C-Suite Data Answers

If we want AI to start spotting cross-sell opportunities quickly, we don’t need perfect data, but we do need a baseline level of completeness and consistency. Think of it like building a house: you don’t need fancy trim work, but you definitely need a solid foundation. Here’s the bare minimum I’d say we need in […]

Short answer:There is real financial risk in moving ahead with AI before you’ve got a solid data governance framework in place. It doesn’t mean you can’t start anything, but you need to be clear-eyed that you’ll be trading speed for a higher chance of rework, inconsistent results, and in some cases, regulatory exposure. Let me […]

If we want to get serious about AI-driven automation, the main thing we have to look at first is whether our data infrastructure can actually support it end to end. Right now, we’re not quite there, but it’s fixable if we focus on a few areas. Here’s what I’d say we need to improve: Centralizing […]

Let me walk you through why centralizing our financial and customer data is such a big deal for AI forecasting. At the simplest level, AI models learn by finding patterns across all the relevant data we have. If that data is scattered in different systems—some in finance, some in CRM, some in spreadsheets—then the model […]

Short answer:If we skip data quality work up front, measuring ROI becomes far more uncertain and potentially misleading—because you won’t know whether disappointing results came from the AI itself or from flawed data. Let’s be clear about how this plays out in practice so you can weigh the trade-offs: 1. What Happens If We Skip […]

Not all data cleanup is created equal. If we try to “clean everything” before doing anything, we’ll burn time and budget without clear ROI. Instead, we should triage ruthlessly and focus on the few areas where better data quality will unlock the most value from AI. Here’s where I’d recommend we focus first: 1. Customer […]

Short answer:Based on what I know of our current data environment, implementing a robust AI-driven pricing strategy in six months is possible but will be challenging without first addressing some foundational gaps. Let me break this down carefully so we’re clear-eyed about what’s feasible. 1. What AI-Based Pricing RequiresTo do this credibly, we need:✅ Transaction-Level […]

Short answer:There are AI tools that can help drive revenue growth quickly, but their success depends far more on our readiness—data quality, integration, process alignment—than on the tool itself. If we try to shortcut foundational work, even the best AI platforms will underperform. Let me break this down pragmatically: 1. Tool Capabilities: Fast Revenue Impact […]

Here’s exactly what I need from you and the rest of the leadership team to move this forward effectively in the next quarter: 1. Clear Business Priorities and Use CasesPredictive analytics is only valuable if it’s aimed at problems that matter. I need your input to pinpoint 2–3 priority questions or outcomes you want to […]

That’s a fair question, and it’s one I hear from a lot of executive teams—especially when competitors are marketing flashy AI-powered personalization. Let’s start by being clear: AI personalization is not a feature you simply buy and turn on. It’s an outcome of foundational capabilities you build deliberately over time. If we’re not “there yet,” […]

Here’s a simple, no-fluff way to explain this to your COO: Use a relatable analogy + a plain-language example. Analogy:“Imagine trying to plan meals for a month without knowing what’s in your fridge. You might think you have enough ingredients, but you’re missing key items—or worse, you buy duplicates you didn’t need.” Example Scenario:Let’s say […]