Key Takeaways

• There is a measurable optimism gap between executives and employees on AI: Approximately 70% of C-suite executives report high optimism about AI, while roughly 70% of general workforce employees report low optimism. Director-level optimism sits around 60%, manager-level around 50%. This gap is both a risk and a change management opportunity.
• Only 15% of workers report having a clear understanding of AI ROI expectations: The absence of clear ROI definition — not skepticism about AI itself — is the primary driver of workforce hesitation. Organizations that define ROI concretely and communicate it broadly close this gap and accelerate adoption.
• AI coaching at scale is already delivering measurable results: A Fortune 100 company using Nadia scaled from 700 in-person coaching meetings per year to 38,000 over three months, achieving a 72% NPS. This is not a future possibility — it is a current enterprise reality.
• The top three HR AI concerns are also the top three AI opportunities: Driving adoption, reskilling the workforce, and implementing governance frameworks rank as both the biggest concerns and the biggest priorities for HR leaders — a signal that the organizations addressing these challenges are simultaneously capturing the greatest opportunities.
• Workflow reimagination must come before technology implementation: The most effective enterprise AI deployments begin with workflow analysis, not tool selection. Organizations that map and redesign processes first are better positioned to achieve scale and realize the 20% greater EBITDA that top AI adopters are achieving.
• Large companies have structural advantages that will compound over time: Greater investment capacity, better and more consistent data, deeper talent and technology expertise, established change management infrastructure, and stronger vendor relationships all position large enterprises to outpace smaller competitors in AI adoption — once execution catches up with intent.

Full Transcript

The Current State: Signal, Noise, and the C-Suite vs. Workforce Divide

[00:00:00]

Tim: Good afternoon, everyone. As you've heard today in so many ways, we're at a precipice of change — and yet your roles are driven really by separating the signal from the noise. One of the challenges right now is that sometimes the noise is the signal and vice versa. What I'd like to do is go through what we're hearing, and then why, done right, AI is well-suited for large companies. All of the issues you've heard today around adoption, the skepticism of the workforce, the excitement of executives — it all pervades.

Let me go back to how Ethan started the day. He talked about workers and employees not having a clear-cut view of how the enterprise views AI. That's true in some organizations and not in others. This was based on a Section AI survey reported on two weeks ago in the Wall Street Journal. Among the C-suite, there is a lot of optimism — they're talking about time saved, impact to the organization, and clear lines of authority in terms of policy.

With the rank and file, it's a different view. Not real clarity on what they're expected to do — and because of that, they're either not admitting they're using AI or not using it at all. Only 15% of the workforce says they have a clear understanding of ROI expectations. On the other side, high optimism among about 70% of executives, low optimism among about 70% of the general workforce. At the director level, about 60% optimism. Manager level, about 50%. There's a real disconnect.

I've had conversations in the hall today, and you heard it in earlier panels: everyone believes they're behind. But if everybody's behind, no one's behind. It's a great opportunity to ask: what do we need to do to be effective? We did our first AI meeting among our members in March of 2023. At that point, the question was simply, 'What is this?' Huge excitement as people were discovering it. A year later, they said, 'Okay, we kind of know it — now we're moving into implementation.' But now we're back into an anxiety phase, because the technology is moving so quickly that we need to put plans, processes, and governance elements in place to get the workforce to do what we want it to do.

▶ The Executive vs. Employee AI Optimism Gap: What the Data Shows

A Section AI survey reported in the Wall Street Journal found that approximately 70% of C-suite executives hold high optimism about AI, while approximately 70% of general workforce employees hold low optimism. Only 15% of workers report having a clear understanding of their organization's AI ROI expectations. This optimism gap — widest at the frontline, narrowing at the director and manager levels — is a primary driver of slow enterprise AI adoption and a defining change management challenge for HR leaders in 2026.

What HR Leaders Are Most Concerned About — And Why the Concerns Are Also the Opportunities

The opportunity for all of us, and what our members are talking about, is putting the right frameworks in place — as you heard from Diane and Alan — but doing so in a way that's effective. Let me share a couple of data points from a survey we conducted in October. We asked our members: what are your top concerns about AI?

Not surprisingly: driving adoption, building skills, and implementing governance frameworks. When we asked what the biggest concern is going forward, it's essentially the same three. Signal and noise are reinforcing each other. Measuring ROI and demonstrating that AI implementation will have a real effect on the business. Reskilling — and I'm most concerned about the current state of learning and reskilling being too esoteric for what we're asking our teams to do. And finally, driving change. Workflow reimagining and the implementation of technology on top of that is something we're still in the middle of figuring out.

CHROs are at the crux of this. You heard Ethan talk about HR as the new R&D. The CHRO role sits at the intersection of every challenge we've discussed: workforce adoption, reskilling, governance, and change management. That's both the burden and the opportunity.

▶ The Three Biggest Enterprise AI Challenges for HR Leaders in 2026

A fall 2024 HR Policy Association survey of CHRO-level members identified three dominant concerns about AI: driving workforce adoption, reskilling employees for AI-augmented roles, and implementing effective governance frameworks. These same three areas also represent the greatest opportunities — organizations that solve for adoption, reskilling, and governance simultaneously are positioned to capture compounding advantages. The CHRO role sits at the intersection of all three challenges, making HR the organizational function with the most leverage over AI outcomes.

Three Enterprise AI Case Studies: What's Actually Working

I want to talk about three case studies that illustrate the opportunity and the savings involved. We launched a Center on Workplace AI in the fall, chaired by Nicole Lamoureux of IBM. These cases come from that work.

Case Study 1: Democratizing Coaching at Scale with Nadia (Fortune 100)

A Fortune 100 company implemented Nadia to democratize its coaching program. They went from conducting about 700 in-person coaching meetings per year to 38,000 over three months. The workforce embraced it — they were invited in, not mandated. The opportunity for implementing Nadia across talent development, workforce upskilling, and talent acquisition was significant, and they achieved a 72% NPS.

▶ How a Fortune 100 Company Scaled Coaching from 700 to 38,000 Sessions in Three Months

A Fortune 100 company deployed Nadia, Valence's AI coaching platform, to democratize access to leadership development across its workforce. The results: coaching sessions grew from approximately 700 in-person meetings per year to 38,000 sessions over three months — a 54x increase — with a 72% Net Promoter Score. The deployment spanned talent development, workforce upskilling, and talent acquisition. Employees were invited into the program, not mandated, which the HR team credits as a key driver of adoption.

Case Study 2: AI-Accelerated Vehicle Approval for a Large Delivery Network

A large delivery company with hundreds of thousands of employees needed to accelerate the seasonal approval of individual drivers and their personal vehicles. Because the company had prioritized other AI projects for broader company benefit, the CHRO partnered with the company's BPO partner to implement an AI-powered solution for vehicle approval and evaluation. Over the course of the next year, this will decrease manual HR work by 20% and decrease time-to-fill by 10%.

Case Study 3: HR Systems Consolidation and Time Savings at a Global Biotech

A biotech company with 95,000 employees reduced its HR systems from 200 to 100 and implemented a new unified process across the organization. The result: approximately 700,000 hours saved for HR teams and roughly 1 million hours saved for employees broadly — across a single year.

▶ Enterprise AI in HR: Three Case Studies and the Business Outcomes They Delivered

Three large enterprises demonstrate what AI adoption in HR looks like in practice. A Fortune 100 company scaled coaching from 700 to 38,000 sessions in three months with a 72% NPS using Nadia. A major delivery network used AI to cut manual HR work by 20% and reduce time-to-fill by 10% for seasonal driver hiring. A 95,000-person biotech consolidated HR systems from 200 to 100 and saved approximately 1.7 million combined employee and HR team hours annually. These outcomes point to a consistent pattern: AI in HR delivers measurable scale, efficiency, and employee experience improvements when implementation is well-designed.

Why 2026 Is the Inflection Year — and Why Large Companies Will Win

As we look at 2026, it really is the inflection year. The opportunity for HR to use the resourcefulness at its disposal — to get solutions done even outside of corporate-wide initiatives — is going to be very important. We're going to need increased proficiency. And the benefit is that if we can get the workflow analysis done effectively, we can develop scale and take advantage of what large companies uniquely have.

Let me close with why large companies will excel at AI adoption:

• Resources and investment: The numbers are significant. And those at the top of the adoption curve are realizing about 20% greater EBITDA on their AI investments.
• Data: There is still work to be done to smooth and make data consistent across enterprises. But larger companies have better data — and better data makes AI implementation meaningfully more effective.
• Talent and technology expertise: Large companies have the internal capability to implement and iterate on AI solutions at a level smaller organizations cannot match.
• Change management infrastructure: Large companies have established the muscle for implementing change at scale. That capability — imperfect as it is — will improve, and it gives large enterprises a meaningful structural advantage.
• Scale: Once a process is in place that's effective and efficient, the gains compound rapidly. Large companies are positioned to capture those gains across thousands of teams and millions of interactions.
• Vendor relationships: The ability to partner with best-in-class providers — like Nadia for coaching and development — is something large companies are uniquely positioned to leverage and negotiate.

There are real opportunities for business leaders, HR professionals, and executives to put these elements in place. I would encourage you to think hard about how you're going to implement change management — and how the public sector will need to evolve alongside this transition.

[00:11:38.642]

Das: Thank you, Tim.

▶ Why Large Enterprises Are Structurally Positioned to Lead in AI Adoption

Despite the complexity of deploying AI across large organizations, enterprise-scale companies hold meaningful structural advantages: greater investment capacity, better and more consistent data, deeper internal talent and technology expertise, established change management infrastructure, and stronger vendor relationships. Top AI adopters among large enterprises are already realizing approximately 20% greater EBITDA compared to their peers. The key is translating these structural advantages into effective implementation — starting with workflow analysis before technology selection.

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