Most executive teams are approaching AI transformation as a technology deployment problem. Buy the tools, distribute access, train employees on features, and wait for productivity gains to materialize.
Julie A. Stone, Group Vice President and Chief Learning Officer at TTEC, has seen that sequence consistently fail across large enterprises. Her diagnosis of where value is actually created cuts through the noise dominating most AI conversations at the executive level. “AI doesn’t create outcomes,” Stone says. “Performance is created by people working with AI inside a deliberately designed system.” To close that gap, Stone has developed a five-pillar framework that reframes AI transformation as a human performance and systems design challenge.
1.Strategy: Align AI and Workforce Decisions to Business Outcomes
The most expensive mistake organizations make is launching AI tools before defining what those tools are supposed to change. Employees complete training, return to their roles, and lack clarity on where the technology fits into their daily work or what a measurably different result looks like. Thus, adoption stalls and the investment underperforms.
Rather than asking where AI can be used, Stone advises flipping the question entirely: where must performance change to move the business forward? Define a small number of high-value outcomes, such as revenue growth, margin improvement, or customer experience improvement, and work backwards through existing processes to identify the gaps. Then determine where AI can be inserted to produce a fundamentally better result, not just a faster version of what already exists. “The real opportunity is not automation – it’s redesign.” Stone says, “You have to make deliberate decisions about what work humans must do, what AI can automate, and what AI can augment.”
2. Skills: Build Capabilities for Human-AI Collaboration
The skills gap in AI transformation is not purely technical – it is also behavioral and operational. While AI tool training is necessary, it is not sufficient on its own. As AI takes over certain tasks, organizations must simultaneously develop higher-order human capabilities: applying contextual judgment, framing problems correctly, prompting effectively, and iterating with AI as a genuine collaborator. Equally critical are workflow and performance skills. Most employees have never been expected to identify performance baselines, use data to measure improvements, or deliver measurable gains in productivity and quality. “The real reason most upskilling is failing,” Stone says, “is because it is disconnected from the work itself. If workflows do not change after training and leaders do not reinforce new expectations, employees revert to business as usual.”
3. Leadership: Prepare Leaders to Manage Human-AI Performance
AI transformation requires leaders to unlearn a core assumption: that performance is purely human. With AI, performance now emerges from a system – humans and AI working together. That requires a fundamental shift from managing people to orchestrating outcomes across a human-AI ecosystem. From an accountability standpoint, leaders must define with good looks like with greater precision, ensure the system is designed to produce it, and actively coach teams on how to work effectively with AI. “This is fundamentally a leadership transformation,” Stone says. “At the end of the day, if leaders do not change how they define and manage performance, no amount of technology is going to compensate for that.”
4. Operating Model: Redesign Work Between Humans and AI
The productivity gains that Stone’s team achieved at TTEC – up to 9 times improvement in key workflows – did not come from layering AI on top of existing processes. They came from deconstructing workflows to the task level and asking: Should this be done by a human, by AI, or by both? Then rebuilding the work around that answer. Critically, redesigned workflows require redesigned roles. Stone’s instructional designers evolved into learning experience designers, with a new skill set, a defined certification pathway, and updated performance expectations. “That is the piece many companies are missing,” Stone says. “If you don’t redesign the full ecosystem, you are going to sub-optimize how much value you can actually get out of bringing AI into your organization.”
5. Culture: Create an Environment Where Human and AI Performance Thrives
Technology adoption rarely fails because employees lack technical skills. It stalls because they do not feel safe experimenting, do not trust the tools, and have not been shown a clear picture of what the organization is trying to achieve. Stone grounds her culture framework in three prerequisites. Every person must feel valued for what they contribute and who they are. They must feel they belong, that people like them can succeed. They must feel they can grow. “Culture is a multiplier,” Stone says, “but only when it is grounded in value, belonging, and growth.”
Leaders who build that foundation through trust and transparency create the conditions where experimentation is possible, and AI adoption can accelerate. Those who skip it create precisely the fear and anxiety that stalls adoption regardless of how capable the technology is. The organizations that will lead the next decade are not the ones that deployed AI earliest. They are the ones that built all five pillars deliberately and designed their people around AI with genuine intention.
Follow Julie A. Stone on LinkedIn or visit TTEC for more insights on human-AI team design, workforce strategy, and enterprise performance transformation.
