While many organizations struggle to scale AI, one group of high-maturity users—the augmented strategists—are building a path to ROI by designing roles intentionally for AI-powered business.
In this article
We’ve officially entered a new era of enterprise AI. While the past several years were defined by AI adoption, it’s now ubiquitous across the business world. And according to Workday’s latest research, Beyond Productivity: Measuring the Real Value of AI, there’s one AI benefit that nearly all respondents agree on: higher efficiency.
Eighty-five percent of employees say AI helps them save time, and 77% say it’s made them more productive. But a deeper look shows that’s not the whole story.
“The real question isn’t whether AI can boost productivity,” wrote Michael Brenner, Workday VP of thought leadership and customer advocacy, in his perspective on the research. “It’s whether leaders are ready to redesign work so that productivity translates to real business impact.”
It’s a challenge many leaders still struggle to solve, and the gap shows up primarily in role design. Nearly 9 in 10 organizations report that fewer than half of their roles have been updated to incorporate AI-related skills. Employees who use AI most heavily are also most susceptible to time lost to rework and low-quality output.
A matrix based on the research captures why that’s happening. It shows where and how AI is creating true gains versus where it’s absorbing outsized time and energy. One group—augmented strategists—consistently achieve both AI-driven efficiency and net value, building workforces that can adapt and perform in an AI-powered business world.
“The real question isn’t whether AI can boost productivity. It’s whether leaders are ready to redesign work so it translates to real business impact.”
Michael Brenner
Workday VP of Thought Leadership and Customer Advocacy
Augmented strategists represent the gold standard for AI scalability and ROI. They consistently convert AI-driven efficiency into business value. And they don’t just work faster—they work smarter by leveraging AI to systemically expand their own capabilities. Their success is defined by three core pillars.
Augmented strategists treat AI as a systemic support to amplify human judgement, data-backed decisions, and complex thought:
AI success is actively cultivated. Augmented strategists are two times as likely to receive substantial skills training compared to struggling users. Organizations that produce these strategists prioritize:
Augmented strategists are two times as likely to receive substantial AI skills training compared to struggling users.
This group skews toward experienced professionals—71% are aged 35 to 44—primarily in roles like IT and marketing, where the ability to interpret data and apply reasoning is critical.
Augmented strategists sit in the optimal quadrant of the net productivity matrix, ahead of their peers in AI practice. Because they treat AI as operational support (rather than a replacement) for stronger human reasoning, they achieve a rare balance: increasing efficiency while elevating output quality.
Augmented strategists share a consistent success pattern: AI enhances human judgment and improves outcomes when organizations deliberately protect quality standards. Maximizing this level of ROI requires intentionally designing roles to work confidently and effectively alongside AI. The following focus areas are critical to getting that redesign right.
Decision: Explicitly map which tasks AI can execute autonomously and where human judgment must play an active role in the process.
Why it matters: Execution alone does not equal ownership. Accountability can only be maintained when clear guidelines for human judgement exist.
At Fortune’s Most Powerful Women Summit, Aashna Kircher, senior vice president of HR products for Workday, explained that as we integrate AI more deeply, leaders must ask: “How do we retain decision-making and judgment?”
“You can hold somebody accountable,” Kircher emphasized, “but if all they’re doing is pressing a button and saying yes, then they’re not actually applying judgment.”
Defining clear AI assist versus human judgement zones keeps people from outsourcing the thinking along with the task.
What to document:
What breaks when this step is missing: Standards splinter across the team. Some over-rely on AI and ship work that can’t hold up. Others over-audit every output, creating a hidden workload that absorbs time savings.
“AI should do the complex work under the hood so people can focus on judgment, creativity, and connection.”
Gerrit Kazmaier
Workday President of Product and Technology
Decision: Define the specific dimensions (examples: accuracy, tone, rigor) that determine whether AI-supported work meets the bar.
Why it matters: Quality standards protect the time freed up by AI, keeping it available for the work that moves outcomes. This is also where speed advantages turn into something more durable.
According to Gerrit Kazmaier, Workday president of product and technology: “AI should do the complex work under the hood so people can focus on judgment, creativity, and connection. That's how organizations turn AI‑powered speed into durable and human‑led advantage."
Quality standards operationalize that promise so that employees aren’t left wiring things together and fact-checking every answer on their own.
What to document:
What breaks when this step is missing: Output looks polished and moves quickly, but then fails under scrutiny. Teams get a false sense of productivity and pay for it later through downstream correction and rework.
Decision: Make review an explicit responsibility within the role design, with clear ownership and escalation triggers.
Why it matters: As AI accelerates production, quality control becomes a designed part of how teams work.
Chris Ernst, Workday chief learning officer, aptly summed up the importance of balance between humans and AI: “The path forward is not a zero-sum game,” he noted. “It’s an opportunity to architect a symbiotic relationship, where technology expands our capabilities and humans continue to grow and make progress.”
That requires review to be designed into the role, with ownership and escalation triggers explicit so that verification doesn’t become invisible labor concentrated on the same few people.
What to document:
Review ownership: Who reviews which outputs (manager, peer, cross-functional partner) based on the deliverable.
Review depth: What qualifies as a light review versus a structured review, tied to risk and audience.
Escalation triggers: Risk level, missing inputs, uncertainty, policy sensitivity, unclear sourcing, high people impact.
What breaks when this step is missing: The correction burden concentrates on a handful of individuals. Bottlenecks form, quality varies by reviewer availability, and rework shows up late when it is most expensive to fix.
Decision: Align performance evaluation with outcome quality and strategic impact rather than volume.
Why it matters: Metrics drive behavior. When speed becomes the dominant metric, quality and judgment often become secondary. In practice, the two need to be complementary.
“The future of work isn't human or AI; it's a partnership between the two,” Ashley Goldsmith, Workday chief people officer, explains, “driven by a deep understanding of when, where, and how to deploy human talent and AI capabilities. Humans should always be at the center, and AI is here to help us maximize our potential and focus on what we uniquely do best.”
Success measures should reinforce that partnership by rewarding net value and outcomes that hold up, not just output volume.
What to document:
What breaks when this step is missing: Speed becomes the dominant signal. Output volume rises, rework rises with it, and organizations struggle to move roles into consistent net-positive outcomes.
“The future of work isn’t human or AI; it’s a partnership between the two.”
Ashley Goldsmith
Workday Chief People Officer
Decision: Intentionally reinvest time saved by AI into collaboration, mentorship, and strategic thinking.
Why it matters: Efficiency gains often get absorbed back into more task volume.
Chris Ernst warns: “When we stop prioritizing building social connection, organizations risk leaving tremendous value for their employees—and businesses—on the table.”
The enabling conditions associated with augmented strategists include increased investment in team connection, which supports shared standards, better handoffs, and consistent execution.
What to document:
What breaks when this step is missing: Teams get faster at tasks but weaker at coordination. Work becomes more siloed, standards drift, and long-term AI ROI flattens as culture and collaboration erode.
Role design is the clear key to activating full AI maturity. Workday’s productivity matrix makes the split visible: Struggling users operate in roles where judgment, standards, and ownership are left to individual discretion, while augmented strategists operate in roles where those expectations are intentionally designed.
The fastest path to enterprise AI ROI is to make role expectations unambiguous. When leaders define where AI supports execution, where human judgment is required, and what standards must be met before work moves forward, employees stop spending their time validating and repairing output and start owning outcomes that matter.
These five redesign decisions can be applied as a role-by-role operating system. Pick a high-impact role, document the AI/human boundary, set the quality threshold, assign review and escalation, and update success measures so net value—not volume—wins.
This article covers just part of the insight found in the Beyond Productivity research. To dive deeper into how your organization can realize full ROI from its AI investments, download the full report.
As skill demands shift and the talent landscape evolves, workforce intelligence gives HR leaders the insight needed to build a workforce ready to adapt and succeed.
More Reading
Learn why CHROs must prioritise reskilling in the age of AI, and the five steps you can take in 2026 to transition from AI adoption to business impact.
Our North Star remains rooted in our AI ethics principles and a robust responsible AI framework.
Amanda Gervay shares how building a culture based on decency, community and inclusion creates the foundation for high performance in an AI-powered workforce.
