Stefan Agamanolis, Associate Director of Strategic Research Initiatives
As AI reshapes industries, companies of all sizes are seeking ways to leverage its power to secure and expand their competitive edge. For small to medium-sized businesses, especially those navigating leadership transitions or operating with lean resources, adopting AI can feel daunting. Yet the benefits AI offers are not limited to large corporations—any business can lay the foundation for sustainable growth by taking steps to capture its unique expertise, organize its data effectively, and use AI strategically to expand its potential. Let me highlight three steps can help unlock this AI transformation: preserving the nuanced expertise of senior employees, capturing and organizing valuable data assets, and using AI as a catalyst to fuel new growth opportunities or reinforce competitive advantages.
Preserving institutional expertise as a legacy for growth
In any business, long-tenured employees hold valuable, often subtle know-how accumulated over years of experience. As senior employees approach retirement, there is an eagerness not to “replace” their insights with technology, but to ensure that this invaluable knowledge is preserved as a legacy that future teams can build on. This step is not simply about documenting knowledge; it’s about creating a dynamic and enduring resource that captures unique insights, decision-making nuances, and the lessons learned from countless problem-solving experiences.
For example, through structured interviews or targeted discussions, businesses can capture in-depth expertise on processes, product knowledge, and best practices. AI-powered tools, like speech-to-text technology, can transform these rich interactions into searchable content that future teams can consult and build upon. By participating in this knowledge preservation, experienced employees can help ensure that the organization not only retains its legacy but also lays a foundation for growth and resilience that benefits from their years of dedication and expertise.
Building a strategic data foundation for AI-driven insight
For AI to be transformative, it requires access to high-quality, well-organized data. However, in many companies, valuable data remains scattered across different sources—such as customer records, technical documentation, product logs, and even external sources like industry research. A strong data foundation involves more than simply merging these sources; it’s about strategically organizing and prioritizing data in a way that enables AI to deliver real business value.
This step begins with understanding what data already exists, what additional data could be captured, and the relative importance of each data source in driving business goals. By assessing the completeness, quality, and interconnectedness of these data assets, businesses can develop a data architecture that is both efficient and flexible, allowing them to continuously expand and refine their AI capabilities. Whether it’s identifying patterns in customer behavior, tracking product performance, or spotting operational inefficiencies, a well-designed data strategy will support the long-term scalability and relevance of the AI system.
Leveraging AI as a catalyst for market expansion and resilience
Once a business has preserved its unique expertise and built a strong data foundation, AI can be used as a catalyst to achieve a range of strategic objectives—from recapturing eroding market share to expanding into adjacent markets or transforming existing products and services. AI provides opportunities not just for operational efficiency but also for creating new avenues of value, such as offering personalized customer support, developing data-driven services, or identifying new revenue streams.
For instance, an AI model trained on customer support data and product knowledge can assist service teams in real time, enhancing the customer experience while reinforcing the human touch that many businesses prioritize. Beyond customer service, AI can support product development by identifying trends and patterns that inform new features or uncover underserved markets. In some cases, the comprehensive data a company has organized may even have value to other parties, opening possibilities for alternative revenue streams through data partnerships or licensing.
Transforming with AI on a practical scale
For small and medium-sized businesses, achieving these steps doesn’t require costly consultants or proprietary platforms. By adopting a targeted, practical approach, companies can make strategic progress toward an AI-driven future without high upfront investments. Our xLab program at Weatherhead School of Management has helped businesses take these first, impactful steps by conducting assessments, identifying high-impact opportunities, and developing proofs of concept that demonstrate the specific ways AI can drive value, helping businesses explore what’s possible, de-risk further investment, and develop a roadmap for sustainable transformation.
This journey isn’t only about adopting new technology—it’s about using AI as a powerful tool for growth, resilience, and the next generation of business success.