Dr Aruna Dayanatha PhD
Introduction
Artificial intelligence (AI) is rapidly reshaping how organizations create value across every industry. The pressure is on for companies to develop their people alongside advancing AI capabilities. Talent and learning leaders recognize they must upskill their workforce not just to use AI, but to thrive alongside it in order to remain competitive. However, many firms have so far taken a narrow approach: training employees on specific AI tools in an ad-hoc fashion (for example, how to use the latest chatbot or analytics software). This tool-centric training yields only fragmented, micro-level improvements—a slight efficiency boost here, a solved task there—but fails to build the strategic capabilities needed for long-term transformation.
The real challenge for leaders is to elevate AI adoption from tool training to strategic orchestration. The strategic imperative for CEOs, then, is to shift from ad-hoc tool training toward long-term people development aligned with AI’s transformative power. This means framing AI not as a one-off technical fix, but as a catalyst for building new organizational strengths. AI can enable employees to know better (gain deeper insights and knowledge), do better (execute processes with greater efficiency and quality), and ultimately transform the business.
In the sections that follow, we outline this three-part framework—What can we know better? What can we do better? How can we transform?—and why it matters. We contrast fragmented tool training with strategic capability building, introduce the concept of cognitive scaffolding for individual growth, and show how combining organizational automation with individual augmentation can lead to true business model transformation. Finally, we provide actionable recommendations for CEOs and leadership teams to start developing “AI orchestration” skills across their workforce, ensuring their people—not just their technology—are ready for an AI-driven future.
Beyond Ad-Hoc Tools: From Fragmented Skills to Strategic Capabilities
Many companies’ first response to the AI wave is to roll out training on specific AI platforms or to pilot AI tools in various departments. While well-intentioned, this approach often amounts to a scattershot of micro-initiatives. Employees might learn to use a new chatbot or an automation script, but without a unifying strategy these efforts remain tactical and piecemeal. The organization sees a few isolated wins—yet no substantial impact on its overall performance or innovation capacity.
Contrast this with a strategic capability-building approach. Here, leadership treats AI as core infrastructure for learning and working, not a quick fix. Instead of chasing hype, they align AI initiatives with a unified vision of how the business will compete and operate in the future. Rather than training people to use one tool, they invest in developing data literacy, critical thinking, and cross-functional problem-solving skills alongside AI. They embed AI into workflows and encourage employees to experiment and share learnings enterprise-wide.
When AI is used as a platform for learning and capability growth, the benefits compound. Employees not only perform tasks faster, but also make better decisions and innovate new solutions. Importantly, a strategic approach mitigates the risk that AI simply replaces human work without upskilling the workforce. Leaders avoid the trap of seeing AI as mere labor substitution and instead focus on using it to augment human strengths and build new skills.
To center development efforts, CEOs should anchor on three strategic questions in the age of AI:
- What can we know better?
- What can we do better?
- How can we transform?
This framework moves thinking from a narrow focus on tools to a holistic focus on capabilities—knowledge, execution, and ultimately business innovation.
1. What Can We Know Better? – Augmenting Insight and Learning
In an AI-enabled organization, knowledge is power—and AI dramatically expands what and how people can know. “Knowing better” means leveraging AI to improve insight, foresight, and learning at both the individual and organizational level. Rather than relying on periodic reports or gut instinct, employees can use AI systems to instantly access information, analyze data patterns, and generate insights. But reaping these benefits requires developing your people’s capacity to work with AI as a knowledge partner, not just a query engine.
This is where the idea of cognitive scaffolding comes in. Cognitive scaffolding refers to using AI as a supportive framework that helps individuals extend their cognitive reach and gradually build new expertise—much like scaffolding helps a construction worker reach higher levels. An AI assistant might guide a financial analyst through a complex forecast, suggesting next steps or pointing out anomalies. Initially, the analyst leans heavily on the AI’s support; over time, as they learn the patterns, the AI fades into the background. The goal is to cultivate human expertise and independence, not perpetual dependence.
AI used as a cognitive scaffold acts as a personalized cognitive partner: it provides robust guidance at first but ultimately enables the human to perform at a higher level on their own. For CEOs, enabling people to “know better” starts with training that goes beyond rote tool usage. It means fostering AI-augmented learning. Some leading organizations are already doing this—using AI to recommend personalized learning, identify skills gaps, and design individual career development paths.
Yet, technology alone isn’t enough. The workforce must be prepared to use AI-derived knowledge wisely. Building capability here means emphasizing critical thinking, data literacy, and ethical judgment in training programs. AI can generate a hundred ideas or analyze a million data points; the human’s job is to ask the right questions, interpret results, and apply context and ethics. Knowing better with AI is a human-plus-machine effort: AI provides breadth and speed of information, while humans contribute depth of understanding, values, and strategic judgment.
By developing this disciplined approach to AI-augmented knowledge work, companies enable their people to know more, faster—without sacrificing wisdom. An AI-empowered employee base that continuously learns and adapts will far outperform one that simply follows static training on yesterday’s tools.
2. What Can We Do Better? – Reimagining Execution through Automation and Augmentation
The second strategic question addresses execution: how can our organization do things better with AI? This encompasses efficiency, quality, and capability in operations. AI technologies excel at automating routine, repetitive tasks and this automation can dramatically speed up processes and reduce errors. But “doing better” isn’t achieved by automation alone. The real gains come when automation is combined with augmentation—using AI to enhance, not replace, human work in more complex activities.
Employees must learn how to work alongside AI tools to amplify their productivity and creativity. For example, AI-driven robotic process automation (RPA) can take over high-volume tasks, freeing employees from drudgery. Those employees can be retrained to leverage AI assistants in higher-value work—such as using AI to analyze customers’ needs or simulate project outcomes. The employee’s role evolves into a human-AI orchestrator: overseeing automated systems and injecting human insight where needed.
A key skill here is AI orchestration—the ability to coordinate and manage both human and AI resources to accomplish work. Orchestration skills require understanding which AI tools to use, when to apply them, and how to integrate their outputs into end-to-end workflows. Training programs should help employees develop these orchestration skills: delegating to AI, supervising AI outputs, and redesigning workflows so that humans and AIs each play to their strengths.
Furthermore, organizational processes should be redesigned to maximize the human-AI partnership. When routine work is automated, freed-up human hours should be redirected to high-value activities rather than eliminated. Employees become outcome orchestrators—ensuring automated and augmented parts of work all drive toward business results. People essentially move up the value chain, overseeing processes and focusing on continuous improvement.
In sum, to “do better” with AI, companies should train their workforce in both automation literacy (identifying what can be automated and how to manage it) and augmentation skills (using AI tools to amplify one’s own capabilities). When people are empowered as creative partners with AI—rather than passive tool operators—the organization can execute with a speed, quality, and agility that far surpasses its traditional processes.
3. How Can We Transform? – From Capability Building to Business Model Innovation
Answering the first two questions lays the groundwork for the ultimate goal: transformation. Once we harness AI to enhance how our people learn and how we operate, what new possibilities open up? Forward-looking firms are using AI not just to improve what they already do, but to do entirely new things—entering new markets, offering new services, or reinventing customer experiences. People development is critical here because an AI strategy without an accompanying talent strategy will stall out at incremental improvements. It’s the combination of organizational automation and individual augmentation that unlocks transformative change.
AI has already begun shaping entirely new business models, not just making existing models more efficient. For example, AI enables shifts from reactive to predictive or proactive services. These changes require employees capable of working in new ways and roles created by the AI era. Organizational automation plus individual augmentation equals capability to pursue innovation.
Transformation is not an overnight event—it’s a gradual, cumulative process of change. As AI systems handle more repetitive work, humans—equipped with richer information and more available time—can focus on creativity, strategy, and relationships. Over time, this leads the company to pursue opportunities previously impossible. The transformation journey is both technological and human: an organization that continually learns and adapts with AI will outperform one that does not.
Ultimately, transformation is orchestrated through people development. When employees become orchestrators of knowledge, automation, and innovation, the entire business model evolves toward continuous adaptation and value creation.
Recommendations for CEOs: Building Orchestration Skills and a Learning Workforce
- Align AI Strategy with People Strategy: Make workforce development a core part of your AI roadmap. Treat AI adoption not just as a tech upgrade but as an organizational change that demands new skills at all levels.
- Cultivate a Growth Mindset and Learning Culture: Encourage a culture of experimentation, where employees feel safe to try new AI-driven approaches and share learnings.
- Integrate Cognitive Scaffolding in Training: Design training programs that use AI to teach. Allow AI systems to guide employees through learning but gradually reduce assistance as skills develop.
- Build AI Orchestration into Leadership Development: Train managers to direct both human and AI resources. Teach leaders to delegate to AI, review AI outputs, and design human-AI workflows.
- Emphasize Problem-Solving and Systems Thinking Over Tools: Focus on durable skills—complex problem formulation, design thinking, and systems analysis—rather than tool-specific skills that become obsolete.
- Redesign Roles and Teams for Human-AI Collaboration: Revisit roles and team structures to include AI fluency. Encourage teams to