Presented by BCG

The C-suite wants to capture the massive potential of generative AI, but many are hovering on the threshold instead. Only 6% of companies have managed to train more than 25% of their people on gen AI tools so far, and two-thirds of the executives surveyed believe that it will take at least two years for AI and gen AI to move beyond the hype. Many boards and executive teams are cautious, and asking the big questions: How does this technology work, and how will it change the way we do business?

“We’re now moving into a phase with companies focused on ways to deliver real impact,” says Matthew Kropp, CTO and managing director and senior partner of BCG X, the technology build and design unit of Boston Consulting Group. “We see clients taking big swings and spending millions of dollars on big objectives with the goal of changing how their business works. While we haven’t seen lots of value put in the bank, we’re getting there.”

But mastering the technology itself is only one part of reaching gen AI’s potential. Organizations are recognizing that gen AI is more than a tool that can be handed over to employees as if they were asking them to install a Microsoft Word update. Instead, it’s an opportunity to think about where and how generative AI can transform an organization and how work is done. That requires breaking down employee processes, functions and roles into their component parts to understand where and how generative AI can replace drudgery while augmenting and enhancing valuable human work.

But that also means addressing the second point -- and it’s not a trivial one: the major cultural shift necessary to turn potential employee resistance or fear into enthusiasm. AI adoption is difficult, in part because it’s simply so new, and most folks have not yet learned how to effectively work with these tools yet. But not only that -- employees have to want to use them, and want to learn how to use them effectively.

“As companies start to experiment, aiming for resolving the pain points and delivering real impact, they’re going to start to run into employee resistance for many reasons."

“As companies start to experiment, aiming for resolving the pain points and delivering real impact, they’re going to start to run into employee resistance for many reasons, including resistance to learning new ways of working or simply refusal to use a new technology that they believe is coming for their jobs,” Kropp says.

Identifying business potential and capturing employee buy-in

From the top down, every company is going to have a few big opportunities, Kropp says, but that may not be the place to start.

“If you run a giant call center, that's a big opportunity to make that call center more efficient. If you spend billions of dollars on marketing, there's a big opportunity to streamline the way that you create content,” he explains. “But there are also thousands of small opportunities that are in everybody's day-to-day work.”

To find those opportunities, you need to get the whole organization engaged by educating them, which is key to managing this type of cultural change. For instance, giving them access to tools such as Enterprise ChatGPT, training them how to use it and encouraging them to think about how to redesign their work.

“Employees need to believe that the end goal is to improve their jobs in a way that actively amps up the joy they take in their work, talent and skill, and to make them happier in their roles.”

“You need grassroots ideation -- people getting excited about the possibilities of the technology, realizing that they can take the toil out of their work, while giving them the time and space they need to dig into the parts of their job they truly enjoy,” he adds. “Employees need to believe that the end goal is to improve their jobs in a way that actively amps up the joy they take in their work, talent and skill, and to make them happier in their roles.”

AI and gen AI tools deployed in search of productivity alone ignore employees and their needs. But research from BCG managing director and senior partner Debbie Lovich and her team at BCG's Henderson Institute shows that employees who enjoy their work are 49% less likely to say they would consider taking a new job.

"Focus on minimizing employee toil and maximizing joy. Look at the employee’s work processes and figure out what they do, and what parts are painful or unfulfilling. That's what you automate."

“So, we have to reframe,” Kropp says. “Our recommendation is that you focus on minimizing employee toil and maximizing joy. Look at the employee’s work processes and figure out what they do, and what parts are painful or unfulfilling. That's what you automate. But humans can never be replaced when you need creativity, diversity of thinking, risk management, relationship-building and more -- the interesting and engaging aspects of work, and they’re the very human aspects that technology will never be able to take over.”

For instance, one of BCG’s clients, a financial institution with over 12,000 engineers, is implementing GitHub Copilot, a generative AI tool that can both write code and aid engineers as they’re coding. In the rollout, they’re focusing not just on basic training and best practices for using the tool effectively -- they’re also demonstrating how the tool eliminates many of the tedious parts of the job while keeping the more rewarding parts of the work in the hands of the engineers.

“Generative AI is really good at writing test code, and engineers get really excited about this because they realize they can spend more time doing the creative, problem-solving part of their job,” he explains. “They actually start to see this is not replacing me, this is letting me operate at a higher level. It's letting me add more value. It’s letting me think more creatively. It's letting me do more of the fun stuff and less of the stuff I don’t like to do.”

Incorporating joy into forward-looking AI strategies

BCG has developed the ADORE framework as a roadmap to successfully implementing AI while increasing employee well-being. It can be used by the organization as a whole, to transform a function end-to-end, or by a single team hoping to change its processes and the way it functions.

A: Aim for Outcomes. The first step is articulating precisely what you aim to achieve by incorporating AI into a business process, whether that’s improving customer satisfaction, reducing costs or increasing selling time.

D: Diagram status quo. Once you’ve identified what you’re hoping to achieve, you diagram every step of the process you’re targeting from start to finish.

O: Optimize for AI. Here you examine each step of the process to identify which steps are toil, and which are joy, or in other words: What parts of the process can and should be redefined with generative AI and which things should remain in the hands of humans?

R: Redesign the process. Once you’ve identified the places generative AI’s strengths will add value to a process, you can redesign it. It could mean adding automation to one part of the workflow -- or it can mean rethinking and redesigning the entire process from the ground up.

E: Ensure outcomes. Here the right metrics are put in place to measure outcomes and ensure you’re reaching the goals and seeing the outcomes that were set out at the beginning. It’s also important to measure things like employee adoption, productivity gains and the level of joy employees get from work.

Why experimentation is still critical

Big-swing use cases like call center efficiency and accelerating software development are just the tip of the iceberg, Kropp says. For instance, we’re seeing the transformation of AI-powered knowledge management, which began as a chatbot layered over a vector database. Today knowledge management can synthesize insights from the proprietary data that lives in every facet of an organization.

"You need to continue to dig deep to find these opportunities, and ways to engage employees with lasting, positive change that AI represents."

Meanwhile, engineers are learning new ways of working, supported by code-generating agents. Biopharma companies are slashing the length of R&D cycles to bring drugs to market faster. Insurance companies are using gen AI to dramatically speed up the underwriting process, while consumer product companies are creating new direct-to-consumer sales channels through virtual sales agents and more. But you need to continue to dig deep to find these opportunities, and ways to engage employees with lasting, positive change that AI represents.

“Unearthing powerful top-down goals like these, and identifying the ways gen AI will transform the company is critical in order to effect real and lasting change from the top,” Kropp explains. “So it’s very important that organizations are actively experimenting and investing in application development. But the most important work of the next three to five years will be addressing employee concerns and ensuring that employee engagement stays central to your generative AI efforts.”

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com