Artificial Intelligence Is Not the Answer to Information Challenges (www.hcisi.org)

The piece argues that AI is not a cure-all for organizational information problems: instead of simplifying fragmentation, advanced models often make it invisible and amplify confusion. Because every team lives in its own stack and metrics, AI learns and reproduces siloed assumptions and biases, producing fluent but potentially misleading summaries and answers. The author invokes a “law of diminishing returns” on data—after an initial phase where additional data sharpens insight, most added information becomes clutter—and warns that scaling processing without cleaning the underlying data just scales bad conclusions faster. As an alternative, the Human-Centered Information Systems Institute proposes “Clean Slate Information Management” and digital minimalism: a repeatable rhythm to Reveal (map assumptions and overlaps), Reduce (eliminate or merge tools), Reframe (define what information must support), and Rebuild (reintroduce technology, including AI, as a servant of clarity). Practically, this implies prioritizing data hygiene, unified informational architecture, and purpose-driven metrics so AI becomes a tool for testing coherence and surfacing unknowns—not a turnkey replacement for judgment. The takeaway: success in the AI era will hinge less on model power and more on the clarity of the informational foundations those models are trained on.