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Straight Talk on Data + AI Integration: Insights from the TDWI Expert Panel
19 Jun, 2025
During the recent TDWI Expert Panel on Integrating Your Data and AI Platforms, experts from across industries came together to discuss how unifying enterprise data and AI environments can accelerate innovation, scale insights, and drive real business outcomes.
The session explored some of the most pressing challenges organizations face today—from proving ROI to aligning governance and driving adoption. These are the real-world issues that determine whether data and AI investments succeed or stall.
We’ve distilled a few of those themes below—and shared HSO’s perspective on what it really takes to make integrated data and AI platforms work.
Shifting the Business and IT Dynamic
Historically, IT has been seen as a support function—reactive and separate from business strategy. But the most successful organizations we work with treat IT as a strategic partner.
When IT understands business goals and co-owns outcomes, it can proactively bring technology solutions to the table. The key is to start small: solve a specific, high-impact business problem, and show tangible results. Early wins build trust, create momentum, and set the stage for deeper collaboration between business and IT.
Building a Stronger Business Case for Data + AI Investment
Big IT projects often struggle with long timelines, high costs, and shifting requirements. That’s why we recommend an agile, value-first approach.
Rather than boiling the ocean, focus on rapid delivery of business value. Target one critical use case. Prove ROI in weeks—not months or years. This method reduces risk, increases adaptability, and keeps stakeholders engaged with visible, iterative outcomes that align to business priorities.
Why Semantic Models Are More Than Buzzwords
It’s easy to dismiss semantic models as just another name for data schemas—but they play a critical role in aligning business and IT.
Where schemas define structure, semantic models define meaning. They translate raw data into consistent, understandable business terms—like “Net Revenue” or “Active Customers.” This shared language is essential for trusted BI and AI outputs. It ensures everyone is looking at the same numbers, using the same logic, and driving decisions from a single source of truth.
Unifying Governance Across BI and AI
BI and AI initiatives often develop in parallel—but their success depends on the same foundation: clean, secure, well-governed data.
Rather than managing governance in silos, create a unified framework that covers all data consumers—dashboards, models, and everything in between. Build on existing BI governance practices, then expand to include model monitoring, ethical reviews, and AI-specific access policies. The result? Faster deployment, less duplication, and more trust in the insights being delivered.
Preparing for AI with a Targeted Data Foundation
Launching AI without understanding your data landscape is a recipe for failure. But mapping everything at once isn’t realistic.
We advise starting with one business use case. Identify the necessary data. Map, clean, and govern just that slice. This focused approach proves the value of responsible data practices, reduces scope creep, and ensures your AI efforts are grounded in real business needs—not hypothetical ones.
Driving Adoption with Trust and Usability
Even the best AI solution fails without user trust. That starts with surfacing the right insights at the right time—especially during a user’s first interaction.
Dashboards and agents shouldn’t be data dumps. They should guide users to the application’s “aha!” moment—the place where it instantly proves its value. This builds confidence, encourages habit, and sets the stage for broader adoption. Keep it simple, clear, and focused on answering the user’s most important questions first.
Want to keep the conversation going?
If your organization is looking to move from experimentation to execution with integrated data and AI platforms, let’s talk.