Many companies are unable to take full advantage of the huge potential of AI because employees don’t trust AI tools enough to turn their work over to them and let the machine run. Resistance to disruptive, technology-driven change is not unusual. Specifically, many people resist AI because of the hype surrounding it, its inherent lack of transparency (its decision-making seems to take place in a black box), their fear of losing control over their work, and the way it disrupts familiar work patterns. So, what can companies do to help employees become more comfortable working with AI systems? Overcome AI resistance by running experiments, creating a way for employees to visualize the decision process of the AI, and engaging constituencies who would benefit from the technology. The sooner you get people on board, the sooner your company will be able to see the potential results that AI can produce.
David Maister was angry. He had been surprised and annoyed to learn that his company had set up a new AI-based marketing system that was doing most of what he thought was his job as digital marketing manager at Global Consumer Brands: deciding what ads to place where, for which customer segments, and how much to spend. And when he found that the system was buying ads for audiences that didn’t fit the company’s customer profile, he stormed in to his boss’s office and yelled, “I don’t want men and women over 55 buying our product! It’s not our audience!” Maister demanded that the system vendor modify it to enable him to override its recommendations for how much to spend on each channel and for each audience target. The vendor scrambled to give him the controls he wanted. However, after being given the reins on budgeting and buying decisions, Maister saw his decisions were degrading results. For example, despite the company’s younger customer profile, men and women over 55 were buying gifts for their children, nieces, and grandchildren, making them, in fact, a very profitable audience.
Maister returned control to the system and results improved. Over the ensuing weeks, he began to understand what the system did well, and what he could do to help it. He learned to leave decisions about where to spend and whom to target to the system. He focused on introducing more strategic parameters, such as the aggressiveness of a campaign, or a limit on spending, and on testing different approaches to execution. The results continued to improve throughout 2017 as the system learned and got smarter, while Maister learned how to improve the Brand’s strategy in response to the insights produced by the AI. Within the first three months of using the system in new channels, the Brand saw a 75% increase in purchases from paid digital channels, a 77% increase in purchase value, a 76% increase in return on add spend, and a significant decrease in cost per acquisition.
The names in this story have been changed, but the moral is clear: If you give control over AI experiments to employees to keep them involved, and to allow them to see what the AI does well, you can leverage the best of both humans and machines.
Unfortunately, companies will be unable to take full advantage of the huge potential of AI if employees don’t trust AI tools enough to turn their work over to them and let the machine run. This problem of low AI adoption rates is increasing as businesses of all kinds are seeing successful applications of AI and realizing it can be applied to many data-intensive processes and tasks even as AI technology — once only available at large companies like Google, Amazon, Microsoft, and IBM — is now becoming less expensive and easier for smaller companies to access and operate, thanks to AI-as-a-Service.
Resistance to disruptive, technology-driven change is not unusual. Specifically, many people resist AI because of the hype surrounding it, its lack of transparency, their fear of losing control over their work, and the way it disrupts familiar work patterns.
