Data readiness for AI

Latest articles on Data readiness for AI from Cortado Group

  • Your AI is Blind, and Your "Clever" Naming Strategy is the Culprit

    By David Russell

    The first step in any AI implementation is data. This is the core truth **we** bring to every client engagement. AI inherently needs data to succeed-it can't forecast *your* close rates until it knows *your* current close rate. It can't tell if 90-day-old deals are dead if *you're* not accurately tracking open and close dates. It needs this fuel to do its magic.

    Everyone talks about 'data readiness' for AI, but few address the most fundamental hurdle: naming things clearly. This article reveals how the very first step an AI takes-reading file and directory names-can be a dead end if your data isn't organized for machine understanding.

  • Conversations to Conversations 4: Integrating AI Systems and Human Expertise

    By David Russell

    This week, our work on our AI focused on a dual objective: advancing the core architecture of our AI-driven meeting intelligence platform while simultaneously…

    This is our candid report from the front lines: most AI errors are data process problems, not model problems. We share the unglamorous-but-critical refactors for data normalization and transcript cleanup required to make AI work reliably.

  • AI is a Chainsaw. Are You Using It Without a Guard?

    By David Russell

    AI can be a powerful tool, but without proper safeguards, its misuse can lead to costly mistakes and missed opportunities.

    “Garbage in, garbage out” isn’t just a cliché-it’s the root cause of most AI failure. This post explores how poor input data, missing provenance, and unchecked automation undermine intelligent systems.

  • SKU 101 Foundations

    By David Russell

    Solid SKUs are essential for effective Go-to-Market AI, as they ensure accurate insights and boost ROI by preventing costly data errors.

    Messy SKUs create messy AI. Learn why structured SKU systems are the essential first step to making GTM AI deliver accurate insights and measurable ROI.

  • Getting AWS Q running isn’t about getting Q running

    By David Russell

    Amazon just rolled out Amazon Q, promising to make BI as easy as asking a question. Sounds magical-until you realize the hard part isn’t Q itself. It’s the messy groundwork: stitching together source systems, wrangling permissions, and proving that spreadsheets aren’t just folklore. The real story? Success with Q has far less to do with turning it on, and everything to do with whether your data foundation is ready to support it.

    Successful AI adoption starts with clean, connected data. Learn how to prepare enterprise systems, validate assumptions, and ensure data readiness so tools like Amazon Q deliver real business value.