Almost all of my recent GenAI conversations with leaders have focused on tactics. How do they fund projects with existing budgets? What’s the path from experimentation to industrialization? How can they build stronger data foundations? While these are interesting and essential, I’m always struck by how people have become an afterthought in the GenAI discourse.
I’ve seen this throughout my data industry career. Technology rollouts leave the human element to the end, getting by with standardized individual or group training curriculums. This has mostly been sufficient since the new applications or technology focused on changing how something was done – e.g., a new ERP transaction recording process, building a CRM customer record, or similar tasks.
GenAI requires employees to have a new and diverse set of skills. The ongoing exchange and dialogue of GenAI experiences are unfamiliar to most employees. They need to understand a wide range of data dynamics: where it’s coming from, what might be missing, what decisions need more data, and how their data use impacts other functions. They also must figure out when and how to apply intuition and context to GenAI-powered insights to create the best outcomes.
As data leaders democratize GenAI, they must realize success depends on significant and ongoing change management. The recent increased emphasis on data literacy and efforts to become more data-driven is a start, but most are siloed and not uniform across the organization. GenAI roll-out strategies must meet people wherever they are on their data journey and account for an entire spectrum of skills and comfort levels.
Change Management for the GenAI Workforce
There are two central aspects of change management for GenAI. First, leaders need to spend much more time educating and creating awareness. Change creates fear, and decision-makers too often leave employees in the dark during GenAI deployments. Think about long-haul drivers worried about autonomous vehicles taking their jobs, or a copywriter thinking that ChatGPT will replace them.
Anyone interacting with a GenAI-enabled product or process should be provided a transparent and clear understanding of the changes happening, details on how they will affect their role, and a direct path to appropriate training to leverage new capabilities.
Second, enablement must meet the user where they are in their specific journey. I’ve recently spoken with entrepreneurs from fields ranging from medical services to designers to the building trades. There is real interest in using AI and GenAI. What they lack is time! They’re too busy day-to-day to spend hours researching and trying out tools. As an industry, we need to make it easier for potential users to understand what solutions are right for them, and how to fit tools more easily into existing workflows.
This is an extension of the ideas championed by data literacy thought leaders for a while now. They’ve seen too many companies that expect employees to train themselves during off hours or cram learning into daily workloads. That approach sets the organization and staff up for failure, with most people usually falling back on old habits or misusing/underutilizing new tools to limited benefit. The organization suffers from many issues, including an inequitable playing field, substandard results, significantly disengaged or disgruntled employees, and projects with limited ROI.
We need to learn from these past mistakes. The transformative opportunity with GenAI is too great to squander. Leaders deploying GenAI must ask themselves: are we being as transparent as possible about changes and the potential impact on individual roles? Do we have a plan to meet individuals wherever they are on their journey to understanding and embracing data in their work? Do our plans respect and enable incorporating human intuition, context, and experience?
Answering these questions clearly and with positive intent is crucial to creating the conditions for GenAI’s success. By drawing on people’s innate curiosity and willingness to learn, we can empower employees to reimagine their roles and become partners in designing a GenAI-driven future.
