The Unintended Consequences of Artificial Intelligence

Organizations continue to make significant investments in Enterprise AI, bringing Agentic and Generative AI solutions into their own operations and systems, with the goals of improving their operations through automation and machine learning. Within the context of knowledge, data, and information management, we’ve seen organizations make meaningful strides with AI, implementing solutions that combine the collective knowledge assets of the organization to deliver actionable intelligence at the point of need for their employees, automatically identifying and proactively filling gaps in knowledge and information, and improving the quality and reliability of their vast knowledge assets across the enterprise.  Though most organizations have thus far struggled to achieve true enterprise-level artificial intelligence capabilities, many have realized varying levels of success, with some moving beyond prototypes and implementing and scaling pilots to production. For these organizations that have begun to realize the value of AI in production, however, we’re now noting several unintended consequences emerging. Not all of these consequences are negative or inherently risky for the organization, but they do all bear consideration for organizations at any stage of an AI initiative and will require thoughtful planning and design in order to leverage or mitigate.