Competing in the Dark | Digital Data Design Institute at Harvard

New research reveals that firms are playing a game of catch-up they didn’t even know they were losing

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Leaders know they must innovate to survive, and they don’t make decisions in a vacuum: they watch rivals, draw inferences, and position themselves accordingly. But what happens when their picture of the competitive landscape is fundamentally wrong? The new NBER working paper, “The Innovation Race: Experimental Evidence on Advanced Technologies,” co-written by a team of researchers including D^3 associate Zoë B. Cullen, takes this question seriously. Embedded in the Bank of Italy’s long-running INVIND survey, the study ran a randomized field experiment with roughly 3,000 Italian firms to test whether correcting firms’ misperceptions about competitors’ technology adoption changes their own investment plans. What they found should matter to any leader thinking about AI, automation, and the pace of organizational change.

Key Insight: Racing Without a Scorecard

“These data provide a unique opportunity to measure the beliefs firms hold about their competitors’ adoption decisions—and to identify their causal effects on firms’ own adoption behavior.” [1]

The central question driving the research is deceptively simple: are firms more likely to adopt advanced technologies like AI when they expect competitors to adopt them? This is a problem economists call ‘strategic complementarity’—the idea that one firm’s incentive to act depends partly on what others around it are doing. It’s easy to theorize about, but hard to test with real firms making real decisions.

The researchers solved this through a massive field experiment. They first asked firms what share of their competitors were currently using AI or robotics, then randomly provided half of them with the actual adoption rates of peers in their specific sector and size class. By measuring how these firms updated their 2027 adoption plans after seeing the data, the researchers could cleanly identify whether knowing a rival’s move actually changes your own. In the real world, if two firms adopt AI at the same time, it’s hard to tell if one is copying the other or if they are both just reacting to something like a new tax break or a labor shortage. By introducing a controlled “information shock,” the researchers could prove that it was the knowledge of competitor behavior itself that drove the change in strategy.

Key Insight: We Are More Alone Than We Think

“On average, prior beliefs underestimated actual adoption by 24.6 pp.” [2]

The research surfaced a striking baseline finding: firms are deeply mistaken about how technologically advanced their peers already are. On average, firms underestimated the share of competitors using AI or robotics by about 25 percentage points, a gap so large it suggests companies are making strategic decisions based on a competitive landscape that no longer exists. But when treated firms received accurate peer-adoption figures, they meaningfully revised their expectations upward—a response consistent with rational updating, and proportional to how far off their original estimates had been.

However, one of the most fascinating findings of the study was that not all technologies are equal when it comes to peer pressure. While information about competitors significantly increased intentions to adopt robotics, it had almost no measurable effect on plans for AI. The researchers offer several explanations: (1) AI adoption was already higher at baseline (roughly half of firms planned to use it by 2027), leaving less headroom for growth. (2) Robotics is a mature, deeply embedded technology in Italian manufacturing, so firms that see rivals using it extensively are receiving a clear legible signal. (3) AI, by contrast, is newer and often adopted experimentally, so competitive signals carry more ambiguity. Perhaps the return on investment for AI is still shrouded in uncertainty.

Key Insight: Information Campaigns

“[G]overnments could deploy information campaigns that raise firms’ awareness of the productivity benefits of new technologies and the extent to which their peers are adopting them.” [3]

Traditionally, governments try to spur innovation through expensive financial incentives and subsidies. However, this research points to a much cheaper and potentially more effective tool: the information campaign. If the primary reason firms aren’t adopting new technology is a misperception of the competitive landscape, then simply publishing accurate, sector-specific adoption data could do more to modernize an industry than a mountain of tax breaks. The researchers note two mechanisms that may be at work: a competition channel, where firms fear falling behind rivals, and a learning channel, where they use peer behavior to infer a technology’s productivity potential. Evidence from firms in concentrated markets suggests both channels are active, though neither can be fully isolated with current data.

Why This Matters

For executives and business leaders, this research surfaces a concrete and often underappreciated source of strategic risk: competitive misperception. If your organization is making AI and automation investment decisions based on an outdated view of where your industry actually stands, you may be systematically underinvesting, not because you lack capital or ambition, but because you lack accurate signals. The practical implication is that competitive intelligence on technology adoption is a direct input into investment strategy. For those thinking about the longer arc of AI diffusion, the contrast between robotics and AI results is instructive: behavioral responses to peer signals are strongest when a technology has a proven track record. As generative AI matures from experiment to infrastructure, the competitive spillovers documented here for robotics will likely be coming for AI next.

Bonus

This research shows that learning what peers are actually doing with advanced technology can shift decision-making. So here’s a question worth asking: how well do you really know where GenAI adoption stands in the broader workforce? The Generative AI Adoption Tracker, built by a team including D^3 Associate David Deming, offers a data-grounded answer. Drawing on five nationally representative U.S. surveys and 25,000 respondents, it tracks GenAI use at work and at home, adoption rates among working-age adults, and the productivity time savings already being realized. Consider it your lamp in the darkness.

References

[1] Cullen, Zoë B., Ester Faia, Elisa Guglielminetti, Ricardo Perez-Truglia, and Concetta Rondinelli, “The Innovation Race: Experimental Evidence on Advanced Technologies,” NBER Working Paper 34532 (2025), 2. https://doi.org/10.3386/w34532.

[2] Cullen et al., “The Innovation Race,” 3.

[3] Cullen et al., “The Innovation Race,” 26.

Meet the Authors

Zoë B. Cullen is Associate Professor of Business Administration at Harvard Business School and Associate at the Digital Data Design Institute at Harvard (D^3).