Artificial intelligence (AI) is transforming how companies operate and compete. Yet, most are still early in their AI journey. A McKinsey study found that while 92 percent of organizations plan to increase AI investments, only one percent consider themselves fully mature.
The gap between ambition and execution often comes down to readiness. AI readiness measures whether an organization has the right strategy, data, technology, and culture to adopt AI responsibly and at scale. For leaders, it’s a technical concern and strategic priority—one that sits at the heart of effective digital transformation.
What Is AI Readiness, AND Why It Matters for Business Leaders
AI readiness is an organization’s ability to adopt and scale artificial intelligence in ways that strengthen performance and advance its business strategy. For leaders, it’s not just about implementing tools—it’s about creating the right conditions for AI to deliver lasting value.
Although related, AI readiness differs from AI maturity and AI governance. AI maturity reflects how advanced an organization already is in using AI, while AI governance focuses on responsible oversight.
Ultimately, AI readiness enables companies to move beyond operational efficiency to unlock AI’s strategic benefits. Harvard Business School Professor Iavor Bojinov explains this in the online course AI for Leaders, saying, “The key difference is that operational benefits make you more efficient, while strategic benefits can make you more differentiated, resilient, or scalable.”
AI readiness helps leaders turn new technology into lasting business value.
How to Conduct an AI Readiness Assessment
Auditing your company’s AI readiness requires alignment among strategy, technology, and people. A structured approach can help identify where your organization stands today and what must evolve to drive adoption across teams.
To assess your organization’s AI readiness, audit the following areas:
1. Define Strategic Intent
Start by clarifying why AI matters to your organization. What business problems should it solve? What advantages will it create? A clear vision ensures AI investments align with long-term goals rather than short-term experimentation.
2. Audit Data and Technology Foundations
Check whether your data is accurate, accessible, and ethically managed. Confirm your infrastructure can support scalable AI applications before expanding initiatives.
3. Assess People and Culture
AI adoption depends on the people using it. Assess whether teams have the skills, trust, and flexibility to integrate AI into daily decisions. Identify where additional training or communication is needed to build employee confidence and engagement.
4. Evaluate Governance and Risk Management
Ensure frameworks guide responsible AI adoption, manage compliance, and reduce bias. Strong governance enables innovation while protecting organizational integrity.
By following these steps, leaders can uncover the structural and cultural shifts required for sustainable AI success.
Related: Listen to Harvard Business School faculty discuss AI and navigating the future of the workplace on The Parlor Room podcast, or watch the episode on YouTube.
Why Change Management Is Key for AI Readiness
Building AI readiness depends on people just as much as technology. Successful adoption requires leaders to guide their organizations through change.
“How do you get an organization to learn how to use a new tool, how to think, to build or rebuild the business and work with new tools?” asks Moderna CEO Stéphane Bancel in AI for Leaders. “How do you make sure leaders—senior leaders especially—understand the technology so that they can drive change within their organization? Those are the types of questions that a leader needs to think about to set the right AI strategy.”
At its core, scaling AI is about change management. As emphasized in AI for Leaders, technology accounts for only a small part of the equation. The greater challenge is cultural—helping people build trust in new systems, adapt habits, and view AI as a tool that enhances their work rather than threatens it.
Successful adoption begins with leadership. Executives must guide their organizations through new ways of thinking, working, and making decisions. Leaders set the tone, provide vision, and ensure teams have the resources needed to experiment safely.
Common Challenges and Pitfalls in AI Readiness
Even with a strong strategy, many companies struggle to achieve AI readiness. Common challenges include:
- Data silos and quality issues: Inconsistent or inaccessible data can undermine AI projects before they start
- Poor governance: Lack of policies for ethical use, risk management, or compliance can stall adoption
- Implementation costs and decisions: Choosing between developing AI systems in-house or adopting external solutions, and deciding how much to scale, can slow progress and increase costs
- Capability-adoption gap: The difference between what technology can do and what people are ready to do
Leaders can mitigate these risks by fostering transparency, investing in upskilling, and creating clear accountability structures. Avoid common mistakes, such as rushing adoption without auditing readiness, treating AI as a one-off project, or underestimating the importance of change management.
Addressing these challenges proactively ensures that AI initiatives are not only implemented but scaled effectively—delivering operational efficiencies and strategic impact.
How Moderna Scaled AI Readiness
At Moderna, AI adoption has been driven as much by the organization’s culture as by technology. Leadership recognized that becoming an AI-enabled organization is a change management challenge—not just a technical one.
“The biggest challenge to becoming an AI company is change management,” Bancel says in AI for Leaders. “Humans have to first learn how to use the tool, be comfortable with it, and then go through changing business processes. That’s really where the challenge lies.”
To build readiness, Moderna reduced friction from the outset by launching an internal version of ChatGPT, offering training, and encouraging peer-led demonstrations. Town halls and cross-functional communication reinforced learning and normalized AI use.
“Creating a safe space, providing vision, but also providing the tools and flexibility for employees to engage is a critical component of any AI adoption,” says Moderna’s Chief People and Digital Technology Officer Tracey Franklin in AI for Leaders.
While adoption varied across teams, consistent leadership support and a learning-focused culture allowed Moderna to close the capability-adoption gap, embed AI into workflows, and turn technological potential into strategic advantage.
Moderna’s experience demonstrates that evaluating and strengthening AI readiness are essential steps for leaders seeking to turn AI’s promise into measurable business impact.
Preparing for Transformation
AI readiness is no longer optional. Companies that align strategy, data, technology, people, and governance are best positioned to realize the strategic benefits of AI-powered digital transformation.
For leaders, assessing readiness is a strategic imperative. It informs investments, guides change, and ensures AI initiatives create operational efficiencies and long-term differentiation.
Starting with a readiness assessment and establishing clear frameworks allows organizations to move from experimentation to scaled adoption. Embedding change management practices ensures that progress is sustained. The sooner leaders evaluate their organization’s AI readiness, the faster they can turn AI opportunity into measurable results—and create a foundation for long-term AI transformation.
Ready to assess your organization’s AI readiness? Explore HBS Online’s AI for Leaders course or dive into our Digital Transformation and AI Learning Track, which enables you to complete three digital transformation and AI courses to earn a Certificate of Specialization. To explore all our digital transformation and AI programs, download our free course flowchart to find the right fit.
