By Barry Scharfman, Ph.D., Diana Saravelas, and Ryan McCreedy, Psy.D.
While “unprecedented change” is by now the default description of the artificial intelligence (AI) era, disruption is hardly new. Organizations have long faced technological, economic, and social upheaval.
What is new is the speed at which those forces now collide and the strain that speed puts on how organizations operate. In this context, it’s not change that’s unprecedented but the demands of adapting to change.
In the rush to adopt AI, many organizations mistake momentum for progress. Tools roll out, pilots multiply, and confidence rises—but the capabilities necessary to achieve results remain underdeveloped. The dissonance is striking: 68% of leaders and employees surveyed in Slalom’s 2026 AI Research Report say they can keep pace with AI, yet 93% report that such workforce barriers as underdeveloped skills and inadequate training limit their progress.
To overcome this capability gap, organizations must first redesign themselves to adapt at the rate AI demands. However, that level of adaptability doesn’t emerge on its own; it’s built through a series of deliberate steps to align leadership, talent, and value creation.
Step 1: Develop leaders who embrace exploration over expertise.
Today’s leaders were largely taught to be operators, rewarded for their knowledge, efficiency, and decisiveness. Adaptive organizations need something different: leaders who can think beyond their domain, challenge assumptions, and guide teams with clarity, not control.
This requires a new set of leadership muscles that prioritize:
• Curiosity over certainty.
• Sense making over jumping to solutions.
• Co-creation over top-down direction.
• Comfort with saying, “I don’t know. Let’s figure it out.”
But these skills don’t come from a three-day off-site retreat or a commoditized AI tool. To develop leaders who can think critically and tackle complex challenges, organizations must invest in real-world learning, executive coaching, and intentional cross-functional collaboration. Adaptability is developed through repeated experience, and embedding learning into real decision making creates leaders capable of sustaining innovation over time.
Step 2: Turn agentic AI into a catalyst for human potential.
As leaders become stewards of adaptability, they must also reshape work around the new division of labor. While agentic AI helps organizations move more quickly and efficiently, the human advantage lies in orchestrating how these systems are adopted, interpreted, and refined. AI expands capacity, but people bring the context, judgment, and accountability to drive the right outcomes.
Instead of replacing roles, the real value of AI comes from automating the routine tasks within them, freeing people to focus more on creativity, critical thinking, and problem solving. To make that shift, organizations must:
• Reassess how work is shared. Regularly evaluate which tasks AI can handle today, which are automatable within the next 18 to 24 months, and which will reshape roles over time based on feasibility, human nuance, and business value.
• Redesign processes first. Simplify end-to-end workflows before automating them. Otherwise, AI will scale broken processes faster, leaving teams to handle the fallout.
• Make AI outcomes core to the job. Redefine responsibilities to prioritize owning AI results, rewarding this shift through recognition or career advancement.
• Allow for experimentation. Enable teams with hands-on training, guardrails, and feedback loops to test, learn, and iterate safely.
With this foundation in place, introducing agentic AI can strengthen, rather than sideline, human potential.
Step 3: Treat value creation as a portfolio.
While agentic AI raises the ceiling of value for many organizations, it often emerges in ways no spreadsheet can predict. Traditional return-on-investment (ROI) models collapse under that speed and fluidity, whereas adaptive models thrive in it.
Instead of relying on fixed business cases, adaptive organizations take a more iterative approach, using rough estimates, small tests, and results measured in ranges. ROI then evolves through iteration, and teams can double down as evidence appears.
However, there are some ways to increase value up front:
• Assess readiness first, for such factors as data, architecture, and governance, and decide on build-or-buy approaches based on speed, risk, and long-term value.
• Prioritize viability over novelty, avoiding impressive but unrealistic use cases.
• Pilot quickly and measure early, using tight feedback loops to refine ROI.
• Evaluate outputs throughout the agentic process, creating step-level guardrails so results remain reliable as AI scales.
• Treat enablement as its own source of value, since adoption unlocks returns that licenses alone cannot.
Using these tactics as a baseline, leaders can then develop a balanced portfolio of ROI built on two approaches: AI solutions applied to specific use cases and broader enablement that embeds AI into daily work. Use cases deliver early returns, while enablement compounds value over time. Both are essential.
Building an Adaptive Future
AI is no longer about rethinking technology; it’s about leaders and teams rethinking how to drive meaningful innovation together. The organizations that embrace adaptability as a defining part of how they learn, operate, and grow will be the ones to turn capability gaps into the capacity to truly transform.
Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value. From strategy through delivery, our agile teams across 53 offices in 12 countries collaborate with clients to bring powerful customer experiences, innovative ways of working, and new products and services to life. Learn more at slalom.com.
Barry Scharfman, Ph.D. leads Slalom’s Data & AI New England practice.
Diana Saravelas leads Slalom’s Transformation New England practice.
Ryan McCreedy, Psy.D. leads Slalom’s Talent & Culture discipline for the Americas