At this point, orgs aren’t asking whether to use AI — they’re trying to figure out how to make AI a part of their strategy with the data they already have.
For many (or maybe most) companies, that’s their biggest roadblock. It’s not that the tech isn’t capable of executing their use cases – it’s because the data environment isn’t built to support them.
Relevance, clarity, and adaptability matter— and most strategies fail to address these requirements for successfully utilizing AI.
This week, we’re sharing three practical ways to strengthen your data foundation — so your data can carry the weight of your AI efforts.
Clean data isn’t enough. Cloud-based isn’t enough.
AI-ready data has to be structured, contextualized, and constantly evolving — or your models won’t work.
💡 Here’s where to focus:
1. Start with the problem, not the pipeline
AI works best when it solves a specific business problem — not when it’s applied to a generic dataset. That’s why relevance must come before refinement. Yes, your data needs to be clean — but it also needs to be the right data for the question you’re trying to answer.
→ Before you pull data, work with business and technical teams to define which inputs influence the outcome you’re targeting.
Why it matters: You’ll waste time (and budget) prepping data that doesn’t move the needle unless you define what “relevant” looks like upfront. It’s not about volume — it’s about clarity.
2. Govern with context, not just controls
It’s not enough to say who can access data. You need to document what the data means — and how that meaning shifts across teams. Does “revenue” mean gross, net, or recognized? Does “active user” mean login, purchase, or session?
→ Start embedding definition reviews into your governance process — not just permissions. That’s what makes models usable outside the data team.
Why it matters: AI can’t infer what you didn’t define. When context is missing, your model might still give an answer — it just won’t be the right one. That’s how trust erodes.
3. Validate constantly, not just when things break
Your data might be clean today — but what about next week, when a vendor changes a format or a new data source sneaks in unexpected values? If you’re not running freshness checks, drift detection, or regression tests, you’ll only catch issues when a model fails in production.
→ Build feedback loops from end users. Make validation part of your day-to-day workflows — not a one-time quality gate.
Why it matters: Bad data doesn’t announce itself. If your team is constantly troubleshooting AI outputs, you might be wasting time trying to fix the model when your dataset is the real problem.
5 Pillars of an Effective Gen AI Strategy
Prepping your data is only part of the equation. To get real results from AI, you need a plan that connects use cases to infrastructure, governance, and talent.
