30 Apr 2026
4 min read
AI initiatives often struggle not because organizations lack advanced technology, but because they move too quickly without aligning people, data, and operating models.
In a recent TAG Data Talk episode sponsored by Wavicle, Dr. Beverly Wright speaks with Chris Klene about what it really takes to build AI on a strong foundation and why foundational knowledge ultimately determines success.
Here are five key highlights from the conversation on building AI on bedrock the right way.
Speaker details
- Host: Dr. Beverly Wright – Executive in Residence at Institute for Insight, Georgia State University
- Guest Speaker: Chris Klein – Director of BI and Analytics, Genuine Parts Company
1. AI acceleration is being driven by pressure, not readiness
AI adoption has been accelerating across organizations, influenced by what leadership teams and employees were seeing in the market and media. This visibility created a growing desire to act quickly and demonstrate progress, even when organizations were still assessing their ability to execute effectively.
“More and more companies at the leadership level, and then down to the individual contributor level, are reading in the news about the presence of AI and what that means. I feel like there’s a lot of desire and potentially pressure to find ways to leverage these tools.”
Momentum is strong, but readiness often trails behind it.
2. Weak foundations don’t fail immediately. They create silos and friction later
Foundational gaps are not always apparent at the start. As teams work independently and approaches diverge, inconsistencies begin to surface. These gaps create friction and make it harder to sustain progress across the organization.
“Without that foundation, you end up in a state where you create silos in the organization. You minimize the ROI on projects, and ultimately you’re not positioning yourself for long-term success.”
What feels effective early on becomes increasingly difficult to scale.
3. Foundational work may feel unexciting, but it enables long-term progress
Core investments such as data governance and integration are often less visible than AI tools themselves, but they play a critical role. These efforts ensure data can be trusted, reused, and scaled across use cases.
“Having those data foundations enables a lot of things. It enables self-service, it enables traditional machine learning, it enables generative AI.”
Foundational work does not slow innovation. It makes it sustainable.
4. AI success depends on enablement, not just access
As AI tools become more accessible, the challenge is no longer just deploying technology. It is making sure people across the organization can use those tools effectively and within the right guardrails.
“The tools are positioned in a way to enable people but at the same time protect the company.”
Adoption, trust, and enablement are essential to turning AI access into real business value.
5. Bedrock requires ongoing attention, not a one‑time effort
Foundations cannot be treated as something that is completed and left unchanged. As technology and business needs continue to evolve, the underlying bedrock must evolve as well.
“A lot of companies want to build a bedrock and believe it’s this static thing. But no, not at all.”
Long‑term success depends on continual learning and adaptation.
Final takeaway
This conversation reinforces one core idea: AI does not fix weak foundations. It magnifies them. Sustainable AI success comes from investing in data quality, governance, shared standards, and people enablement before scaling ambitious initiatives.
Watch the full podcast below:
As organizations work to strengthen data quality, governance, and decision readiness, Wavicle helps enterprises build the foundations required for responsible, scalable AI. Get in touch to schedule an AI readiness assessment.
Disclaimer: Quotes in this blog are taken directly from the TAG Data Talk conversation and have been edited only to remove filler words for clarity.
