By Im Byung-min (IBM), Columnist
Artificial intelligence (AI) is changing the foundations of corporate competitiveness. Its applications are expanding from document drafting and information retrieval to strategy, operations, customer experience, and product and service development. Companies therefore face a broader task: integrating AI into how they organize and run their businesses. The ability to create new value with AI could become a significant source of competitive advantage.
Drafting documents, summarizing meetings, and deploying chatbots for customer inquiries are useful starting points. These applications reduce repetitive work and free up employees’ time. Yet competitors can adopt similar tools, making lasting differentiation difficult to achieve through adoption alone. The next challenge is to turn those efficiency gains into capabilities unique to the business.
Connecting Proprietary Data and Operational Knowledge
Capital, facilities, technical expertise, talent, and brands remain important foundations of competitiveness. The ability to combine a company’s accumulated data with AI adds another dimension. Customer purchasing patterns, production and quality data, research and development records, sales expertise, and supply chain information reflect years of operational experience. Lessons from successes and failures, together with employees’ practical knowledge, are also assets that competitors may struggle to replicate quickly.
Whether these assets are available in a form the entire organization can use is a separate question. Data may be scattered across departmental systems, while the reasoning behind important decisions remains in individuals’ memories. Under these conditions, adopting AI may leave much of the company’s knowledge untapped. An AI strategy should therefore address model selection alongside an assessment of what the company knows, what data it holds, and where its strengths lie.
Data’s value depends on its quality and connectivity as well as its volume. Companies need to identify the information they already hold, fill gaps relevant to their work, and connect data across systems. Practical experience must also be documented and organized so that colleagues and AI systems can draw on it. When this knowledge becomes part of everyday decision-making, proprietary data can contribute to a stronger competitive position.
A salesperson using AI to draft a report improves the efficiency of an individual task. A company can go further by linking customer transaction history and market analysis to the identification of potential needs, product recommendations, and proposal preparation. Extending this process to follow-up activities broadens AI’s role across the sales cycle. Workflow redesign connects the information and time gained at each stage to subsequent decisions and actions.
AI agents can retrieve relevant information and perform analyses and tasks across multiple systems to pursue a defined goal. Companies can use these capabilities to build workflows in which people and AI work together. They must decide which tasks to automate and where employees should focus their attention. Reducing time spent on routine information processing and reporting can free up capacity for customer relationships, creative problem-solving, strategic judgment, and identifying business opportunities.
Turning Small Experiments into Customer Value and Business Results
This transformation requires coordination across the business. It should be treated as a company-wide management priority involving both executives and frontline employees. Production, sales, research and development, and other teams need to identify tasks that AI could improve, while management connects those initiatives to corporate strategy. A structure for documenting and sharing implementation experience helps turn departmental lessons into organizational knowledge.
Starting with a clearly defined task is more practical than attempting to build a comprehensive system from the outset. Small experiments with measurable results help establish which approaches work. When outcomes fall short of expectations, companies should examine the causes and improve the underlying data and procedures. Proven applications can then be extended to other departments and workflows, allowing the approach to develop around the company’s needs.
Performance evaluation should examine the benefits customers receive alongside cost savings and productivity gains. Relevant questions include whether AI improves understanding of customer needs, enables more personalized services, or creates opportunities for product and service development. Directing resources freed up by greater efficiency toward these improvements can help AI investment support business growth. The same process can reveal new revenue models grounded in the company’s data and experience.
Companies using the same AI technology may achieve different results. Some may focus on productivity in individual tasks, while others extend its use across products, services, and operations. Outcomes depend in part on how effectively data is used, how practical knowledge is captured, how responsibilities are assigned to people and AI, and how lessons from implementation are shared. Developing these capabilities deserves attention alongside choosing the technology.
Management’s questions must also broaden. Executives should consider which AI tools to adopt, what distinctive value their company offers customers, and how the technology can strengthen that value. They should explore which products and services could emerge from combining existing data, expertise, and experience. Opportunities that competitors might pursue first also merit attention, with priority given to concrete initiatives that build on the company’s strengths.
Future competitiveness depends on how effectively a company brings its data, technology, experience, people, and culture together in applying AI. It must decide what to improve and what new value to create. Experimentation and validation can then guide the expansion of successful applications. The ability to redesign workflows and business operations with AI will help build a foundation for sustained competitive advantage.
