The AI Paradox: Why SMEs Might Be Losing Ground Just When They Thought They’d Caught Up

Posted on December 3, 2025 by Eugene Ivanov

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.

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Putting the Cart Before the Horse: What We’re Getting Wrong About AI

Posted on November 10, 2025 by Eugene Ivanov

The debate about artificial intelligence has become exhaustingly predictable.

On one side, we have doomsayers who celebrate every misstep—a misdrawn map of Europe, a miscounted number of r’s in “blueberry”—as proof that AI is fundamentally flawed. The word “hallucination” has been weaponized to dismiss technology that, despite its imperfections, has made extraordinary strides in reliability. On the other side, we have enthusiasts, armed with an ever-expanding toolkit of specialized models and applications, who rush to integrate AI into every conceivable business process.

Both camps, I would argue, are missing the point.

The skeptics’ position barely warrants discussion. Yes, AI makes mistakes. So do humans—with alarming regularity. The relevant question isn’t whether AI is perfect, but whether it’s useful. And on that measure, the evidence is overwhelming. Major language models have dramatically reduced their error rates, and their capabilities continue to expand at a pace that would have seemed impossible just years ago. Dismissing this technology because it occasionally stumbles is like rejecting automobiles because they can’t navigate every dirt path that a horse can.

But here’s where it gets interesting: even among those who embrace AI’s potential, most are approaching its implementation backwards. I call this the technology-centric trap. The thinking goes something like this: “We have these amazing AI tools available. Which of our existing business processes can we automate with them?” It’s a natural question, especially given the dizzying array of AI applications flooding the market, each promising to revolutionize some aspect of operations.

The problem is that this approach assumes our current business processes are fundamentally sound—that they just need a technological upgrade to run faster and cheaper. But what if the processes themselves are the problem? What if they’re outdated, inefficient, or built on assumptions that no longer hold in today’s environment? Bolting AI onto broken workflows doesn’t fix them; it just automates dysfunction at machine speed.

The correct sequence is elegantly simple, though harder to execute: identify the problem first, then find the solution. Not the other way around.

This isn’t theoretical musing. My experience with crowdsourcing taught me this lesson clearly. Successful crowdsourcing doesn’t start with assembling a crowd and asking what they can solve. It starts with identifying a specific problem, tracing it to its root cause, and defining it with precision. Only then do you present it to potential solvers. Skip those preliminary steps, and you’ll get solutions to the wrong problems—or no workable solutions at all.

The same principle applies to AI integration. Before asking which AI tools you should deploy, ask: What processes are genuinely holding us back? Where are the bottlenecks that constrain growth? Which workflows were designed for a different era and have simply persisted out of habit? These questions require honest, sometimes uncomfortable introspection about how your organization operates versus how it should operate.

Only after answering these questions does it make sense to survey the AI landscape. If appropriate tools exist, deploy them. If they don’t, consider building them or adapting what’s available. But the technology choice flows from the problem definition, not the reverse.

IBM’s recent paper on AI agent architecture makes this point compellingly. Their analysis reveals that many AI agent deployments stall after the pilot phase not because the technology fails, but because organizations are trying to force-fit advanced AI onto fundamentally broken workflows. Technology works fine; the underlying processes don’t.

This isn’t about being anti-technology or advocating for needless delays. It’s about being strategic. AI offers unprecedented opportunities to reimagine how work gets done, but only if we’re willing to question the status quo first. The businesses that will truly benefit from AI aren’t those that deploy the most tools the fastest. They’re the ones that take the harder path: examining their operations critically, identifying what needs to change, and then—and only then—leveraging AI to build something better.

The future belongs not to those who automate the present, but to those who redesign it first.

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Unlocking SME Innovation: Why AI-Based Problem-Solving is the Great Equalizer

Posted on September 7, 2025 by Eugene Ivanov

In mid-October 2009, I was visiting with a client, a large, Midwest-based paint and coating manufacturing company.

As part of their product development process, the company’s engineers built a powerful outdoor pump to paint industrial buildings. The pump worked beautifully in indoor testing, but when the engineers tried to use it outdoors, the pump started to clog frequently, making it essentially useless. The client wanted me to help them run a crowdsourcing campaign aimed at redesigning the pump.

When speaking with my counterparts at the client’s innovation group, I pointed out to them that the indoor and outdoor conditions they used to test the pump weren’t identical: the indoor testing was done during the summer, with the temperature even in the air-conditioned lab often reaching the mid-seventies, while the outdoor temperature in the Midwest at this time of year rarely hit the 60°F mark. Could it be that the clogging was somehow caused by the temperature shift?

My hunch turned out to be correct. The problem was not the pump design. The problem was the paint: it was rapidly becoming viscous with a small drop in temperature, causing the pump to clog. The engineers fixed the problem by simply adjusting the paint formulation.

As an innovation manager, I like to remind my clients that the most important part of the problem-solving process is to correctly define the very problem they’re trying to solve.

The sad reality is that many large organizations, both corporate and non-profit, fail to identify the root cause of their problems. Instead, they immediately start looking for something—anything!—that may look like a solution.

To me, this is equivalent to taking Tylenol to relieve a headache even before knowing what caused it: hangover, mild cold, chronic migraine, or advanced glioblastoma.

The situation is even worse for small- and mid-sized companies (SMEs). They’re under constant pressure to innovate, but often lack the dedicated innovation departments, large budgets, and internal resources that their larger competitors rely on. While traditional consulting firms primarily cater to enterprise-level clients, SMEs are often left underserved, leaving their internal problem-solving capabilities ad hoc at best.

The AI Revolution: A Generational Moment for SMEs

Advances in AI, particularly large language models (LLMs), dramatically reduce the cost of domain expertise. By using tools like ChatGPT, Claude, or Gemini, everyone can now tap into knowledge that just a few years ago was accessible only to large and resource-rich organizations.

This presents a generational opportunity to level the playing field. The AI-based tools can stimulate the creative process, energize problem-solving, and support decision-making at SMEs with unprecedented speed and affordability.

What we’re witnessing isn’t simply an upgrade to existing business tools—it’s a fundamental shift in how problems get solved. Consider the cognitive cleanup that AI enables: where SMEs once struggled to sift through mountains of data, identify patterns, or generate multiple solution pathways, AI tools can now process complexity in real-time, offering structured thinking frameworks and systematic approaches to innovation challenges.

This transformation enables real-time business unblocking. When an SME faces a technical hurdle, market challenge, or operational bottleneck, AI tools can immediately provide relevant domain expertise, suggest alternative approaches, and help teams think through consequences before committing resources. The days of waiting weeks for external consultants or struggling in isolation are rapidly ending.

The emergence of problem-solving intelligence through AI represents more than efficiency gains—it’s about democratizing strategic thinking itself. SMEs can now engage in sophisticated scenario planning, competitive analysis, and innovation forecasting that were previously the exclusive domain of large corporations with dedicated strategy teams.

What makes this moment truly generational is the compound effect: as AI tools become more sophisticated and SMEs become more adept at leveraging them, the competitive advantages traditionally held by larger organizations begin to erode. Agility becomes more valuable than resources. Creative problem-solving trumps bureaucratic processes.

The Future of Innovation Services

This is also the moment to redefine traditional consulting by combining human expertise with AI tools and bringing cutting-edge innovation practices to SMEs across industries. It’s time to introduce AI-augmented innovation services for SMEs.

The new paradigm isn’t about replacing human insight with artificial intelligence—it’s about amplifying human creativity and judgment with AI’s processing power and knowledge synthesis capabilities. This hybrid approach enables SMEs to punch above their weight class, competing not just on price or niche expertise, but on the quality and speed of their innovation processes.

It’s not a transient trend. It’s a blueprint for the next generation of SME decision-making. The organizations that embrace this shift now will find themselves equipped with sustainable competitive advantages that compound over time, while those that hesitate risk being left behind in an increasingly AI-augmented business landscape.

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The Brainstorming Renaissance: How GenAI Tools Are Rewriting the Rules of Creativity

Posted on May 24, 2025 by Eugene Ivanov

What if the best idea in your next big innovation meeting didn’t come from your star designer, but from a chatbot?

This isn’t a futuristic thought experiment; it’s happening now. Generative AI tools like ChatGPT, Midjourney, and Stable Diffusion are infiltrating brainstorming sessions, product design sprints, and even poetry readings. They’re not just helping — they’re outperforming human contributors on key metrics like speed, idea quality, and production cost.

As the ideation landscape is redrawn, it raises profound questions: Are AI-generated ideas better than human ones? Who benefits most from these tools — the seasoned expert or the curious novice? And more provocatively, is this the death of creativity, or its long-overdue rebirth?

Let’s unpack this creative renaissance in two acts.

Act I. GenAI vs. Human Brains: The Battle of Ideas

Quality, Novelty, and Feasibility: The Metrics That Matter

The old belief that “creativity is uniquely human” is quickly eroding. A landmark 2023 study by Girotra and colleagues compared ideas generated by ChatGPT-4 with those brainstormed by students at an elite university. 

The task? Inventing commercially viable products. The results? Staggering.

ChatGPT-4 produced ideas with higher average quality, measured by consumer purchase intent. It also dominated the high-performance tier — 35 of the top 40 ideas came from the model, not the humans. And it did all this at 40 times lower cost than its human counterparts.

Similarly, Meincke et al. (2024) showed that when GPT-4 was fed a few high-quality examples (a technique known as few-shot prompting), its outputs significantly outpaced those from human ideators across multiple dimensions of perceived value, though humans still edged out the machine on idea novelty.

This novelty gap has consistently surfaced across domains. In innovation tasks, artistic expression, and even scientific ideation, humans tend to produce slightly more novel ideas. But here’s the twist: being novel doesn’t always mean being better.

In real-world innovation, novelty without feasibility might be just noise. That’s where GenAI shines — balancing utility with surprise. In the words of Joosten et al. (2024), AI-generated ideas often have higher customer benefit and overall value, even when they are only moderately novel.

Similar things happen in the art world. When human evaluators were asked to judge whether a poem was written by a human or ChatGPT-3.5, they failed to tell the difference, and often preferred the AI version. The reason? AI poetry was rated higher on rhythm and beauty, two key markers of aesthetic impact.

The creative playing field isn’t just leveling — it’s shifting.

Speed and Cost: The Unfair Advantage of GenAI

Creativity has always come at a cost: time, energy, expertise. Generative AI blows this equation wide open.

In a 2024 study by Boussioux et al., AI generated high-quality business ideas at a fraction of the time and cost compared to human crowdsourcing. Human-generated solutions cost $2,555 and 2,520 hours. GPT-4 produced comparable (and in many cases better) ideas in 5.5 hours and for only $27.

In artistic domains, the same pattern holds. Zhou and Lee (2024) analyzed over 4 million artworks and found that artists using GenAI tools experienced a 25% increase in productivity and a 50% boost in engagement metrics like likes and shares. GenAI didn’t just amplify quantity; it elevated quality, especially when human artists actively filtered and curated the outputs.

But this productivity surge comes with a subtle risk: homogenization. Studies consistently show that GenAI outputs, particularly when used en masse, tend to be more similar to each other. The diversity of ideas — that raw, unpredictable chaos of human thought — gets smoothed out by the statistical instincts of the machine.

Prompt engineering can mitigate this to an extent. Techniques like chain-of-thought reasoning or persona-driven prompts have shown promise in boosting AI’s creative variance. But for now, GenAI is a volume weapon, not a chaos engine.

Act II. Who Gains More? Novices vs. Experts in the GenAI Era

The Democratization of Ideation

In many ways, GenAI is the great equalizer.

Doshi and Hauser (2024) found that low-creativity participants improved their storytelling by 11% when given access to AI ideas. Not only did their performance increase, but the creative gap between novices and high performers virtually disappeared. AI raised the floor without lowering the ceiling.

This has profound implications for innovation. Students, junior employees, or people outside traditional innovation roles can now participate meaningfully in ideation. As Girotra and Meincke’s work suggests, with a few examples and a well-engineered prompt, anyone can contribute viable, high-quality ideas.

Art mirrors this trend. In AI-assisted haiku creation, collaborative efforts between humans and machines consistently outperformed both pure AI and pure human poems in aesthetic evaluations. GenAI helps amplify latent creativity, especially for those who lack formal training or confidence.

In short, GenAI levels the playing field.

The Expert Paradox: When Experience Gets in the Way

Ironically, experienced professionals don’t always benefit from GenAI — and in some cases, it may undermine their performance.

A striking example comes from a study by Eisenreich et al. (2024). When experts were shown AI-generated ideas for inspiration, they performed worse than either “pure” AI or “pure” human ideators. Why? The explanation seems to be anchoring — AI outputs may constrain creative thinking rather than catalyze it among seasoned minds.

This insight challenges the assumption that more expertise means better outcomes when using AI tools. Instead, it suggests a new skill is required: the ability to effectively collaborate with AI via guiding curating, and edited, but without being creatively boxed in.

Artists face the same challenge. In visual domains, Zhou and Lee (2024) found that those who simply plugged ideas into AI tools produced more generic work. But artists who curated and refined AI outputs saw the biggest boosts in evaluations and audience engagement.

The future expert isn’t just a creator. They’re a creative director, orchestrating a human-machine ensemble to push boundaries rather than settle into comfortable patterns.

Conclusion: From Brainstorming to Brainhacking

We are witnessing a historic shift — not just in how ideas are generated, but in who gets to generate them and what those ideas look like.

GenAI tools have redefined the ideation process. They produce more, faster, and often better. They empower novices, disrupt experts, and challenge our deepest assumptions about creativity. Yet they also introduce risks: homogenization, bias, and the temptation to outsource too much of our thinking to machines.

The challenge isn’t to resist GenAI, but to use it wisely. To know when to prompt and when to pause. To explore widely, then filter ruthlessly. To let GenAI flood the canvas, but retain the brush.

So the next time you need a breakthrough idea, don’t just think outside the box. Ask your favorite bot what it thinks the box should be made of.

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The End of the Crowd? (Why AI Won’t Fully Replace Human Crowdsourcing — Yet)

Posted on May 12, 2025 by Eugene Ivanov

AI has already claimed its seat at the innovation table — and it didn’t even knock. It barged in, armed with large language models (LLMs) like GPT-4, reshaping how companies ideate, prototype, and solve problems. 

With astonishing speed and minimal cost, these tools are outperforming humans in tasks ranging from code generation to business model design. So, here’s the billion-dollar question: if AI can already outperform human crowds in many areas, is traditional crowdsourcing about to die?

A compelling study by Boussioux et al. (2024), titled “The Crowdless Future? Generative AI and Creative Problem Solving,” puts this debate into sharp focus. Their experiment pitted human-generated business ideas against those created using a human-AI hybrid approach. The results? AI-assisted solutions, especially when guided through strategically refined prompts, scored significantly higher in value, including financial and environmental impact, and overall quality. And they came with a price tag of just $27 compared to over $2,500 for the human-only submissions.

Translation? AI isn’t just good at creative problem-solving. It’s lean, scalable, and often better than the crowd, at least when measured by implementation potential and perceived value.

But if AI is that efficient, why aren’t we declaring the death of crowdsourcing right now?

While AI may outpace us humans in cost and consistency, there are at least four powerful reasons why traditional human crowdsourcing is far from obsolete.

Novelty: The Spark of the Unexpected

Boussioux et al. found that human-generated ideas consistently ranked higher in novelty, especially at the upper end of the scale. In other words, when you’re looking for that one-in-a-million idea — the weird, wild, breakthrough concept that no dataset can predict — humans may still have the edge.

AI models, no matter how advanced, are trained on what has been, not what could be. Their “creativity” is fundamentally synthetic — it’s a remix of the past. Human crowds, on the other hand, bring serendipity, fringe thinking, and unpredictable combinations. And in innovation, sometimes it’s one crazy idea, not a dozen “good” ones, that changes everything.

Ownership: Who Gets the Credit (and the IP)?

With AI-generated content, the question of intellectual property is still a legal and ethical minefield. If an LLM produces a groundbreaking idea based on prompts from your team, who owns the output? Your team? The model’s creators? The crowd of internet texts that the model was trained on?

Crowdsourcing sidesteps this ambiguity. A human contributor generates a breakthrough idea and signs an agreement transferring all IP rights to this idea to the crowdsourcing campaign sponsor in exchange for a reward, all in a legally transparent and unambiguous way. For organizations wary of future legal headaches, sticking with human solvers may feel like a safer bet, at least until AI governance frameworks catch up.

Marketing Value: Crowdsourcing as Innovation Theater

Let’s be honest: not all crowdsourcing is about getting the best ideas. Sometimes, it’s about signaling. When a company launches an open innovation contest — say, “Reimagine the Future of Food” — it’s making a statement: We’re listening to our customers. We’re cutting-edge. We’re engaged. Investors love this!

An AI prompt doesn’t generate press releases, Instagram buzz, or goodwill. But a vibrant campaign with real people submitting ideas does. For companies looking to boost their image as forward-thinking and innovative, the crowd still offers a potent narrative tool.

Community: It’s Not Just About the Ideas

Crowdsourcing doesn’t just produce solutions — it builds communities. When done right, it creates a network of passionate participants who care about a problem, become brand advocates, and sometimes even co-founders of spinoff ventures.

AI, by contrast, is transactional. It doesn’t care. It doesn’t get excited. It won’t show up at your hackathon or promote your brand on social media. That human energy — the sense of being part of something bigger — is still irreplaceable.

So, will AI replace crowdsourcing?

In many ways, it already has — for tasks where speed, scale, and strategic value matter most. But for organizations chasing radical novelty, craving emotional connection, or navigating uncertain legal waters, the human crowd still has a job to do.

Maybe the future isn’t crowdless — it’s crowdsmart. A hybrid world where AI augments, not replaces, the wisdom of the crowd. Where LLMs help us sift, refine, and accelerate, but humans still supply the spark.

In the end, it’s not AI vs. the crowd. It’s AI + the crowd. And when those two forces align, innovation doesn’t just scale — it soars.

Bold claim? Perhaps. But when the sparks fly from both silicon and soul, that’s when real innovation begins.

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Knowing Where You’re Going and Who’s Driving: How AI Is (and Isn’t) Reshaping Human Work

Posted on March 25, 2025 by Eugene Ivanov

Integrating AI into business practice has gone from a fringe conversation to a boardroom imperative. From productivity gains to fears of de-skilling, the debate is divisive—some see AI as a game-changer for human potential; others worry it’s a slippery slope toward dependence and displacement. 

Negative sentiments notwithstanding, the real question is not whether to use AI, but how to use it wisely. Four cutting-edge studies provide a nuanced view of this evolving frontier, shedding light on when AI helps, when it hinders, and how it may redefine not just work—but workers and teams themselves.

Inside the Frontier: When AI Knows What It’s Doing

The “jagged technological frontier” isn’t just a catchy metaphor—it’s the heart of a massive field experiment run with 758 consultants at the Boston Consulting Group. Researchers introduced GPT-4 to professionals tasked with solving complex business problems. 

The key insight? AI is only effective when operating within its capabilities—or “inside the frontier.” These are tasks that AI can complete reliably: structured analysis, clear communication, or ideation based on known patterns. “Outside the frontier” lies the domain of ambiguity, tacit knowledge, and judgment—and it’s here where AI stumbles and sometimes misleads.

The study’s surprising twist was who benefited the most from using AI tools. It wasn’t the top performers, but the consultants with below-average baseline performance. For these individuals, AI acted as an accelerant—boosting quality by over 40% and productivity by 25%. In contrast, for tasks outside the AI’s comfort zone, consultants with AI were 19% less likely to deliver the correct solution. These findings don’t argue against AI—they reveal its shape. Like any tool, AI is powerful only when used in the right context. Success comes from recognizing where AI’s frontier lies and then adapting accordingly.

Too Smart to Help? When Better AI Backfires

What happens when AI becomes too competent? 

In a striking counterpoint to exuberant techno-optimism, a 2022 work by Dell’Acqua earlier explores a phenomenon the author dubbed “falling asleep at the wheel.” In a field experiment with 181 professional recruiters, participants evaluated resumes with AI assistance. But this time, the quality of the AI tool varied—some recruiters received high-accuracy recommendations, others received low-accuracy ones.

Counterintuitively, the recruiters using lower-performing AI tools made better decisions. They were more engaged, spent more time reviewing resumes, and were more likely to challenge AI suggestions. Meanwhile, highly accurate AI caused the human effort to drop. Recruiters deferred too quickly to machine judgment and became less accurate in their assessments.

This wasn’t a fluke—it was particularly true for experienced professionals, whose own skills were diluted by over-reliance on the algorithm. 

The takeaway is clear: high-quality AI can displace rather than augment human expertise. In such settings, algorithmic excellence may seduce users into disengagement, suppressing their cognitive muscle memory. Maximizing joint performance may sometimes require less powerful AI—at least when keeping humans in the loop is critical.

Smarter Isn’t Always Better—But Sometimes It Is

Otis and colleagues offer a compelling twist to this narrative. In a randomized trial involving 640 Kenyan entrepreneurs, participants received business advice either from a traditional guidebook or via a GPT-4-based AI mentor on WhatsApp. Unlike the recruiter study, this AI tool helped top performers—boosting revenue and profits by over 20%. But it harmed low performers, who saw their performance dropping by about 10%.

Why this contradiction? It comes down to task selection and user discretion. Entrepreneurs had autonomy in when and how to use the AI, and high performers asked better questions on more manageable tasks. In contrast, low performers sought help on complex, ill-structured problems—those outside the AI’s frontier—leading to bad advice and worse outcomes.

This study makes more nuanced the notion that better AI leads to disengagement. It shows that it’s human judgment about what AI can and cannot do that is the real driver of success. When users are savvy about AI’s limitations, even powerful systems can be transformative. When they’re not, AI becomes a mirage—confidence without clarity.

Teaming Up with the Machine: A New Era of Collaboration

If the first three studies examined AI as a co-pilot for individuals, the just-published experiment conducted by a Harvard/Wharton team reimagines AI as a collaborator for entire teams. Conducted with 776 professionals at Procter & Gamble, the study asked: can AI fill the collaborative roles typically occupied by humans?

Participants were randomly assigned to four groups: individuals working solo, human teams of two, individuals with AI, and human teams with AI. All tackled real product development challenges. The results were eye-opening: individuals with AI matched the output of human teams. Even more striking, teams with AI outperformed all others, including human-only teams.

AI’s impact wasn’t just in better performance: it flattened functional silos. Without AI, participants generated ideas aligned with their functional background: R&D workers generated more technical proposals, and commercial workers more business-oriented. With AI, all produced more balanced solutions, regardless of background. Emotional benefits were evident too—users reported more positive feelings and less frustration when working with AI.

The implication of this study is profound: AI isn’t just a tool; it’s evolving into a cybernetic teammate, one that enhances creativity, bridges knowledge gaps, and even mimics the social glue of teamwork. (Who could predict this even a couple of years ago?) 

This shift could redefine how we structure teams, allocate expertise, and manage work across the enterprise. The age of the solitary “AI-enhanced worker” is giving way to something richer—and potentially more disruptive.

The AI Edge Depends on the Human Hand

Across four major field studies, a clear pattern emerges. AI can supercharge performance—but only when we understand how, when, and who should use it. It’s not the intelligence of the algorithm that matters most; it’s the alignment between task, user, and tool.

GPT-4 boosted underperformers—if tasks were within their skill set. High-quality AI backfired—if users relied on it blindly. Entrepreneurial outcomes varied—based on users’ understanding of AI’s strengths and limits. And now, AI isn’t just augmenting individuals—it’s enhancing teams.

As businesses race to adopt generative AI, the lesson is both simple and sobering: AI is only as good as the people who know how to use it. And isn’t it a case for all other tools?

So, are we ready to treat AI not just as a tool, but as a teammate? As a manager? Feel free to scratch out the last question.

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In Silico Creativity. Part 3. AI and Creative Art

Posted on March 4, 2025 by Eugene Ivanov

In Part 1 and Part 2 of this three-part series, I reviewed what is known about AI’s ability to generate poetry and music. This article is about what AI can do for creative art. 

As AI systems like DALL-E, Midjourney, and Stable Diffusion aggressively penetrate the art world, questions arise regarding how AI-generated works are perceived, valued, and integrated into human creative workflows. Two studies—one by Bellaiche et al. (2023) and another by Zhou & Lee (2024)—offer key insights into this debate, exploring AI’s role in augmenting human creativity and highlighting the biases influencing our appreciation of AI-generated art.

AI? No, please!

Bellaiche et al. (2023) investigate whether people prefer human-created artworks over those made by AI and, if so, why. Through a series of experiments, they find that individuals tend to rate artworks labeled as “human-created” more positively than those labeled “AI-created,” even when all images were, by the study design, produced by AI. The study shows that people associate greater meaning, effort, and emotional impact with human-made works—and this contributes to higher aesthetic evaluations.

Interestingly, participants with positive attitudes toward AI exhibited reduced bias against AI-labeled artwork. Additionally, people who scored lower on cognitive reflection tests were more likely to rate human-labeled art as more beautiful. That suggests that AI-created artwork is subject to top-down biases rather than bottom-up sensory judgments. 

These findings have important implications for AI-generated content in creative industries. While AI can produce high-quality artwork indistinguishable from human-made pieces, public perception remains a significant barrier to AI’s acceptance in creative fields. As AI-generated art becomes more common, overcoming biases and fostering appreciation for AI as a creative tool will be crucial.

AI? Yes, please!

While Bellaiche et al. focus on biases against AI-generated art, Zhou & Lee (2024) explore how AI enhances human creative productivity. Analyzing a dataset of over four million artworks, their research shows that AI can help art creators: AI-assisted artists experience a 25% boost in creative productivity and a 50% increase in positive evaluations of their work by peers.

However, the study also uncovers a paradox: while peak creativity—measured as content novelty—increases over time, average novelty declines. This suggests that while AI enables some artists to push creative boundaries and produce exceptionally novel artifacts, many others relying on AI’s capabilities begin producing aesthetically pleasing but less original work. 

The authors introduce the concept of “generative synesthesia,” describing the harmonious blending of human ideation and AI execution as a new form of creative workflow. This positions AI’s role not as a replacement for human creativity but as a tool that expands the creative process when used effectively.

The Evolving Landscape of Art Creation 

Together, these two studies offer a nuanced perspective on the role of AI in artistic creation. While AI can meaningfully enhance artistic output, psychological perceptions still favor human-made art. In other words, AI’s impact on creativity depends on how artists engage with AI tools and how audiences perceive the resulting work.

As AI-generated art will continue to proliferate—and there must be no doubt about that—it will be essential to address these biases and develop a more comprehensive understanding of creativity in the age of AI. Will society embrace AI-assisted art as a legitimate form of creativity, or will human authorship remain the gold standard? 

One thing remains clear though: the definition of art and the role of the artist are evolving, and AI is at the center of this transformation.

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In Silico Creativity. Part 2. AI and Music

Posted on March 1, 2025 by Eugene Ivanov

As Artificial Intelligence (AI) keeps conquering creative fields, from visual arts to music, the debates on whether AI can be truly “creative” show no signs of abating. But a much more practical question is, do listeners perceive AI-composed music differently from human-composed? 

Two recent studies, one by Shank et al. (2022) and another by Zlatkov et al. (2023), explore these questions and reveal interesting insights into how people judge AI-generated music.

Do We Like AI-Composed Music Less?

The study by Shank et al. (2022) investigated whether people like music less when they believe it was composed by AI. In a first set of experiments, participants listened to excerpts of classical and electronic music and rated how much they liked them while also guessing whether they were composed by a human or AI. The results showed that listeners were more likely to assume that electronic music was AI-composed and tended to like it less if they believed this was the case.

In the next set, the researchers directly manipulated the information given to listeners about composer identity. They found that for classical music, participants liked the excerpts less when they were told it was AI-generated. This suggests a clear bias against AI composers, particularly in genres, like classical music, that are traditionally associated with human emotional expression and creativity.

…Or We Don’t Care?

The study by Zlatkov et al. (2023) explored a similar question from a different angle. Their experiment involved 163 participants who listened to five human-composed and five AI-composed musical pieces. The participants were divided into two groups: one was told the correct composer identity, while the other was deceived. The researchers hypothesized that those who knew that a piece was AI-generated would rate it lower.

Surprisingly, the evidence didn’t support this hypothesis. Unlike previous findings, Zlatkov et al. found that listeners did not necessarily dislike music just because it was AI-generated. However, researchers acknowledged limitations in their study design as they didn’t explore the role of musical style, listener background, and other contextual factors in shaping perceptions of AI-composed music.

…And Should We Care?

Both studies provide yet another example of the complexity of human perception of AI creativity. While one suggests that people have an inherent bias against AI-generated music, particularly when it challenges traditional notions of musical craftsmanship, the other indicates that this bias may not be as universal as previously thought; instead, it depends on context and how AI music is introduced to listeners.

The larger point, however, is that AI-composed music is good enough to fool people into believing that it was human-generated.

So, as AI-generated music becomes increasingly sophisticated, it’s not its quality but rather human perception that will represent a major hurdle to its adoption. Whether AI compositions will ever be embraced on equal footing with human-created music will therefore depend not just on technical advancements but also on changing cultural attitudes toward creativity itself.

And let’s be honest. You’re listening to a piece of music that gets you. Does it matter who—or what—composed it?

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In Silico Creativity. Part 1. LLMs and Poetry (and Short Stories)

Posted on February 25, 2025 by Eugene Ivanov

In my previous article, “In Silico Ideation,” I reviewed academic literature describing the application of LLM algorithms to generating new product ideas. Now, I want to review what is known about LLMs’ ability to generate other creative content. This article is about poetry (and short stories). 

Can You Tell Who Wrote That Poem?

If you think you can easily spot the difference between AI-generated and human-written poetry, think again. In a 2024 study by Porter and Machery, 1,634 participants were randomly assigned to evaluate poetry from 10 well-known poets and poems generated by ChatGPT-3.5 written in the style of each poet.

Guess what? The participants failed to tell the difference between the two sets. Even more surprising, they were more likely to mistake AI-generated poems for human work than the other way around. Moreover, ChatGPT-3.5-generated poetry not only passed as human-written but was rated higher for overall quality, rhythm, and beauty compared to works by famous poets.

The researchers call this the “more-human-than-human” effect. When people like a poem, they tend to assume it must have been written by a human. This bias plays out consistently across experiments, regardless of participants’ experience with poetry.

However, there was a twist: when explicitly told that a poem was AI-generated, participants rated it lower than when told it was human-written, revealing persistent biases against machine creativity.

Enhancing Human Creativity

AI isn’t just creating content on its own—it’s also changing how humans create it. A 2024 study by Doshi and Hauser found that prior access to a pool of AI-generated “seed” ideas improved the novelty and usefulness of human-written short stories by 6.7% and 6.4% respectively. Stories inspired by AI prompts were also rated as more enjoyable and better written.

The most intriguing finding? AI appears to be a great equalizer. Writers with lower measured creative abilities saw improvements of up to 11% when using AI “seed” ideas, effectively closing the gap between them and their more naturally creative peers. 

The Collaboration Sweet Spot

It also appears that generating creative content is more effective when humans collaborate with LLMs rather than when either party works alone. A 2023 study by Hitsuwari and colleagues found that while AI-generated haiku and human-made haiku were rated equally beautiful, AI-generated haiku with human intervention received the highest beauty ratings. 

Again, participants couldn’t reliably distinguish between human and AI authors. Moreover, the higher the AI-generated haiku was rated, the more likely people were to believe it was human-made.

The Diversity Angle

There’s a potential downside to AI-induced creative enhancement. The Doshi and Hauser study found that AI-assisted stories showed higher similarity to one another and the AI-generated prompts. This suggests a reduction in the diversity of creative output, raising questions about AI’s role in fostering true originality over time.

Implications for the Future

These studies collectively point to several important implications:

1. Indistinguishable creation: The line between human and AI creativity is rapidly blurring, at least for shorter creative formats like poetry.

2. Democratization of creativity: AI tools can help level the playing field, potentially allowing those with less natural creative talent to produce work of similar quality to highly creative individuals.

3. The collaboration advantage: The highest quality creative output may come from human-AI partnerships rather than either working independently.

As AI continues to evolve, so will our understanding of creativity itself. Rather than seeing AI as a replacement for human creativity, these studies suggest we might be moving toward a future where AI becomes an extension of human creative capabilities—enhancing, equalizing, and potentially transforming how we create art.

The question isn’t whether AI can be creative, but how our collaboration with LLM systems will reshape the very notion of creativity itself. I’ll come back to this topic in my future articles.

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In silico Ideation: How Large Language Models (LLMs) Help Generate New Ideas

Posted on February 11, 2025 by Eugene Ivanov

As with every emerging general-purpose technology, Generative AI (GenAI) is searching for problems to solve. Finding the most fitting will take time. I consider it pointless to look for the things that GenAI can’t do; instead, I prefer focusing on what it already can.

One of the few areas where GenAI has already demonstrated its usefulness is innovation. In a recent PPT presentation, “Powering Front-End Innovation with AI/LLM Tools,” I explored how AI can enrich the front-end of the innovation process. In this article, I’ll review academic literature describing the application of LLM algorithms to one specific stage of this process: generating new ideas. 

Faster, Cheaper, Better 

Meincke et al. (2023) appear to be the first to use an LLM algorithm to generate new product ideas. The authors took advantage of a pool of ideas created by MBA students enrolled in a course on product design in 2021 (that is, before the wide availability of LLMs). The students were given the following prompt:

“You are a creative entrepreneur looking to generate new product ideas. The

product will target college students in the United States. It should be a physical good, not a service or software. I’d like a product that could be sold at a retail price of less than about USD 50…The product need not yet exist, nor may it necessarily be clearly feasible.”

200 ideas generated by the students were used as a benchmark to compare with two pools of ideas generated by OpenAI’s ChatGPT-4 with the same prompt. One set comprised 100 ideas generated by ChatGPT with minimal guidance (zero-shot prompting); the other 100 ideas generated by the model after providing it with a few examples of high-quality ideas (few-shot prompting).

The first important discovery made by Meincke et. al. was that ChatGPT was generating new product ideas with remarkable efficiency. It took one human interacting with the model only 15 minutes to come up with 200 ideas; a human working alone generated just five.

This dramatically reduces the cost of new ideas generated by ChatGPT. Under specific conditions described in the article, generating one ChatGPT idea costs $0.65 compared to $25 for an idea generated by a human working alone. That means a human using ChatGPT generates new product ideas about 40 times more efficiently than a human working alone.

Faster and cheaper. But what about the quality of the ideas?

To assess the quality of all 400 ideas, the purchase intent measurement through a consumer survey was applied. Measured this way, the average quality of ideas generated by ChatGPT is statistically higher than the ones generated by humans: 47% for ChatGPT with zero-shot prompting and 49% with few-shot prompting vs. 40% for human-generated ideas.

Moreover, among the 40 top-quality ideas (top decile of all 400), 35(!) were generated by ChatGPT. 

The only consolation for us humans was that the mean novelty of human-generated ideas was higher than the ones generated by the model: 41% vs. 36%. Besides, ChatGPT-generated ideas, especially with few-shot prompting, exhibited higher overlap, limiting their diversity compared to human ideas. Unfortunately, the novelty itself didn’t affect purchase intent.

Prompting Diversity

In a follow-up study, Meincke et al. set out to improve the diversity of ChatGPT-generated ideas by using 35 different prompting techniques. The authors used the same framework as in the previous study: seeking ideas for new consumer products targeted to college students that can be sold for $50 or less.

Meincke et al. show that of all 35 prompting approaches, Chain of Thought (CoT) prompting, which asks the LLM to work in multiple, distinct steps, resulted in the most diverse pool of ideas; its diversity approached the level of the ideas generated by the students.

The authors also showed a relatively low overlap between ideas generated using different prompt techniques. That means that a “hybrid” approach—using different prompting techniques and then pooling the ideas together—might be a promising strategy for generating large sets of high-quality and diverse ideas.

From Students to Professionals

One of the limitations of the above two studies was that human-generated ideas were created by students. One might argue that students, being less experienced, couldn’t come up with higher-quality ideas that would beat the algorithm. 

This limitation was addressed by the study of Joosten et al (2024). In this study, professional designers and ChatGPT-3.5 were assigned identical tasks of generating novel ideas for a European supplier of highly specialized packaging solutions. A total of 95 ideas were generated, 43 by humans and 52 by ChatGPT. All the solutions were evaluated, in a blind fashion, by the company’s managing director, a seasoned innovation expert.

The results show that when assessed by the overall quality score, ChatGPT generated better ideas than professionals. More specifically, ChatGPT-generated ideas scored significantly higher than humans’ in perceived customer benefit, while both sets scored almost identically in feasibility.

Interestingly enough—and in contrast to the results of Meincke et al.—ChatGPT-generated ideas scored significantly higher in novelty. As a result, ChatGPT produced more top-performing ideas in terms of novelty and customer benefit.

Similar results were obtained by Castelo et. al (2024). These authors compared ideas for a new smartphone application that were generated by GPT4 and professional app designers. The authors showed that GPT4-generated ideas were ranked as more original, innovative, and useful.

Furthermore, Castelo et al. used a text analysis approach to determine what specifically made GPT4-generated ideas superior. To do so, they compared two types of creativity—creativity in form (when the language used to describe an idea is more unusual or unique) and creativity in substance (when the idea itself is more novel)—and found that GPT4 outperformed humans in both types of creativity.

Complementing the above two studies is the work by Si et al. (2024) who analyzed the ability of Claude 3.5 Sonnet to generate research ideas (in the field of Natural Language Processing), rather than new product ideas. Comparing ideas generated by the LLM model with those generated by professional NLP researchers, the authors showed that the LLM-generated output was ranked as more novel, although slightly less feasible, than the one generated by human experts.

LLMs vs. Crowds

Of all known idea-generation techniques, crowdsourcing is considered one of the most effective, a consistent source of ideas whose novelty, quality, and diversity exceed those created by individuals and small groups (of experts and laypeople alike). One, therefore, could hope that at least a crowd of people would beat an LLM algorithm in an idea-generating competition. 

Alas. 

Boussioux et al. (2024) designed crowdsourcing content to generate circular economy business ideas. In total, 234 ideas were generated (and evaluated by 300 independent human judges): 54 by a human crowd of creative problem solvers and 180 by GPT-4. 

Indeed, solutions proposed by the human crowd exhibited a higher level of novelty, both on average and at the upper end of the rating distribution. Yet, GPT-4 scored higher in the ideas’ strategic viability for successful implementation, as well as environmental and financial value. Overall, the solutions generated by the algorithm were rated higher in quality than the crowd-generated solutions.

Elaborating on findings by Meincke et al. (2024), Boussioux et al. found that a special prompting technique, prompt-chaining, resulted in the enhanced novelty of GPT-4-generated solutions without compromising their overall quality.

Once again, the authors demonstrated the high cost-efficiency of the LLM-assisted idea-generation process: under specific conditions used by the authors, it took 2,520 hours and $2,555 to generate 54 “human” solutions; the same numbers for LLM-generated solutions were 5.5 hours and $27. 

Some Final Thoughts

As recently as a few years ago, the conventional wisdom was that AI tools would only be used to automate routine knowledge work but that the creative part of this work would remain in the human domain. Recent developments forcefully disprove this discourse. 

One can split proverbial hairs while assessing the novelty or feasibility of ideas generated by LLMs. But one thing is clear: the overall quality of LLM-generated ideas is at least as high as the one generated by us humans. And all this is only at a fraction of the time and cost of human ideation.

That means that in silico ideation is here to stay, which allows firms to shift their attention from the ideation stage of the innovation process to later stages, such as idea incubation and prototyping.

At least until LLMs show us they are better at these stages too.

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