AI Mania Is Eviscerating Global Decision-Making
A consultant’s claim that every AI initiative they’ve seen in the last 18 months has failed sparks a broader examination of today’s “AI mania” in corporate life. Commenters contrast flashy, executive‑driven chatbot and automation projects—often overpromised, poorly scoped, and riddled with selection bias—with quieter, more modest gains from tools like code assistants or semantic search. Many see the current wave as another tech hype cycle driven by fear of missing out and accountability avoidance at the top, with real but limited productivity benefits and a high risk of wasted effort and burnout if organizations lack clear goals and guardrails.
Scope of “AI projects” and quiet successes
- Commenters question what counts as an “AI project” (LLMs, classic ML, chatbots, vision, regressions, etc.).
- Several say narrow, unsexy uses (spellcheck-like tools, dictation, semantic search, code assist, SQL generation) work well but aren’t branded as “AI projects.”
- Quiet productivity gains for individuals (e.g., advanced SQL, learning complex APIs, faster prototyping) are widely reported, but often not visible as big corporate “initiatives.”
Critique of the article’s 0% success claim
- Many see “0% success” as hyperbole and note it ignores long-standing, effective AI/ML.
- Others argue the consulting firm self-selects for failing, mismanaged projects and has explicitly refused AI implementation work, so its sample is biased.
- Some think the tone is entertaining but grandiose, light on technical detail (no discussion of RAG, evals, retrieval quality, etc.).
AI projects vs. AI-assisted work
- Strong distinction between:
- People using off-the-shelf tools (code copilots, chatbots) to assist their own work → largely positive but modest gains.
- Organizations building custom AI systems (internal chatbots, automation, agentic workflows) → frequently over-promise and under-deliver, hard to make robust, often fail on guardrails and consistency.
Organizational dysfunction and failure modes
- Many say most failures resemble traditional software failures: bad management, unclear goals, chasing trends, and valuing speed of creation over maintenance.
- AI can amplify bad ideas: easy to dress up weak proposals, generate huge low-quality PRs, and waste whole organizations’ time.
- Some argue leaders should fix processes (education, planning, quality checks) rather than blame AI or quit.
Mania, hype cycles, and decision-making
- Strong theme of “AI mania” in the C‑suite: FOMO, unrealistic 10–100× productivity claims, token-usage KPIs, and pressure on staff to profess belief.
- Several compare this to past bubbles (dot-com, blockchain, railway mania); AI is seen as both genuinely useful and overhyped.
- Debate over long-term impact: some expect transformative change or even a singularity; others note the lack of clear, large-scale success stories and suspect we’re mid-bubble.