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Underwriting Data Extraction: ACORD Forms, Submissions and Loss Runs
Underwriting Data Extraction: ACORD Forms, Submissions and Loss Runs
How insurers extract clean data from ACORD forms, broker submissions and loss runs with AI, where extraction breaks, and how to measure accuracy.
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Knowledge Graphs and LLMs: When You Need Both
Knowledge Graphs and LLMs: When You Need Both
Knowledge graphs and LLMs solve different problems. Learn when vector RAG is enough, when to add a graph, and how to combine both in one assistant.
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LLMOps vs MLOps: What Actually Changes
LLMOps vs MLOps: What Actually Changes
LLMOps vs MLOps compared: what carries over from your MLOps platform, and what changes in evaluation, retrieval data, monitoring, cost, security and releases for LLM applications.
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Build vs. Buy vs. Orchestrate: The Real AI Capability Decision
Build vs. Buy vs. Orchestrate: The Real AI Capability Decision
When organizations start looking at AI, the conversation often starts with the classic build vs buy AI question: “Should we build it or buy it?” From the discovery conversations I have been part of, I think that question is becoming too narrow. Today, organizations have a third option: orchestrate. The real question is: What should […]
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The AI Services Market Is Splitting: Headcount Sellers vs. Outcome Engineers
The AI Services Market Is Splitting: Headcount Sellers vs. Outcome Engineers
AI is splitting IT services into headcount sellers and outcome engineers. See how outcome-based pricing works and what to ask your AI services partner.
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AI in Underwriting: Reading ACORD Forms and Loss Runs
AI in Underwriting: Reading ACORD Forms and Loss Runs
AI in underwriting is only as good as the submission it reads. How AI handles ACORD forms, broker emails, spreadsheets and loss runs, where it goes wrong, and how to measure it before you rely on it.
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When Software Gets Cheap, Your Data Becomes the Moat
When Software Gets Cheap, Your Data Becomes the Moat
When anyone can build software with AI, proprietary data becomes the moat. Here’s what a real data moat looks like, and three moves to start building yours.
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If AI Writes Code, Who Tests It?
If AI Writes Code, Who Tests It?
AI can write working code in seconds. The harder question is who makes sure it is right for the customer, the business and the risk you are willing to accept. A QE leader on why quality engineering has to move upstream.
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Enterprise Search That Cites Its Sources: How It Works
Enterprise Search That Cites Its Sources: How It Works
AI enterprise search that cites its sources links every answer to the passage behind it. Here is how the seven-step pipeline works, where it goes wrong and how to measure it before go-live.
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Why Agentic AI Deals Stall Between the Demo and the Statement of Work
Why Agentic AI Deals Stall Between the Demo and the Statement of Work
Agentic AI pilots rarely fail on technology. They stall because the business case was never documented, governance arrived late and the champion was left to sell it alone. Here is how to sequence the next pilot so it reaches production.
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Document AI in 2026: From Messy Documents to AI-Ready Data
Document AI in 2026: From Messy Documents to AI-Ready Data
Document AI turns PDFs, scans and emails into AI-ready data. See how intelligent document processing works in 2026, plus a 90-day roadmap to production.
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Mid-Market AI: The Enterprise Playbook Doesn’t Scale Down
Mid-Market AI: The Enterprise Playbook Doesn’t Scale Down
Why the enterprise AI playbook fails at mid-market companies, and what works instead: AI that runs on imperfect data, needs no dedicated team, has governance built in and pays back within a quarter or two.
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AI Use Case Prioritisation: A Scoring Framework
AI Use Case Prioritisation: A Scoring Framework
A practical, weighted scoring framework to prioritise AI use cases by value, feasibility, risk, cost, time to value and strategic fit, with a worked example and a value-feasibility matrix.
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Copilot vs Agent vs Assistant: Terminology That Matters
Copilot vs Agent vs Assistant: Terminology That Matters
Copilot vs agent vs assistant: how the three differ on autonomy, cost and risk, plus a one-question test for what a vendor is really selling.
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What Is Agentic AI? A Practical Definition for Enterprise Teams
What Is Agentic AI? A Practical Definition for Enterprise Teams
Agentic AI is software that takes a goal, plans its own steps and acts on real systems. Learn how it differs from AI agents and RPA, and where it pays off.
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Enterprise RAG Explained: Architecture, Costs and Failure Modes
Enterprise RAG Explained: Architecture, Costs and Failure Modes
Enterprise RAG explained: how the architecture works stage by stage, what it really costs, the 7 failure modes that break retrieval, and what to test before you build or buy.
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What Is Enterprise AI? The Answer Is Not the Technology
What Is Enterprise AI? The Answer Is Not the Technology
Nine in ten large companies run AI somewhere, but only 6% see it move earnings. What enterprise AI is made of, why it stalls, and what the 6% do right.
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Security Doesn’t Own Security
Security Doesn’t Own Security
Why IT and security have to stop operating like two companies
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10 enterprise AI use cases in 2026
10 enterprise AI use cases in 2026
Explore 10 enterprise AI use cases for 2026, from customer service and IT to finance and talent, and learn how to identify high-ROI opportunities. The enterprise AI use cases delivering measurable value in 2026 are narrow and operational: customer service and voice automation, document and contract intelligence, IT incident triage, software quality engineering, finance exception […]
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Enterprise Guide to Hybrid Cloud Integration: Tools, Architecture and Use Cases
Enterprise Guide to Hybrid Cloud Integration: Tools, Architecture and Use Cases
A quick guide to improving flow, automation, and cloud delivery.
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10 Real-World Applications of Generative AI in Healthcare You Didn’t Know Were Already Saving Lives
10 Real-World Applications of Generative AI in Healthcare You Didn’t Know Were Already Saving Lives
Why service, content, and product design all need to work together.
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The Coworker Every Doctor Needs: Intelligent Automation in Healthcare
The Coworker Every Doctor Needs: Intelligent Automation in Healthcare
How to structure data products that improve speed, clarity, and measurable value.
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10 Applications of Intelligent Automation in Healthcare
10 Applications of Intelligent Automation in Healthcare
A practical look at quality checks, feedback loops, and delivery guardrails.
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