Insights
Generative AI, machine learning, optimization, AI platforms and responsible AI governance in practice.
34 articles
GPU solvers and a maturing open-source ecosystem are reshaping decision optimization. Here is what changed, where it helps, and how to test it on your own planning models.
Most optimization models never make it past the pilot. This DecisionOps playbook covers the data, testing, deployment and monitoring practices that keep solver-based decisions reliable.
AI agents are powerful but unpredictable. Agentic orchestration places them inside governed end-to-end processes alongside people, bots and systems. Here is how the model works.
The AI Omnibus delayed high-risk rules, not the whole EU AI Act. A practical guide to what is enforceable today, what moved, and how to use the extra runway well.
Most enterprise AI is bought, not built. This checklist shows what to ask AI vendors about data, models, testing, contracts and exit before you sign or renew.
AI regulation fragmented in 2026. Here is how to map Vietnam, Korea, US state and EU obligations onto one practical, reusable AI compliance control set.
Sovereignty has moved from data residency to control over models, operations and supply chains. Here is how to design an AI platform that meets sovereignty needs without stalling innovation.
Token prices fall while AI bills rise. This playbook shows platform teams how to meter tokens, tie them to business value and apply the engineering levers that cut cost per outcome.
Hyperautomation is being rebuilt around orchestration of AI agents, robots and people. Here is what is genuinely new, what still belongs to RPA, and how to modernize without joining the projects that get canceled.
Shadow AI now includes personal chatbot accounts, browser extensions and employee-built agents. This playbook shows how to discover, triage and govern it without killing productivity.
Prompt injection, excessive agency and poisoned memory are now everyday engineering problems. This playbook turns the OWASP LLM and Agentic Top 10s into concrete controls.
Planning is shifting from fixed monthly cycles to decisions made when conditions change. Here is what agentic planning really means, where AI agents fit, and how to build autonomy safely.
MCP now sits under the Linux Foundation’s Agentic AI Foundation and A2A has reached version 1.0. Here is what these open standards mean for the AI platform you build next.
All three hyperscalers now offer managed agent platforms. This guide compares what AgentCore, Foundry Agent Service and Gemini Enterprise Agent Platform provide and how to choose.
MCP is now the default plumbing between AI agents and enterprise systems. This playbook covers what changed in 2026, the reference architecture, the security risks and a phased rollout plan.
Open-weight models are strong, permissively licensed and everywhere, but the frontier gap reopened in 2026. Use this decision guide to choose per workload.
Finance teams no longer fund AI on demos and productivity promises. This guide shows how to build a generative AI business case with baselines, unit economics, honest costs and stage gates.
Language models can now draft optimization models from plain-English problem statements, but only a disciplined pipeline makes the results trustworthy. This playbook shows how to build one.
Foundation models have reached spreadsheets. Here is what tabular foundation models like TabPFN and TabICL do well, where gradient boosting still wins, and how to pilot them.
The RAG-versus-fine-tuning debate has become a four-way choice. Here is how to decide between context engineering, long context, retrieval and model customization in 2026.
Open-source, commercial and GPU solvers now overlap more than ever. This guide gives planning and analytics leaders the criteria, scoring approach and roadmap to choose a solver stack.
Before adding agents or bots, find out how your processes really run. This playbook shows how object-centric process mining turns event data into a ranked, evidence-based automation backlog.
The classic RPA Center of Excellence was not built to govern thousands of AI agents. This maturity model shows how automation operating models evolve toward agent operations by 2030.
Agentic AI is the most hyped and most canceled category in enterprise AI. Here is how to choose the right use cases, set autonomy deliberately and engineer agents that hold up in production.
As model usage spreads across teams, the AI gateway has become the control point for cost, security and reliability. This guide covers capabilities, design choices and a phased rollout.
Generative AI has transformed what document processing can handle, from invoices to contracts. This playbook shows how to design, validate and scale agentic IDP safely.
Most enterprises have an AI policy; few have governance that runs. This guide lays out a practical operating model: who decides, how use cases are tiered, and how controls live inside delivery.
ISO/IEC 42001 is becoming the proof customers ask for. This playbook covers scoping, controls, impact assessments, audit stages and the pitfalls that delay certification.
A good demo proves an AI feature can work once. This guide shows how to prove it works reliably, using offline evals, calibrated LLM judges, red teaming and online evaluation.
Reasoning is now a dial, not a model choice. Here is how effort settings, hidden reasoning tokens and reasoning state change the cost, latency and design of enterprise GenAI.
LLM applications break the assumptions classic MLOps was built on. This guide lays out the LLMOps operating model — artifacts, platform components, observability, cost and incident practices.
Most ML models degrade quietly after launch. This playbook shows how to monitor drift, estimate performance before labels arrive and retrain on evidence, not on a calendar.
Frontier models are not the right tool for every job. A practical guide to when small language models win, how to build them from production traffic, and how to run a hybrid model portfolio.
Predicting who will buy is not the same as knowing who a discount will persuade. This guide explains when causal ML and uplift modeling beat prediction and how to start.
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