The first wave of AI transformation focused on access: getting tools into people’s hands and encouraging adoption. The focal point is now on what businesses need to change around the technology for it to create value. In practice, that comes down to two things: the work itself and the workforce expected to do it differently.
Organizations need to wrestle with big questions about how work should be redesigned, how roles and routines should evolve, how teams will need to be supported, and how leaders will define what better work looks like.
The ServiceNow Workforce Skills Forecast 2026 helps bring that challenge into focus, confirming that the roles that will matter most require both human skills and technical skills.
They include change specialists, enablement teams, business partners, organizational psychologists, and strategy professionals who can connect technical possibility with human judgment and measurable outcomes.
Many businesses are finding that their leadership’s AI ambitions are moving faster than the organization’s ability to put them into practice. Teams may understand the strategy but still lack the clarity, permission, or support to change how work actually happens.
Progress depends on helping teams change their day-to-day work. To think differently about process and workflow with AI is a critical component. That’s why organizations need people who can interpret the change, clarify the outcome, build confidence, and help employees understand how their roles and routines will evolve.
The practical starting point is a simple sequence: work, workforce, workbench. Leaders should first define the work that must change and then identify the skills and capabilities needed to perform it differently. Only then can they decide whether AI tools or agents belong in the workbench.
AI usage is an important signal, but organizations need to avoid mistaking activity for impact. The true measures are productivity, speed, quality, capacity, employee experience, and customer outcomes.
More doesn’t always mean better outcomes. If leaders are unable to define what they expect from AI before using it, they’ll struggle to know whether the transformation is working.
This distinction matters because AI can create strategic choices after productivity gains appear. If a team produces more capability, leaders must decide whether to invest it in higher-value work, accelerate delivery, redesign roles, or reduce costs. These are choices about how the business will run and how people work, not software decisions. This is one of the most effective ways to capture value from AI.
Middle managers are the critical link between leadership intent and frontline behavior. Because they’re closest to the work, they know where processes break down, where employees stumble, where customers feel friction, and where AI might meaningfully change the work.
AI should be seamless and integrated into workflows so that employees barely notice it. Technology should not be designed to be complex; the best AI is sometimes almost invisible. But even when the technology is right, the transformation will stall if managers and leaders can’t translate AI into new routines, expectations, and measures of success.
Many organizations haven’t empowered managers and leaders to actively own AI transformation in their teams. They passively receive centrally assigned tools, creating a disconnect between what AI was supposed to do, how it’s used, and what it delivers.
To make that connection stronger, leaders need to clarify expectations, provide practical support, and equip managers to:
- Rethink the processes they own
- Identify innovation opportunities
- Explain what AI means for their teams
- Measure progress against business outcomes
The goal is to help them make meaningful transformation in the work their teams do every day.
As AI agents become more capable, they can work autonomously across organizations to support incident resolution, employee onboarding, and more. That shift raises practical questions that parallel workforce management:
- How are AI agents onboarded?
- Who’s accountable for their outputs?
- How are they governed?
- How do they fit into human teams?
The implications extend beyond governance to workforce development. As AI agents perform increasingly specialized tasks, organizations may become flatter and more agile. Smaller, empowered teams could be the way of the future.
Expertise that once required specialized teams may become more broadly available, increasing the premium on generalists who combine technical understanding with judgment, communication, systems thinking, and the ability to guide human-agent work toward a clear result.
AI transformation also increases the need for a more dynamic understanding of workforce capability. Work and technology have never changed this fast. This moment requires a level of agility that was unimaginable a few years ago. Leaders need a clearer view of the skills, experience, and potential within the organization.
A one-time skills assessment can provide an initial workforce “X-ray.” But the stronger model is an ongoing talent signature—a system that evolves as employees learn, perform, receive feedback, and move across work.
That kind of workforce intelligence enables a more fluid model of talent deployment. Rather than defining employees only by job title or fixed role, organizations can match people to work based on skills and business needs.
In an environment where capabilities change quickly, this “flow to work” model may become one of the most important ways organizations build resilience and speed. This kind of agility may fuel the competitive advantage of future organizations.
Leaders should prioritize preparing the workforce to make AI useful. Focusing on five priorities can help drive meaningful adoption:
- Define the work before the solution: Identify the workflow, tasks, decisions, and outcomes AI should enhance before selecting or scaling technology. What work needs to be done, and what workforce (human plus AI) is needed to complete that work?
- Equip managers to lead this transformation: Give managers the responsibility, skills, tools, and metrics to redesign work locally and bring teams through change.
- Track value, not just usage: Monitor productivity, quality, speed, capability, employee experience, and customer outcomes so that AI investment can be evaluated by impact.
- Govern AI agents as accountable assets: Clarify ownership, access, accountability, supervision, and integration rules for AI agents before embedding them in teams.
- Measure value before scaling: Test AI in context, validate value with real users, and understand workforce implications before restructuring roles or teams.
AI transformation will reward organizations that move quickly, but it will favor those that build the human capability to move effectively.
At ServiceNow, we learned this lesson early in an AI implementation that we believed could solve a specific problem. After realizing we hadn’t brought employees along, we focused on helping them build confidence in the tool—and adoption accelerated.
Competitive advantage won't come from AI adoption alone. More button clicking isn’t what we need. Instead, advantage will come from people who can bridge technology and behavior, lead teams through change, and convert new tools into measurable—and compounding—enterprise value.
Find out how ServiceNow can help your organization develop talent and plan for future workforce needs.
