The four Es of AI: Keeping up with the trend that never sleeps
With regard to creating that scale, Chris Davis, a Partner at the digital advisory Metis Strategy and a leader of his firm’s AI practice, worries less about scaling the technology than he does about people’s role in that scale.
With regard to creating that scale, Chris Davis, a Partner at the digital advisory Metis Strategy and a leader of his firm’s AI practice, worries less about scaling the technology than he does about people’s role in that scale. “Someone has to develop, train, and supervise the models,” he explains. “…the irony is that people could actually be the limiting factor.”
As a means of overcoming this limitation, he stresses how necessary it is that organizations revisit—and where appropriate, revise—their operating models. “You need to re-envision business strategies with the exponential scale of AI in mind,” he says. “And train product managers on how they might weave AI into anything—core digital products, customer experiences, employee experiences, and so on.” He goes on to explain, that means also ironing out the roles and responsibilities among various players in your organization: “AI laboratories, data scientists, product teams—they all have to know how to work together efficiently every step of the way, from identifying use-cases to building algorithms and models, from following AI operating procedures to monitoring any models that are already in use.”
And there’s plenty of evidence to support Davis’s point. For example, after recently redefining the roles, responsibilities, and delivery methods of its IT product teams to suit its specific AI ambitions, a global financial services provider discovered many gaps in its capacity: some that it could address through upskilling, but also some that would require it to hire new people.
Looking forward. Meanwhile, hyperbolic headlines will continue to outpace adoption; yet, they won’t outpace the exponential rate at which the volume of data is growing, especially as technologies such as 5G and IoT hit their stride. So, if you, too, want to leverage AI to its fullest extent, you must first look in the mirror: Can I manage this growing volume of data? If you can’t convert the data into something meaningful, then, as Lenovo’s tech chief, Art Hu, suggests, you might lose ground: “If you don’t figure out as a company how to (manage a growing volume of data) effectively and efficiently, the competitor that does is potentially going to have a significant advantage.”
As you mature your data strategy, remember that you have many data-driven tools at your disposal, only one of which is AI. It’s wedged between an ocean of use-cases to the North and your core data foundation to the South, and progress in each of these layers is linked to the other two inextricably. There’s no use in thinking of your data strategy as something binary, as if it were a building under construction that will one day be complete. Those that educate, explore, experiment, and expand, perpetually, with the right pace and sequencing, are those most likely to win with AI.
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