1. The new power partnership is People + Technology

For years, one of the most important partnerships for HR leaders was with the CFO. That relationship still matters, but Sunaina believes AI has created a new critical partnership: People leaders and technology leaders working side by side. At Omnissa, Sunaina is co-sponsoring the company’s AI transformation with their Head of R&D. The key is that they’re not treating AI as just a technology rollout — they’re treating it as a business, culture, and mindset transformation.

“This is not just a technology change. This is a mindset shift and transformation for the organization.”

That means the People team has to be involved in questions of governance, responsible usage, cost, training, adoption, and behavior change — not just communications and enablement. Sunaina’s view is that the CHRO should not sit in a narrow HR swim lane when AI is reshaping how work gets done.

2. Don’t build a massive AI committee. Build a small, agile team.

One of the biggest lessons Sunaina shared: when it comes to AI transformation, small is better than big. Omnissa has a small but mighty AI transformation team of four people supporting the work across a global organization of roughly 4,000 employees. That might sound counterintuitive, but the logic is simple: AI work requires speed, clarity, and iteration. A 25-person committee can slow things down before the organization even gets started.

“The very nature of what we’re doing with the transformation is all about agility and resilience and change management.”

The takeaway for People leaders: don’t confuse representation with effectiveness. You need the right voices in the room, but you also need a team small enough to make decisions, test quickly, and keep momentum. Big governance can come later. Early on, the priority is creating a foundation that lets the organization learn without getting stuck.

3. Thoughtfulness beats urgency in AI adoption

Sunaina pushed back on the idea that companies should copy whatever big tech is doing with AI. Just because another organization rolled something out doesn’t mean it’s the right move for your company, your people, or your risk profile. Omnissa has taken a more intentional approach: piloting tools, learning how employees use them, and thinking carefully about guardrails before scaling more broadly. They’re not trying to turn everything on overnight.

“Don’t feel pressured by what other people are doing.”

That’s especially important when customer data, security, governance, and cost are involved. Sunaina talked about the need to balance enablement with responsibility — giving people room to experiment without creating unnecessary risk. Move fast enough to learn, but not so fast that you have to pull everything back later.

4. AI fluency needs to become part of the employee lifecycle

Omnissa has already built AI fluency expectations into its external hiring process, using rubrics to assess both hard and soft skills. Internally, they’re starting to think about how those expectations show up across the employee lifecycle — from hiring to development to performance to rewards. Sunaina was clear that AI fluency can’t just be a one-time training program. It has to become part of how the organization defines, recognizes, and reinforces the behaviors it wants to see.

“What gets rewarded gets done.”

But she also cautioned against expecting every employee to transform their role overnight. Not everyone needs to use AI in the same way, and not every function will have the same use cases. The goal is to help people understand where AI can create uplift in their work — removing drudgery, improving productivity, and opening up more time for higher-value work.

5. Performance management should feel less like paperwork and more like a real conversation

Sunaina shared one of the most practical AI use cases her team is exploring: reducing the administrative burden of performance management. The goal isn’t to make performance conversations colder or more automated. It’s the opposite. Omnissa wants to use AI to take away the operational drag so managers and employees can spend more time on meaningful conversations about goals, development, achievement, and support.

“How do we make performance conversations more joyful?”

Right now, too much time is spent filling out forms, documenting goals, typing up notes, and managing the process. Sunaina’s team is looking at how to reduce the time burden significantly — from an average of 237 hours down to something closer to 50–70 hours. Don’t use AI to replace the human parts of performance management. Use it to remove the friction that gets in the way of those human parts.

6. Managers are the unlock for employee experience

Sunaina also shared that Omnissa is introducing the concept of the “year of the manager.” The idea is to focus more intentionally on what managers need — from development, tooling, and support — so they can create a better experience for their teams. That matters because the manager remains one of the biggest drivers of engagement. AI may change the way work gets done, but managers still shape how employees experience that change.

“We know that the single biggest uplift from an employee’s engagement is via their manager.”

For People leaders, this is a reminder not to over-index on tools at the expense of manager enablement. A great AI strategy will fall flat if managers don’t know how to explain it, model it, coach through it, and build trust around it. The future of work may be increasingly AI-enabled, but the employee experience still runs through the manager.

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