Building a Lego Cat

Bottom Line Up Front

This article draws from notes taken during a discussion between a Harvard Business School professor, Joeseph Fuller. and OpenAI’s Chief Economist, Ronnie Chatterji on the future of work and talent in an AI-driven world.

The integration of AI into the electric utility industry’s supporting capabilities will significantly reshape workforce roles, placing greater emphasis on human skills such as empathy and communication. Organizations must proactively prepare employees through structured experiential learning, new management skills for overseeing virtual AI colleagues, and reconfiguring workflows to align with AI best practices. Immediate actions include conducting skills audits, initiating regular training workshops, and developing specialized AI oversight roles to effectively navigate this essential transition.

Building a Lego Cat

My daughter, Hannah, spent months carefully saving every penny she earned, eagerly anticipating the moment she could finally purchase an elaborate Tuxedo Cat Lego set. When the Lego set arrived, she sat down, excited but a bit overwhelmed by the sheer complexity of the pieces laid before us. Initially, she thought she could simply dive in and complete the project quickly, relying solely on her enthusiasm and the provided instructions. But soon, she realized the value of seeking guidance from me and her brother, as both of us had built similar complex models before. It wasn’t just about following instructions; it was about benefiting from experienced insights, learning proven techniques, and understanding the underlying principles of constructing something new and intricate. Hannah’s initial hesitation and gradual confidence mirror the uncertainty and eventual empowerment employees may feel during significant organizational changes.

We stand at the threshold of immense change. Generative AI isn’t simply a new tool—it’s a fundamental shift in how we manage talent and workflow. Roles previously stable will evolve dramatically, with some opportunities closing and new ones emerging.

Organizations must proactively guide their teams through this transition, helping employees identify skills worth cultivating. When routine tasks become automated, what remains valuable? The answer lies in distinctly human capabilities: social interactions, emotional intelligence, and nuanced interpersonal skills. These soft skills, often overlooked, will soon become paramount.

Yet, many anticipate AI adoption as an overnight shift—a binary moment. Reality, however, is more nuanced, with changes unfolding gradually across industries. In the electric utility industry, complexities such as regulatory hurdles, operational reliability, and legacy technology infrastructure naturally slow technology adoption. We’re in transition, and it’s unrealistic to immediately expect incoming talent to fully embrace new AI competencies without the guidance of those who’ve navigated similar changes before.

This is a call for organizational reflection. Traditional roles must be reconsidered—perhaps the countless PowerPoint presentations we’ve grown accustomed to would be more effectively delivered interactively through AI-driven agents or bots. For example, internal HR briefings might transform into AI-driven systems that provide personalized answers on employee benefits or career development paths, significantly improving employee satisfaction and reducing administrative workload.

AI will reshape organizational structures dramatically. Today, typical corporate hierarchies consist of executives (1%), managers (9%), and employees (90%). While AI will not change this ratio, it will change 41% of the tasks typically handled by employees. We might see fewer managerial layers, more specialized AI oversight roles such as AI Ethics Managers or AI Integration Specialists, and agile cross-functional teams emphasizing human-AI collaboration within supporting capabilities such as HR analytics, finance budgeting, or procurement strategy. Additionally, employees will increasingly manage virtual AI team members, requiring them to develop management-oriented skills previously expected only from supervisors or managers, such as task delegation, workflow oversight, and performance monitoring.

The sluggish pace of AI integration often stems from the question: “How can we use this to improve our current processes?” It’s a natural response to something like AI, but adopting this mindset is limiting. A transformative approach asks instead: “How can we fundamentally reconfigure our processes to fully leverage AI, aligning ourselves with the best practices AI inherently understands?” For instance, instead of merely enhancing current financial reporting processes, utilities might entirely reimagine budgeting and forecasting using AI-driven financial analytics platforms, enabling real-time insights, reduced errors, and enhanced decision-making speed.

Legacy firms, including electric utilities, face substantial risk if they focus on just improving their processes and resist wholesale re-engineering their workflows. New generative AI startups, unhindered by traditional structures and regulatory baggage, pose significant competitive threats by rapidly adopting AI-driven methodologies in supporting areas like automated HR processes to streamline recruitment, intelligent procurement systems to enhance supply chain efficiency, AI-driven finance analytics for precise and agile budgeting, and virtual training and onboarding programs to rapidly equip employees with necessary skills. Delaying adoption can place utilities at a severe competitive disadvantage.

Experiential learning emerges as critical to navigating this shift. Employees and leaders must engage in hands-on, interactive education guided by AI veterans who’ve also successfully navigated technological and organizational transitions in the past. For supporting capabilities, this might look like HR teams regularly practicing with AI-driven recruitment and retention tools, finance departments running real-time analytics exercises for agile budgeting, or procurement teams conducting simulations with intelligent supply-chain management software. Regular practice—meaning structured activities scheduled consistently, such as weekly workshops or monthly training sessions—accelerates comfort, reduces resistance to adoption, minimizes errors, enhances productivity, and ensures smoother transitions, particularly in risk-averse environments like utilities.

It’s natural for employees to experience concern or uncertainty about the integration of AI into their roles. Organizations must proactively acknowledge and address these concerns, reassuring employees through clear communication, education, and demonstrating how AI complements rather than replaces their expertise.

To effectively prepare for this new emphasis on soft skills, organizations should first clearly identify the interpersonal and emotional competencies most relevant to their industry and workforce. Skills such as empathy, active listening, effective communication, and emotional intelligence can be assessed through targeted surveys, feedback sessions, and performance reviews. Once identified, training can include role-playing scenarios, peer-to-peer coaching, interactive workshops, and continuous feedback mechanisms, ensuring that these essential human skills are cultivated deliberately and effectively.

Actions to Take Now

• Conduct a skills audit to identify human-centered skills that will grow in importance.
• Launch structured, regular AI experiential learning workshops.
• Establish clear communication channels addressing employee concerns about AI adoption.
• Begin piloting AI solutions in supporting capability areas, like HR, procurement, or finance.
• Foster mentorship programs connecting AI veterans with employees new to AI technology.
• Define specialized AI roles and begin developing talent internally to fill these positions.

Returning to the Lego set, Hannah quickly learned the value of expert advice. She discovered strategies that streamlined our building process and deepened our enjoyment of the project. It wasn’t about doing everything ourselves or suddenly becoming Lego masters overnight—it was about recognizing the wisdom and experience available around us. Similarly, our organizational journey with AI should rely on insights from those who’ve faced parallel challenges. Embracing their expertise, we’re better equipped to build a future that’s innovative, resilient, and deeply human. Hannah’s growing confidence and eventual joy in mastering the complex Lego set can similarly reflect the emotional journey employees will experience as they adopt and master new AI-driven processes. Like Hannah, they will likely move from initial uncertainty to increased confidence, satisfaction, and fulfillment as they successfully navigate this important transition.