Strengthening human agency in the age of AI

Most organizations are investing heavily in AI to gain a competitive edge. But far fewer are addressing the human readiness gap – the uncertainty, resistance, and lack of preparedness among people expected to work alongside these systems. 

According to McKinsey, while 92% of companies plan to increase AI spending, only 1% consider their AI deployment “mature” and fully integrated into workflows and decision-making. This gap highlights a critical truth: the biggest barrier to AI success is no longer technology; it’s people. 

The real objective, then, is not just implementation, but enabling superagency: systems where individuals use AI to amplify uniquely human strengths like creativity, judgment, and adaptability. Organizations that invest in human readiness through workflow redesign, AI fluency, and human-centric leadership will be the ones that turn AI potential into real advantage. 

This blog explores the challenges people face in an AI-driven world, what leaders must do to bridge the gap between AI potential and human capability, and how teams can prepare for continuous change.

AI transformation often stalls at the pilot stage, not because the technology fails, but because the human system around it does. 

The leadership blind spot 

There is a persistent misalignment between leadership perception and employee behavior. According to McKinsey: 

• Leaders are 2.4x more likely to blame employees for lack of readiness than to question their own alignment  

• Yet employees are using generative AI tools three times more than leaders believe  

This reveals a critical gap: employees are more willing to engage with AI than leaders assume, but that willingness is not being effectively channeled. Leaders often underestimate both the appetite for change and their own role in enabling it. 

The trust and enablement gap 

Willingness alone is not enough. Without clear guidance, safeguards, and trust in the tools, adoption remains shallow. 

Employees may experiment with AI, but hesitate to rely on it fully, second-guessing outputs, using it inconsistently, or avoiding it in high-stakes decisions. This limits both productivity and innovation. 

While third-party benchmarking can strengthen trust, it remains underutilized. Among the 39% of C-suite leaders who benchmark AI, only 17% prioritize fairness, bias, transparency, privacy, and regulatory compliance, factors critical to building confidence in AI systems. 

The real barrier: Human factors 

This is why AI transformation is ultimately a human challenge. Research reveals that organizations cite human factors, not technical limitations, as the primary barrier to AI implementation. 

These include: 

• Lack of clarity on when and how to use AI  

• Fear of job displacement  

• Insufficient training and support  

• Misaligned incentives and workflows 

As AI systems increasingly take on cognitive tasks, reasoning, coding, analysis, the role of humans must adapt. Instead of competing with machines, we should focus on capabilities that machines cannot replicate. 

Irreplaceable leadership traits

1. Aspiration

AI can optimize processes to achieve business goals, but it cannot define them or enroll others in the vision. However, we can set bold and ambitious goals that are unique but appropriate for the context and business. Aspiration requires imagination, belief, and emotional connection which allows humans to inspire others and set goals and objectives that would resonate with them. 

2. Judgement

AI can generate recommendations, but it lacks accountability. No matter how advanced AI gets, humans must make decisions when they involve ethics, values, or ambiguity, because judgment requires context and responsibility. At the end of the day, the final call remains with humans.

3. Designing for nonlinear outcomes

AI operates as an inference engine, optimizing based on patterns and historical data. In contrast, humans can envision and drive 10x breakthroughs that defy those patterns. Growth requires innovation, and this comes from breaking patterns and thinking out of the box, which is an ‘only human’ trait. 

AI fluency: The new core skill 

One of the fastest-growing capabilities in the workforce is AI fluency, which has increased sevenfold in just two years. 

AI fluency is not purely technical. It includes: 

• Framing effective questions and prompts 

• Interpreting outputs critically 

• Identifying bias and limitations 

• Integrating AI into decision workflows 

Employees who are AI-fluent do not simply use tools, they collaborate with them effectively, optimizing human-AI adoption. 

The Skill Change Index (SCI) 

A Skill Change Index (SCI) was developed to measure automation’s potential impact on each skill used in today’s workforce. We must shift from skills that face high exposure to automation, such as accounting and coding, to interpersonal skills such as negotiation, coaching, and conflict resolution. While nearly all occupations are expected to experience skill shifts in the next few years, humans can lean into the interpersonal capabilities that are low exposure but high value.  Organizations that invest in these human skills will create a more resilient workforce. 

Traditional leadership models need to change with the introduction of AI into the workplace. It is no longer about being the smartest person in the room. Here are 3 important steps leaders can take to be an effective leader in the current context.  

1. Leadership orchestration 

Leaders must transition from managing tasks to a leadership orchestration system: 

• Coordinating humans, AI agents, and automated processes 

• Conducting workflow redesign where each component complements the others 

• Ensuring seamless collaboration between humans and machines 

The leader’s role becomes less about doing and more about enabling the system to perform. 

2. Setting the context 

In an AI-driven organization, leaders cannot control every decision. Instead, they must provide clearly defined guardrails; clear values, ethical boundaries, and decision rights. After this is done, they must foster a culture of experimentation and curiosity within these parameters. This shift from command to context empowers teams to act autonomously while staying aligned. 

3. Direct engagement with AI 

Effective leaders do not outsource AI understanding to technical teams. 

• They experiment with tools themselves 

• They understand capabilities and limitations firsthand 

• They model behavior for the organization 

This direct engagement builds credibility and accelerates adoption across teams.

Human-AI adoption does not happen organically. It requires deliberate design choices that reduce friction and build confidence. This can happen through hardwiring vs softwiring, workflow redesign, and human-centric design.   

Hardwiring: Embedding AI into the system 

Hardwiring refers to formal structural changes. This can be done by redefining roles and responsibilities, establishing accountability frameworks, and creating escalation protocols (knowing when humans must override AI decisions).   

Softwiring: Strengthening the human edge 

The human edge is as important and can be built via:  

• Psychological safety, easing any uncertainty regarding AI 

• Leadership behaviors that encourage curiosity 

• A culture that encourages questions and experimenting with AI 

Softwiring ensures that employees feel safe and motivated to engage with AI, not threatened by it. 

Workflow redesign 

A common mistake is to apply AI to isolated tasks rather than rethinking entire workflows. Instead of looking at a particular task to automate, leaders should reimagine the entire end-to-end workflow. Think of asking the question: “How should this workflow be redesigned end-to-end with AI in mind?” 

This approach unlocks exponential gains rather than incremental improvements. 

Personalized interaction models 

AI should not be one-size-fits-all. Its role should adapt to the user’s needs, whatever that might be. For some businesses it might be to provide feedback as a coach, for some a thought partner and for others a direct report. Matching AI roles to human preferences increases both effectiveness and adoption. 

As AI becomes more accessible and used widely, the technology itself is no longer a sustainable differentiator. What will set organizations apart is how their people use them. The ultimate competitive advantage lies in accountable leaders who are willing to learn and teams that are AI-fluent.  

Human-AI adoption is not a one-time initiative, it is an ongoing journey of learning. In the end, Superagency is not about replacing humans with machines. It is about elevating human potential through intelligent collaboration. 

If you are curious to learn more about how you can build teams that can thrive in the age of AI, talk to our AI and digital advisory team today.