5 Leadership Skills for the AI Era: What Changed and What's New

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We covered reskilling mid-career leaders and AI leadership skills at last week’s Coffee With Digital Trailblazers. Graduates and entry-levelers should listen to our advice for the class of 2026 in a June episode.

What’s a key takeaway from both of these episodes? Even though many leadership skills are always important, Digital Trailblazers apply them very differently in the AI era. And then, several leadership skills probably wouldn’t have made this list five years ago.

3 Leadership Skills AI Rewrote — and 2 That Didn’t Exist Five Years Ago

Reskilling for AI opportunities

Every leader has a wake-up call. Have you watched a promotion go to someone else or lost a job as the number two candidate? I suggest digging deep into the underlying cause. You leaders will find the mindsets, skills, and leadership competencies they need as their businesses evolve.

I reviewed current AI opportunities and projected into the near future. Here are my picks for the five skills senior talent and leaders need to stay relevant.

Leadership skills for delivering business value

Change managemebt in digital transformation

Change management – Fewer than 50% of knowledge workers say change management is optimized for AI. Organizations that want to deploy more POCs in production, increase adoption, and deliver business value will need more leaders with change management skills.
- Pre-AI – Change management was a role on large-scale enterprise programs. Many SMBs left this discipline out, only to find IT deploying new tech and employees underusing the capabilities.
- AI Era – Change management differentiates companies that scale AI agent adoption versus others stuck in pilot purgatory. Only 25% of organizations have moved 40% or more of their AI experiments into production, according to The State of AI in the Enterprise.

Skills for driving innovation with AI

Critical, creative, and analytical thinking – Last year’s World Economic Forum Future of Jobs Report says 70% of employers list analytical thinking as the most sought-after core skill and 66% say creative thinking is on the rise.
- Pre-AI – Organizations hired strategy teams and management consultants to be the most critical strategic thinkers. Creative thinking fell within the domains of design thinking, customer experience (CX), user experience (UX), or marketing. Data scientists were the premier analytical thinkers, and many businesses struggled to become data-driven organizations.
- AI Era – Critical thinking is a foundational skill for asking questions, writing prompts, and validating an LLM’s responses. I made the comment, “Does the LLM have the right context, or is it fishing in the wrong sea?” in the Coffee With Digital Trailblazers episode on covering critical thinking and digital transformation. Security, marketing, and IT operations professionals who used to be task-driven will find more opportunities to apply their analytical thinking to strategy, lead initiatives, and collaborate with AI on decisions. Creative thinking is essential for businesses that seek more than AI productivity gains and need to drive transformation in their products and services.

AI amplifies this skill’s scope and complexity

The first three skills illustrate how AI amplifies and evolves skills from the pre-AI era. The next two show two new areas in AI governance and AI agent orchestration.

StarCIO AI Strategy and Governance Workshop

Data and AI governance – The urgency to deliver business value from AI is outpacing many organizations’ ability to communicate and implement it. Data integration is a top AI challenge, reported by 78%, and 52% report that limited data unification is holding back their AI initiatives.
- Pre-AI – Data governance was a compliance function largely implemented in financial services and other regulated industries. I advocated for proactive data governance pre-AI that aimed to enable data-driven organizations and data product opportunities. But most organizations pre-AI were focused on dataops and minimally addressing data debt. So while data governance existed pre-AI, it was a role and organizational function, not a responsibility or skill set, and largely limited to regulated industries.
- AI Era – Data governance has taken on new meaning in the AI era, and AI governance is a new skill set. Many organizations are working to develop data fabrics, address data management debt, and upgrade the intelligence extracted from unstructured data sources. Data quality KPIs are crucial for validating whether data is AI-ready and for building trustworthy AI agents. To address the challenges, many organizations recognize the need to coordinate a combined plan that covers both AI strategy and governance.

A key new AI skill for Digital Trailblazers

AI agent orchestration – Who will coordinate work between people and AI agents, ensure that AI agents are performing accurately, and identify when a business process requires reengineering? AI agent orchestration is a new platform category, built on top of MCP and agent-to-agent protocols, providing a control plane for managing production AI agents. (Note: I have an upcoming research paper on AI agents and orchestration platforms.) But it’s also a new skill set required as more organizations deploy thousands of AI agents.
- Pre-AI – AI agent orchestration didn’t exist. Instead, workflow was established in business process automation tools, managed by people, automated with robotic process automation, and reengineered by six-sigma-trained black belts.
- AI Era – Managing AI agents and overseeing complex workflows is a management skill for those overseeing operations, combining process engineering, analytical skills, and management competencies.

AI is only reshaping business, not transforming. Build up these skill sets to be one of your organization’s Digital Trailblazers.