AI Strategy, Audit & Execution

"​Enterprises don’t fail at AI because the technology doesn’t work. They fail because execution is misaligned with reality."

Executive Context: Why This Service Exists

Most enterprises today fall into one of three categories:

This service exists to solve exactly that gap.

RSC’s AI Strategy, Audit & Execution engagement is designed to help enterprises pause intelligently, assess their real AI readiness, and move forward with confidence, control, and defensibility — not hype.

What This Engagement Is (and Is Not)

What it is 

What it is not

Core Objectives of the Engagement

By the end of this engagement, leadership will have:

  1. Clarity on which AI applications actually matter to the business
  2. Visibility into risks, gaps, and readiness (data, governance, talent)
  3. A prioritized roadmap for MCP, production-grade LLMs, or other AI systems
  4. A clear execution choice — build, partner, or hybrid
  5. Optional execution support from RSC for implementation and rollout

Engagement Structure (4–8 Weeks)

Phase-Based Diagnostic & Execution Framework

Phase Focus Area Key Outcomes
Phase 1 AI Landscape Audit Current-state mapping of tools, pilots, data, governance
Phase 2 Use-Case Prioritization High-impact AI opportunities aligned to business goals
Phase 3 Architecture & Governance Design MCP / LLM / AI stack blueprint with controls
Phase 4 Execution Roadmap Phased implementation plan with ownership & KPIs
Phase 5
(Optional) 		Hands-on Execution RSC-led build or co-build of AI systems

Typical Engagement Outputs

By the end of the engagement, clients receive:

Who This Service Is For 

This engagement is best suited for:

Why RSC for this Enterprise AI Engagement   

RSC brings a rare combination:

We do not sell tools. We design decision systems.

Engagement Duration & Commercials (Indicative)

This service exists to help leadership move from experimentation to advantage — with clarity, confidence, and control.

Comparative Table: AI Strategy & Execution

Dimension Big Consulting Firms RSC (AI Strategy, Audit & Execution)
AI Engagement Starting Point Begins with AI vision decks or vendor-aligned roadmaps Begins with a ground-truth audit of business, data, and governance
Understanding of Production-Grade LLMs Often tool-centric or model-centric System-centric: LLM + RAG + Governance
MCP & Orchestration Knowledge Superficial or experimental Deep architectural understanding with execution pathways
Vendor Neutrality Frequently tied to hyperscaler or platform partnerships Fully vendor-agnostic; architecture-first, not tool-first
Handling of Regulatory & Compliance Risk Treated as a parallel legal workstream Embedded directly into AI system design
Ability to Say “Don’t Build This Yet” Rare — incentive to expand scope Explicitly willing to pause or kill non-viable AI initiatives
Execution Ownership Handed off to SI or internal IT teams Optional hands-on execution and co-build with accountability
Human-in-the-Loop Design Often added late or inconsistently Designed upfront as a core system principle
Auditability & Traceability Retrofitted after deployment Engineered from day one
Knowledge Reuse & Compounding Project-based; knowledge dissipates Designed to compound institutional intelligence
Time-to-Truth for Leadership 10–16 weeks to surface real constraints 2–4 weeks to surface hard realities
Engagement Flexibility Rigid, multi-quarter programs Time-bound, outcome-driven (4–8 weeks)
Willingness to Execute Advisory-heavy, execution-light Advisory + execution (optional, scoped, accountable)
Commercial Incentives Optimized for program expansion Optimized for decision clarity and ROI