Stepping into a leadership role today looks very different from just a few years ago. With global artificial intelligence (AI) adoption reaching 78 percent, most organizations are investing heavily in these technologies. Yet only one percent believe they’ve reached AI maturity. What’s missing isn’t better tools—it’s better leadership readiness.
Research
shows that the biggest barrier to scaling AI transformation isn’t talent or technology; it’s leadership that isn’t moving fast enough to guide transformation. Despite widespread excitement, many leaders are held back by a costly misconception: that technical expertise is required to lead AI initiatives effectively.
Harvard Business School Professor Iavor Bojinov, who co-teaches the online course AI for Leaders with HBS Professor Karim Lakhani, puts it simply: “Scaling AI isn’t just a technical challenge. It’s about getting people to use the tools—and integrate them into how work actually gets done.”
Non-technical leaders are uniquely positioned to lead that charge. Their business acumen, cross-functional understanding, and change-management experience are exactly what organizations need to turn AI from isolated experiments into meaningful, high-value impact.
This guide outlines what non-technical leaders truly need to lead AI success, the competitive advantages they bring, and how they can confidently manage AI projects from strategy to execution.
What Technical Literacy Actually Means
Leading AI initiatives requires technical literacy, not technical expertise—and the distinction matters.
- Technical expertise is knowing how to build models, write code, and architect systems.
- Technical literacy is understanding what AI can and can’t do, its risks, benefits, how training shapes system behavior, and business outcomes.
As Bojinov notes in AI for Leaders, “Technical understanding isn’t just for engineers—it’s a critical part of using AI well.”
This doesn’t require learning Python. It requires the fluency to ask key questions, spot limitations early, and make confident strategic decisions.
Why Non-Technical Leaders Have an Edge
Non-technical leaders bring four distinct advantages that often prove more valuable than deep technical skills:
1. They Know Which Problems Are Worth Solving
The first challenge in AI isn’t building technology—it’s determining where it belongs. Leaders excel when they start with business problems, not algorithms.
VideaHealth, a healthcare AI startup featured in AI for Leaders, illustrates this clearly. The company used AI to analyze dental X-rays and help clinicians spot issues they might otherwise miss. But the founders quickly realized that the real challenge wasn’t model performance—it was trust and clinical AI adoption.
As VideaHealth founder and CEO Dan Bachiochi explains in AI for Leaders, “If you connect the dots here, we’re not actually replacing dentists with AI. We’re helping dentists provide better care to their patients because they’re not as stressed. It’s easier for them to find the things that they really need to act on.”
That outcome mattered—and validated the product’s value. But earning trust required an understanding of workflows, risk tolerance, and how clinicians rely on their own judgment.
This raised critical questions:
- How would AI fit into clinical workflows?
- Would it support or undermine the dentist–patient relationship?
- What level of performance would earn practitioner confidence?
These questions weren’t about code; they were about roles, responsibility, and human judgment—areas where non-technical leaders shine.
VideaHealth’s journey highlights a key lesson: an effective AI strategy begins with choosing problems that truly matter, understanding the level of performance users expect, and creating technology that strengthens and supports human expertise.
2. They Ask Questions That Reveal What Matters
Once a high-value problem is identified, the next responsibility is to pressure-test the solution. Because they aren’t building models themselves, non-technical leaders create impact by asking questions that determine feasibility, value, and safety.
As Lakhani emphasizes in AI for Leaders, “When you hear someone say, ‘Our system is 90 percent accurate,’ don’t stop there. Ask, ‘Accurate in what way? What about the other 10 percent—what kinds of mistakes does the system make?'”
This mindset matters because not all errors carry equal weight, and not every technically impressive model delivers business value.
According to AI for Leaders, effective leaders probe three essential dimensions:
- Strategic alignment: Does the project support organizational goals? Is AI the right tool?
- Measurable impact: What’s the return on investment (ROI) beyond cost savings? What success metrics matter?
- Augment or replace: Will AI enhance human work or automate tasks? Where does it sit on the human skill curve?
These questions shape everything, from model design and evaluation to governance
structures and rollout plans. Ultimately, asking the right questions keeps organizations focused on building AI solutions that are practical, trustworthy, and aligned with real business needs.
3. They Understand the Human-AI Partnership
Some of AI’s toughest challenges aren’t technical—they’re ethical, organizational, and human. Here, non-technical leaders play an essential role.
In the online course AI Essentials for Business, Lakhani, who co-teaches the program with HBS Professor Marco Iansiti, compares leading with AI to flying with a copilot: humans are the captains.
“You need to make the decisions,” Lakhani stresses in AI Essentials for Business. “And so, as you start to bring AI more into tools, this captain role will need to be well-defined and thought through along the way.”
Building an effective human–AI partnership requires:
- Clear decisions about how work is shared
- Accountability for outcomes
- Guardrails that ensure AI is used responsibly
- Space for experimentation and learning
JPMorgan Chase, highlighted in AI for Leaders, exemplifies this approach. Rather than relying solely on technical teams, the company built a comprehensive governance framework for generative AI by involving business leaders, compliance experts, and technical specialists—demonstrating how essential non-technical leaders are in shaping ethical, strategic AI use.
4. They Commit to Continuous Learning—and Lead by Example
AI evolves rapidly. What’s cutting-edge today may be outdated in months. Leaders don’t need to understand every technical detail, but they do need to stay curious and adaptive.
Bratin Saha, vice president and general manager of Machine Learning Services at Amazon AI, notes in AI Essentials for Business: “One of the things that’s a little unique about machine learning and AI is it’s very experimental, very iterative…you need to have that patience. You need to be tolerant of failures.”
Practical ways to build fluency include:
- Experimenting with industry-specific AI tools
- Staying current on how competitors and peers are using AI
- Investing in programs, such as AI Essentials for Business and AI for Leaders, to build the foundations required for leading AI initiatives
When leaders model a learning mindset, they create psychological safety—encouraging teams to explore, fail fast, and engage confidently with AI.
How Non-Technical Leaders Can Take the Wheel on AI Projects
Leading AI initiatives isn’t about technical mastery—it’s about shaping an environment where AI can thrive. Your role is to set direction, clarify priorities, and ensure teams have what they need to make progress.
AI projects intersect with data scientists, engineers, product managers, compliance specialists, and domain leaders. Because you sit at this intersection, you’re uniquely positioned to bridge communication gaps, coordinate efforts, and keep teams aligned.
A critical part of the role is broadening access to AI across the organization. When employees feel encouraged to experiment, curiosity spreads, and teams begin spotting opportunities that might otherwise be missed.
This is where encouraging democratization becomes particularly powerful. As Lakhani explains in AI for Leaders, democratization “refers to the process of making something accessible to everyone…by empowering individuals across an organization to use tools, data, and resources that were previously limited to specialized teams.”
Once an initiative is underway, measurement becomes essential. While technical teams track precision, recall, and model drift, leaders should anchor on business-related key performance indicators (KPIs)—such as customer experience, cost savings, revenue growth, and productivity gains—to guide the project toward meaningful outcomes.
Non-technical leaders move AI forward by defining the destination, ensuring responsible decisions, and rallying teams around what matters most. Leading AI projects doesn’t require technical skills—it requires leadership.
Lead Any AI Initiative with HBS Online
Whether you’re just beginning your AI leadership journey or preparing to guide larger initiatives, HBS Online offers two powerful pathways, ideal for leaders without technical backgrounds.
AI Essentials for Business provides the foundation. You’ll build the literacy to understand what AI can and can’t do, evaluate opportunities, assess risks, and ask the questions that keep projects moving forward.
AI for Leaders
moves from understanding to implementation. You’ll learn to assess organizational AI readiness, accelerate AI adoption, and guide AI transformation ethically and effectively.
Are you ready to apply AI strategically and guide your team from concept to execution? Explore AI Essentials for Business and AI for Leaders, or dive into the Digital Transformation and AI Learning Track, which allows you to complete three courses in the subject area to earn an advanced Certificate of Specialization. Not sure where to begin? Download our free course flowchart to choose the right digital transformation and AI course for you and your team.
