Editorial
Research Report
The State of the Digital Workplace
Organizations are investing in AI. Many are experimenting. Some are seeing pockets of value. But very few are realizing meaningful, measurable returns.
These themes kept surfacing at the World Leaders in Data and AI forum and in my discussions with senior leaders around what human performance means in the age of AI. The pattern remained consistent across industry and stage of AI adoption.
Data suggests that only 20% to 30% of companies are achieving real ROI from AI. Even more telling: roughly three-quarters of AI’s economic value is captured by just 20% of companies, and many CEOs say they are seeing little impact.
That gap is not accidental. It reflects a set of patterns, both helpful and harmful, that are shaping outcomes. If the goal is real value creation, not just experimentation, leaders need to understand where organizations are getting stuck, and what it actually takes to move beyond it.
Table of Contents
- 1. Incremental Thinking Is Limiting Transformational Outcomes
- 2. The Gap Between Vision and Reality Is Wider Than Most Leaders Expect
- 3. Technology Isn't the Constraint, Human Cognitive Capacity Is
- 4. Ownership Isn't About Role – It’s About Capability
- Where Real AI Impact Will Come From
- The Gap Is Human, and So Is the Answer
1. Incremental Thinking Is Limiting Transformational Outcomes
One of the clearest patterns is the reliance on incrementalism.
Pilots and small-scale use cases are useful for building familiarity and reducing risk. The problem is when pilots become the strategy rather than the stepping stone.
This creates a pattern of layering small efficiency gains onto existing processes. But if the underlying process is flawed, AI simply accelerates or marginally improves something that was never optimal to begin with.
Rather than “how do we automate this?” better questions are:
- Should this work even exist in its current form?
- What would this process look like if it were designed today, with AI as a core capability?
- What work disappears? What changes? What becomes newly possible?
Organizations seeing real ROI are not just automating tasks, they are redesigning how work happens.
"The biggest shift isn’t adopting AI — it’s redesigning how the enterprise makes decisions. Most companies are still using AI as a tool to drive efficiency on top of existing processes — AI leaders are already transforming their operating model and driving growth aligned to business strategy,” said Jennifer Colapietro, PwC Partner and Digital Core Modernization Platform Leader.
This redesign operates at multiple levels. Companies need to rethink the three layers simultaneously, said Rebecca Maffei, Chief Information Officer at Fashionphile: the nature of the work being done, the composition of the workforce (including both humans and agents), and the capabilities required of human employees within that system.
Or as Beth Falder, AVP Data Management & Analytics at Nuvance Health, puts it: "The age of the use case is over. It’s time to pivot AI strategy tied to value chains aligned with strategic initiatives."
Without this level of rethinking, organizations may achieve marginal gains, but will fall short of what AI makes possible.
2. The Gap Between Vision and Reality Is Wider Than Most Leaders Expect
Leaders can clearly articulate what AI could enable: faster decisions, better customer experiences, new revenue streams. In many cases, that vision is entirely valid.
But there is a significant gap between that vision and the operational reality required to deliver it — one frequently widened by turf wars disguised as strategy, battles for ownership instead of outcomes, or the many AI myths that still run rampant.
That gap shows up in several ways:
- Infrastructure readiness: Data quality, integration, and accessibility are frequently not at the level required for scaled AI.
- Operating models: Decision rights, governance, and workflows are not designed for real-time, AI-supported execution.
- People readiness: Teams are expected to adopt new ways of working without the necessary skills, context, or support.
- AI fluency: Understanding of what's happening inside these systems — distillation, model behavior, capability thresholds — is advancing far more slowly than the technology itself.
When we don't acknowledge this gap, we make decisions based on aspiration rather than reality. The result is predictable: missed expectations, delayed outcomes and mounting pressure on teams.
Closing it requires more than ambition, it requires precision. Colapietro recommends to “move from AI pilots to enterprise-scale impact by identifying a small set of critical decisions and redesigning them to operate in real time — with clear ownership, guardrails and embedded agents.”
This shifts the conversation from broad transformation to targeted, high-impact change — and aligns with what leading organizations are doing: investing not just in use cases, but in the underlying capabilities that allow those use cases to scale.
Falder's top AI priority for the next 12 months reflects exactly this: to "create a roadmap for AI/agent workforce and redesign at least one value stream to the new agent/human oversight model."
Staying ahead also demands vigilance on what's emerging. Danielle Crop, Executive Vice President Digital Strategy & Alliances at WNS a Capgemini Company, plans to stay close to and research emerging models and tools — not for experimentation alone, but to ensure her organization can move quickly with the partners and technologies that will shape the future.
The organizations that close the gap aren't the ones with the boldest vision. They are the ones most disciplined in translating that vision into executable steps.
3. Technology Isn't the Constraint, Human Cognitive Capacity Is
A common assumption is that the primary barrier to AI success is technical. In reality, the limiting factor is far more human.
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The pace of change is accelerating. At the same time, cognitive and emotional demands on leaders and teams are increasing with more decisions, more ambiguity, more complexity. Human capacity is not keeping up.
This matters because the skills most required in an AI-enabled world are not diminishing, they are intensifying: complex judgment and discernment, systems thinking and orchestration, empathy and human connection, the ability to navigate the unknown and make high-stakes decisions with incomplete information. These are not low-effort capabilities. They require high cognitive capacity to execute well under sustained pressure.
AI doesn't eliminate the need for human excellence, it concentrates it. The remaining work is more complex, more ambiguous and more consequential. And yet many organizations are attempting transformation while their workforce is already operating at or beyond capacity.
That is neither sustainable nor wise.
If leaders are serious about where their organizations will be in the next 3–5 years, human readiness cannot be an afterthought. It must be built deliberately, by measuring, building and sustaining the human capacity that transformation demands.
Because ultimately, AI scales the quality of thinking behind it. If that thinking is fragmented, reactive or depleted, the outcomes will reflect it.
4. Ownership Isn't About Role – It’s About Capability
The question of AI ownership comes up frequently. Should it sit with the CIO? The CDO? The CHRO? The question is too narrow when framed this way.
AI is not just a technology deployment, and not just a people transformation. It sits at the intersection of business strategy, technology capability and human behavior. The more useful question is: Who has the capability and capacity to own it effectively?
The leaders succeeding in this space tend to share a specific profile:
- Enough technical understanding to make informed decisions.
- Grounded in business value creation and growth.
- Understand human dynamics and can lead change at scale.
- Trusted and respected across the organization.
Not deep specialization in one domain, but integration across all.
In many cases, this may require redefining roles or even organizational structures. But the differentiator is not structure. It's how these leaders operate. They are accountable not just for decisions, but for how decisions are made, the speed, the quality, the inputs and the accountability structures around them. And critically, they have the capacity to do it well. A leader driving AI transformation while depleted, reactive or overwhelmed will struggle to make the kind of high-quality, adaptive decisions this environment demands.
“Many Data & AI leaders know major change is needed to move from efficiency plays to growth plays with AI, but struggle to get their C-suite leaders to either understand or to listen. This is not a new problem for data leaders. C-suite leaders from purely business backgrounds often underestimate the value of the integrated technical-and-business understanding required right now for Agentic transformation. The companies who understand that this talent is the key and empower them will be the winners; the rest risk becoming another Kodak or Sears,” said Crop.
The implication is clear: this is as much a leadership challenge as it is a technological one.
Where Real AI Impact Will Come From
Across all of these themes, there is alignment on where AI will create the most value. Not in isolated use cases, but in how decisions are made and executed across the organization.
“The biggest impact will come from accelerating decision velocity across core value streams — especially those closest to the customer — where faster, smarter decisions drive customer impact and business growth,” said Colapietro.
Crop points to impact coming from "blending proven AI/ML with newer generative models to support end-to-end agentic decision-making." And Falder describes the shift as "moving away from solving complex problems through committees and manual work that takes days, to an agent/human model solving the same problem in a day."
For many organizations, the highest-impact opportunities will be industry-specific. Maffei pointed to agentic commerce and customer support as priority areas, where AI-driven decisioning directly enhances customer interactions and measurable outcomes.
The pattern is consistent: value concentrates where decision-making, customer impact and business outcomes intersect.
The Gap Is Human, and So Is the Answer
The organizations that realize the promise of AI will not be the ones that adopt it the fastest. They will be the ones that fundamentally rethink how work is designed, how decisions are made, how humans and technology operate together and what leadership looks like in that system.
The gap between experimentation and ROI is not a technology gap. It is a human one, rooted in readiness, behavior and how we work.
Closing it requires leaders willing to move beyond incremental change, to redesign how their organizations operate and to fully accept what that demands of them.
Editor's Note: What else is AI demanding of our organizations and leaders?
- The Cognitive Economics of AI — AI promised to free up our time, but it's making us busier than ever. Here's why protecting time to think is a leadership imperative.
- Your Employees Are Getting Things Done – But for How Long? — Burnout doesn't announce itself. It builds quietly, one borrowed burst of energy at a time. Here's what leaders need to know before the debt comes due.
- Digital Literacy vs. AI Literacy: Why Your Organization Needs to Know the Difference — Digital literacy and AI literacy aren't the same thing. Yet organizations are building one and calling it the other.
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