The future of human-machine teaming

A conversation with AI strategy consultant Kathleen Featheringham

AI penetration in the enterprise has tripled since 2018, with 37% of companies now deploying artificial intelligence in some form, according to a 2019 Gartner survey. Meanwhile, IDC estimates that 40% of all corporate tech spending in 2019—more than $2 trillion—will go toward digital transformation projects that apply AI, machine learning, digital workflows, and other emerging technologies.

Will this digital boom lead to happier and more engaged workers? That’s every company’s hope, but the challenges are huge. The majority of workers today remain disengaged from their jobs, workplace stress levels are on the rise, and many worry about the impact of automation and AI on long-term job security.

CTOs and CIOs all of a sudden want to talk about the people elements because they’ve watched too many of their AI projects fail.

How should smart companies plan for the future? For starters, they can invest in technologies that improve the employee experience. They also need to get serious about change management and culture-building, says Kathleen Featheringham, director of AI strategy and training at Booz Allen Hamilton. In a conversation with Workflow, Featheringham explained why companies need to focus on human machine teaming rather than tech.

How can CIOs and CHROs keep the focus on people during digital transformation projects?

Most people think of change resistance as binary: “No, I don’t like it.” Or, “Yes, I do.” But there are stages, and you finally get to a state which is: “I believe that this actually helps me do my job better.”

HR leaders should ask, what is the employee journey in the digital transformation process? Then start communicating. The biggest mistakes happen when executives take whole groups and say, “We will take this skill that we no longer need and we’re going to train them to be this now.”

I’ve also spent a fair bit of time with CTOs and CIOs, who all of a sudden want to talk about the people element because they’ve watched too many of their AI projects fail. Figuring that out isn’t just an HR problem. It’s a problem technical leaders need to account for as well.

Why do AI and other big technology deployments often backfire with workers?

You’ll see resentment when employees don’t understand what someone’s role is going to be. I’ve seen [executives] say things like, “See this grouping of people here? We’ll be able to add in some algorithms that can replace all of them.” That’s a bad way to start. It’s bad even in terms of understanding where the technology is at, because sometimes AI doesn’t do what you think it can.

Companies need to start with demystifying what AI is and what it means for the organization. Then you can map out the journey for your organization and see where all of the different people fit in.

Where do you see the most pushback?

It’s not resentment from entry level—it’s from the middle. Your middle managers tend to be the heart of your organization. These are people who have been there for a long time and who have worked every job. Senior executives are calling the shots, telling them, “We must do this, it’ll be great.” If you’re not clear, you are challenging what they take pride in—their expertise, their identity, their world. And then you’re asking them to manage all this.

We refer to this group as the frozen middle. You need to make sure you’re training them. They need the demystification—what AI is and what it isn’t, what it means for them and why their role is so important. They have the institutional knowledge and they’re the change agents of the organization. If you empower them with good training, they will encourage the staff below and work harder for those above.

Do you have confidence that AI can be part of the solution?

Yes, but AI can’t solve the problem of adoption on its own. Change management is about changing human behavior and, to an extent, human beliefs. For true adoption, for human-machine teaming, leaders must understand AI’s potential, continually evaluate it to move an organization forward, and act within a culture of trust and respect.

Businesses don’t have a great track record of guarding workers’ interests during periods of tech upheaval. Do you think companies today will buck the trends?

I think companies have bucked that trend and will continue to do so. The reason is simple: AI is not yet a plug-and-play technology. It requires human-machine teaming. For example, Gartner estimates that 80% of analytics insights won’t deliver business outcomes through 2022. If organizations want to see the return, they will have to go at this differently.

Another prediction is that 85% of big data projects will fail, due to a lack of adoption by people. It may seem counterintuitive, but people will play an increasingly important role in how companies manage technological change.


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