultaneously commoditizes it. If your AI tools can generate sophisticated market analysis, so can your competitors.’ If you can produce detailed competitive intelligence reports, so can everyone else in your industry. Explicit knowledge, once a source of competitive advantage, becomes a common baseline rather than a differentiator.

And as explicit knowledge loses its strategic value, tacit knowledge becomes the critical differentiator. The paradox is complete: AI democratizes explicit knowledge while elevating the importance of the very thing that can’t be democratized — human experience, judgment, and intuition embedded within organizations.

The SME Threat: Winning the Battle, Losing the War

This paradox poses a particularly acute threat to SMEs, and it’s one I didn’t fully appreciate in September.

Yes, SMEs can now generate the same volume of explicit knowledge as their larger competitors. They can produce equally sophisticated analyses, reports, and strategic frameworks. “We have access to the same knowledge as you guys!” they might justifiably claim.

But here’s the problem: explicit knowledge is only half the equation—and increasingly, it’s the least important half.

Large organizations possess something SMEs often lack: a critical mass of accumulated tacit knowledge. They have teams of experienced professionals who’ve navigated multiple market cycles, managed countless customer relationships, and learned through trial and error what works and what doesn’t. They have established decision-making processes refined over decades, institutional memory that prevents repeated mistakes, networks of expertise that span functions and geographies, and organizational cultures that know how to translate insights into execution.

This tacit knowledge infrastructure is what turns data into decisions, and decisions into results. It’s the interpretive layer that determines which AI-generated insights matter and which don’t, the judgment that knows when to act boldly and when to proceed cautiously, and the execution capability that transforms analysis into competitive action.

So, here’s the cruel irony: by democratizing explicit knowledge, AI may widen the gap between SMEs and larger players. SMEs gain access to knowledge abundance but lack the tacit knowledge infrastructure to leverage it effectively. They’re drowning in insights but starving for wisdom.

Fighting Back: Building Tacit Knowledge at Scale

Should SMEs surrender to this paradox? Absolutely not. But they need to be strategic about how they compete in an AI-augmented world.

First, SMEs must recognize that their competitive advantage won’t come from AI-generated knowledge itself — it will come from how they apply that knowledge through their unique tacit knowledge capabilities. This requires intentional investment in building organizational wisdom, not just accessing information.

Second, SMEs should focus on what they can do better than large organizations: developing deep, contextual understanding of their specific customers and markets. Large companies have breadth; SMEs can have depth. Know your customers not just through data, but through relationships, repeated interactions, and intuitive understanding of their unstated needs.

Third, create tight-knit, high-trust teams where tacit knowledge flows naturally. In smaller organizations, this is easier to achieve than in large bureaucracies. Use this structural advantage to build learning cultures where experience is shared, mistakes are discussed openly, and collective judgment improves continuously.

Fourth, implement deliberate knowledge transfer mechanisms — mentoring programs, case study discussions, post-project reviews — that capture and disseminate tacit knowledge across your organization. Don’t let experience remain siloed in individual heads.

Finally, use AI strategically to augment your tacit knowledge, not replace it. Let AI handle the explicit knowledge generation: data analysis, report creation, and pattern identification. This frees your people to focus on interpretation, judgment, and creative application — the tacit knowledge work where you can still differentiate.

The Window Is Closing

The adoption window for AI is compressing rapidly. Following historical patterns, we likely have only 3-4 years until peak adoption in 2028-2029. SMEs that spend these precious years simply celebrating access to AI-generated explicit knowledge will find themselves competitively disadvantaged despite being technologically enabled.

The winners will be those who recognize the paradox and act on it now: embracing AI for what it does best while urgently building the tacit knowledge capabilities that AI cannot replicate.

The great equalizer might not be so equal after all. But for SMEs willing to play a different game — one focused on wisdom rather than just information — the opportunity remains extraordinary.

I’m grateful to Daniel Martinez Villegas, whose recent presentation at the Berkeley Open Innovation Seminar drew my attention to the explicit vs. tacit knowledge dichotomy.