Why This Director of CX thinks AI transformation is all about change management

As organizations build AI strategies, cross-functional collaboration determines success. Technical leadership provides the platforms and tools. HR designs the training frameworks. Product builds the roadmaps. And customer experience leaders bring critical operational expertise.

Shanta Bodhan, Customer Success Leader at Cornerstone OnDemand, believes CX leaders possess unique expertise essential for AI transformation. That’s because Customer Experience teams not only understand the technology, but also because they manage organizational change, context complexity, and workflow design daily.

Her message: AI transformation succeeds when CX leaders partner with technical and HR leadership from the strategy phase, not just the implementation phase.

"Customer experience leaders need to really embrace that, pull up a seat at the table for making this change because they're primed and they're ready and they have all of the skills already in their back pocket." — Shanta Bodhan

CX Leaders Are Change Management Expert

Shanta believes CX leaders possess change management expertise. Every customer interaction is change management:

• Diffusing volatile situations in real-time with zero runway
• Building trust on the fly when customers are frustrated or confused
• Resolving complex problems in the moment without perfect information
• Getting teams excited about challenging, high-pressure work

"Nobody calls customer support to say they had a really great day. You're constantly dealing with these very volatile situations that require change management." — Shanta Bodhan

This expertise in managing change at scale, under pressure, every single day, makes CX leaders essential partners in AI strategy and implementation.

AI transformation, Bodhan explains, isn't just about technology. Rather, it's about “preparing your team and company” to adopt new workflows, trust with new systems, and adapt to new ways of working. These are core competencies CX leaders already demonstrate daily.

The Opportunity Mindset

Team concerns around AI displacement are inevitable. "Will AI replace us?" "Are we training our replacements?"

CX leaders are uniquely positioned to address these concerns because they understand both the operational workflows and the team dynamics.

"I really look at this with an opportunity mindset instead of a fear mindset." — Shanta Bodhan

Shanta believes the common fear that AI will eliminate jobs is "completely false." Instead, she sees AI taking on certain tasks, but then creating new roles and opportunities. She highlighted skills like prompting and machine learning oversight — areas where there’s a shortage of skills today — are new areas that are emerging.

Shanta also emphasizes a skills-based approach to both hiring and AI transformation. "I would look for folks that have those types of skills... not pigeonholing one person within one specific industry," she explains. Emotional intelligence, the ability to handle ambiguity, and hands-on product capability matter as much as technical expertise.

This philosophy extends to AI adoption: success depends less on someone's background or department, and more on their ability to manage change, build trust under pressure, and design workflows that actually work.

From Implementation Partner to Strategic Partner

Most AI implementations fail not because of technology, but because of organizational readiness. Organizations invest in cutting-edge tools without addressing the foundational elements that drive successful adoption.

Shanta emphasizes that the right approach depends on organizational context. Startups may be more nimble and change-enthusiastic but have limited resources. Enterprises have budgets and methodologies but may face change fatigue. "I wouldn't say that one is better or worse," she notes. "Either one can actually do this successfully or not based on their environment and how they approach it."

Cornerstone OnDemand's approach demonstrates the power of cross-functional collaboration:

• Executive-Level Commitment: AI is embedded as a core company value with executive sponsorship. Critically, implementation follows a partnership model where CX, HR, and technical leadership collaborate as strategic peers rather than operating in silos.

• Safe Experimentation Spaces: They created an AI Hub for internal testing with different LLMs. Teams run AI hackathons to experiment with daily workflows. They practice a "customer zero" approach by testing internally before deploying to customers.

• Continuous Upskilling: Prompting is treated as a learnable skill requiring training. Human-in-the-loop oversight roles are created for AI tool refinement and on-going maintenance. All of this makes AI a continuous iteration, not a one-time project.

The Context Challenge

"The faster they can get that information, the better it is for them. Usually that's housed in disparate systems." — Shanta Bodhan

A Support team capacity for consultative support is increasing as AI agents handle more mundane queries. But consultative support requires context: customer history across touchpoints, product usage data, previous support interactions, account health signals, documentation, and knowledge bases.

CX leaders are uniquely positioned to answer:

• What information teams need in the moment
• Which systems hold critical context
• How context fragmentation impacts resolution times
• What "consultative conversation" requires in practice

Solving this requires both technical capabilities and workflow design expertise, which is why CX and engineering partnerships matter.

When Shanta discusses working with partners to solve context challenges, she's acknowledging what many CX leaders already know: Getting the right information to the right place at the right time remains a challenge.

Taking Strategic Ownership

AI transformation requires leadership at the design level, not just implementation execution.

Three actions for this quarter:

1. Assess readiness: Is your team change-fatigued or change-enthusiastic? Shanta emphasizes designing for your environment: "You may want to make sure that they're designing that AI change management and that platform so it leads to an outcome that you can celebrate and build off of with that trajectory." Quick wins work for change-fatigued teams; rapid experimentation suits enthusiastic cultures.

2. Join strategic conversations: Collaborate with technical leadership and HR on vendor evaluation and implementation roadmaps. Contribute CX operational expertise to inform platform selection and workflow design.

3. Run experiments: Customer zero testing, workflow prototypes, quick wins to build momentum. As Shanta says, "Just step up to the plate, run some experiments, see what you can do."

"Customer experience leaders need to really embrace that, pull up a seat at the table for making this change because they're primed and they're ready and they have all of the skills already in their back pocket." — Shanta Bodhan

The job market will evolve. Skills will shift from task execution to AI enablement. Organizations that include CX operational expertise in AI strategy—not just implementation—will build more successful transformations.

CX leaders already manage change at scale daily, build trust under pressure, and understand what consultative support requires in practice. When this expertise shapes strategic conversations alongside technical and HR leadership, AI implementations address real workflow challenges, not theoretical ones.

About the Interview

This article is based on a conversation between Omar Nasser, Inkeep’s Growth Lead with Shanta Bodhan, Customer Success Leader at Cornerstone OnDemand, a leader in corporate learning and talent management.