The C-suite is eager to tap into the vast potential of generative AI, but many executives are still cautious. So far, only 6% of companies have trained more than a quarter of their workforce on generative AI tools. Two-thirds of executives surveyed believe it will take at least two years for AI to progress beyond the initial hype stage. Company boards and executive teams are still asking critical questions about how this technology works and how it will impact their business operations.
Matthew Kropp, CTO and managing director at BCG X, the technology design unit of Boston Consulting Group, mentions that we’re now in a phase where companies are looking for ways to create real impact. Clients are investing significantly, aiming to revolutionize their business processes, although tangible results are still emerging.
However, mastering the technology is only part of the puzzle. Organizations need to view generative AI as more than just a tool to be handed over like a software update. It’s about understanding how generative AI can transform work by breaking down processes and roles to see where AI can eliminate mundane tasks and enhance valuable human work.
Another major challenge is the cultural shift required to ensure employees welcome this new technology. Adopting AI is difficult partly because it’s so new, and most people haven’t yet learned how to work with these tools effectively. Employees need to want to use them and see their benefits, which means overcoming resistance.
As companies experiment to solve pain points and create impact, they will encounter employee resistance. This includes reluctance to learn new methods or refusal to use technology that they fear might replace their jobs.
Kropp states that while every company will find some big opportunities, these might not be the best starting points. For example, improving efficiency in a call center or refining marketing processes can deliver significant gains. However, identifying smaller opportunities in everyday work across the organization is also crucial. Engaging the entire company through education, access to tools like Enterprise ChatGPT, and encouraging them to rethink how they work is key.
Employees need to feel that the goal is to improve their jobs, enhancing the joy they find in their work by removing routine tasks and allowing them to focus on what they enjoy. Research by BCG reveals that employees who are happy at work are far less likely to consider leaving their jobs.
By focusing on reducing tedious tasks and maximizing joy, organizations can ensure technology serves human needs. Human creativity, diversity of thinking, risk management, and relationship-building are aspects that technology cannot replace.
For example, a financial institution with over 12,000 engineers is implementing GitHub Copilot, a generative AI tool that helps write and aid in coding. The focus is not just on training but also on demonstrating how the tool can make their job more enjoyable by taking over the less interesting tasks.
BCG has developed the ADORE framework to successfully integrate AI while enhancing employee well-being. This involves:
– Aim for Outcomes: Clearly defining what the business hopes to achieve with AI.
– Diagram status quo: Mapping out the current process from start to finish.
– Optimize for AI: Identifying which steps can benefit from AI and which should remain human-driven.
– Redesign the process: Adjusting the workflow to integrate AI’s strengths.
– Ensure outcomes: Measuring results to ensure goals are met and employee satisfaction is maintained.
Experimentation remains critical. While big use cases like improving call centers and speeding up software development are evident, there’s much more potential. AI-powered knowledge management and advancements in fields such as biopharma, insurance, and consumer sales are emerging. However, it’s crucial to continually explore these opportunities while keeping employee engagement central.
Unearthing major goals from the top and transforming company processes with generative AI is essential for lasting change. Organizations need to actively experiment and invest in AI development while prioritizing employee concerns to ensure that their engagement remains at the core of AI initiatives.