Unite.AI, Sunday, October 18th, 2026
What Is a Reranker? The Second-Stage Model That Improves Search Results
A primer on rerankers, the second-stage models that reorder retrieved candidates for relevance in search and RAG.
more →
Back Issues/Search Home → Archive → RSS → Subscribe → Services → Popular →
All sections ›
Archive
AI — Archive
2,118 articles · page 1 of 43
Fast Company, Sunday, October 18th, 2026
What Happens When People Have to Clean Up AI's Mess?
A Drexel professor describes latent risk: people losing the skills they need to step in when reliable AI fails.
more →
Fast Company, Saturday, October 17th, 2026
Why Companies May Be Liable for AI Errors
Two CPAs explain why responsibility for AI mistakes falls on the professionals and businesses that rely on the output.
more → The Register, Saturday, October 17th, 2026
Open-Weight Models Win Hearts, Minds, and Wallets
Vercel gateway data shows open-weight models handled 71.5% of tokens and nearly a quarter of spend in September.
more →
Unite.AI, Friday, October 16th, 2026
What Is Tool Calling? How AI Models Turn Decisions Into Actions
A primer on tool calling: schemas, validation, permissions and failure modes in the loop behind AI agents.
more → InformationWeek, Friday, October 16th, 2026
Shadow AI Has Gone Agentic. Are Organizations Ready?
Employees now build unsanctioned AI agents that act on systems; an IT exec argues for guardrails rather than shutdowns.
more →
The New Stack, Friday, October 16th, 2026
Muse Wonder vs. Claude Fable 5.1: Meta's New Model Came Closer Than I Expected
A head-to-head test on agentic and memory tasks finds Meta's cheaper model close to Fable, but costlier in tokens.
more →
TechTarget, Friday, October 16th, 2026
Enterprise Controls Can't Keep Up With AI Agents
Companies are granting AI agents authority faster than they can inventory, monitor or stop them.
more →
TechTarget, Friday, October 16th, 2026
Cost Per Task: A New Method for AI Spend Management
Seat-based budgeting fails for token-metered AI; measuring full cost per successful task gives finance a usable unit.
more → Unite.AI, Friday, October 16th, 2026
AI Often Cites Web Pages It Has Never Even Read
Imperial College research finds search-augmented LLMs often answer from snippets without opening sources, producing stale answers and bad citations.
more → Techstrong.ai, Friday, October 16th, 2026
Agentic AI Memory in Production: Why Enterprise Agents Forget and How to Fix It
A practical guide to the three memory layers production agents need and the write-back mistakes that make them forget.
more →
CIO, Thursday, October 15th, 2026
Token Efficiency: Building AI That Spends Intelligence Wisely
Rebuilding a platform with AI showed token waste comes mostly from architecture, not model choice.
more →
MIT Sloan Management Review, Thursday, October 15th, 2026
The Defining Challenge of Responsible AI Is Designing Systems That Preserve Human Judgment When and Where It Counts
An MIT SMR-BCG expert panel says responsible AI must design systems that keep trustworthy human judgment available when it matters.
more →
CIO, Thursday, October 15th, 2026
How to Make Sense of the Agentic AI Tools Ecosystem
A guide to the categories and leading vendors in the emerging agent tooling stack, from orchestration to governance.
more → SC Media, Wednesday, October 14th, 2026
Why Teams Must Take a Least-Privileged Access Approach in the AI Era
Overprivileged AI tools can cause chaos without malice; least privilege and runtime monitoring of agents are the fix.
more →
Techstrong.ai, Wednesday, October 14th, 2026
Wharton Study: 80% of Enterprises See AI Returns, but Only 28% Achieve Strong ROI
Wharton and GBK Collective find most firms see positive AI returns, but strong ROI depends on growth metrics and leadership.
more → AI Business, Wednesday, October 14th, 2026
DeepSeek and What Enterprises Should Learn From AI Agent Exploits
DeepSeek's model used exposed API keys in its sandbox to call other models, a warning to keep agents supervised.
more →
InfoWorld, Wednesday, October 14th, 2026
5 Agentic AI Trade-Offs to Consider Before Production
A practitioner lays out the latency, accuracy, cost and model-size trade-offs teams should settle before building agents.
more →
Wednesday, October 14th, 2026: 11:00 AM to 12:00 PM
Your AI Agents Are Only As Good As Your Data Layer (Oct. 14th)
Most AI agent projects stall before production, and it's usually a data problem, not a model problem. In this short webcast, see why the data layer decides whether agents ship, and how the Couchbase AI Data Plane gives them persistent memory, governed access to live data, and real-time context.
more →
MIT Technology Review, Tuesday, October 13th, 2026
Open Models Have Real Benefits. Do They Outweigh the Risks?
An explainer on the open versus closed AI model debate, weighing democratization and research benefits against misuse risks.
more → Security Boulevard, Tuesday, October 13th, 2026
Monitoring AI Use Before It Reaches the Network
Local models, certificate-pinned desktop apps and home connections hide AI use from proxies, so monitoring must move to the device.
more →
Computer Weekly, Tuesday, October 13th, 2026
Enterprises Are Growing the Use of AI, but Proving What It Earns Is the Harder Part
ABN Amro, Santander, Axa, ING and AMD describe how they force AI projects to show measurable business value.
more →
VentureBeat, Monday, October 12th, 2026
Your AI Agents Can Execute Every Tool Call Correctly and Still Break Every Enterprise Rule
Tool calling makes single actions deterministic but leaves the agent's decision layer probabilistic and the tool boundary ungoverned.
more →
AiThority, Monday, October 12th, 2026
The AI That Watches the AI: Building Intelligence to Monitor Other Intelligent Systems
As AI agents gain autonomy, enterprises need a supervisory layer that judges AI behavior, not just uptime and latency.
more →
TechZone360, Monday, October 12th, 2026
From AI Experiment to Enterprise Strategy: Making Claude Work at Scale
Scaling Claude beyond pilots depends on clean data, governance and fitting AI into the work employees already do.
more →
SiliconANGLE, Sunday, October 11th, 2026
The Hidden Tax on Enterprise AI: Why Data Architecture Is the ROI Problem Nobody Budgeted For
Data gravity creates a compounding hidden cost in centralization-first AI architectures that surfaces 12 to 24 months in.
more →
Security Boulevard, Sunday, October 11th, 2026
Your Brand's AI Answer Is an Attack Surface
Companies are using AI recommendation poisoning to bias assistants, a risk that falls between marketing and security teams.
more → CSO Online, Friday, October 9th, 2026
When Building an AI-Native Security Program, Start With Outcomes
Lean security teams should apply AI where critical business outcomes are delivered inconsistently, not by tool category.
more →
AI Business, Friday, October 9th, 2026
Western Open-Weight Push Could Give Enterprises More AI Control
New Western open-weight models give regulated enterprises an alternative to Chinese models, with new responsibilities.
more →
CIO.com, Friday, October 9th, 2026
AI Won't Empty the Software Factory: Why the New Engineers Act Like Foremen
Executives cutting engineering headcount over AI misread the job; demand for engineers who steward AI-built code will grow.
more →
CIO, Friday, October 9th, 2026
AI Is Your Newest Hire. Manage It Like One
Firms that onboard AI like a new hire, with context, connections and self-assessment, get results others miss, says a CIO.com author.
more → CIO Dive, Wednesday, October 7th, 2026
Who Actually Controls Enterprise AI?
Thoughtworks survey of 3,200 CIOs finds no dominant AI ownership model, yet 94% of U.S. CIOs expect blame for AI failures.
more →
TechTarget, Wednesday, October 7th, 2026
When Is AI Work Really Done? CIOs Need a Clearer Finish Line
AI 'done' can mean task, workflow or business outcome; CIOs must stop confusing them when judging AI productivity and value.
more →
TechTarget, Wednesday, October 7th, 2026
Scaling AI Puts Enterprise Architecture to the Test
BearingPoint finds 74% of firms see measurable AI impact but only 13% scaled as planned, exposing data, legacy and architecture gaps.
more → CIO Dive, Wednesday, October 7th, 2026
AI Platform Sprawl Undermines Enterprise ROI
Few firms run AI on one shared platform; FICO's analytics chief says CIOs should push an AI standard to curb sprawl and costs.
more → Customer Experience Dive, Wednesday, October 7th, 2026
A Health Insurance Agency's Journey From Pilot to Full-Time Voice AI Agent
eHealth used a voice AI agent to prescreen Medicare callers, ending six-hour waits during a 500,000-call enrollment surge.
more →
SmartData Collective, Wednesday, October 7th, 2026
10 AI Trends That Should Shape Your 2027 Business Plan
Ten AI planning trends for 2027, urging budgets that tie AI spending to measurable outcomes, defined costs and accountable owners.
more →
TechRadar, Tuesday, October 6th, 2026
When Identity Isn't Human: Securing the Agentic Enterprise
IAM built around human logins can't govern autonomous agents; security must shift to controlling what happens after access is granted.
more →
Computerworld, Tuesday, October 6th, 2026
How to Build a 'Safe-to-Fail' Culture for IT Teams and Why You Should
IT staff won't experiment with AI if failure hurts reviews; contained, reversible experiments and protected time unlock learning.
more →
Analytics Insight, Tuesday, October 6th, 2026
Artificial Intelligence Terms: A Practical AI Glossary for 2026
A plain-language glossary of modern AI terms, from machine learning and foundation models to agentic AI, RAG, evals and observability.
more →
BigDataWire, Monday, October 5th, 2026
The Real Bottleneck in Enterprise AI Comes After the Demo
An enterprise AI product leader argues data cleanup and outcome definition, not demos, decide AI success.
more →
Techstrong.ai, Monday, October 5th, 2026
Building AI, Fearing AI: The Movements, Money and Personal Rivalries Behind AI's Power Networks
Techstrong.ai special report maps the donors, institutions and rivalries shaping AI safety, and asks who can hold AI builders accountable.
more →
Analytics Insight, Friday, October 2nd, 2026
Edge AI: Why Companies Are Moving Analytics Closer to Where Data Is Created
Edge AI moves ML inference onto devices near data sources, cutting latency and bandwidth for real-time decisions.
more →
RTInsights, Thursday, October 1st, 2026
Enterprises Don't Need Bigger AI Models. They Need Better AI Architecture.
Enterprise AI advantage is shifting from model selection to system design that routes work to the right model or tool.
more →
CIO, Thursday, October 1st, 2026
Building an Enterprise AI Benchmark Changed How I Evaluate AI
Nutanix co-founder Dheeraj Pandey says enterprise AI evaluations should test context assembly and permissions, not just model intelligence.
more →
CIO, Thursday, October 1st, 2026
Where AI Agents Are Showing Real IT Savings
IT leaders identify coding, cloud cost optimization and tier 1-2 support as the areas where AI agents are producing measurable savings.
more → TechRadar, Wednesday, September 30th, 2026
Where to Draw the Line on AI: Lessons From Digital Forensics
SANS's Heather Barnhart says AI can speed forensic triage, but human investigators must own the conclusions.
more →
Schneier on Security, Wednesday, September 30th, 2026
I Want Better Reporting on AI Genie Behavior
Bruce Schneier argues 'rogue AI' headlines misplace responsibility and proposes 'genie behavior' for AI that fulfills prompts in harmful ways.
more →
InfoWorld, Wednesday, September 30th, 2026
Observability for AI-Native Systems: New SLIs Beyond Latency and Error Rate
Defines AI-specific SLIs such as task accuracy, hallucination rate and cost per successful task for LLM systems.
more →
Analytics Insight, Wednesday, September 30th, 2026
Agentic AI: How Businesses Are Using Autonomous AI Agents at Scale
Businesses are moving agentic AI into production across service, IT and operations, but reliability and oversight remain hurdles.
more →