The key to AI implementation might just be a healthy skepticism - here's why (www.zdnet.com)

A new IEEE survey finds organizations moving past experimentation into deliberate generative-AI adoption: 39% of technology leaders plan to use generative AI regularly (a 20% year-over-year rise), 35% are rapidly integrating it expecting bottom-line impact, and 91% intend to ramp up agentic AI for data analysis. Top use cases cited include real‑time cybersecurity detection (47%), accelerating software development (39%), supply‑chain automation (35%) and R&D tasks like disease mapping and drug discovery. The shift signals that gen‑AI is being treated as an operational tool — a coach, collaborator and automation layer — rather than a novelty, and many firms are prioritizing role‑based agents and connectors (email, SharePoint, CRM) to embed AI into workflows. The report also warns of a growing need for healthy skepticism: half of respondents flagged over‑reliance and model inaccuracies as top concerns, and industry claims of productivity gains remain mixed. Practical technical approaches seen at companies like Dayforce include using public LLMs (OpenAI) with RAG‑augmented retrieval/search, focusing on smaller domain LLMs for statistical advantage, and piloting agents for specific roles. Organizationally, success requires transparency, retraining, AI champions to evangelize best practices, and stronger ethics and data‑science skills — ethics is now the top skill projected for 2026 — to govern deployment and avoid overconfidence in imperfect models.