Arm your sales teams with credible, independent terminology definitions. Your clients will trust you more when you speak their language—and call out the hype.
Uses foundation models combined with tools to plan and execute multi-step workflows. Orchestrates LLMs with function calling, loops, and error handling.
Creates "virtual co-workers" to autonomously complete tasks like data analysis, content generation, customer service, and workflow automation.
Enables productivity gains and new AI co-worker service models. Drives investment in orchestration frameworks and agent platforms.
Raises autonomy, accountability, and safety concerns for human-AI collaboration. Questions about who's responsible when agents make mistakes.
"Autonomous reasoning systems that think independently and make complex decisions without human intervention"
"A language model orchestrated with tools and loops to follow a multi-step plan—useful but not truly autonomous. Still requires guardrails, error handling, and human oversight at critical checkpoints."
When "Agentic AI" dominates terminology but "Governance" lags behind, it signals that organisations are rushing to deploy autonomous systems without adequate oversight frameworks. This pattern preceded major AI failures in financial services (Q3 2025) and healthcare (Q2 2025).
Action: If your vendors are pitching "agentic" systems, insist on seeing their governance framework first. Ask about error rates, escalation procedures, and human review processes.
LLM generation pipeline that integrates a search step to ground responses. Queries external knowledge bases before generating answers.
Produces fact-checked answers by querying external knowledge bases. Used in customer support, analytics, and enterprise Q&A systems.
Improves reliability of AI services. Reduces hallucination risks. Creates market for retrieval infrastructure and vector databases.
Improves trust by grounding outputs but introduces dependency on curated knowledge bases. Quality depends on retrieval sources.
"Produces perfectly grounded factual responses with zero hallucinations"
"An LLM plugged into a search step—quality depends entirely on the retrieval sources and prompts used. Significantly reduces hallucinations but doesn't eliminate them. Only as good as your knowledge base."
RAG is one of the few AI techniques where the vendor hype actually matches reality. It's the "hidden gem" of 2025—boring, reliable, and actually works in production.
Action: When evaluating AI solutions for customer support or knowledge management, ask vendors if they use RAG. If they claim "zero hallucinations" without mentioning RAG, they're either lying or don't understand their own system.
Including: Nostalgia Award, Regulation Radar, Underground Darling, and the special "Wait, That Worked?" category
Provide Feedback to Unlock Full AwardsUse the "Business Impact" and "Honest AI Dictionary" columns to separate signal from noise when evaluating vendor pitches and technology investments.
Example: When a vendor pitches "autonomous AI agents," check our definition. We've saved executives from millions in hype-driven investments by revealing what "autonomous" actually means.
Compare vendor claims against our independent analysis. If a vendor's pitch doesn't align with our "What it actually is" definition, ask hard questions.
Example: Vendor claims "zero hallucinations" without mentioning RAG? Red flag. They're either lying or don't understand their own system.
Before investing in AI training, understand what you're actually buying. Our definitions help you evaluate whether training addresses real capability gaps or just teaches vendor-specific features.
Example: If "prompt engineering" training costs £2,000/person, is it worth it? Our analysis shows it's now basic literacy—like charging for "Google search training."
You're viewing this as a beta tester. We're testing the "12th Edition" gambit (cheekily implying this has been refined since 2013), the daily report format, the awards concept, and the overall value proposition. Your honest feedback determines whether we launch this in November 2025 or refine further.
If we launched a daily (or twice-weekly) email digest with terminology reports like the samples above, would you subscribe? Would you recommend it to colleagues?
Does the "12th Edition" framing (implying iterative refinement since 2013) enhance credibility, or does it feel like we're overselling? Would you call us out on it?
Do the Terminology Awards feel like helpful industry commentary, or too cheeky/mean-spirited? Would you share the awards announcement on LinkedIn?
If we compiled all daily reports + awards + analysis into an annual PDF compendium, would you (or your organisation) pay £35-50 for it? Or should it remain free as lead generation content?
What would make this more valuable? What's unclear? What would you change? Even quick reactions ("I'd change X") are incredibly helpful.
Email your responses to: [your-email@aibok.org]
Or download the full strategic document (PDF) for comprehensive context.
Timeline: We'd appreciate feedback by Friday, 1 November 2025 if possible, so we can incorporate it before launch.