Andrea Elliott
CEO & Founder, EMG | JD, MBA, AIGP | (E)GRC & Foresight Practioner providing Anticipitory Governance | ESG | Privacy
Atlanta, Georgia, United States
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Andrea Elliott is the CEO, Founder, and Managing Partner of EMG Advisory, the AI-native governance, risk and strategy practice serving regulated, high-stakes industries including Quantum Computing and Financial Services. She most recently served as Chief Compliance and Ethics Officer at a publicly traded global payments technology company, where she led a global risk and compliance transformation, built the company's AI governance framework, and led the outstanding CFPB & State AG Consent Orders to full regulatory compliance with zero violations. She also designed and led the GRC technology transformation to drive internal risk intelligence, transparency, and enterprise accountability, providing an end-to-end, 360 perspective on risk throughout the enterprise. With 15-plus years across risk, compliance, governance, ethics, and legal, Andrea brings a foresight practitioner's perspective to AI governance failures and mitigation. She is the author of (1) The Missing Middle, (2) Bounded Autonomy, (3) The Compounding Bet, (4) The Quiet Rewiring, (5) The Architecture of Intuition, (6) The Proportionality Problem, and (7) The Deadline on Implicit Governance. She holds a JD from Emory Law [Bankruptcy Journal & Transactional Certificate], an MBA from UGA [Finance focus with an emphasis in Consulting], and the AIGP credential from IAPP. Her Undergrad studies at Auburn University included Architecture, Industrial Design, and Business Administration.
From Andrea: "AI is rewriting what industries can do, and the leaders who govern it with seriousness and imagination will reshape theirs rather than scramble to catch up. I help those leaders imagine beyond what AI makes possible today, then usher them through the transformation for the betterment of the people their industries serve.
It starts with something deceptively simple: helping organizations make sound AI decisions they can stand behind, quickly and confidently. Done right, that discipline is not a brake on ambition. It is what lets a company reimagine what is possible with AI.
My work sits at the intersection of law, risk, ethics, strategy, and systems design. After 15+ years in risk and compliance roles, most recently as Chief Compliance Officer where I built the company's AI governance framework from the ground up, I founded EMG Advisory on a conviction: AI does not need more hype. It needs structure, accountability, and leaders who treat risk as a strategic discipline.
That is the part most people miss. Governance, Risk, and Compliance done well, is not what slows AI down. It decides where you can afford to move fast and where you cannot, and it is inseparable from strategy; your AI strategy does not survive without it.
The other half of how I work is AI-native wiring. I think in systems, synthesize fast, and collaborate with AI at the conceptual layer as a cognitive partner, translating complexity into strategic clarity. It is also how EMG itself is built: I practice the responsible, operationalized AI I help my clients deploy.
I work with organizations that want to use AI strategically, responsibly, and with foresight. I help leaders:
- Turn AI risk and compliance into strategic advantage
- Make defensible AI decisions at the speed of business
- Translate regulatory ambiguity into operational clarity
- Anticipate second- and third-order effects before they surface
- Design governance that works in practice, not just on paper
The regulatory landscape shifts faster than most AI roadmaps, and the patchwork of overlapping laws and standards leaves even willing organizations unsure what they must comply with. Pair that with AI that stalls in committee and governance impeccable on paper that no one operates by, and the gap becomes clear. Closing it is where I do my best work.
But closing the gap is the floor, not the ceiling. The organizations that govern AI with seriousness and imagination will not just keep up; they will transform what their industries can do for the people who depend on them. That is the future I am building toward."
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What It Takes to Win the AI Era: Why Human Psychology Is the Key
Why does AI feel intuitive to some leaders and opaque to others? It is not coincidence. It is by design. AI was built in our image. It is not a foreign intelligence; it is a mirror. Machine learning was modeled on how humans learn: pattern recognition, feedback loops, reward signals, iterative refinement. Leaders who already understand reward systems, habit formation, and identity-based change speak AI's native language. The leaders who do not are still translating. Why companies fail at AI is behavioral. What it takes to win is human psychology.
The data confirms the diagnosis. Most enterprises measure AI success on a narrow scoreboard: the technology runs, outputs match expectations, hours saved translate into headcount cuts. By that scoreboard, AI looks like it works. But 95 percent of enterprise generative AI projects fail to deliver measurable ROI (MIT NANDA). Across 140 implementations analyzed, only 23 percent of failures trace to technology; 77 percent trace to strategy, governance, and change management (Folio3). The technology is doing what it was built to do. The humans around it are not.
Translation is never enough. Surface-learners master the current tool and scramble when the next capability releases. They benchmark against a linear curve in an exponential environment: the next four years will feel less like 2022 to 2026 and more like 1980 to today. Deep-learners aggregate cross-disciplinary skills that suddenly compound into AI fluency. Learning AI is learning how learning happens, and it cannot be outsourced; if you did not build the intuition, you cannot govern it. Automating today's processes is itself a trap: it optimizes for a market that may not reward the same things by 2030.
The Human Layer is the psychological ecosystem inside every AI initiative: workforce, customers, and leaders designing the incentives and safety conditions that determine whether either group will engage. Identity emerges from rewards, not directives. Most enterprises reward efficiency with more work or elimination, so people hide AI use rather than build identity around it. Psychological safety, not policy compliance, makes responsible AI use the path of least friction. This is survival, not altruism: misaligned incentives and unclear decision rights implode the AI strategy from inside.
The session lays out three behavioral requirements: leadership-level behavioral fluency that cannot be outsourced, depth of learning that compounds across AI cycles, and identity-incentive-safety design that rewards experimentation over efficiency-punishment.
Learning objectives:
Diagnose the behavioral root causes of the 80% AI initiative failure rate.
Apply The Human Layer framework to evaluate AI risk posture upstream of governance architecture.
Use three Monday-morning diagnostic questions to surface the behavioral gap behind any stalled AI program.
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