Strategic Clarity for Enterprise Leaders
We're building the analytical backbone of executive AI decision-making. ExecRiskAI democratizes institutional-grade risk analysis for organizations navigating strategic initiative optimization.
Our Story
ExecRiskAI was born from a simple observation: enterprise leaders making billion-dollar AI decisions were operating with incomplete information. Our founders spent two decades in financial services and consulting, watching executives oscillate between over-optimism (driven by vendor claims) and paralysis (driven by uncertainty). We realized that the gap between venture-scale startups and enterprise Fortune 500 decision-making frameworks created a blind spot—exactly where we saw opportunity.
In 2022, we began aggregating anonymized AI project data from 450+ organizations. By 2024, we had built and validated models predicting cost, timeline, and resource impact across 18 distinct AI initiative categories. Today, ExecRiskAI is used by executive teams at Global 2000 companies to make informed, defensible decisions.
What We Stand For
Rigor
Built on peer-reviewed research and 450+ enterprise datasets. Every model is continuously calibrated against real-world outcomes. Our analysis is as defensible in the boardroom as in an audit.
Transparency
We believe in assumption visibility and confidence intervals. Every recommendation includes sensitivity analysis so clients understand the "why" behind the "what."
Partnership
We're extensions of your strategy and finance teams, not consultants you hire. Your success is measured by better decisions and stronger execution.
Our Journey
Foundation & Research
Founded on the hypothesis that institutional AI decision-making could be transformed by rigorous data analysis. Began aggregating anonymized project data from enterprise organizations.
Model Development
Built and validated predictive models across cost, timeline, and resource impact. Reached 250+ dataset contributions from early-stage partners and pilot customers.
Platform Launch
Released beta platform to select enterprise cohort. Expanded dataset to 450+ projects. Achieved 94% accuracy in timeline predictions across independent validation set.
Enterprise Adoption
Scaled to Global 2000 customers. Integrated NFA management guidance and expanded capabilities to 18 distinct AI initiative categories.
Leadership Team
Built by leaders with proven track records in enterprise technology, financial services, and applied research.
Sarah Chen
Former VP Analytics at Goldman Sachs. 15 years in institutional AI risk.
Marcus Rodriguez
PhD Machine Learning, Carnegie Mellon. Built ML infrastructure at McKinsey.
Jennifer Park
Former Head of Product at Palantir. 12 years building enterprise software.
Trust & Governance
We operate with institutional rigor and transparency. Our governance framework includes annual third-party model validation, independent security audits, and board-level oversight of data ethics and methodology.
450+
Enterprise AI projects analyzed
94%
Accuracy in timeline predictions
$18.2B+
Combined initiative value assessed