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AI and Data Trust Savings Calculator

Calculate the annual cost of ungoverned AI in your organization. Four inputs, real-time results, methodology grounded in Gartner and Forrester benchmarks.

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AI and Data Trust Maturity Assessment

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Featured Research

Your AI Budget Is Upside Down.

Enterprises spend the most on AI where the ROI is lowest, and the least where it's highest. The fix isn't more AI. It's better allocation.

BDC Original Research — March 2026 · 66 Research Nodes · 400+ Sources

Enterprises that invest in governance infrastructure first consistently outperform those that prioritize AI capability expansion. The correlation is not marginal: BCG finds governed AI programs deliver 3× higher ROI than ungoverned ones. Yet enterprise spending still runs 70% toward capability and 30% toward governance — the inverse of the evidence.

The deeper problem is aggregation: 74% of enterprises claim positive AI ROI, but only 29% can actually measure it. The gap between claim and evidence is where strategic risk accumulates. 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

Deep research on what matters.

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

AI Governance

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

Data Governance

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

Unified Trust Frameworks

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

Data Quality

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

AI Strategy & Adoption

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

Change Management

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

Research publications launching Q2 2026. Subscribe below to be notified.

The Methodology

Powered by Ahab Deep Research.

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.

How Ahab Works

  • Autonomous subtopic tree decomposition: complex topics broken into 20-50 individually researchable nodes
  • Multi-source triangulation: academic papers, industry reports, practitioner guides, regulatory documents
  • Admiralty grading: every source rated for reliability (A-F) and every finding rated for confidence (1-6)
  • Mandatory disconfirmation: actively searches for evidence that contradicts the emerging consensus
  • Quality loop: automated keep/revert gate ensures only findings that meet the reliability threshold survive

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