Enterprise AI scaling

Latest articles on Enterprise AI scaling 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.

    You can't scale what you can't find. Learn how the hidden costs of inconsistent data naming can cripple your enterprise AI scaling efforts, turning institutional knowledge into institutional noise and preventing your AI from ever reaching its full potential.

  • 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…

    Our core lesson for scaling AI: Reliability scales faster than agent count. This post details why we're focusing on structured handoffs between agents and a persistent data architecture, rather than just spinning up more models. This is how you avoid a brittle system.

  • Want Fewer Hallucinations? The AI Whisperer’s Most Underrated AI Prompt

    By David Russell

    Discover how a simple AI prompt can reduce hallucinations and transform your approach to AI governance for better business impact.

    Most enterprises have mastered the pilot phase-but not the scale phase. Learn why the organizations that win are those that govern AI output, not just generate it.

  • One Prompt to Rule Them All? Not Quite

    By David Russell

    Discover how to effectively choose and adapt prompting frameworks to enhance your AI outputs, ensuring clarity and precision for every task.

    You can’t scale AI on improvisation. Shared frameworks make results repeatable across squads, tools, and use cases-so success scales, not just individual heroics.

  • Choosing the Right AI Evaluation for Your Go-to-Market Strategy

    By David Russell

    Discover how to enhance your go-to-market strategy by choosing the right AI evaluation methods for trust and accuracy in sales.

    You're ready to scale Enterprise AI. But without solid evaluation frameworks, you're building on shaky ground. Dive into the dual-strategy approach (code + LLM) that guarantees your AI systems remain reliable, accurate, and trustworthy as you grow.

  • Rethinking GTM: How Custom AI Delivers Real Pipeline Impact

    By David Russell

    For go-to-market (GTM) leaders, AI has moved past the "what if" stage and is now a critical "how-to" for hitting aggressive targets. The real challenge isn't…

    Your AI pilot was a success. Now what? The biggest challenge in AI is moving from a single use case to production. This guide outlines the key to Enterprise AI scaling: building secure, centrally-governed tools that avoid the pilot-to-production chasm and deliver repeatable success.

  • 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.

    Scaling AI in the enterprise is about more than flipping a switch. Dive into the challenges of connectivity, security, and data modeling that make or break AI at scale.