When I sat down with Teresa Tung from Accenture for Episode 2 of AI Confidential (you can find this episode on Youtube in addition to Spotify), I was struck by a stark reality that many enterprise leaders are facing: while 75% of CXOs recognize the critical need for high-quality data to power their generative AI initiatives, nearly half lack the trusted data required for operational deployment.
Read the full conversation breakdown in our newsletter →
This gap isn’t just a statistic—it’s a story I’ve seen play out repeatedly across boardrooms and technical teams. As companies rush to embrace generative AI, they’re discovering that the real challenge isn’t implementing the technology—it’s protecting and leveraging their most valuable asset: data.
Teresa shared a fascinating perspective from her work at Accenture that resonated deeply with me. She pointed out that in the next five years, industry leadership will be determined not by who has the most advanced AI models, but by who can effectively control and utilize their data. It’s a shift that reminds me of the early days of digital transformation, where companies that failed to adapt quickly found themselves in a Kodak-like situation.
The Security Paradox
Here’s the challenge that keeps enterprise architects, CTOs, and CIOs up at night: the most valuable data for AI applications is often the most sensitive. Whether it’s financial records, customer interactions, or proprietary research, this “crown jewel” data holds transformative potential but comes with enormous risk.
During our conversation, Teresa shared an illuminating example from an automotive manufacturer grappling with this exact dilemma. The company saw tremendous potential in using AI to enhance customer interactions but faced the fundamental challenge of keeping sensitive data secure while making it actionable.
Beyond Pilot Purgatory
What’s become clear through my conversations with technology leaders is that many organizations are stuck in what I call “pilot purgatory”—they can experiment with AI on non-sensitive data, but can’t scale to production because they lack frameworks for securing sensitive data at scale.
This is where technologies like Confidential Computing enter the picture. As Teresa and I discussed, it’s not just about encrypting data at rest or in transit anymore—it’s about maintaining security while data is being processed. This capability is transforming how companies can approach AI implementation, enabling them to:
- Process sensitive data while maintaining encryption
- Share insights without exposing raw data
- Create new business models through secure multi-party computation
The Path Forward
For technology leaders navigating this landscape, the message is clear: the winners in the AI race might be determined partly by who moves fastest, but whoever builds the most trustworthy and secure foundations will endure and stand the test of time. As Teresa pointed out, successful AI implementation requires treating data as a product—with all the quality controls, supply chain considerations, and security measures that implies.
Looking ahead, I believe we’re entering a new era of AI adoption where security and scalability must be considered from day one. The companies that thrive will be those that can balance innovation with protection, speed with security, and ambition with responsibility.
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What challenges are you facing in scaling AI while maintaining data security? I’d love to hear your thoughts in the comments below.
Aaron Fulkerson 