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Introducing the B.O.A.R.D. Handbook for Strategic AI Oversight

  • Writer: Andreea Bodnari
    Andreea Bodnari
  • Jan 6
  • 5 min read

After working with dozens of enterprises on AI risk monitoring at ALIGNMT AI, we kept seeing the same problem:

Brilliant AI initiatives. Multi-million dollar investments. Talented teams.

But no structured governance framework to ensure business value while managing risk.

Today, we're releasing the B.O.A.R.D. Framework Handbook for Strategic AI Oversight—a comprehensive, practical guide for governing AI from pilot to production.


The Problem: AI Governance Crisis

The numbers tell the story:

Real consequences:

Organizations need practical governance frameworks—not just principles, but implementation roadmaps.

Why We Built This

At ALIGNMT AI, we saw organizations struggle with the same governance failures repeatedly. Companies spent $1-5M on AI initiatives with no documented baseline, making ROI calculation impossible. We encountered enterprises running different cloud platforms and 3+ ML toolsets simultaneously, with teams independently solving the same problems. Models were deployed without proper validation, only to be discovered during regulatory examinations. Shadow AI proliferated on personal laptops across business units, while successful pilots couldn't scale beyond initial deployments. The pattern was clear: technical capabilities were strong, but governance discipline was absent.

We partnered with Lake Dai from Carnegie Mellon University—an AI governance expert, CMU professor, and advisor to governments and Fortune 500 companies—and spent months distilling lessons from healthcare and financial services, two industries where AI governance is both critical and mature.


The result: The B.O.A.R.D. Framework.

The B.O.A.R.D. Framework: Five Essential Dimensions


B - Business Value & Baseline

Principle: "If you can't measure it, you can't manage it. If you can't tie it to P&L, you can't justify it."

Every AI initiative needs:

  • ✅ Clear P&L linkage (not "efficiency"—specific line item)

  • ✅ Documented baseline metrics (not "slow"—actual measurements)

  • ✅ Expected ROI within 18-24 months

  • ✅ Go/no-go criteria with decision points

Real example: UnitedHealth Group automated 30% of prior authorizations, reducing processing from 3-5 days to 24 hours. They documented baselines, set targets, measured quarterly—enabling additional revenue through faster throughput.

Workbook 1: Complete business case development in 30 days.

O - Organization & Operating Model

Principle: "Clear accountability + centralized enablement + distributed execution = AI that scales."

Three essential layers:

1. Strategic Accountability (C-Suite)

  • JPMorgan Chase: Chief Data & Analytics Officer reports to COO, 600+ AI use cases in production

  • Cleveland Clinic: First Chief AI Officer appointed August 2024

  • Industry trend: 40% of Fortune 500 will have Chief AI Officers by 2026

2. Centralized Enablement

  • Data infrastructure, ML platform, governance framework, talent programs

3. Distributed Execution

  • Business unit teams who understand domain problems and own P&L

The cost of getting it wrong: One firm had 5 cloud platforms, 8 ML toolsets, 12 teams solving same problems. Estimated waste: $4-6M annually.

Workbook 2: Operating model design and gap analysis in 90 days.

A - Architecture & Assets

Principle: "AI is a supply chain. Treat data, compute, and models like strategic inputs—with enterprise standards and capacity planning."

Key Decisions:

Data Infrastructure

  • Montefiore Health: $41M investment in data platform

  • Impact: Data scientists' time on wrangling dropped from 60-70% to <25%, enabling 3x faster deployment

Compute Capacity

  • Training Meta's Llama 3.1: $483M in cloud costs

  • Dell/NVIDIA study: On-premise can be 62% more cost-effective for sustained workloads

  • Data centers could triple electricity use by 2028

Model Sourcing

  • 🛒 Buy: Commodity capabilities (OCR, speech-to-text)

  • 🔧 Build: Core competitive advantage, unique data

  • 🎨 Customize: Foundation models for domain adaptation

Model Registry

  • Every production model documented (risk, performance, approvals)

  • 90% of enterprises concerned about shadow AI

Workbook 3: 3-year infrastructure strategy in 90 days.

R - Risk, Regulation & Responsible AI

Principle: "Map every guardrail to AI risk management frameworks. Regulation is inevitable."

Six Categories of AI Risk:

  1. Model Performance - Predictions, drift, failures

  2. Fairness & Bias - Discrimination, penalties ($2.5M Earnest settlement)

  3. Privacy - Unauthorized access, re-identification

  4. Security - Model theft, attacks ($460M Knight Capital loss)

  5. Operational - Downtime, lack of explainability

  6. Regulatory - EU AI Act (€35M penalties), SR 11-7, FDA, fair lending

Regulatory Landscape:

EU AI Act (in force):

  • High-risk systems: Credit, insurance, medical, hiring

  • Requirements: Risk management, data quality, documentation, transparency

  • Penalties: €35M or 7% of global revenue

US Regulations:

  • Financial: SR 11-7, fair lending laws

  • Healthcare: 692 FDA-authorized AI/ML devices, ONC, HIPAA

What leaders do: Proactively engage regulators with quarterly meetings, not reactive responses.

Workbook 4: Complete risk framework in 180 days.

D - Dashboards & Decisions

Principle: "You can't govern what you can't see. Quarterly scorecards turn oversight into portfolio management."

Quarterly AI Scorecard (Four Dimensions):

  1. Portfolio Health: Investment, value delivered, ROI, project counts

  2. Business Value: Revenue + cost savings + risk mitigation

  3. Risk & Compliance: Model compliance, audits, findings, incidents

  4. Organizational Health: Workforce, platform adoption, training

Four-Decision Framework:

Every initiative gets ONE decision:

💰 FUND - Approve new initiative

  • Criteria: ROI >1.5x, documented baseline, resources available

  • Example: "$2M fraud detection, projected $8M savings"

📈 SCALE - Expand proven pilot

  • Criteria: Pilot met targets, infrastructure ready

  • Example: "Churn model: 23% reduction vs. 15% target—scale to 150K customers"

🔧 FIX - 90-day remediation

  • Criteria: Root cause clear, specific plan

  • Example: "Model accuracy degraded—retrain with recent data, 90-day target"

🛑 SUNSET - Terminate failed initiative

  • Criteria: No path to ROI, superseded by better solution

  • Example: "$1.2M invested, 8% adoption—sunset, reallocate team"

The impact: Organizations with structured ROI measurement achieve 5.2x higher confidence in AI investments (Gartner).

Workbook 5: Scorecard setup in 90 days, ongoing quarterly reviews.


Who Should Read This

Essential for:

  • 🎯 Board Members - Oversight on AI investments

  • 🎯 C-Suite (CEO, COO, CFO) - Strategic alignment

  • 🎯 Chief AI Officers / CDOs - Framework building

  • 🎯 VPs of AI/Data - Pilot to production

  • 🎯 Risk & Compliance - Regulatory navigation

  • 🎯 Enterprise Architects - Governed infrastructure

Especially valuable for:

  • Healthcare organizations (FDA/ONC/CMS)

  • Financial services (SR 11-7, fair lending)

  • EU AI Act preparation

  • Companies with $5M+ AI budgets

  • Organizations with 10+ AI initiatives

Your 30-60-180 Day Roadmap

Within 30 Days:

  • Inventory all AI initiatives >$500K

  • Verify documented baselines exist

  • Confirm P&L linkage

  • Complete Workbook 1

Within 90 Days:

  • Establish ROI thresholds

  • Map governance to NIST AI RMF

  • Approve 3-year compute capacity plan

  • Complete Workbooks 2 & 3

Within 180 Days:

  • Audit initiatives against baselines

  • Sunset underperforming projects

  • Approve risk management framework

  • Present first quarterly scorecard to board

  • Complete Workbooks 4 & 5

Why It's Free

At ALIGNMT AI, our mission is to make enterprise AI safer and more governable.

We're releasing this free because:

  1. The industry needs it (42% failure rate = systemic gap)

  2. Regulation is accelerating (EU AI Act, state laws)

  3. Stakes are high ($460M losses, $2.5M settlements)

  4. Better governance benefits everyone

Our belief: Every enterprise deserves practical AI governance guidance.


Implementation Webinars

Join us at HLTH's upcoming webinar sponsored by ALIGNMT AI: "The Next Chapter in Healthcare AI: Why Production Oversight is Your Competitive Edge" - featuring insights from the B.O.A.R.D. Framework applied to healthcare AI governance.

Get Started Today

AI governance doesn't have to be overwhelming. With the right framework, you can ensure AI investments deliver measurable value while managing risk.

Download the handbook. Start with Workbook 1. Take action in 30 days.

 
 
 

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