People make technology go round

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Artificial intelligence is falling into the same trap that many technologies have fallen into over the decades: grab gobs of the latest shiny new technology, drop it on top of the organization, and wait for the overnight transformation to take shape – which never does.

Just as handing someone a pile of expensive film-making gear won’t turn them into the next Steven Spielberg, all the AI tokens in the world won’t turn a workforce into a forward-looking force in the market. It takes a forward-looking culture, open to innovation from all its ranks, to make AI a success.

That lesson is being learned anew in the AI era, and, as usual, after pouring in millions of dollars, euros, rupees, and pounds to acquire the latest technology. “There’s a reflex to solve every problem by buying more AI, adding more tools, or pushing people to use AI whether or not it helps,” state the authors of a recent report out of Glean’s Work AI Institute, a collaborative effort with AI experts at top universities such as Stanford University, University of California at Berkeley, and Harvard University.

“High AI achievers don’t just prompt and pray,” the study’s authors state.

The 6,000 workers involved in the study estimate that AI automation saved them at least 11 hours every week. At the same time, only 13% say their organizations are performing significantly better as a result.

The study’s authors separated out the top performers in AI (people who report both productivity and quality gains from using AI) versus the rest. The data shows that the successful companies – 13% of the sample – aren’t “buying more AI tools, burning more tokens, or building adoption dashboards that glow a triumphant shade of green. They’re doing the harder work of treating AI as a work-design problem, not a procurement one.”

The successful AI organizations “start with the work, selecting tools and platforms that fit the job instead of letting vendor contracts dictate their AI strategy, the authors point out. “And they understand that giving AI access to data is not the same as giving it context.”

Tellingly, more than half of workers, 53%, say critical information they need to do their jobs is not accessible through their AI systems. By contrast, workers in “context-rich” AI organizations are 64% less likely to feel worn out by AI, 52% less likely to ship work they can’t explain, spend 9% less of their AI time botsitting, and are 31% less time botshitting.

Such organizations are still the exception. “Most organizations will keep learning the hard way that AI’s time savings aren’t free,” the Glean authors state. "The hours workers save come back as botsitting. The judgment they offload comes back as
botshitting. The workplace fills up with work that looks finished, sounds confident, and is hollow enough that some exhausted human — usually without credit or reward — still has to mop it up."

The AI achievers are 18% more likely to refrain from using AI on certain tasks, the data also shows. And “they’re also more likely to bend or break the rules to get value from it: 54% use unapproved tools or approved tools in noncompliant ways, and 36% hide how much AI is helping them — often because they’re working around an official system that is too slow, too narrow, or too disconnected from how the work actually gets done.”

The companies pulling ahead aren’t just swapping out tasks for AI; they’re actively redesigning work, the study also concludes. In top-performing organizations, 90% say their employer treats AI as a “chance to redesign work,” compared with 54% of the lagging organizations,

Very importantly, 90% of workers in advanced AI organizations say their employer provides enough AI training and support, compared with 52% at less-engaged organizations. Reward systems also are being redesigned around AI – 84% of the AI leaders say their employer formally rewards AI skills, compared with 48% of the laggards.