The people aspect of an AI Strategy
AI never exists in isolation. AI tools will always be part of a wider system. AI is always interacting with people somewhere. For example, Large Language Model (LLM) outputs often act as prompts to human thinking as much as humans provide the prompts into LLMs. But understanding how the technology is best applied to individuals, teams, or organizations differs.
Empowering an individual isn’t the same as empowering a team. Let’s say an AI lets someone write more emails. If those are internal emails rather than mass marketing, it probably wouldn’t make the team more effective. Especially if their ability to respond to e-mails hasn’t improved. Supercharging the quality of the output of the individual, rather than just the volume makes more sense in that setting.
Aiding individuals will be helpful, but is unlikely – alone – to revolutionise the performance of their team. There is a good example from Wharton Business School, a team that builds educational tools. Incorporating AI to give feedback about the tool prototype saved the bandwidth of testers. Using AI to act as a live participant in a meeting, to take notes, collect human feedback, and make code changes all saved time. But it was only by changing workflows and patterns of behavior across the whole team, weaving AI into those systems, were they able to cut delivery times from over two weeks down to days. The astute reader here will already have seen the cost and potential source of resistance to overcome. Change like this is far more disruptive than simply giving a piece of AI software to an individual. There might have to be a change in how the entire team works with the introduction of AI.
The final level is the ‘organization’; this is quite distinct from the ‘team’. A team is small and connected enough that any one member can have tacit knowledge about all the other people in the team. Who they all are, what they are doing, who needs or can offer help. An organization is simply too big for that. It would be impossible for any one individual to know what everyone in HSBC or Google did for example. Organizational changes are either simple additions like the addition of a new software package all employees might use, like empowering the individual but at scale, or they can be large structural changes on how the business functions.
To understand the link between disruption and productivity, an example I use comes from the era of electrification.
Before electrification, many factories’ tools were belt-driven. The belts transferred mechanical power from overhead transmission rods running across the factory driven by some central engine. When electrification first happened, there wasn’t an immediate productivity boom. At least not a big one. Power was only required when the tool needed to run, overhead transmission rods weren’t required to turn continuously; but this was a small energy saving. The real change happened when the layouts of the factories themselves were changed. Production processes could be completely refined. In the belt driven era, the factory layout was governed by the efficiency of the rod-belt power lay out; not the ergonomics of activity required to make the product. The assembly line as we recognize it now was essentially impossible. Once the tools could be placed anywhere, they could be set out to enable a vastly more efficient and effective assembly process of people and machines. How the organization flowed had to change to realize the benefit, not simply swapping in substitute technologies into the existing business layout.
Understanding individual behavior, team behaviors and processes, and organizational workflows, is fundamental to ensuring the success of AI deployment.
