Govern AI with confidence and care.
BCBSIL is putting structure and accountability around AI before scaling it across the enterprise — the right instinct for a health plan millions of members trust. Here's how Motion Consulting Group helps you build that foundation: design the operating model, implement the controls, and run the system of record that keeps AI safe, compliant, and worthy of that trust.
Govern AI before you scale it.
AI is no longer an experiment at BCBSIL — it's a strategic capability that touches members, care, and claims. The leaders who scale it well put the rules, ownership, and accountability in place first. That's exactly the foundation this engagement builds.
You've already laid the groundwork — GitHub and Copilot are in place, and teams are using AI today. The question now isn't whether to use AI; it's how to govern it so adoption scales safely. For a health plan, that bar is high: AI decisions can affect a member's care and coverage, which means governance has to hold up to HIPAA, the NAIC Model Bulletin on AI that states are adopting, model-risk scrutiny, and — above all — member trust.
Get this right and governance isn't a brake on innovation. It's what lets you move faster with confidence: protecting members, earning trust, and reducing risk, all at once.
The member-first frame: every control in this program traces back to a simple promise — that AI at BCBSIL is used responsibly, explainably, and in the member's interest.
Assess. Design. Implement. Operate.
A clear, four-phase path — and the difference is the back half. Many firms will hand you a policy and a gap assessment. MCG designs the operating model and then builds and runs it — the part that turns governance from a document into a working system.
Assess
Inventory your AI estate, classify risk, map regulatory exposure, and benchmark against your target framework.
Design
Operating model and decision rights, AI policy, the use-case intake and review process, and your risk taxonomy.
Implement
Stand up the model registry, intake gate, controls, and Copilot guardrails; integrate with your existing GRC. Where MCG builds.
Operate
Continuous monitoring for drift and risk, board reporting cadence, and audit readiness — run as a managed service. Where MCG stays.
Start small, prove it, scale it. The assessment opens the engagement and gives you a concrete, prioritized picture — not a year-long commitment before you see value.
Make the most of the investment you've already made.
GitHub Copilot is already in your environment. The fastest, most tangible win is governing it well — so developers move faster and the organization stays protected.
Usage standards & guardrails
Clear, practical rules for what Copilot can and can't touch — protected data, sensitive repos, approved use patterns — so teams have confidence, not guesswork.
Secure-SDLC controls
Code-generation risk addressed where it lives — IP and license hygiene, secret-scanning, security review, and quality gates wired into your pipeline.
Developer enablement
Adoption playbooks and training so the benefit is realized broadly and consistently — not just by the early adopters.
Measured value
Track adoption, productivity, and cost so the Copilot investment is governed and demonstrably paying off.
What you're left running.
Governance only works if it operates day to day. This engagement stands up a working model that plugs into the risk infrastructure BCBSIL already has — it doesn't bolt a parallel process beside it.
- A joint review body — the right people (security, privacy, compliance, data, and the business) reviewing production-intent AI, with clear, documented decision rights.
- An intake gate — a single front door where every new use case is described, risk-classified, and approved before it reaches members or production.
- A model registry as the system of record — every AI system inventoried, tiered, owned, and tracked across its lifecycle.
- Bias & fairness review — disparate-impact testing wherever AI informs care, coverage, or member-facing decisions, so health equity is governed, not assumed.
- GRC integration — because the standard we build on aligns to your existing risk management, governance slots in rather than standing alone.
- An audit and board cadence — what's measured, reviewed, re-certified, and reported, on a rhythm leadership can rely on.
Built on the standards your regulators and auditors expect.
These frameworks stack rather than compete. We use ISO/IEC 42001 as the management-system backbone and NIST AI RMF as the risk method, then layer the cybersecurity substrate, the AI-specific threat surface, and a healthcare-AI overlay on top — and map every deliverable to the rule it satisfies.
| Framework | What it is | Why it matters to BCBSIL |
|---|---|---|
| ISO/IEC 42001 | Certifiable AI management system; aligns to ISO 27001/9001 | The governance backbone — and it plugs into your existing risk stack |
| NIST AI RMF | Govern / Map / Measure / Manage risk method | A defensible, widely-recognized way to measure and manage AI risk |
| HIPAA | Protected health information safeguards | Non-negotiable wherever AI touches member health data |
| NAIC AI Model Bulletin | Insurer expectations for governing AI use, adopted state by state | Directly on point for a health plan — be ready as states adopt it |
| Model-risk discipline | Validation, monitoring, and documentation of consequential models | Applies the rigor regulators already expect to your AI estate |
| NIST CSF 2.0 · CIS Controls v8.1 | Current cybersecurity framework and controls baseline | The security posture your AI inherits — governed to current versions, not last decade's |
| OWASP LLM Top 10 · MITRE ATLAS | The AI-specific attack surface — prompt injection, model abuse, agentic risk | What a security-only review misses — real wherever AI faces members or sensitive data |
| The Joint Commission · CHAI model cards | Responsible-AI guidance for health + the model "nutrition label" for health AI | Speaks a health plan's world — accreditation-aligned and member-facing, not generic |
One crosswalk, not silos. Instead of a separate binder per regulation, you get a single map: each control, written once, traced to every standard it satisfies — far easier to operate and to defend in an audit.
From ad-hoc use to a governed practice.
As AI use spreads, consistency becomes the risk. We help BCBSIL turn scattered, individual usage into a repeatable, governed practice the whole organization can rely on.
Prompt engineering & usage standards. Practical standards for how AI is prompted, reviewed, and used safely — especially where outputs touch members or sensitive data — so quality and safety don't depend on who happens to be at the keyboard.
An AI Center of Excellence. A home for patterns, reusable controls, training, and shared learning — so good practice spreads and every team isn't solving the same governance problem from scratch.
Know where you stand. See the path to scale.
Before you build, you get a clear, honest read on where BCBSIL is today — and a sequenced roadmap for getting to enterprise-scale, responsible AI.
Readiness assessment
A grounded view of your AI estate, governance maturity, regulatory exposure, and the gaps that matter most — prioritized, not a laundry list.
Enterprise roadmap
A phased plan that sequences the highest-value, lowest-risk moves first, with change management so the framework is adopted — not shelved.
Governance designed by people who build and run AI.
Advisory firms can hand you a framework. MCG designs it, implements it, and operates it — backed by Kelly's scale and talent bench. For a regulated enterprise that has to live with this every day, that's the difference that matters.
Advisory-only firms
- Assess and recommend a framework
- Hand over a policy and a gap report
- Strong on design — then the work goes back to your teams
Motion Consulting Group
- Design the operating model and build it
- Stand up the registry, intake, controls, and monitoring
- Run it as a managed service — engineering + Kelly's talent at scale
Independence where it counts. The team that builds and runs your controls is firewalled from the party that attests to them — so a build-and-run model still produces an assurance your auditors, board, and regulators can rely on.
Proof points (representative — final, cleared references to be confirmed): AI governance and delivery across regulated, high-stakes environments — healthcare (the closest analog for BCBSIL), a top-tier US telecommunications carrier, energy, and biotech.
Let's start with a focused assessment.
The simplest first step is a scoped Phase 1 assessment: a clear picture of your AI estate, your governance gaps, and a prioritized plan — delivered quickly, with no long commitment to begin.
Let's scope a working session.
A short session with your security, privacy, compliance, and data leaders to walk this framework, hear your priorities, and shape a Phase 1 assessment tailored to BCBSIL.
Working session — scheduled through your MCG contact