It was a pleasure to attend the 20th International Conference on Knowledge Management (ICKM) as part of the combined GFIC-CIKI-ICKM event hosted by Iscte – Instituto Universitário de Lisboa from November 27-28, 2025, in Lisbon Portugal. The theme of the conference: “Shaping the Future of Data, Knowledge and Innovation with AI for Global Sustainable Change”.
I was one of the invited speakers to participate in the panel discussion: “Leveraging AI Driven Data and Knowledge for Competitive Advantage in a Digital Economy.” This session included, Christian Stary, Tereza Raquel Merlo, Ph.D , and Angel Ford, EdD, moderated by Daniel Alemneh.
Our session was lively and insightful with thought provoking comments from the audience as the panelist answered a series of questions. I want to thank the conference organizers for inviting me and I look forward to participating in future conferences. It was a personal thrill for me to have my son Jaren Rhem with me and for him to also add his perspective about AI use among his college colleagues at Penn State University to the panel discussion.
The following represents a few of the questions asked and my detailed response to these questions.
Questions/Answers:
Knowledge is required in many contexts of organizational operation that can be supported. How can organizations identify which types of data (internal, external, structured, or unstructured) will create the greatest competitive advantage when paired with AI?
To start the organization must develop an AI strategy to detail how it sees AI being utilized (its vision) and the purpose of using AI (mission). Once an organization has determined its vision and mission for AI, to build a sustained competitive advantage with AI, organizations must focus on the types of data that unlock differentiation, speed, and precision in decision-making. One area to start is with customer data. Customer data sits at the core, providing deep analysis of behavior, preferences, and purchasing patterns which allows companies to personalize interactions, refine marketing strategies, and enhance the overall customer experience in ways competitors cannot easily mimic. Operational data could be the next focus providing insights drawn from supply chain performance, inventory movements, production workflows, and internal processes enable businesses to streamline operations, reduce waste, and boost efficiency. Market data further strengthens competitiveness by helping organizations stay aligned with shifting trends, competitor actions, and broader industry movements, allowing them to anticipate change rather than react to it. Finally for manufacturing companies, converging the physical and digital systems, IoT and sensor-generated data provide real-time visibility into equipment performance, product usage, and customer interactions, creating opportunities for predictive maintenance, product innovation, and new service models. Above all, proprietary and highly trusted data, unique datasets that competitors cannot access or easily reproduce form the foundation of long-term strategic advantage. By integrating these diverse data sources into AI-driven systems, businesses can make more informed decisions, innovate faster, and maintain leadership in an evolving marketplace.
Which technique could be advised for data clearance to achieve reliable AI-driven knowledge for competitive advantage? How can businesses measure the ROI of AI-driven knowledge systems, especially when competitive gains may be indirect or long-term?
Measuring the return on investment for AI-driven knowledge systems is inherently complex, yet it becomes manageable when organizations focus on a clear set of performance indicators. Productivity gains offer a foundational metric, captured through reductions in manual effort, time saved by users, and overall process efficiency improvements. Enhanced decision-making serves as another critical measure, reflected in the accuracy, speed, and quality of decisions supported by AI-generated insights. Cost savings provide tangible evidence of value, emerging from automation, error reduction, and improved allocation of resources. Revenue growth can also be directly linked to AI when increases stem from system-driven recommendations, optimized operations, or more effective customer interactions. Additionally, user adoption and engagement reveal the true impact of the system high satisfaction, frequent use, and broad adoption signal that AI is meaningfully improving daily work. To quantify ROI, these metrics must be compared to pre-implementation baselines or relevant industry benchmarks, allowing organizations to clearly identify the financial and operational lift generated by the AI-driven knowledge system.
AI-driven data systems enable automation that goes beyond routine tasks. In what ways does AI shift the balance between human expertise and machine-driven insights in strategic decision-making, and how should companies adapt their organizational structure accordingly?
I don’t believe that AI is shifting the balance between human expertise and machines, because we will always need the human-in-the-loop. What is evolving is that AI is redefining how decisions are made and how professionals deliver value. Rather than replacing human roles, AI serves as a powerful augmentation tool, enhancing individual capabilities and allowing experts to devote more time to strategic thinking, complex problem-solving, and high-impact decisions. This shift drives organizations toward a more data-driven (or what I like to say data informed) decision-making culture, where intuition is supported and often challenged by AI-generated evidence and analytical depth that humans alone cannot match. As AI becomes embedded in workflows (through agentic solutions), new forms of expertise will emerge; professionals must learn to interpret, validate, and contextualize AI-generated recommendations, blending technical literacy with domain knowledge. The result is a model of collaborative intelligence where humans and AI systems work side by side, each contributing complementary strengths. Human judgment, ethics, and creativity combined with AI’s speed, scale, and analytical precision. This will ultimately enable organizations to solve problems more effectively and with greater confidence.
As always, I welcome your comments and questions. For more insights on AI, listen, and subscribe to my podcast RHEMINISCING with Dr. Tony Rhem, AI past, present, and the possible!
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