Insights
Original research on AI and data trust, powered by deep autonomous investigation. Every claim sourced. Every finding graded.
Original Research
Each paper synthesizes autonomous deep research across dozens of nodes and hundreds of sources. Every claim is sourced and graded.
BDC Original Research — April 2026 · 36 Research Nodes · 300+ Sources
Enterprise AI governance faces a structural double gap. Enterprises cannot comply with regulations: over 50% lack AI inventories, only 25% have governance programs, and compliance timelines are already 37% over budget. And the enforcement infrastructure is not ready: harmonized standards are 8+ months late, notified bodies are not operational, and the EU AI Office has filed zero cases. The real risk surface is neither gap alone but their intersection: private litigation that does not wait for regulators.
50%+
of organizations lack
AI system inventories
Multiple sources
8+ mo
EU harmonized standards
are late
EU Commission
0
enforcement cases filed
by EU AI Office
EU AI Office
Sources: EU AI Act, FTC, Mobley v. Workday, McKinsey, BCG, Gartner, WEF | Admiralty Grade: A/1 to B/2
Read the Paper →BDC Original Research — March 2026 · 66 Research Nodes · 400+ Sources
Governance maturity is the strongest predictor of enterprise AI value, confirmed independently by McKinsey, BCG, Gartner, and the World Economic Forum. Yet enterprise spending runs 70% toward capability and under 10% toward governance. This paper maps the inversion, diagnoses why it persists, and defines the reallocation path.
95%
of GenAI pilots deliver
zero P&L impact
MIT NANDA
7%
of organizations have data
ready for AI
McKinsey
29%
can actually verify
their AI ROI claims
IBM IBV
Sources: McKinsey, BCG, Gartner, WEF, MIT NANDA, IBM IBV | Admiralty Grade: A/2
Read the Paper →Research
BDC publishes original research across AI governance, data governance, and the intersection of trust and technology. Every finding is sourced, graded for reliability, and built to inform real decisions.
Research Domain
Frameworks, risk management, responsible AI, and the evolving regulatory landscape. How organizations build governance that works in practice, not just on paper.
3 deep research runs completed
Research Domain
Operating models, data quality management, metadata strategy, and the organizational structures that make governance sustainable beyond the initial engagement.
2 deep research runs completed
Research Domain
Where AI governance meets data governance. Integrated approaches to trust that span the entire data and AI lifecycle, from ingestion to autonomous decision-making.
2 deep research runs completed
Research Domain
Measurement frameworks, remediation strategies, and the business case for investing in data quality as the foundation of AI trust.
1 deep research run completed
Research Domain
How organizations move from AI experimentation to governed production. Adoption barriers, enablement models, and the change management that makes AI investment pay off.
Research in progress
Research Domain
The human side of governance. Resistance patterns, champion networks, and what separates organizations where governance takes hold from those where it stalls.
Research in progress
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This is why governance matters. See where you stand.
Explore the Tools →The Methodology
BDC's research is produced by Ahab, an autonomous deep research engine purpose-built for exhaustive topic investigation. Ahab decomposes complex topics into subtopic trees, sources across academic, industry, and practitioner literature, and grades every finding using the Admiralty reliability system.
Every research run produces 20 to 50 individually sourced findings with explicit confidence grades. Contradictions are surfaced, not hidden. Gaps in the literature are documented as findings, not papered over with assumptions.
The result: research you can cite in a board presentation, defend in a regulatory conversation, and build a governance program on.