The AI World Moves Toward Organizational Deployment

It's about time..

Stanford professors Bob Sutton and Melissa Valentine at AI + Organizations Conference

Much of the effort and attention around AI for the last several years has been around technical developments. New model announced! New benchmark surpassed! New contract for massive data centers! New world-class technologists hired! You know the drill.

I am happy to say, however, that things are beginning to change in this regard. AI companies are beginning to realize something that many corporate executives knew intuitively. What matters isn’t the technology—OK, that’s important too—but the ability of organizations to deploy it effectively and get value from it.

There are several signals of this shift. I participated in one a couple of weeks ago, giving a talk at the first conference held by the “AI and Organizations Lab” at the Stanford Institute for Human-Centered AI (HAI). The lab, headed by Stanford professor Melissa Valentine (a co-author I have enjoyed working with), is one of the first—and certainly the most prominent among—AI research centers to focus on organizational issues.

The first day of the conference was attended by academics, reflective business practitioners, and representatives of the lab’s key sponsor—none other than Google DeepMind. That one of the world’s great centers of AI technology believes that organizational factors are important to the success of AI overall is a big step forward. There is an ongoing research center at DeepMind on organizational AI topics, headed by Martin Gonzalez.

The second day involved a large group of organizational behavior academics and Ph.D. students. The goal was to begin outlining some areas of interest and methods for effectively researching and publishing about generative AI. I am often frustrated with how long it takes for academic research to be published, but there was an exception represented at the meeting. Aruna Ranganathan and a co-author published the HBR article “AI Doesn’t Reduce Work--It Intensifies It” earlier this year, and it’s a fine example of how organizational behavior research can relatively quickly shed light on AI’s impact on people and companies.

AI Companies Addressing Deployment as Well

In addition to Google, a couple of other major AI providers recently announced initiatives to address deployment issues. OpenAI, for example, announced “Frontier Alliance” partnerships with BCG, McKinsey, Accenture, and Cap Gemini to implement AI in customer firms. OpenAI had previously begun to hire “forward deployed engineers”—the new phrase for a technically-focused consultant—in 2024 to help their customers deploy AI use cases and move beyond pilots, and the consulting firms will partner with them. The widespread use of the FDE role, perhaps driven in part by the stock multiple of Palantir, which invented the term, probably still isn’t sufficiently focused on organizational and behavioral issues judging from this job post, but it’s a step in the right direction. OpenAI also announced recently that it would partner with several large private equity firms to help deploy AI in their portfolio companies. The OpenAI Deployment Company, like many OpenAI initiatives, also seems like a way to raise more capital.

Anthropic’s moves are very similar to OpenAI’s. The Claude vendor created an “Applied AI” capability to aid deployment and has also formed a partnership with private equity companies, the goal of which is to establish a new AI services company.

Granted, neither of these organizations are particularly focused on the human issues involved in putting AI systems into production. The deployment-oriented roles they have created are primarily focused on technology architecture, optimizing performance, and other technical aspects of AI implementation. The umbrella and somewhat generic term “change management” doesn’t appear anywhere in job descriptions or marketing copy.

Is Tech All That Matters?

Even at Google DeepMind’s organizational AI group, I suspect there will be a strong focus on using technology to address organizational issues. For example, DeepMind and Stanford HAI sponsored a “grand challenge” among university teams that proposed various problems and approaches to organizational adoption. The winner in what I assume was an objective evaluation (there were judges from Stanford and Google, but also several other universities) was a proposal from Stanford. That’s fine, but it was pretty technical, as this description suggests:

Wang and Goldberg will use the modern transformer architecture for machine learning, which excels at finding patterns in sequential data, to build a “large coordination model” that learns how successful teams coordinate their work and predicts which sequence of actions will work best for a given scenario in the future.

I have no objection to this effort, although I suspect that human individuals and teams often employ some behaviors that are difficult to predict with a generative AI model.

I am confident that Valentine and her collaborators at HAI (of which I hope to remain one) will not reduce all issues in the AI and organizations domain to technology issues. Perhaps someday even the AI tech firms will realize that the adoption and effective use of AI results from a complex web of human factors including workflows and workarounds, skills, rational and irrational behaviors, emotions, power, and economics. Maybe someday AI will be able to predict and resolve all these factors, but I don’t think we’re close to that yet.