In November 2025, enterprise consulting powerhouses aligned on something rare: 12 definitive playbooks for scaling agentic AI. McKinsey, PwC, BCG, Deloitte, and peers released authoritative frameworks aggregated by Stephane Grill into a free resource vault—stephanegrill.com/fagenticaiplaybook. These aren't generic trend reports. They're battle-tested orchestration strategies, ROI models, and stress tests that reveal what enterprise AI's next phase actually looks like.
But here's the catch: most executives don't know which 4 to read first. The playbook vault is comprehensive—spanning strategy, operations, roadmaps, and risk. If you're building teams, scaling beyond solo agents, or pitching AI to boards, you need to understand which guides apply to your phase, your risk profile, and your timeline. We decoded the top 4 and found the patterns hiding in all 12.
What Is This Playbook Vault?
Context matters. Stephane Grill curated 12 playbooks into a lead-gen landing page that functions as a free resource library for enterprise leaders. No vendor lock-in, no proprietary tooling—just frameworks and ROI math you can adopt immediately. The vault mirrors a familiar pattern: consulting firms publish authoritative reports, practitioners aggregate them, and leaders finally have a single source of truth.
The 12 playbooks, released across November and early December 2025, address:
- Strategy & Vision: How to frame agentic AI to boards and investors
- Orchestration Architecture: Agent mesh, routing, and coordination
- ROI Models: Cost breakeven from pilot to scale
- Operations: Automation of internal workflows (HR, finance, reporting)
- Customer-Facing Automation: Sales, support, and customer interaction layers
- Risk & Guardrails: Stress tests, bias detection, failure mode analysis
If you work at a company with 50+ employees and one AI initiative underway, this vault is required reading. If you're a solo founder experimenting with agents, skip the playbooks—they target teams and capital.
The 4 Essential Playbooks Worth Your Time
1. McKinsey – Agentic AI Scaling Playbook (Nov 2025)
Core Thesis: Agentic AI isn't "one model"—it's orchestration of 5–10 specialized agents working in concert. Single-LLM systems fail because they try to solve everything with one tool.
Key Frameworks:
- "Agent Mesh" Architecture: Think of agents as microservices. A research agent gathers information, a drafting agent synthesizes, a validation agent fact-checks, a routing agent decides what happens next. Human stays in the loop for high-risk decisions (financial, legal, customer-facing).
- ROI Math: 3–5x efficiency on knowledge work when orchestration maturity exceeds 50%. Median pilot saves 2,000 person-hours annually in a 500-person organization.
- Failure Modes: Single points of failure (agent A breaks, entire system stalls) vs. distributed resilience (agent A fails, agent B routes work to human or backup agent).
Nexairi Perspective: McKinsey's framework is gold for enterprises with teams >20. For smaller groups, orchestration overhead exceeds benefit. But the "agent mesh" concept is foundational—it's how AI agents avoid rogue behavior through proper data governance and orchestration.
Who Should Read This: CTOs, VP of Engineering, heads of AI operations. Skip if you're still hiring your first ML engineer.
2. PwC – Agentic AI Reinvention (Nov 2025)
Core Thesis: "Reinvent from the customer back." Agentic AI wins happen at the interface—where agents interact directly with customers, not hidden in internal spreadsheets. 70% of value comes from customer-facing automation (sales, support, fulfillment).
Key Frameworks:
- 4 Maturity Stages:
- Stage 1: Agents draft internal documents. 5–10% manual review
- Stage 2: Agents handle routine customer queries. 20–30% escalation
- Stage 3: Agents propose actions to humans. 40% of interactions are agent-handled end-to-end
- Stage 4: Agents execute autonomously within guardrails. 60%+ autonomous interactions
- "Invisible AI" Positioning: Customers don't know they're talking to agents—they just know problems get solved faster. This shifts the pitch from "AI" to "responsiveness."
- Rollout Playbook: Pilot with internal support team (30 days), expand to 1 customer segment (60 days), scale to full customer base (90+ days).
Nexairi Perspective: PwC's framework is cynical and correct. Enterprise AI gets funded when it touches revenue or cutscosts visibly. Travelers cut 1,200 call center roles using NLP agents—that's Stage 4 adoption, and it drove board approval in weeks.
Who Should Read This: Product leads, sales ops, customer success leaders. Every SaaS founder should read this three times.
3. BCG – Emerging Agentic Enterprise (Nov 18, 2025)
Core Thesis: Agentic AI creates "digital twins of decision processes." Run 1,000 simulations of a sales strategy before committing budget. Test hiring decisions before onboarding. Reduce real-world risk by stress-testing in silico first.
Key Frameworks:
- Cost Model: $1–3M pilot (6 months), $20–50M Phase 2 (18 months), $50–100M enterprise scale (3 years). Non-linear because process complexity is non-linear.
- Risk Matrix: High-ROI/Low-Risk (reporting automation, repetitive decisions) vs. High-Risk/Medium-ROI (strategy simulation, M&A analysis). Start low-risk, move methodically up the curve.
- "Digital Twins" Definition: Agents that model decision processes without executing them. Marketing can ask "what if we increased paid spend by 30%?" and run 100 agent simulations in 5 minutes.
Nexairi Perspective: BCG's cost model is the closest thing to ground truth published. The $1–3M pilot number is realistic for mid-market; expect longer for enterprises with legacy systems. Digital twins are where agentic AI gets strategic—not just automating tasks, but stress-testing decisions before humans commit capital.
Who Should Read This: CFOs, board members, and anyone securing budget. This playbook is your CFO deck.
4. Deloitte – Agentic Horizon Stress Test Roadmap
Core Thesis: Agents fail in predictable ways. Stress-test for 7 failure modes before production: hallucination, bias, data drift, security breaches, action errors, audit trail gaps, and recovery failures. Without stress tests, production agents become liability centers.
Key Frameworks:
- 7 Failure Mode Tests:
- Hallucination: Does agent invent facts? (Red-team with contradictory data)
- Bias: Does agent discriminate by demographics? (Test with diverse customer profiles)
- Drift: Does agent degrade over time? (Run same test monthly for 6 months)
- Security: Can agents be prompt-injected? (Adversarial testing)
- Action Errors: Can agent execute irreversible actions safely? (Sandbox-only first)
- Audit Gaps: Can compliance teams trace every decision? (Log everything, make queryable)
- Recovery: If agent fails mid-task, can humans resume cleanly? (Checkpoint every step)
- 6-Month Pilot → Production Guardrails: Month 1–2: baseline tests. Month 3–4: adversarial tests. Month 5–6: production hardening (monitoring, alerting, circuit breakers).
Nexairi Perspective: Deloitte's framework is the "don't get fired" checklist. 45% of AI-generated code contains OWASP vulnerabilities—agentic AI will fail the same way unless stress-tested first. Start with this playbook if you're risk-averse or regulated.
Who Should Read This: CISOs, compliance officers, anyone responsible for audit trails. This is non-optional for finance, healthcare, and regulated industries.
Cross-Playbook Themes: What the 12 Playbooks Agree On
When 12 independent consulting firms align on patterns, listen. Here's what they all say:
| Theme | Prevalence (out of 12) | Key Quote |
|---|---|---|
| Orchestration > Models | 10/12 | "Single LLMs fail; agent teams win." [McKinsey] |
| Human-in-Loop Mandatory | 11/12 | "Agents propose, humans decide." [PwC] |
| ROI in Ops First | 9/12 | "Start with reporting → sales automation → strategy." [BCG] |
| Customer-Facing Wins Matter Most | 8/12 | "70% of value comes from customer interactions, not internal ops." [PwC] |
| Stress Testing Non-Negotiable | 12/12 | "Test for failure before deployment." [All guides] |
Interpretation: Enterprise consensus has formed. It's not about the model anymore—GPT-4, Claude, Llama all work. What matters is orchestration (how agents coordinate), human oversight (where humans stay in control), and customer experience (where users feel the impact).
The Real Agentic Playbook: A Nexairi Synthesis
If we synthesize the 12 guides, the actual enterprise agentic AI playbook looks like this:
- Months 1–2: Know Your Use Case Start with low-risk, high-ROI targets: reporting, email drafting, customer support triage. Not strategy. Not M&A. Not irreversible decisions.
- Months 2–3: Design Your Agent Mesh Map dependencies. Which agents need to talk to which? Where do humans override? What's the fallback if an agent fails? McKinsey's orchestration framework applies here.
- Months 3–4: Stress Test Run Deloitte's 7 failure mode tests. Red-team for prompt injection. Test bias across customer segments. Simulate production load. This is the tedious, non-optional phase.
- Months 4–6: Pilot with Real Users PwC's maturity model applies: start internal, expand to one customer, scale incrementally. Measure Stage 1 (5–10% manual review), then optimize toward Stage 2 (20–30% escalation).
- Months 6–12: Scale & Monitor BCG's cost model kicks in: expect $20–50M in Phase 2 infrastructure if you're enterprise. Monitor for drift, bias, security. Audit trails everywhere.
Timeline Reality Check: If you skip any phase, you'll get burned. Rushing from Month 1 to Month 4 (skipping stress tests) results in production agents that hallucinate, discriminate, or get hacked. No exceptions.
Who This Actually Applies To
Download the vault (and actually read it) if:
- You have 50+ employees and a dedicated AI team
- You're building agentic systems that touch customer data or make financial decisions
- You need to pitch AI to boards or secure >$5M in budget
- You're regulated (finance, healthcare, compliance-heavy)
Skip it if:
- You're a solo founder experimenting with AI agents
- You're still in "ChatGPT exploration" phase
- Your org has <20 people
- You don't have budget to spend $1–3M on a pilot
The Bottom Line
Enterprise agentic AI is past hype—it's now about orchestration, stress tests, and customer-facing wins. The 12 playbooks prove this consensus is real. McKinsey, PwC, BCG, and Deloitte didn't coordinate; they all reached the same conclusions independently. That's signal, not noise.
Download the vault. Read the top 4. Build your roadmap. And don't skip stress testing—the one thing all 12 guides agree on.
