When a client wants to deploy an LLM internally but their data governance is a mess, do you take the engagement and fix the data first, or walk away?

Here's a Reddit-style body for that question: **When a client wants to deploy an LLM internally but their data governance is a mess, do you take the engagement and fix the data first, or walk away?** Looking for some honest takes from people who've been in this position, because I keep seeing the same pattern and I'm not sure my firm is handling it well. Client comes to us, usually mid-market or larger, and says some version of: "We want to deploy an internal LLM. Our competitors are doing it. The board is asking. Can you help us build a chatbot over our internal knowledge base / a copilot for our analysts / an AI assistant for our support team?" Sounds great on paper. Then you start the discovery and find out: * Their "knowledge base" is 14 SharePoint sites, 3 Confluence instances from acquisitions, a shared drive nobody has cleaned since 2017, and a guy named Dave who knows everything but is retiring in 8 months. * Sensitive customer data is sitting in spreadsheets that anyone with a corporate login can read. * They have no data classification policy, or they have one on paper that everyone ignores. * Half their "documents" are screenshots of emails saved as PDFs. * Access controls are basically vibes. So now you're standing at a fork. You can: **A) Take the engagement and quietly fix the data layer first.** Bill it as "AI readiness" or "knowledge foundation work." Spend 6-9 months doing the unglamorous data hygiene, governance, and access control work nobody wants to pay for. Then deploy the LLM on top of a clean foundation. The client gets a real outcome but they're impatient and the CFO is asking why we haven't shipped anything yet. **B) Build the LLM anyway on the mess.** Slap some RAG on top, ship something demo-able in 8 weeks, collect the fees. Watch it hallucinate, leak data it shouldn't have access to, or surface that one HR doc with everyone's salaries. Hope you're out the door before the lawsuit. **C) Walk away.** Tell them they're not ready, recommend a smaller scoped engagement, lose the deal to the consultancy down the street who will happily do option B. In practice my firm does some flavor of A but the commercial pressure to start showing "AI value" within the first quarter is brutal. The clients hear "data governance work" and their eyes glaze over. They hear "we'll have a chatbot in 6 weeks" and they sign the SOW. A few things I'd love to hear from this sub: * How are you scoping these engagements at signing time so the data foundation work is non-negotiable, not an upsell? * For folks at the bigger firms, are you walking away from deals where the client isn't ready, or are you taking the work and managing the risk? * Has anyone actually had success doing option B and not getting burned, or is that survivor bias talking? * How are you handling the partner/principal pressure to "just ship something" when you know the foundation isn't there? I genuinely think a lot of the "AI projects fail at 80% rate" headlines trace back to this exact decision point, and we're collectively not being honest about it with clients.