As industries and customer expectations evolve, businesses face mounting pressure to leverage emerging technologies that provide a competitive edge. Today, that often means having a clear strategy for using artificial intelligence (AI) and generative AI to develop new products and services, streamline operations, boost productivity, and avoid falling behind.
AI-powered digital transformation is no longer optional; it’s essential—whether your organization is ready or not. According to a recent McKinsey & Company survey, 88 percent of organizations already use AI in at least one business function. Of those, 31 percent are scaling their efforts, while seven percent are realizing value from widespread deployment.
“Many organizations are still in the early stages of exploration,” says Harvard Business School Professor Karim Lakhani, who co-teaches the HBS Online course AI for Leaders with HBS Professor Iavor Bojinov. “They’re experimenting and testing, but they haven’t fully integrated generative AI into their workflows. This creates a growing gap between what this AI technology can do and how it’s actually being used.”
The guide below explains what digital transformation is, explores how artificial intelligence can facilitate it, and highlights four key business areas ripe for AI innovation.
What Is Digital Transformation?
Digital transformation is the process of adopting new technologies to fundamentally reshape how your business operates and creates value. It often involves:
- Rethinking business models
- Optimizing operations
- Improving customer experiences
It’s not simply driven by technology but rather by addressing inefficiencies, enhancing agility, and making data-driven decisions.
In an HBR IdeaCast episode, Columbia Business School Professor Rita McGrath recommends a measured transformation approach.
“Instead of launching it like a great big bang and running the risk of a huge failure, you take it more step by step,” McGrath says. “So it’s building up digital capability, but in a very step-by-step kind of way. And that allows the organization to much more readily absorb the change.”
In AI for Leaders, Lakhani echoes the need to take things slow while rolling out an AI initiative: “This journey rarely starts with bold, sweeping changes. It begins with clarity and credibility—identifying high-impact use cases that solve real business problems and earn trust across the organization. Early wins matter; they don’t just show what’s possible, they shift mindsets and build momentum.”
If you’re considering integrating AI into your company’s operations, here are four areas where it can drive digital transformation.
4 AI Levers Driving Digital Model Transformation
1. Strategy
As you embrace digital transformation, AI becomes pivotal to reshaping business strategy. Unlike traditional strategies that rely on static data and human judgment, AI-driven ones continuously evolve by leveraging advanced technologies such as machine learning and data analytics.
“When you have digital networks, AI, machine learning, all this kind of cool stuff around, your options for strategy really increase,” says Harvard Business School Professor Marco Iansiti, who co-teaches the online course AI Essentials for Business with Lakhani. “And you can do strategy in a bunch of different ways.”
For example, AI is core to Amazon’s business strategy and drives its digital transformation. By analyzing real-time data, Amazon anticipates stock shortages, reroutes deliveries, and improves shipping times.
AI-driven strategies don’t merely improve existing practices; they fundamentally transform them. With AI, you can adopt predictive analytics to anticipate customer needs, automate processes to reduce costs, and personalize services to enhance customer satisfaction.
Netflix offers another example of AI-driven transformation. Using AI and machine learning algorithms to analyze data—including viewing habits, ratings, and search queries—the streaming service generates personalized recommendations for users, transforming how they consume content. This allows Netflix to predict audience preferences, optimize its content library, and make data-driven investment decisions about shows and movies.
2. Governance
Strong data governance becomes more crucial as you integrate AI into your business because it provides the structures, policies, and processes to manage the risk advanced technologies pose.
Without governance, your company can experience unintended consequences like:
- Security vulnerabilities
- Ethical missteps
- Operational inefficiencies
Governance is also critical to addressing ethical concerns around algorithmic bias, which occurs when AI systems are trained on prejudiced or unrepresentative data, leading to unfair outcomes. In a business setting, these outcomes could be as varied as:
- Discriminatory hiring: Reinforcing gender bias by disproportionately rejecting women who take time off for caregiving.
- Unequal access to resources: Hindering underrepresented employees’ career growth by allocating fewer professional development resources, like training and mentorship, to those who haven’t historically received them.
- Workplace bias: Marginalizing underrepresented candidates by assigning higher performance scores to those whose backgrounds align with training data.
“We need to be sure that, in a world that’s driven by algorithms, the algorithms are actually doing the right things,” Iansiti says in AI Essentials for Business. “They’re doing the legal things. And they’re doing the ethical things.”
To mitigate algorithmic bias, build your AI systems with regularly audited, diverse data sets. Recruit a diverse team to participate in developing and reviewing those systems to further promote inclusivity and fairness.
Additionally, governance facilitates accountability and oversight, ensuring digital initiatives align with your business’s overall strategy. It helps create clear lines of responsibility to monitor and measure digital transformation efforts effectively.
By establishing benchmarks, reviewing performance, and continuously refining processes, governance enables you to stay agile and responsive in a rapidly evolving digital landscape.
3. Architecture
A robust digital infrastructure is also essential. Your organization’s systems, networks, and platforms should allow for the seamless integration of AI and other emerging technologies.
“The plumbing—the architecture of the plumbing—is the architecture of the business,” Iansiti says in AI Essentials for Business. “And so, if you really want to take this digital operating model seriously, you’ve got to take these kinds of terms and these kinds of ideas seriously.”
Without a well-structured architecture, AI integration can falter, limiting information flow, collaboration, and scalability.
Creating an environment where AI technologies can thrive requires:
- Cloud-based platforms
- Scalable AI models
- Interconnected systems that support data sharing
For example, General Electric (GE) transformed its industrial operations by implementing cloud-based platforms across its machinery and sensors, creating a unified structure for real-time data analysis to predict equipment failures, optimize maintenance, and improve efficiency.
You, too, can lay the groundwork for AI to drive innovation, streamline operations, and support sustained transformation by prioritizing a well-designed digital architecture.
4. Culture
Beyond technology and infrastructure, digital transformation heavily relies on organizational culture. Not every organization has the culture to support digital transformation. Many struggle with outdated communication methods and departmental silos, which slow the adoption of new technologies.
AI can be vital to breaking down those barriers. By enabling improved data sharing, workflows, and real-time insights, AI helps teams collaborate and make decisions more quickly and effectively.
For example, Microsoft shifted its organizational culture by adopting a growth mindset, emphasizing cross-functional collaboration, and leveraging AI to provide data-driven insights to make faster, smarter decisions. This approach allowed the company to fully embrace AI and cloud computing, positioning it as a digital innovation leader.
By integrating AI into decision-making processes, you can not only drive change but also ensure your team has the tools and insights to support and sustain cultural shifts.
Still, success depends on openness to change.
“Scaling AI successfully requires more than advanced technology,” Lakhani says in AI for Leaders. “Sustained impact relies on transforming culture—aligning leadership, nurturing new skills, building trust, and supporting ongoing adoption.”
The Urgency of AI-Driven Transformation
Companies that fail to evolve risk being left behind in a rapidly changing landscape. As Iansiti says in AI Essentials for Business: “If you don’t transform, you’re out of here.” This stark reality underscores the urgency of embracing AI and broader digital transformation to compete.
If you’re ready to begin leveraging AI for your organization’s digital transformation, 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 start? Download our free course flowchart to find the right fit.
This post was updated on January 27, 2026. It was originally published on September 25, 2024.
