#048: Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential

Are your leaders capable of unlocking the tremendous potential of AI in GxP? Or are they so busy so the the AI boat just sails away leaving your company way behind?

Source: Superagency in the Workplace: Empowering people to unlock AI’s full potential, McKinsey and Company, Jan 2025.

1.0 AI Adoption: High Interest, Low Maturity

Businesses are enthusiastically investing in artificial intelligence, but very few have achieved true AI maturity. In fact, almost all companies are working on AI in some form, yet only about 1% of business leaders feel their organization’s AI efforts are fully scaled or mature. The report warns that in this pivotal moment for technology, the real risk is not overestimating AI, but underestimating it – thinking too small rather than too big. Key findings on AI adoption include:

• Widespread investment, rare maturity: Only 1% of companies consider their AI initiatives to have reached maturity, underscoring that most firms are still in early stages.
• Slow progress acknowledged: Nearly half (47%) of C-suite executives say their company is developing AI solutions “too slowly,” even though 69% started investing in AI over a year ago. This suggests awareness that progress has been sluggish despite early efforts.
• Continued commitment: An overwhelming 92% of companies plan to increase AI investments over the next three years, indicating that business leaders intend to push AI efforts forward. However, these investments need to translate into scaled impact – currently only 1% feel they’ve achieved that goal.

The takeaway is that while AI adoption is nearly universal in principle, scaling it to a transformative level remains rare. Leadership must accelerate the journey from pilots to enterprise-wide deployment. As the report puts it, AI today is analogous to the early internet era – a technology with massive potential where timid ambitions could leave companies behind. Leaders should therefore approach AI with bold vision to avoid “thinking too small.”

‘I’ve always thought of AI as the most profound technology humanity is working on . . . more profound than fire or electricity or anything that we’ve done in the past.’ - Sundar Pichai, CEO of Alphabet

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?2.0 Employee Readiness and Attitudes

One of the report’s central themes is that employees are more ready and willing to embrace AI than many leaders realize. The workforce is already adopting generative AI tools in their day-to-day work and largely expects AI to change how they work in the near future. At the same time, employees have some understandable concerns and want more support (especially training) to navigate this transition. Notable findings about employee readiness include:

• Higher AI usage than leaders expect: Employees have jumped on AI much faster than management thinks. Three times as many employees are using generative AI for a substantial portion of their work than executives estimate, according to the survey. Over 70% of all employees believe that within two years, at least 30% of their work will be impacted by AI. In other words, the people on the front lines anticipate significant workflow changes due to AI and many are already experimenting with these tools.
• Optimism mixed with apprehension: A slight majority of workers are AI optimists, but a large minority remain cautious. 41% of employees are apprehensive about AI’s impact on their jobs. These more hesitant employees will need extra support and reassurance as AI is rolled out. Overall, employees are eager to gain AI skills, but change management is important to bring everyone along.
• Younger workers leading the charge: Millennial employees are the most familiar and comfortable with AI. They are 1.4× more likely than those in other age groups to report extensive familiarity with gen AI tools, and also more likely to expect their workflows to change within a year. This suggests younger managers and staff can act as advocates and mentors for AI adoption, helping foster a culture of experimentation.
• Demand for training and support: Employees clearly signal that they need training to successfully adopt AI. Nearly 48% rank training as the most important factor for embracing generative AI, yet almost half of employees say the support and training they currently receive is only “moderate or less”. This gap highlights a major opportunity (and responsibility) for companies to educate and upskill their workforce on AI tools.
• Concerns about accuracy and security: Workers do have concerns about AI, especially regarding its risks. About half of employees worry about AI inaccuracies or cybersecurity risks associated with using these tools. Other noted worries include data privacy and job displacement, though to a lesser degree. These concerns mean that employees want AI adoption to be handled thoughtfully, with safeguards in place.
• High trust in employers: Despite their worries, employees tend to trust their own company to implement AI properly. According to the survey, 71% of employees trust their employer to deploy AI responsibly and ethically, a higher level of trust than they have in other institutions like governments or big tech firms. In fact, employees are 1.3× more likely to trust their company to get AI right than to trust outside institutions. This is a crucial point: it gives leaders a “permission space” to introduce AI—employees are generally supportive and expect leadership to balance innovation with safety. Earning and maintaining this trust will require transparency and responsible AI practices, but it means the workforce is inclined to give their leaders the benefit of the doubt in driving AI forward.

In summary, most employees are eager to leverage AI and even anticipate major changes to their work from it. They crave more knowledge and training to use AI effectively. While some employees feel uneasy, they largely trust their companies’ leadership to guide the AI transition in a safe and ethical way. This readiness and goodwill among staff is an asset companies should maximize when rolling out new AI initiatives.

3.0 Leadership Challenges in AI Implementation

‘[It] is critical to have a genuinely inspiring vision of the future [with AI] and not just a plan to fight fires.’?– Dario Amodei, cofounder and CEO of Anthropic

If employees are largely ready for AI, the bigger challenges lie with leadership and management. The report finds that the primary bottleneck to scaling AI is not technical capability or worker resistance, but leadership mindset, alignment, and pace. Many executives underestimate their people’s readiness and overestimate the obstacles, resulting in cautious or fragmented AI efforts. Several leadership challenges highlighted include:

• Perception gap – leaders vs. workers: There is a clear disconnect between leaders and employees on AI readiness. Surveyed executives were 2.4× more likely to cite “employee readiness” as a barrier to AI adoption than to admit issues with their own alignment or strategy. In reality, employees report being quite ready and are using AI actively. This misperception can cause leaders to pump the brakes unnecessarily. In short, leaders often blame employee reluctance, but the data shows employees are not the ones holding back AI progress. Closing this gap in understanding is critical – leadership must recognize that their workforce is primed for change.
• Need for alignment at the top: Getting the whole leadership team on the same page about AI is a major hurdle. “Securing consensus from senior leaders on a strategy-led AI roadmap is no simple task,” the report notes. Different executives have different risk appetites and priorities, which can slow down decision-making. Yet alignment is crucial: leaders collectively need to define where AI will drive value, how to manage risks, and how to measure success. The report suggests appointing a dedicated AI leader or cross-functional AI task force to orchestrate these efforts and keep all parts of the business moving in the same direction. Without strong leadership unity, AI initiatives can remain siloed and stall out.
• Leading the transformation (not just the technology): Adopting AI at scale is as much an organizational and cultural transformation as a technical one. Leadership needs to treat it accordingly. The report emphasizes that success with AI hinges on visionary, bold leadership – merely deploying technology isn’t enough. With the technology maturing rapidly and employees eager, it really falls to leaders to set an ambitious vision. They should articulate how AI will transform the business and then drive that change. In fact, leaders likely have more latitude to act than they realize; employees are giving them permission to experiment with AI and move faster. This means executives can and should stretch their ambitions – for example, reimagining core business processes with AI – rather than limiting AI to small pilots.
• Balancing speed with trust and safety: Both leaders and employees want to see faster implementation of AI, but they are rightly concerned about doing it in a responsible way. About half of employees are anxious about issues like AI making mistakes or security breaches, and undoubtedly leaders share worries about risks and reputation. The report finds that employees do trust their companies to handle these issues, placing the onus on leadership to “prove them right” by making bold and responsible decisions. This is a classic speed-versus-safety dilemma. The recommendation is that leaders move decisively on AI (to capture value quickly), while instituting proper safeguards (to ensure accuracy, cybersecurity, transparency, and ethical use). In practice, this could mean establishing clear guidelines for AI use, oversight mechanisms (like AI model audits or review committees), and starting with use cases that have manageable risk. By showing that AI can be deployed in a trusted way, leaders will build credibility and momentum for further adoption.

In essence, the leadership challenge is to stop under-leading on AI. Rather than pointing to unready employees or waiting for perfect information, executives must align around a bold AI strategy and drive it forward. They should leverage the goodwill and readiness of their workforce, act swiftly to implement AI in key areas, and concurrently address the valid concerns around risk. The report’s message: strong, proactive leadership is the linchpin for unlocking AI’s full potential in the workplace. Companies where leadership steps up – setting clear vision, investing in people, and coordinating execution – are far more likely to leap from pilot projects to transformative AI impact.

4.0 Scaling AI in Business: From Pilots to Transformation

Many organizations have experimented with AI in limited pilots or specific use cases. The next big step is scaling AI across the enterprise to drive significant business value. The report cautions that companies risk “losing ground in the AI race” if they don’t move beyond experimentation and embed AI into core processes and products. As the initial hype around AI settles, the focus should shift to practical, high-impact applications that can deliver competitive advantage. Key insights on scaling AI include:

• Bold goals vs. incremental gains: After the hype, there is a temptation to aim only for incremental improvements. The report argues that companies should do the opposite – set bold goals for AI integration. Practical applications that empower employees in their daily work and yield measurable ROI should be prioritized. By investing in strategic use cases (for example, AI to personalize customer service or streamline supply chain decisions), organizations can create competitive “moats” that set them apart. In short, scaling AI is not about doing a thousand tiny pilot projects; it’s about identifying a portfolio of transformational initiatives and pursuing them with purpose.
• Operational headwinds to overcome: Achieving AI at scale is challenging, and the research identified five major “AI headwinds” that companies must overcome. These are the common obstacles that slow execution of AI programs:

These headwinds are significant, but they are addressable with the right strategies. For example, to tackle cost uncertainty, some leading companies keep flexible budgets and use dynamic planning so they can invest aggressively in what works (and pull back where it doesn’t). To ensure leadership alignment, businesses are forming cross-functional AI councils and appointing AI coordinators to keep everyone focused on common goals. And to deal with talent gaps, they are both hiring specialized experts and upskilling current employees to build AI capabilities internally. The overarching point is that scaling AI isn’t just a technical endeavor – it requires rewiring the organization (governance, people, processes) to integrate AI deeply. Companies that systematically remove these barriers put themselves in position to realize AI’s full benefit at scale.

5.0 Recommendations for Leveraging AI Effectively

For businesses looking to harness AI successfully, the report provides clear guidance. Here are the most critical recommendations derived from the findings:

• Provide visionary leadership and alignment: Treat AI adoption as a strategic, enterprise-wide transformation led from the top. Leaders should align on a bold AI vision and roadmap, defining where AI will create value and how to manage the risks. Establish a united cross-functional leadership team for AI – for example, consider appointing a chief AI officer or an AI task force to coordinate strategy, value delivery, and risk mitigation across the organization.
• Move fast, but responsibly:?Accelerate the rollout of AI solutions to capitalize on momentum – remember that employees are largely ready and even expecting quicker adoption. However, pair speed with strong safeguards. Business leaders must balance urgency with responsible AI practices (addressing accuracy, security, and ethics) to maintain trust. This means implementing governance for AI (policies, oversight committees, ethical guidelines) in parallel with rapid experimentation. Leaders have a mandate to be bold and careful at the same time, which is achievable with proper oversight.
• Invest in people and skills: Back up AI ambitions with serious investment in your workforce. Training and upskilling employees is essential to build AI capabilities and ease anxieties. Companies should roll out extensive education programs on AI tools (e.g. workshops, online courses, hackathons) to help employees at all levels become comfortable with AI. This not only fills skill gaps but also signals to staff that they will be supported through the transition. Likewise, attract new talent in key areas (data science, machine learning engineering) and create an environment that retains these specialists (give them resources, time to innovate, and a compelling mission). A people-centric approach ensures that the organization can actually execute on AI opportunities.
• Focus on high-impact use cases: Prioritize AI initiatives that drive clear business value and can scale, rather than dabbling in dozens of pilots. Identify a portfolio of practical use cases with strong ROI or strategic importance, and concentrate resources there. For example, this might include AI to automate routine tasks, enhance customer experiences with personalization, improve decision-making with predictive analytics, or augment R&D. By embedding AI into critical workflows (sales, customer service, supply chain, etc.), companies can start seeing tangible benefits and build a competitive moat. Early wins will create momentum and justify further investment.
• Overcome scaling barriers proactively: Be deliberate in addressing the operational challenges that come with scaling AI. Plan for flexibility in budgeting – AI projects can evolve quickly, so allow for agile reallocation of funds as you learn what works (this helps optimize cost vs. performance). Secure the needed infrastructure and tools in advance, whether that means cloud capacity, data pipelines, or partnerships with AI vendors, to prevent bottlenecks in deployment. Tackle talent needs by hiring where necessary and reskilling existing teams, and involve a diverse group of employees (not just technical teams) early in AI development to build buy-in and better-designed solutions. Finally, build transparency and explainability into AI systems from the start. Use techniques and tools that make AI outputs interpretable, so that regulators, employees, and customers are comfortable with the technology. By anticipating and managing these factors – costs, talent, tech resources, and trust – companies can smooth the path to scaling AI across the enterprise.

‘It is in [the] collaboration between people and algorithms that incredible scientific progress lies over the next few decades.’ – Demis Hassabis, cofounder and CEO of Google DeepMind

In conclusion, “Superagency in the Workplace” underscores that the ingredients for AI success are largely present: powerful AI technology and a workforce eager to use it. The deciding factor is leadership. Companies whose leaders embrace AI with a bold, cohesive strategy and invest in empowering their people will unlock outsized benefits. Those who hesitate or remain fragmented risk falling behind. As the report succinctly puts it, executives should “make the most of their employees’ readiness to increase the pace of AI implementation while ensuring trust, safety, and transparency”, with the simple goal of capturing AI’s enormous potential to drive innovation and real business value. By acting decisively on these recommendations, organizations can transform themselves into “superagencies” – where people and AI together achieve levels of productivity and creativity that neither could alone.

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?6.0 Latest AI News

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