CIO perspectives on AI, transformation, and the role of PS

AI is no longer just about hype or experimentation. It’s about execution. And professional services are at the center of making that shift real.

At Propel 2025, a panel of senior tech leaders, Gary Sorrentino, (Global CIO, Zoom), Jim Siders, (CIO, Palantir Technologies), Karthik Chakkarapani, (SVP, Corporate Operations & CIO, Zuora), and Kenny Scannell (VP of Global Sales, Rocketlane), shared real-world insights into how AI is driving enterprise transformation, and what they now expect from professional services. 

Read on for a summary of the key takeaways from the discussion.

What CIOs really want from professional services

For enterprise buyers, especially CIOs, software is a bet made with someone else’s money. That means every dollar spent needs to yield measurable and scalable impact. And while product demos may win attention, what ultimately wins adoption is the value delivered after the purchase.

SaaS tools are often built to be easy to sell, not necessarily easy to mold. PS fills that gap, shaping the tool to the business, not the other way around. The nuanced ability to blend domain knowledge with product expertise is what separates good services teams from forgettable ones. As Gary from Zoom put it: “We want your opinion. Don’t just ask what we want, tell us how to use your product best.”

The decision-making process also goes deeper than functionality. Buyers want vendors to start by understanding why a change is needed. What’s broken? What’s the cost of inaction? What capabilities will close the gap, and what ROI will they unlock? 

The best PS organizations can answer those questions without being prompted. They guide buyers toward not just implementation, but transformation, rooted in an understanding of both current-state challenges and future-state ambitions.

And finally, leadership matters. Buyers don’t expect themselves to be PSA experts. They look to vendors for thought leadership, whether that’s reimagining outdated processes or challenging assumptions. 

The bottom line: CIOs are looking for PS teams that lead with clarity and conviction, who guide the customer toward the right way to implement and scale. That’s where real value lies.

AI’s move from engineering to everyday work

While engineering may have been the obvious starting point for AI adoption, it's far from the final destination. Engineering teams were the early test beds, where companies experimented and refined, but the real shift is happening deeper in the business. AI is now showing up in frontline sales, internal operations, and across back-office functions, streamlining the day-to-day for roles that previously had little automation support.

Technology leaders are leaning into this momentum. Their goal: redirect scarce, high-value human resources toward the most differentiated problems. That means retraining teams, offloading routine tasks, and amplifying impact where it matters. This shift is less about cost savings, more about unlocking bandwidth and making better use of expertise.

But there’s a harder truth underneath: AI is ready, but most organizations aren’t. CIOs are now investing in change enablement, phased rollouts, internal education, and safe experimentation environments to close the readiness gap.

3 truths about how AI is reshaping enterprise behavior

A year and a half into the generative AI wave, the reality is setting in. The companies making meaningful progress aren’t the ones chasing flashy demos. They’re the ones treating AI like a high-leverage tool that only works when the basics are already strong.

Here are three core truths emerging from the field:

• Truth #1: AI can’t fix broken fundamentals
  If your data is disorganized, your processes are flawed, or your teams can’t prove value today, AI won’t help. It will amplify dysfunction. Enterprises seeing durable value are embedding AI into already-strong workflows, where it augments high-performing teams and accelerates clear outcomes. 
• Truth #2: Curiosity drives adoption
  The fastest-moving companies are the ones making their teams curious. The best leaders are not just deploying tools, they’re retraining people to be open, adaptable, and hands-on. AI success starts with behavior, not software.
  
  
• Truth #3: Real change spreads through real examples
  Professional services teams play a crucial role here. The most effective ones aren’t just rolling out platforms, they’re storytelling. Sharing real use cases across clients sparks curiosity and action. “This customer cut hiring time in half using AI” does more to drive adoption than any deck or training session.

How CIOs are building AI momentum on the ground

Most companies are still stuck in old-school learning models. Long trainings, passive certifications, and top-down rollouts don’t match the speed of AI.

To drive adoption, the approach to learning needs to be rooted in the knowledge that: 

• Micro-learning beats traditional training
  Instead of long sessions once a year, frequent, bite-sized learning helps build the habit. For instance, four minutes a week, supplemented by a quiz, is more effective than a 45-minute training no one remembers.
  
  
• People don’t need to learn everything, just enough to act
  Content should be immediately useful. If someone’s trying to fix a tire, they don’t need to learn how to build the whole car. The goal is just-in-time, just-enough learning.
  
  

• The focus needs to shift from training to rethinking how people work
  Learning is only part of the equation. Real change happens when employees start applying what they’ve learned to reshape their work. You want employees to ask, “How can I use this to save myself 5 hours this week?” When that happens, it spreads organically, one success story sparks another.
  
  
• Proof of value > Proof of concept
  Endless evaluation of tools and models slows momentum. Instead, leaders should
  
  
  
  • Set up an AI playground with basic guardrails.
  • Let teams experiment.
  • When something works, promote it to production.
    

• The best use cases often come from non-technical folks
  They’re the ones closest to the pain points and inefficiencies. When they’re empowered to try things, they surface real problems with real gains.
  
  

• Encourage learning loops across teams
  Set up internal meetups. Share AI wins across departments. Involve external players. Zuora, for instance, invites startups to pitch their AI solutions internally. It keeps teams plugged into what's evolving in the market, and when something genuinely useful shows up, they adopt it. It’s a smart way to foster innovation without waiting for top-down mandates.

Check out the rest of our Propel25 recaps here for more insights from the industry’s best.