Artificial intelligence (AI) has existed in various forms since the 1950s. Yet, recent advances in large language models (LLMs), natural language processing (NLP), and data infrastructure have moved it from the lab into everyday life.
AI powers many of the platforms and services we use daily. OpenAI’s ChatGPT is a widely known example, but AI fuels social media algorithms, navigation applications, e-commerce websites, streaming platforms, weather forecasting, and virtual assistants. According to Gallup, 99 percent of U.S. adults use at least one AI-enabled product weekly.
Businesses are no exception. McKinsey reports that 78 percent of organizations leverage AI for at least one function.
There’s a difference, however, between simply using AI and becoming an AI-first company. This guide explains what “AI-first” means, the principles that define AI-first organizations, and steps you can take to make the transition.
What Does “AI-First” Mean?
In the Harvard Business School Online course AI for Leaders, an AI-first company is defined as an organization that integrates AI as a core capability—not just a supporting tool—within its products, services, strategy, and operations. AI shapes decision-making at every level, from day-to-day tasks to long-term strategic planning.
Consider two companies using AI in their content marketing processes:
- Company A maintains its current workflows but uses AI to generate blog posts and graphics.
- Company B redesigns its entire content strategy around AI for keyword research, topic generation, content creation, editing, optimization, and repurposing across channels.
Both use AI, but only Company B demonstrates an AI-first mindset—embedding AI across the entire process rather than treating it as a single-use tool.
Guiding Principles for AI-First Companies
Before transitioning to an AI-first strategy, it’s essential to establish clear guiding principles. These help align stakeholders, clarify priorities, and ensure AI supports your organization’s broader mission.
Common principles among AI-first companies include:
- AI-native strategy: Designing systems and processes with AI integration in mind from the start
- Data-driven decision-making: Using data insights to guide where and how AI adds the most value
- Human-AI partnership: Viewing AI as a collaborator that augments, not replaces, human expertise
- AI as a capability expander: Leveraging AI to unlock efficiencies, streamline workflows, and elevate human contributions
For example, pharmaceutical and biotechnology company Moderna has embraced these principles in its transformation. In AI for Leaders, Brice Challamel, Moderna’s vice president of AI products and innovation, emphasizes that AI-first leadership begins with people: “The first critical decision a leader has to make when embracing AI is to focus on the people and not on the AI. I’d propose this very simple principle: Listen before you think. Listen to your people and ask, ‘What do you think we could do with AI?’”
Steps to Build an AI-First Company
Once your key stakeholders are aligned and you’ve established your guiding principles, the following steps can help you build an AI-first organization.
Step 1: Strengthen Your Data Strategy
Data is the foundation of every AI initiative. Proprietary data—about your product, customers, and operations—drives the insights that power AI models.
As Moderna CEO Stéphane Bancel notes in AI for Leaders, evaluating your data strategy should include:
- Data architecture: The infrastructure that facilitates your organization’s approach to the data life cycle, from collection to processing and analysis
- Data structure: How data is organized to make it accessible and usable by AI systems
- Data access: Who can access data and AI tools, ensuring compliance and security
- Data governance: The rules and policies that ensure responsible data use
Step 2: Identify Clear Business Use Cases
Next, determine how AI can best support your business goals. Identify specific tasks or processes that AI could automate, enhance, or reimagine.
Brainstorm AI use cases across departments and involve leaders in evaluating where AI can deliver the highest impact. Examples from AI for Leaders include:
- Automating follow-up emails after meetings
- Drafting standard contract clauses
- Conducting research and summarizing findings
- Performing compliance checks
- Forecasting product demand and optimizing inventory
- Improving logistics planning and delivery times
- Analyzing marketing performance and return on investment (ROI)
- Ranking and prioritizing sales leads
Step 3: Prioritize High-Impact Opportunities
Once you’ve identified potential AI use cases, prioritize them strategically. Early projects are critical; they shape organizational trust and momentum.
“In the early stages of building AI capability—when you’re still going from zero to one—use cases matter a lot,” says HBS Professor Iavor Bojinov, who co-teaches AI for Leaders alongside HBS Professor Karim Lakhani. “They’re how you prove value. The ones you start with shape trust, momentum, and learning across the organization. That’s why it’s important to choose wisely.”
When ranking opportunities, Bojinov recommends asking:
- Does this use case align with company goals?
- What risks are involved, and are they acceptable for an early-stage project?
- What metrics will define success?
- Will AI augment human work or fully automate it?
- Do you have the necessary data and infrastructure?
- What ethical considerations, such as privacy and bias, must be addressed?
If you’re still having difficulty ranking, Bancel emphasizes using ROI as one factor in prioritization.
“To prioritize, we, of course, try to use ROI as much as we can, because people have many more ideas than we have the capability to execute,” Bancel says in AI for Leaders. “ROI is, of course, a very good metric. Sometimes ROI does not capture all the value that you can create, so that’s why judgment of business leaders is key to be able to prioritize the work.”
Step 4: Integrate AI Thoughtfully
After selecting your first use cases, decide whether to build AI capabilities in-house or outsource solutions, such as third-party tools or consultants.
Outsourcing may be best if you’re early in your AI journey, have limited resources, or need a fast solution. Yet, building in-house might make sense if data is central to your business, you’re pursuing a long-term AI strategy, or need to protect sensitive information.
Many companies adopt a hybrid model—partnering externally for specialized expertise while cultivating internal AI teams for sustainable growth.
“Some companies start with consultants to build momentum and gain experience, while growing their internal teams in parallel,” Bojinov says in AI for Leaders. “Others take a hybrid approach: building core capabilities in-house, but leaning on partners for specialized skills, platform setup, or temporary support. What matters most is not just what works in the short term, but what positions the organization to learn, adapt, and grow over time.”
Step 5: Evaluate, Scale, and Foster Adoption
Once your initial AI initiative is in place, monitor its performance closely. Track results against expectations, identify gaps, and adjust before expanding.
As adoption grows, focus on building AI fluency across your workforce. Offer AI-specific training, upskilling programs, and highlight how AI can improve the employee experience by automating repetitive tasks and enabling higher-value contributions.
Not sure what adoption rates you should strive for? AI for Leaders recommends aiming for a 75 to 80 percent adoption rate for your initial use cases before scaling further.
Preparing to Lead an AI Initiative
AI technology continues to evolve rapidly, introducing new opportunities almost daily. For leaders, the challenge lies not in waiting for perfect conditions, but in acting strategically and learning continuously.
Developing your own AI fluency is key to leading effectively. An online course like AI for Leaders can help you understand AI’s capabilities, assess its impact, and lead with confidence.
If you’re ready to transform your organization into an AI-first company, explore HBS Online’s AI for Leaders course, or dive into the Digital Transformation and AI Learning Track, which allows you to complete three courses in the subject area to earn a Certificate of Specialization. Not sure where to start? Download our free digital transformation and AI flowchart to find the right fit.
