5 hard truths of change management

Mohan Sankararaman calls the old way of doing transformation a trap. As executive vice president and CIO of First Horizon, a regional bank headquartered in Memphis, he used to have the luxury of big, infrequent technology bets — the kind that land every few years and reshape the organization in one push. Now he funds change the way a bank funds risk: incrementally, with room to pull back.

Every CIO is under similar pressure to rethink change management for the AI era. Wanda Wallace, managing partner at Leadership Forum, has advised CIOs on change management for years, and she thinks the job itself hasn’t changed much.

“The hardest and most critical aspect of making change happen and stick is convincing people to adopt a new approach,” she says. “AI doesn’t change that need or that process. It is a human-to-human dynamic.”

Talk to the practitioners and researchers closest to the work, and a version of her view emerges again and again. What has changed is how many things are competing for an organization’s limited capacity to absorb them — AI chief among them. Here are five hard truths IT leaders face about change management today.

1. There’s no finish line

Ashish Parmar, CIO of Standard Industries, a global industrial conglomerate with more than 20,000 employees across roughly 50 countries, has watched the nature of transformation shift beneath him. In the past, he says, change was treated like a project with a start date and an end date — whether the trigger was a new ERP system, a reorg, or a cost-cutting mandate. That model doesn’t hold anymore.

“Today, change is continuous,” Parmar says. “Our strategy is focused on building resilience and adaptability rather than getting to a single destination.”

AI is the clearest example of how the old model breaks down, says Fran Maxwell, who leads Protiviti’s people and change practice, though she’s quick to note it isn’t the only one. Unlike an ERP rollout, which lands as a discrete event, an AI transformation keeps moving.

“The technology evolves continuously, use cases emerge rapidly, and the impact on roles is often uncertain,” Maxwell says. The common misstep is treating any major shift, AI-driven or not, like a one-time project with a training curriculum and a communications plan, she says. The fix is building a permanent capability for adaptation rather than staffing up for a single push.

None of that continuous adaptation is possible if the underlying systems can’t support it, notes Manosiz Bhattacharyya, CTO of Nutanix.

“Technology is not the barrier to transformation; application modernization is,” he says. Years of accumulated dependencies, legacy integrations, and fragmented data are what actually slow an organization down. And layering new tools on top doesn’t make that debt disappear.

“Applying AI blindly does not remove technical debt,” Bhattacharyya says. “It amplifies it.”

2. Bandwidth isn’t just a network problem

A 2026 survey of roughly 3,000 HR leaders by talent firm LHH found that no single cause dominates why companies reshape their organizations: AI and automation, skills mismatches, M&A activity, and strategic shifts were each cited as drivers in the previous year by about a fifth of respondents. In other words, most organizations are contending with several forms of change at once, not just AI.

All that change at once runs into a hard limit: An organization can absorb only so much at a time.

“Every organization’s capacity for change is finite, so leaders cannot endlessly stack new initiatives on top of existing workloads,” Parmar of Standard Industries says.

Rather than treat that ceiling as a constraint, he argues CIOs should use it to force discipline. IT leaders should determine their non-negotiables and point the team’s energy there instead of spreading it thin across AI pilots, reorganizations, and everything else competing for attention.

Sankararaman arrived at nearly the same conclusion at First Horizon. Banking used to reward slow, occasional overhauls, the kind that could take years to prove out, he says. But that approach has become untenable.

“It’s tempting to treat transformation as one big initiative, but with technology evolving this fast, that’s a trap,” he says. Instead, Sankararaman releases funding in stages, each tied to a measurable result before the next is approved. Then, the organization can adapt and build confidence as it goes rather than betting everything on a single multi-year plan. “We reward progress, not perfection,” he says.

Kevin Martin, chief research officer at the Institute for Corporate Productivity (i4cp), has data that supports the value of incremental improvements. When leaders want to move faster, the reflex is to restructure: delayer, widen control, redraw reporting lines. i4cp’s research found no statistical relationship between those structural moves and organizational agility or market performance. What separates agile organizations are routines: scenario planning, faster resource reallocation, clear decision rights, continuous workforce planning, targeted reskilling, and disciplined execution.

“You don’t reorganize your way to agility,” Martin says. “You build it into how the organization operates.”

3. Shadow IT doesn’t belong in the shadows

Employees finding their own tools to get work done isn’t new. Shadow IT has taken on various forms over the years, from personal file-sharing accounts to unsanctioned SaaS subscriptions.

Today, it’s shadow AI, and Sankararaman argues most CIOs still treat it as a security or compliance issue rather than what it really is: information about what the organization needs and isn’t getting.

“Shadow AI is already happening in every organization. If you’re not addressing it through your change management strategy, you’re addressing it too late,” Sankararaman says.

Sankararaman’s approach starts with curiosity rather than restriction: understanding what employees are trying to accomplish with the tools they’ve found on their own, which makes it easier to agree on how the business should govern those tools.

“That’s a change management conversation, not just a policy conversation,” he says.

4. Trust has to be designed in, not repaired later

Every new system that changes how decisions get made must earn trust before it achieves adoption. Agentic AI raises the stakes because it doesn’t just inform decisions; it takes actions on its own within workflows.

That’s a fundamentally different dynamic from anything change leaders have managed before, Sankararaman says. The resistance it produces is often quieter, showing up in questions about how a system reached a conclusion, who’s accountable when it’s wrong, and whether it’s replacing what someone does. “Those questions deserve real answers, not reassurance,” he says.

At First Horizon, IT is building trust into the foundation with permissioned access, centralized guardrails, human oversight, and outputs that are consistent, reviewable, and explainable. “If people can’t understand how the technology reached a conclusion, you haven’t earned their trust,” Sankararaman says. “And without trust, adoption doesn’t hold.”

Employees today worry less about learning a new tool than about what it means for their role, their skills, and how their performance will be judged once a machine does part of the job.

“They worry about career relevance, accountability, job security, and how performance will be evaluated,” Protiviti’s Maxwell says. Closing that gap, in her view, takes more than a rollout plan. It demands transparency about what’s changing, what isn’t, and how people add value once the tool is in place.

5. Tired isn’t the same as unwilling

Ask IT leaders about change fatigue, and they frame it as a capacity problem rather than resistance. Late 2025 saw a wave of five-day return-to-office mandates that landed on top of continued layoffs. Tech companies alone cut more than 66,000 jobs between May and November, according to Newsweek and TechCrunch, exactly the kind of concurrent disruption that erodes an organization’s capacity for change.

Among what i4cp calls “coasting incumbents” — companies that still perform well despite low organizational agility — 51% of employees report finding change fatiguing, and just 8% say change management is an organizational strength. At “agile pacesetters” — the highest-agility, highest-performing organizations in i4cp’s research — only 12% report high fatigue.

“AI is an accelerant,” Martin says. “But organizational friction is the fuel.”

Protiviti’s Maxwell argues that change fatigue is often less about resistance and more about capacity. “Employees are far more likely to embrace change when leaders are clear about what matters most, what success looks like, and just as importantly, what is not a priority right now,” Maxwell says. Her advice to CIOs: Empathize and prioritize before accelerating.

One structural fix most CIOs underuse is shared ownership. “Don’t go at it alone,” advises First Horizon’s Sankararaman. “Partner with others across the business, your CHRO, CFO, COO, and make them co-champions of the change, not just stakeholders who get updates.” A message that arrives from multiple leaders, he’s found, carries more weight and lasts longer than one delivered by IT alone.

The absence of fatigue, in Wallace’s view, is its own warning sign. “If your organization isn’t change-fatigued,” she says, “then I am worried about what you have been doing.”