Competing in today’s market requires adapting to emerging technologies like artificial intelligence (AI) to transform business strategies, streamline operations, and gain a decisive edge.
“Over the last decade, we saw the emergence of firms that are designed and architected to release the full potential of data, algorithms, and AI," write Harvard Business School professors Marco Iansiti and Karim Lakhani, who co-teach the online course AI Essentials for Business, in their book Competing in the Age of AI.
Many traditional business models no longer suffice and now require incorporating AI. But what does an AI-driven business strategy involve?
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An AI business strategy integrates AI into your company’s operations, decision-making, and growth plans. Unlike traditional strategies based on manual processes and historical data, those centered on AI leverage machine learning, data analytics, and automation to drive innovation.
By quickly processing vast amounts of data, AI can enhance everything from customer experiences to supply chain logistics. However, that doesn’t mean strategic planning isn’t important.
In an episode of The Parlor Room podcast, Harvard’s Digital Data Design (D^3) Institute Launch Director Jen Stave discusses the need for a strong foundation when using AI in business.
“It's just understanding the business literature,” Stave says. “Understanding business strategy, understanding all the core components of business that have been around a long time but now are different and affected by AI. But those core principles still exist.”
Listen to the full episode featuring Stave:
Adopting AI isn’t just about deploying technology but reshaping your organization’s business model and aligning its culture, goals, and resources.
No matter what you hope to optimize, recognize that implementing AI is a long-term strategy—not a quick fix. Here are six steps to building a strong AI business strategy.
Related: 5 Key Benefits of Integrating AI into Your Business
6 Steps to Building an AI Business Strategy
1. Understand Business Objectives and Needs
The first step to building an AI strategy is understanding how it helps achieve business goals and objectives. Iansiti and Lakhani recommend using an AI-first scorecard—an assessment of your organization's readiness to adopt and integrate AI technologies—to gauge your capabilities and align stakeholders.
The AI scorecard evaluates your organization’s:
- AI adoption: How well it integrates AI, data platforms, software, and analytics across departments
- AI architecture: Whether its digital infrastructure is strong enough to ensure seamless, standardized data between systems for optimal performance
- AI capability: Development teams’ strength, how agile processes are, and whether it’s structured to promote innovation
“The philosophy behind the scorecard is that the information technology we want to look at—especially data platforms and artificial intelligence—generates a huge range of innovation and opportunities for enterprises, so we don’t want to limit it to a specific thing,” Iansiti says in AI Essentials for Business. “It’s not about one use case. It’s about generating lots of use cases.”
By applying the AI-first scorecard, you can gauge your company's AI adoption, identify gaps, and prioritize actions to meet long-term goals.
2. Conduct a Data Audit
Before implementing an AI business strategy, assess your organization’s data infrastructure and AI maturity—or how prepared it is to leverage AI.
Conducting a data audit—which evaluates data assets’ quality, accessibility, and governance—is crucial to understanding data infrastructure because it:
- Identifies your organization’s data sources, including customer databases, sales records, supply chain data, and financial reports
- Reviews data’s accuracy, consistency, and completeness so AI systems use reliable data outputs
- Ensures relevant teams across your organization can access the data they need without barriers
- Evaluates how you manage data, who can access it, and how secure it is from breaches or compliance violations
In the assessment process, you may uncover challenges like data silos—when different departments separately store and manage data. For example, if your marketing and sales teams manage data separately, it can be challenging for AI to generate valuable insights.
With a data audit, you can connect information across departments to eliminate silos and create an integrated infrastructure capable of supporting AI at scale.
3. Develop an Ethical Framework
As organizations rush to implement AI, many fail to address its ethical considerations, such as data privacy, bias, and transparency. Those must be part of your strategy from the beginning to avoid serious consequences.
“We need to go back and think about that a little bit because it's becoming very fundamental to a whole new generation of leaders across both small and large firms,” Iansiti says in AI Essentials for Business. “The extent to which—as these firms drive this immense scale, scope, and learning—there are all kinds of really important ethical considerations that need to be part of the management; the leadership philosophy from the get-go.”
Not examining those issues puts your organization at risk of legal violations, loss of customer trust, and long-term reputational damage. Preventing such pitfalls requires defining clear ethical standards that cover:
- Data privacy
- Fairness
- Algorithmic transparency
Since AI heavily relies on data, it's vital to have a strong data governance policy detailing how you collect, store, and use data to manage privacy, security, and integrity.
4. Choose the Right AI Technologies and Tools
The next step in your AI business strategy is selecting AI technologies and tools that align with your business goals.
Start by assessing the problems you’re trying to solve with AI, such as automating processes, improving customer experiences, or enhancing decision-making.
AI technologies that can benefit your organization include:
- Machine-learning platforms, which enable systems to learn from data and improve without explicit programming
- Natural language processing tools, which allow machines to understand and respond to human language
- Robotic process automation, which automates repetitive, rule-based tasks and frees up time for more strategic work
When evaluating technologies, consider testing tools on a small scale before full implementation. As Columbia Business School Professor Rita McGrath describes in an HBR IdeaCast episode, you must take digital transformation slowly.
“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.”
5. Prioritize AI Skills Development
AI technologies are complex. Without a skilled team, even the most promising can fail.
To execute your AI business strategy, identify skills gaps in areas like:
- Machine learning
- Data science
- Data engineering
If gaps exist, determine whether to upskill your employees through AI training or recruit new talent. While your information technology and data management teams may be strong, AI often requires specific expertise.
Collaboration is equally important. AI initiatives shouldn’t operate in isolation; they should be alignment on business goals across departments.
Without a team equipped to handle AI’s complexities, even the best strategies can struggle to succeed. By investing in AI training and acquiring the right expertise, your organization can be better positioned to compete.
6. Get Employee Buy-In
As you implement your organization’s AI business strategy, make it a point to gain employee buy-in. AI-driven changes affect not only your systems and processes but employees’ roles, skills, and collaboration.
“How do we actually go about this organizational transformation?” Lakhani says in AI Essentials for Business. “This question is really important because a key lesson in years of academic research in management has shown us that culture eats strategy for breakfast. We can teach you all we can about strategy. We can give you all the frameworks; all the two-by-twos. But if you don’t understand the cultural aspects—the organizational aspects of change—then your best strategies will just simply not work.”
To garner employee buy-in, clearly communicate the vision for adopting AI across your organization. Employees must understand AI’s benefits and how it can positively impact their roles.
According to HBS Professor Tsedal Neeley, framing digital transformation as a transition can help.
“In this role as a digital leader, you’re in a perpetual state of transitioning,” Neeley says in AI Essentials for Business. “There’s no such thing as, ‘OK, we’re changing, we’ve changed, we’re done.’ No. Technology changes. Data and the level of computing that we can achieve change. The ecosystem changes. So you’re entering a perpetually liminal state, and you have to be comfortable with that. And you have to make sure that your organization becomes one that can learn quickly, that can adapt quickly, that can be nimble, and be able to have a core around data and technology.”
Lead in the Age of AI
As AI redefines industries, learning how to harness its potential has never been more critical. Beyond technical know-how, staying ahead involves developing a comprehensive business strategy that integrates AI into every aspect of your organization.
If you need guidance on approaching this transformative journey, consider enrolling in AI Essentials for Business. Through an interactive online learning experience, you can learn how to develop and execute an AI strategy that aligns with your business’s goals and positions it for long-term success.
Are you trying to build an AI business strategy? Explore AI Essentials for Business—one of our online digital transformation courses—and download our interactive online learning success guide to discover the benefits of online programs and how to prepare for one.
