AI & Futurecasting: Preparing Work, Not Just Workers
Every major technological shift changes work. Artificial intelligence is different primarily in the speed at which it is doing so.
Much of the conversation around AI has centered on automation, productivity, and workforce disruption. Those are important discussions, but they can distract leaders from a more practical question.
“What specifically, is going to change inside my organization over the next three to five years?”
For insurance organizations, that question deserves thoughtful attention. Claims processing, underwriting, actuarial analysis, customer service, software development, regulatory compliance, and nearly every other knowledge-intensive function are already beginning to evolve. The challenge is not determining whether AI will affect these roles. The challenge is understanding how.
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That is where Futurecasting becomes valuable.
Futurecasting is a structured way of examining work before change arrives. Rather than beginning with which jobs might disappear, organizations break roles into the activities people actually perform. They consider which activities AI could make faster, where human judgment will become more important, and what entirely new responsibilities may emerge.
The goal is not to predict the future with perfect accuracy. The goal is to prepare people for the future before it becomes urgent. Being directionally correct now is more important than being completely right later.
One of the most important lessons we have learned is that AI rarely replaces an entire role. More often, it changes the composition of the work.
Consider software development. AI coding assistants have dramatically accelerated the production of code, but writing code represents only one part of the development process. Defining requirements, validating architecture, integrating systems, testing, managing security, and ensuring quality do not necessarily accelerate at the same pace.
As more code moves through the workflow faster, pressure builds at these stages, making them relatively more important. The productivity gain does not eliminate the bottleneck—it moves it.
This is why technological change is frequently uneven–or “lumpy”. Different activities advance at different speeds, creating new demands and shifting where people contribute the greatest value.
The same pattern is emerging across insurance. Claims professionals may spend less time documenting files and more time resolving complex customer situations. Underwriters may automate portions of information gathering while dedicating more attention to risk judgment. Customer service representatives may rely on AI to answer routine inquiries while investing more time in empathy, relationship building, and solving unusual problems.
The work shifts, but people remain essential.
This insight has significant implications for leaders. Organizations often think about AI through the lens of efficiency. Futurecasting encourages leaders to think about opportunity.
If AI reduces the time required for lower-value activities, how should that newly available capacity be invested?
Some organizations will simply ask people to complete more work. Others will intentionally redirect that capacity toward innovation, stronger customer relationships, better decision-making, coaching, and strategic initiatives that have struggled to find time in already full calendars.
Those choices will shape an organization’s culture as much as the technology itself. AI may create additional capacity, but leaders ultimately determine whether that capacity translates into more volume or greater value.
We recently worked with a large mutual insurance company to explore how AI could influence work across the organization in the coming years. Rather than beginning with specific technologies, we began with the work.
Through a series of structured workshops, leaders broke key roles into the activities people actually perform. They examined how emerging AI capabilities might influence those activities, where human expertise would become even more valuable, and what new skills would likely become important over time.
Several themes emerged from these conversations.
First, opportunities for automation were uneven. Some activities could change dramatically while adjacent activities changed very little. Looking at entire roles would have obscured those differences and could have led to unrealistic workforce assumptions.
Second, higher-value human work consistently expanded. As administrative effort declined, leaders saw growing demand for collaboration, critical thinking, coaching, relationship management, governance, and cross-functional decision-making.
Third, accelerating one activity often created greater demand elsewhere in the workflow. Returning to software development, faster code generation can create new pressure around system integration, testing, cybersecurity, and organizational adoption. We observed similar patterns throughout the insurance workflows we examined. Recognizing how these bottlenecks could shift helped leaders anticipate new capacity and skill needs and develop more practical strategies for the future.
Together, these insights produced an important shift in perspective: The conversation moved from concern about job loss to curiosity about job evolution. Leaders could move beyond broad assumptions about automation and begin making more informed decisions about how roles, workflows, and development priorities should change.
When employees understand how their work is changing—and why new capabilities are becoming important—learning becomes connected to purpose rather than fear. Development feels like an investment in their future instead of a defensive response to technology.
Organizations benefit as well. By redirecting capacity from repetitive administrative work toward judgment, creativity, empathy, and expertise, they can improve decision-making, strengthen customer relationships, and create more space for innovation. Leaders also gain a clearer foundation for workforce planning because they understand how the work itself is evolving, rather than focusing only on which technologies are emerging.
The organizations that navigate this transition most successfully will not be those that simply adopt new tools. They will be the ones that thoughtfully redesign work, help people grow into higher-value responsibilities, and make intentional decisions about where human judgment creates the greatest advantage.
The future of work is not something that simply happens to an organization. It is something leaders have the opportunity to shape – _and Futurecasting provides a practical framework for beginning that work today.
If your organization is beginning to ask what AI means for your workforce, we would welcome the opportunity to share what we have learned and explore how Futurecasting can help you prepare for the work ahead.
Baton Global’s mission is to provide strategy, innovation, leadership and research services for solving our clients’ most complex challenges, transforming organizations and communities worldwide. For more information about us, see Bâton Global — Strategy, Innovation, Leadership and Research.
Authors:
Wade Britt, MIBS
Chrissy Culek, MPIA
Matthew C. Mitchell, PhD