Decision Guide

A champion needs a business case a CFO will accept. How to calculate AI project ROI honestly—conservative math, transparent assumptions, and a real payback period.

AI vendor pitches all sound the same. A six-criteria framework—production proof, security, evaluation, ownership—to tell real capability from confident hype.

Before AI touches production data, run this security checklist—data handling, access, prompt and model security, and the vendor questions to ask first.

How long does AI implementation take? The real phases—discovery, build, pilot, production, adoption—what drives the timeline, and how to avoid the usual delays.

The build price is a fraction of the real cost. The full TCO of an AI system—inference, data, monitoring, maintenance—so your budget doesn't blow up later.

A vague AI RFP gets vague proposals. How to write one that surfaces real capability—outcome-first, with the reliability and security questions that matter.

Most AI POCs impress and then die. How to run one that's designed to become production—real data, a clear metric, and a scale path built in from the start.

The gap between a promising pilot and production is where most AI dies. The playbook for closing it—data, integration, governance, and adoption.

Comparing AI vendors on vibes leads to regret. A weighted scoring framework—capability, reliability, security, TCO—that makes the decision defensible.

AI adds new data-protection risk—where data goes, what models retain, who can see it. What to know about privacy and compliance before you deploy AI.

Your AI has to work with the systems you already have. The real challenges of integrating AI with legacy software—and the patterns that avoid a risky rewrite.

Scope decides whether an AI project ships or sprawls. How to write a scope that defines the outcome, respects data reality, and says what you won't build.

Traditional SLAs don't fully fit AI. What service levels to expect—uptime, latency, accuracy thresholds, human backup—and what to demand from a vendor.

Build custom or buy off-the-shelf? A clear decision framework—when bespoke software is a real advantage, and when a ready product is simply the smarter buy.

Do you need AI consulting or AI development? When advice comes first, when building does, and why a partner who does both beats a strategy deck that never ships.

An agent demo is cheap; a production agent isn't. What drives AI agent development cost—integrations, guardrails, evaluation—and how to budget for the real thing.

A POC proves it can work; an MVP is the smallest thing real users can use. Which to build for your AI project—and why the distinction decides whether it ships.