People Don't Resist Technology; They Resist Ambiguity: Elena Vekilov on Enterprise AI Readiness

On the latest episode of the AI in Chicago Podcast, I had the privilege of speaking with Elena Vekilov, Chief Privacy Officer and Global General Counsel for Data Security, Product, and AI at NielsenIQ. With responsibility for privacy governance across more than 90 markets, Elena sits at an intersection few executives occupy: where data protection, product innovation, and AI governance collide at global scale.

Our conversation surfaced a crystallizing insight that every leader racing to implement AI should absorb: People don't resist technology. They resist ambiguity.

The Chore Chart Problem

When I asked Elena to summarize the state of data and AI governance in 2025 in a headline, her answer was immediate: "AI finally grew up, but we're still figuring out its chore chart."

The metaphor captures something essential. In 2025, we saw AI move from being a shiny proof-of-concept to an actual coworker, "one that's shockingly fast and efficient, but occasionally misinterprets everything." Elena explains, "It felt like the first year where AI moved from being a demo, a wonky concept in the corner, to actually being a coworker, a sort of eager intern – grown up, but still on a chore chart, still some work to do."

But this maturation created new anxieties. The hesitation Elena encountered wasn't fear of AI itself. It was something more mundane and more urgent: "the trust-gap, the fear of using AI incorrectly."

"Most people aren't lying awake worrying about AI taking over the world," Elena noted, "but people are worrying about it taking over the slide deck or the spreadsheet or the particular tasks they're responsible for. So we saw this very human hesitation of: Can I trust this? How do I verify the output? What happens if the AI is wrong and I don't catch it? It's still on me at the end of the day..."

2025 forced us to confront these new realities; and this is the ambiguity that stalls adoption.

The Third-Grade Origin Story

Elena's path to becoming one of the few executives who has operationalized responsible AI at enterprise scale began earlier than most, built upon her deep-rooted curiosity. At her third-grade graduation, she won an award for asking the questions everyone else might be wondering but is too afraid to ask.

"That instinct to dig deeper, to challenge assumptions, to ask the questions; it's been my brand, my guiding force ever since," she recalls.

That curiosity led Elena through her time advising clients in the highly-regulated energy and infrastructure law sectors, where she first became passionate about innovation and compliance coexisting, and then into the technology and data services sector just as GDPR transformed privacy and data use from a niche concern into an everyday operational reality.

The throughline: "Trust isn't just a legal requirement; it is the foundation for growth and innovation."

Enterprise Blind Spots

Our conversation highlighted three areas where we saw enterprises systematically overestimate their ability to implement and adopt AI in 2025:

The Value Misconception: Overall, industry leaders had to reevaluate assumptions that just Having AI = Value Add. "Most companies thought they were adopting AI," Elena observed. "Maybe it was more just aspirations for it." The IKEA furniture metaphor applies: halfway through assembly, they realized they had the wrong screws, the instructions were upside down, and they were missing the wrench.

That was a lot of the overall enterprise AI journey we witnessed this year, and there are many reasons for it:

The Data Maturity Illusion: Across the board, we saw organizations that believed having lots of data meant having usable data. "Having data is not the same as having data that's clean, structured, governed, explainable," Elena explains. "That beautiful, glamorous celebrity closet they thought they had? It was actually their kitchen junk drawer of random cables." A fundamental readiness overestimation we consistently witnessed this year was in this concept of data maturity.

The Cultural Readiness Gap: We also consistently saw companies announce AI initiatives while their people were doing what Elena called a "say what now?" The disconnect was predictable: "Humans don't resist technology; They resist ambiguity." Companies overestimated their people's emotional readiness and the extent of change management required. Without clear guidance on how AI changes workflows and what's expected, adoption stalls regardless of how powerful the tool is.

The pattern is clear: as Elena explains, "we've learned this year that AI amplifies whatever already exists; whether that is clarity or chaos."

So, overall, where enterprises got it wrong in 2025: trying to sprint before they stretched. To derive full value out of AI technologies, all these pieces must be in place first.

Legal as GPS, Not Roadblock

Elena reserves particular passion for redefining legal's role in AI adoption. "If your legal team is known as the Department of No, you're doing something wrong," she states bluntly.

The alternative is embedding legal into the product development process from the start – not as a final rubber stamp but as a navigation tool. "Think of legal not as a roadblock, but as a GPS: helping the organization reach its goals without unexpected detours or collisions."

The operational mechanism matters: a structured intake process where legal, engineering, architecture, security, and compliance all have seats at the table from day one. "The mistake I see is people bringing in legal at the end for the final rubber stamp," Elena explains. "That's like trying to install a seatbelt after you're already on the highway."

A risk-tiered governance framework enables this efficient partnership between legal and the business. Elena emphasizes, "Not every project is a high-risk moon landing; some are just putting better wheels on the office chair. Matching scrutiny to potential impact lets innovation move quickly while maintaining appropriate safeguards."

Privacy as Innovation Infrastructure

Perhaps the most counterintuitive insight from our conversation: Elena believes "privacy and compliance aren't constraints on innovation. They're prerequisites for scaling it."

"A company that treats privacy like a checkbox is always going to feel like it's choosing between two competing priorities," Elena notes. "But a company that looks at privacy as infrastructure –part of the design process, not an afterthought – actually ends up moving faster and more confidently."

The reasoning is structural. "When privacy and data governance are strong, teams know what data they can use and how. Customers engage confidently. Regulators view the company as a responsible actor rather than a risk requiring intervention. Innovation isn't slowed by uncertainty – it's guided by that clarity that you've provided ahead of time."

"It's like building a highway," Elena offers. "The guardrails don't slow your car down. They let you drive at full speed without worrying about flying off the cliff."

The companies winning the AI race won't be those choosing between innovation and governance. They'll be the ones building innovation on top of governance infrastructure.

Key Takeaways

Build literacy, not just tools: AI competency is now a core professional skill – like spreadsheets were in the 1990s. Invest in strategic fluency across leadership, not just technical teams.

Audit your data diet: You cannot expect Michelin-level AI insights from fast-food data. Data quality is a strategic differentiator, not a back-end IT chore.

Embed legal early: Bring privacy and legal into product development at inception, not as a final stamp. Create standardized playbooks so teams know guardrails before they build.

Match scrutiny to impact: Risk-tier your governance. Perhaps structured workflow tools get lighter review; consumer-facing AI that could affect rights and access gets deeper scrutiny.

Remove ambiguity, not technology: Resistance to AI adoption is rarely about the technology. It's about unclear expectations. Clear guidance accelerates adoption faster than better features.

Treat privacy as infrastructure: Governance enables velocity. When the rules are clear, teams move faster with confidence.

Looking Toward 2026

As enforcement of AI regulations picks up globally, including new provisions of the EU AI Act coming online in August 2026, Elena sees diverging paths necessarily converging. Companies investing now in privacy and governance infrastructure will weather what's coming far more gracefully than those treating compliance as an afterthought.

"Make your governance, your privacy, your compliance functions part of helping your organization be a winner in this innovation market," Elena advises.

The billboard message she'd put in front of every AI founder: Build AI that people trust, want, and will actually use.

Listen to the full conversation on the AI in Chicago Podcast to hear Elena's perspective on contract negotiation friction with GenAI vendors, the underrated power of GDPR's purpose limitation principle, and why West Town captures her leadership style perfectly.

Subscribe and follow the AI in Chicago Podcast for more conversations with the operators, builders, and executives scaling applied AI from Illinois with global impact.