We only publish what we can defend in a vendor meeting. Every claim carries an ID, a review date, and a verdict you can check.
- Our ledger215
- Holding203
- Partial06
- Not holding06
- Industry claims tracked26
- Last reviewtoday
Quiet — no verdict transitions in the last 30 days. See the ledger →
Agent Mode AI — claim-tracked agentic AI analysis
Why AI productivity gains create workforce reduction pressure: the demand ceiling and the competitive trap
The argument that AI-driven productivity lets companies keep all their workers and simply produce more runs into two hard limits: consumer demand and competitive dynamics. Both constraints are structural, operating regardless of management intent, and both resolve in the same direction: fewer workers for the same revenue.
27 years enterprise IT operations. Global organisation. Major incidents. Editorially independent.
- 132pieces
- 215tracked claims
- 14public retractions
The Enterprise Agentic Governance Benchmark. Six dimensions, scored 0–100. Free 5-minute web diagnostic; 30–45 minute Excel for governance groups.
Recently reviewed
Three claims most recently re-tested against their primary sources. Status changes log to the corrections page; nothing quietly vanishes.
- AM-133HoldingQ3 2026 Claim Review Bulletin: which claims moved, which held, and what the EU AI Act enforcement window did to the corpusReviewed 30 Jul 2026Read article →
- OPS-081HoldingGoogle Workspace Studio for small teams: when the no-code agent builder in your Google Workspace is the right call, and when Notion or n8n still winsReviewed 28 May 2026Read article →
- OPS-080HoldingThe EU AI Act for small businesses: the high-risk deadline moved to 2027, but your 2 August 2026 duties did notReviewed 28 May 2026Read article →
Why this publication has a ledger
Most AI commentary gets paid for being loud about what's new. Almost none gets measured on whether what it said last quarter still holds this one. That is the gap this publication exists to close. Every published argument carries an ID, a review date, and one of three verdicts — Holding, Partial, or Not holding — that updates over time as evidence accumulates. The verdict log is the product.
When a claim stops holding, the page says so. The original sentence stays visible. The correction is dated and appended. Nothing is quietly removed. You do not need to trust the author to trust the verdicts — the receipts are public, on a 30–90 day review rhythm, and the corrections record is permanent.
Two registers
Same Holding-up disciplineMid-market and large enterprise. Procurement, governance, EU AI Act, multi-vendor agentic stacks. 30–90 day claim review cadence.
No IT department. Practitioner-advisory voice; faster 30–45 day cadence. Tools, vendor red flags, hours-per-week evaluation budgets.
Topic pillars
Five clusters- 4 articlesNon-human identity
How enterprise IT manages AI agents as first-class identities — lifecycle, credentials, procurement clauses, audit.
- 35 articlesAgent procurement
The contracts, SLAs, and evaluation criteria that distinguish agentic-AI procurement from SaaS procurement.
- 3 articlesShadow AI discovery
Detecting unauthorised agentic-AI deployments inside the enterprise — telemetry patterns, inventory methods, policy response.
- 50 articlesAgentic AI governance
Governance frameworks, oversight patterns, and compliance postures for enterprise agentic-AI deployment.
- 22 articlesEnterprise AI cost
Verifying, tracking, and challenging the ROI claims vendors and analysts make about enterprise agentic AI.
Editor's picks
One per topic cluster- Governance90 days to EU AI Act enforcement: what the corpus says enterprises still haven't done
- Cost economicsThe hidden costs of agentic AI: a CFO's guide to true TCO and ROI modeling
- SecurityClaude Mythos: what 'too dangerous to release' means for your risk appetite and cyber posture
- ArchitectureNon-human identity for AI agents: the 2026 IAM playbook
- StrategyWhy 88% of agentic AI deployments fail
Latest pieces
Full archive →Vendor strategic-narrative proof points: the agentic AI procurement diligence checklist
Every agentic AI vendor pitches a strategic narrative; few are tested against the proof points that distinguish 'this is the future' rhetoric from 'this is what we built and what it does'. The 2026 buying-committee diligence checklist walks seven proof points (named-customer references plus revenue contribution, model-vendor relationships disclosed in the MSA, the engineering team's tenure and turnover rate, the post-revenue-recognition product-roadmap evidence, the regulatory disclosure cadence, the executive incentive structure, and the public technical-content cadence) and produces the structural read on whether the narrative is the product or the cover.
Security-platform agentic AI: evaluating TCO and ROI for the buying committee
Security-platform agentic AI sits in a different TCO category than the general-purpose agentic AI the CFO playbook covers. The unit of analysis is the alert and the analyst hour, not the seat or the token. The 2026 evaluation that survives audit walks the buying committee through five cost components and three discount factors against vendor-supplied ROI numbers, and gates the procurement on a 90-day in-environment baseline, not a vendor demo.
Salesforce platform AI vs Microsoft platform AI: the 2026 full-stack comparison for the buying committee
The product-level comparison of Agentforce against Microsoft Copilot is the conversation the existing /compare/ page already covers. The buying-committee question one tier up is the platform comparison; the Salesforce stack (Einstein + Agentforce + Data Cloud + MuleSoft + Tableau) against the Microsoft stack (Copilot + Azure AI Foundry + Microsoft 365 + Fabric + Power Platform). The two stacks compete on different axes and answer different buying-committee questions; the procurement that treats them as substitutes is the procurement that mis-prices the migration cost in year two.
Okta vs specialized NHI vendors: the enterprise agent identity decision matrix for 2026
Okta's 2025 Identity Threat Detection and Privileged Access additions extended the platform into the non-human identity space that specialized NHI vendors (Astrix, Apono, Britive, Aembit, Andesite, P0 Security) have been purpose-building since 2020. The procurement choice is not 'Okta or specialist' as a binary; it is which work the existing Okta deployment covers natively, which work the specialist closes, and where the federated-trust seam is priced. The 2026 buying-committee matrix walks the agent-identity surface in five dimensions and produces the architecture-not-tool decision the audit will ask about.
Enterprise AI infrastructure vendors: the 2026 SLA and uptime comparison matrix
The agentic AI architecture piece on SLA design is the customer-side specification; the SLAs the major infrastructure vendors actually post are the supply-side reality. The 2026 buying-committee SLA comparison resolves on five dimensions (uptime commitment, latency commitment, support response tier, credit calculation, and exclusions list) and reveals the structural gap most agentic AI buying committees discover at year-two renewal: the headline 99.9% uptime is calculated against a denominator and an exclusions list that materially shifts the customer's effective availability.
Digital transformation RFP: the AI UX assessment question set the existing 60-question playbook does not cover
The 60-question agentic AI RFP playbook covers governance, technical depth, procurement, and audit. The UX assessment is the dimension the existing playbook treats only at the workflow-design level; the digital-transformation RFP that includes agentic AI surfaces the user-interaction question more directly because the agent is the new UI primitive in the customer's environment. The 15 UX-assessment questions below extend the existing playbook into the design and interaction surface that the 2026 procurement evaluates the vendor against.
AWS vs Microsoft vs Google vs OpenAI vs Anthropic: the enterprise agentic AI framework matrix for 2026
The buying-committee comparison of AWS Bedrock AgentCore, Microsoft Azure AI Foundry + Copilot Studio, Google Vertex AI Agent Builder, OpenAI Assistants + Agent Builder + Swarm, and Anthropic Claude Agent SDK is not the comparison the existing /compare/ pairs cover. The five-vendor framework matrix prices the choice as an orchestration-layer commitment rather than a model-tier commitment, with five comparison axes (orchestration primitive, tool-use protocol, deployment topology, observability tier, and exit cost) that resolve differently from the pairwise comparisons the publication already runs.
AI water use in context: comparing the 500 ml claim to coffee, beef, and cotton
The 500 ml-per-prompt claim about generative AI, compared honestly to the water footprint of coffee, beef, cotton, and rice. The aggregate is small. The local concentration is the real story. What CIOs should defend when sustainability committees raise this.
Browse by topic pillar
Five strategic pillarsComing next
Peter's editorial calendar — honest dates, bumped-with-notes if missed.- Week 1726 Apr 2026Non-human identity — the first procurement question CIOs aren't asking yet
Every enterprise agent deployment passes through a credential. Most teams still hand the agent a human's credential. Naming the NHI gap is the next Q2 procurement conversation.
- Week 1803 May 2026Shadow agent sprawl — what telemetry catches and what it misses
The browser-as-agent-runtime pattern creates a detection gap that MDM/CASB don't see. What the first wave of shadow-AI discovery tools actually find, and the three categories they miss.
- Week 1910 May 2026The AI agent MSA — four clauses every enterprise contract needs by August
EU AI Act enforcement activates 2 Aug 2026. The clauses that survive legal review in the next quarter will be the ones that don't pretend the agent is conventional SaaS.