Why Trusted AI? The Key to Driving Value With AI Innovation
No matter how responsible your AI model is, it won’t provide bottom-line business impact unless you can build trust with core decision-makers.
“I don’t trust the results.”
That sentence still resonates in my mind from more than a decade ago.
While working as an advanced analytics practitioner in a major business unit at a large organization, I came across a predictive and prescriptive model built by my team before I joined.
I immediately saw how complete the model was. The internal white paper on it could have been a Ph.D. dissertation.
But even though the model was deployed, the business wasn’t using its results.
While working on another project for the same business unit, I found the main stakeholder for the model and asked him why the team wasn’t using the model’s recommendations.
His response was blunt. “I don’t trust the results.”
I was stunned. The issue wasn’t data cleanliness. Nor was it any risk of bias or personally identifiable information (PII) usage, or even a lack of innovation.
The team didn’t believe the output from their model could be trusted.
How responsible AI is just “sufficient”
Companies and AI practitioners alike often overlook this end-to-end concept of trust. For some, having a highly accurate predictive or prescriptive AI model that’s built on clean, organized, and harmonized data will be sufficient to create value in deployment.
During my 20+ years as a hands-on AI practitioner at leading companies across various industries, I’ve seen countless organizations and data practitioner teams referring to responsible AI from that sufficient perspective. And yet, when facing a situation in which everything is “responsible”—from bias and security to accountability, data, theory, and feasibility—their model doesn’t make it past the proof-of-concept (POC) phase because it isn’t trusted.
No matter how responsible your AI model is, it won’t provide bottom-line business impact unless you can build trust with core decision-makers and stakeholders. Without trust, it won’t be adopted or recognized.
Trusted AI is the new competitive edge
What’s the solution? At Teradata, we believe Trusted AI is the way people, data, and AI work together, with transparency, to create value.
People must be engaged and accountable throughout the AI lifecycle. Keeping humans at the center of AI technology will ensure data security, environmental sustainability, and bias prevention.
AI models and their impact on consumers must be explainable, and the underlying data must be visible. Extending this governance throughout an open and connected ecosystem will create flexibility and accelerate innovation.
AI and data solutions must be fast, reliable, and accurate. Doing that at scale unlocks the type of cost-effective growth and breakthrough innovations that will drive positive impact for people and enterprises alike.
Trusted data empowers Trusted AI
You can’t deliver Trusted AI without trusted data that is integrated and harmonized across the organization. This creates a foundation of reliability, accuracy, and governance that’s essential for helping investments in AI pay off.
Teradata enhances trust in data with technologies like enterprise feature store (EFS) and QueryGrid, our next-gen take on data fabric. We improve productivity and fast-track return on investment (ROI) from AI innovation with our powerful AI engine, ClearScape Analytics™; the ability to bring your favorite models in the most open and connected ecosystem; and best-in-class in-database AI/ML functions, open API integrations, and ModelOps.
By unifying Trusted AI principles with our expertise and capabilities, what we offer—and deliver—is trusted data that enables and empowers Trusted AI.
Learn more about what it means to have trusted data and trusted AI.
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