Here is an excerpt from an article written by Ayelet Israeli,for Harvard Business Review. To read the complete article, check out others, sign up for email alerts, and obtain subscription information, please click here.
Illustration Credit: Greg White
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Because it’s not clear how firms will adopt gen AI, managers face a strategic dilemma: Wait for more clarity and risk falling behind? Or act too soon and invest in applications that don’t deliver?
To address this tension, leaders need to think about gen AI adoption not as a single decision but as a portfolio of organizational experiments. Like A/B testing in digital-product development, these experiments should aim to isolate causal effects—focusing not just on whether gen AI works but also on how it works, for whom, and under what conditions. By testing gen AI applications before scaling them up, managers can reduce risk, refine their strategies, and build internal momentum for change. Experts have been advocating for this approach, but many firms are struggling to implement it. Experimentation therefore remains a relatively novel practice in many organizations.
That needs to change. Experimentation allows companies to transform gen AI uncertainty into a strategic advantage. It helps firms move through their own adoption phase more successfully than their competitors do. And the knowledge generated through experimentation can be leveraged to reinforce existing relationships—or create new ones—within their ecosystems. In this article we’ll describe how firms are getting better at adopting gen AI through experimentation—within their own organizations and across entire ecosystems. Software organizations have been pioneers in this work, but some companies, such as Siemens, are starting to carry it out successfully in the physical world of production.
[Key Insight #1]
The Adoption Challenge
Although the promise of gen AI is great, many organizations have yet to fully embrace it. The fact that gen AI tools generate hallucinations and unreliable results is one reason firms have balked at using them in high-stakes settings. But a deeper reason, various experts say, is that gen AI’s true economic potential lies in creating entirely new systems of value, which are hard for organizations to recognize, let alone pursue. For a historical parallel, think about electricity: Manufacturing plants took almost 40 years to adapt to the technology and optimize themselves around it, according to the late economic historian Paul David.
Embedding gen AI technology at the organizational level will require companies to carefully work out how to integrate it with existing processes, routines, and teams. In manufacturing, the challenge will be even greater, because in that domain the need for performance, reliability, security, and smooth integration with human workers is so strong.
Seen in this light, the slow rate of successful adoption is not surprising. It reflects the larger challenge of making gen AI organizationally useful, not just technically impressive. That’s where organizational experiments can help.
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Fast and rigorous experimentation is emerging as a strategic imperative in the gen AI era. Firms that develop the capacity to test, learn, and adapt in real time—both internally and across their ecosystems—will be better positioned to translate technological potential into organizational advantage. Here’s the uncomfortable truth: While you’re debating gen AI strategy, your competitors may be systematically learning what works. By embracing experimentation as a discipline, businesses can transform uncertainty into a source of strategic differentiation, and in doing so they can shape the future of work.
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