Losing Value from Generative AI Because “People Dimension” Not Addressed

Process reengineering efforts at many companies in “the ‘90s” failed because these efforts did not include effective organization change management. BCG research in 2020 identified the “people dimension” as the “determining factor” as to why companies fail to capture full value from digital transformation.

Company leaders are at high risk of repeating this mistake and drastically undermining the value that gen AI can generate if the critical “people dimension” is not addressed.

Having the “right” technology and “use case” are essential, but not sufficient to generate and capture full value from gen AI. Here’s an illustration why. Take supply chain optimization. Gen AI can analyze vast amounts of data e.g., historical traffic patterns, weather data and fuel costs to generate new shipping routes that are not only faster but also more fuel-efficient, reducing transportation costs. But if your employees do not understand how or when to use gen AI, these performance improvements will not be realized and significant value will be lost to your company. Having employees who are well skilled in the effective use of gen AI, who embrace the performance improving changes that effective use of gen AI can provide, and who pro-actively seek out the right opportunities to use the technology, will maximize the value that gen AI produces.

As companies move from “proofs of concept” to adopting and scaling gen AI across thousands of employees the potential value loss significantly increases. Leaders should ask how can value from gen AI be maximized if:

1. Effective use of generative AI is not embedded in, and an intrinsic part of, the daily work of employees across the enterprise? This requires steps to ensure that gen AI is not used by employees for “a day or so” and then forgotten about. It also requires taking steps to ensure that gen AI is embedded in the daily work of potentially thousands of employees, not just small, individual “pockets” of employees.

2. Employees do not have the skills or behaviors to use gen AI effectively on an ongoing basis – both now and as the technology continues to advance.

3. If a significant portion of employees resist change, e.g. because they fear the loss of their own, or those of their colleagues.

If these challenges are not successfully addressed, full value from gen AI will simply not be captured. Addressing these challenges is the heart of what effective organization change management is about. It is why, top executives must make the essential “people element” that has largely been missing, an integral part of their efforts to adopt and scale gen AI to capture value across the enterprise.

It is important to understand though that conventional organization change management is not fully up to the task. There are unique organization change challenges associated with generative AI that conventional organization change management does not address. These include: the need to embed gen AI in the work of employees; the new behaviors required, e.g. treating gen AI as a collaborator; and addressing potential sources of change resistance e.g. changing employees’ mindsets from viewing generative AI as a threat to viewing developing a proficiency with gen AI as a means of eliminating routine, repetitive work; augmenting and improving their performance, and increasing their attractiveness to both current and potential future employers. Just like the underlying gen AI technology, innovation in organization change management methodology is also required. Fortunately, these innovations in methodology now exist. A carefully orchestrated combination of these change management innovations along with “tried and true” methodologies, must be made an integral part of transformation efforts - alongside the best possible technology and use cases - to help ensure that full value capture from gen AI transformation is achieved.

My thanks to Ted Shelton and Andy Ward.

Note: This is the first time I have posted my own content on LinkedIn. If you have found it helpful, please repost.

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