5 Takeaways From AIGG on Enterprise AI
Head of Sales Engineering
September 23, 2025•2 min read
Transcend recently attended the International Association of Privacy Professionals (IAPP) AI Governance Global (AIGG) conference in Boston, MA. In this post, our Head of Sales Engineering, Jess Dandorph, shares her takeaways from the event.
Every conference has its usual highlights: the sessions, the coffee, the swag. But what I found most valuable at this year’s AIGG were the conversations happening on the floor. Ever since the first AIGG in 2023, the refrain has been consistent: enterprises are excited about AI, but still wrestling with how to scale it responsibly. What struck me this year was how those conversations are evolving. Moving from abstract questions of “if” and “when” to the concrete challenges of governance, transparency, and adoption at scale.
Here are five themes that stood out to me, shaping how operations and engineering leaders can better align to drive enterprise-ready AI.
1. Who owns AI Governance?
Two years in, there’s still no consensus on where AI governance should live. Some companies house it under privacy, others under risk/compliance, and a few are standing up entirely new functions. This lack of uniformity reflects how quickly the field is evolving — organizations are experimenting with different ownership models because the “right” answer hasn’t emerged yet.
That creates both friction and opportunity. On one hand, operations and engineering leaders have to navigate wildly inconsistent stakeholder expectations across organizations. On the other, aligning AI governance with existing privacy or risk programs can accelerate adoption and reduce internal conflict. The takeaway from AIGG: innovation is moving forward regardless, but without clear ownership, governance frameworks will lag and remain fragmented.
2. Transparency is now table stakes
One of the most consistent points raised at AIGG was the need for more transparency into how AI models are built, trained, and deployed. With the EU AI Act and other regulations raising the bar for disclosures, enterprise buyers are making accountability a condition of adoption. For operations leaders, that means demanding clarity from partners to manage risk. For engineering leaders, it means investing in the infrastructure that can provide verifiable signals, not just promises.
At Transcend, we’ve seen this play out firsthand in procurement conversations: enterprises move faster when they can see concrete proof of governance, rather than having to take a vendor’s word for it.
3. Shadow AI remains a growing risk
Even two years after “shadow AI” first became a hot topic, leaders at AIGG made clear that it’s still one of their toughest challenges. Employees experimenting with unsanctioned AI tools continue to create governance blind spots, and the problem is only getting bigger. Gartner projects that 75% of employees will use technology outside IT’s visibility by 2027, up from 41% in 2022. That scale makes visibility and control a board-level concern, not just an IT nuisance.
Other research backs this up: Sift recently found that nearly a third of AI users have entered sensitive information into ChatGPT, and 14% admitted to pasting in company trade secrets. From roadmaps to financial details, employees are trusting public AI tools with data that was never meant to leave the enterprise. That scale makes visibility and control a board-level concern, not just an IT nuisance.
For operations leaders, the priority is identifying where unsanctioned tools are being used and integrating them into governance frameworks. For engineering, it means building controls that bring shadow AI into the light, surfacing usage across the stack, and enforcing policies in real time. Solutions like Transcend’s real-time enforcement help reduce that risk at scale.
4. Governance questions show up earlier in deals
Another shift I noticed at AIGG: for B2B software companies selling into the enterprise, governance isn’t just a back-office issue anymore. Procurement and security teams are raising governance questions earlier in the buying cycle, sometimes before pilots even begin. Leaders are asking about operationalizing capabilities like “Do Not Train” functionality, provable deletion, and auditability as prerequisites to moving forward.
For operations leaders, that means being ready with governance proof points from day one. For engineering, it means embedding these capabilities directly into the core infrastructure, from data discovery and classification to deletion and auditability, so they aren’t add-ons, but part of the system by design. The companies that can demonstrate governance early will accelerate deal cycles; those that can’t will find adoption delayed.
See why Do Not Train and Deep Deletion are essentials for enterprise AI.
Read the full post5. Trust is the differentiator that scales
If there was one overarching message from AIGG, it’s this: innovation will not scale without trust. In 2023, the question was whether governance mattered. By 2025, the question is how you prove it. Enterprises aren’t just asking what your AI can do; they want assurance it’s safe, compliant, and reliable enough to run in production.
For operations leaders, that means carrying responsibility for enterprise credibility, ensuring policies, processes, and procurement signals stand up to scrutiny. For engineering leaders, it means embedding safeguards into the infrastructure itself: explainability, auditability, deletion, and “do not train” enforcement.
Trust is what moves AI from pilots to production. The companies that can demonstrate accountability early, back it up with evidence, and make it easy for customers to verify will be the ones that win adoption at scale.
The conversations at AIGG made one thing clear: AI adoption isn’t slowing down, and governance is no longer a side conversation. For operations and engineering leaders, the path forward is to innovate boldly while proving credibility at every step. The combination that will turn early deployments into lasting enterprise adoption.
And of course, one of the best parts of AIGG was connecting with colleagues and peers on the floor. Sharing perspectives, comparing challenges, and seeing how others are approaching this new era of AI governance.
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Head of Sales Engineering