Artificial intelligence is reshaping work at breakneck speed, but the gap between its promise and its payoff remains wide. After surveying close to 7,000 professionals around the world for Dayforce’s “Annual Pulse of Talent” report, we learned that about a quarter of workers globally said they use AI at work, compared to 87% of executives. The disconnect is clear: Leaders are sprinting while their people are still finding their footing.
For HR leaders, this moment demands a delicate balancing act between intention and experimentation. AI rewards curiosity but punishes carelessness. It needs both speed and structure to evolve from a source of disruption into a driver of growth.
When Intention Meets Experimentation
Intention and experimentation are the twin engines of AI transformation, but they often pull in opposite directions. Many leaders feel compelled to move fast so they don’t get left behind. That pressure leads to experimentation without strategy and risk without reward. In fact, researchers at the MIT Media Lab found that even though there’s been “$30–40 billion in enterprise investment into GenAI … 95% of organizations are getting zero return.”
True progress comes when experimentation operates within intentional boundaries. It’s about identifying where AI can create measurable, human-centered value, not simply deploying every tool available. The greatest ROI comes when adoption is tied to measurable business outcomes through targeted, practical use cases like matching employees to internal job opportunities, recommending personalized learning paths or answering simple HR-related questions.
In other words: AI drives ROI when it solves real problems that matter to people.
Step Back Before Speeding Up
Sometimes, leadership requires going slow in order to go fast. Rolling out AI isn’t just a technology project; it’s a change management initiative on a global scale. A KPMG study on AI’s impact found that only 40% of employees have received any AI training or education, and only 51% “believe they can use AI effectively.” This skills gap slows progress and erodes trust and engagement.
Organizations also stumble when they don’t offer reskilling programs for employees whose roles are impacted by AI. IBM’s Institute for Business Value surveyed 3,000 C-suite leaders, and respondents believed 40% of their employees will need to reskill due to AI’s impact. In fact, nearly 80% of executives felt it was already impacting entry-level positions. Rushing forward without preparing people risks ending up with a two-tier workforce where only some employees feel confident and equipped to use AI, while the rest feel anxious and sidelined.
Stepping back to design with intention means aligning your AI rollout with your workforce strategy. This reflective phase doesn’t slow innovation; it stabilizes it. For example, at Dayforce, we created an AI Forum that comprises our chief digital officer, chief transformation officer, chief marketing officer and me. We discussed key issues ahead of our AI rollout, including:
• Democratizing AI so all employees had equal access and providing training so they felt comfortable experimenting
• Introducing concrete use cases for people to use the technology in their roles and improve productivity
• Creating guardrails to ensure responsible use and data privacy
Balancing Speed And Intentionality
Speed is essential in a competitive environment. But speed without intentionality is a waste of energy. The leaders who succeed with AI adoption will create a rhythm of pilot, learn and scale and treat experimentation as a system. That means testing use cases in limited environments before scaling, using data-driven insights to adapt in real time and embedding trust through transparency and accountability.
As an example, we wanted to streamline Dayforce’s annual lookback process to improve the quality of conversations between employees and their managers. So, we created an AI agent that helps employees more clearly articulate their results and enables managers to more effectively review those results and provide meaningful feedback and development opportunities. The feedback so far is that it’s saving time and leading to richer discussions.
By putting some simple structure around trying new things, HR leaders can stay flexible while still giving people confidence—and move fast without losing trust.
Creating Space For Learning, Failing Fast And Growing
AI is changing not only how we work but also how we learn. To get the most value from it, organizations need to focus on three key areas: training, transition and transparency.
• Training: Make AI learning accessible and embedded in daily work. Use the technology itself to personalize development and close skills gaps.
• Transition: Treat AI as a catalyst for internal mobility. Give people the tools to explore new roles and career paths within the organization.
• Transparency: Communicate openly about how AI is used, who’s accountable and what guardrails are in place.
When HR leaders create a safe space for people to explore AI—where it’s okay to try, learn and even fail—they turn experimentation into real engagement.
From Buzz To Business Value
Speed without strategy only widens the gap between AI optimism and outcomes. The goal isn’t to slow down innovation but to focus it. AI adoption grounded in intention delivers results that endure because it’s built on trust, not trend.
The story of AI in the workplace is still being written. For HR executives, the next chapter depends on your ability to blend two mindsets: the visionary experimenter and the intentional architect. Create the space to test and learn, and anchor every decision in purpose. Because the future of AI at work won’t be defined by who moves fastest but rather by who moves most thoughtfully.