How is your organization preparing for artificial intelligence (AI)? Ask this question of businesses investing in this field today, and the answer almost always comes down to “data”– with leaders talking about “data preparations” or “data science talent acquisition.”
While there would be no AI without data, enterprises that fail to ready the other side of the equation– people— don’t just stunt their capacity for good AI, they risk sunk investment and jeopardize employee trust, brand backlash or worse.
After all, people are the ones building, measuring, consuming and determining the success of AI in enterprise and consumer settings. They’re the ones whose jobs will change; whose tedium will be eased by automation; whose consumption or rejection of AI’s outcomes will be the focus.
People, in short, are those who’ll feel AI’s myriad impacts. That’s why investing in AI is as much about investing in people as it is data.
I wanted to dig deeper into this issue. So, my co-founders and other industry analysts at Kaleido Insights and I surveyed more than 25 businesses that have deployed AI at scale to learn about the ways they’ve invested in people. Here is what we found:
1. Investment in factors beyond technical talent
Hiring a team of data scientists will not cause business processes to magically become automated overnight. Some liken this mistaken assumption to hiring electrical engineers to run a bakery: While the mechanics of ovens are important, it is the experienced baker who best knows how to innovate recipes and inspire customer delight!
Across industries, we found that the successful AI deployments we saw involved at least eight distinct personae:
- Product leaders
- Front-line associates (e.g., customer support agents, field technicians)
- Subject matter experts (e.g., doctors, security admins, legal, etc.)
- End users
- Data scientists & technical builders
In addition to identifying these stakeholders, businesses have to make AI accessible and build trust by educating people and quelling fears. The top recommendation here is to prepare stakeholders by using tactics that put AI into context for each role.
Leadership requires a demonstration of ROI and visualization. AI leaders at FedEx, for example, built simulated dashboards and reports to illustrate the difference between traditional analytics and machine-learning-driven recommendations.