Definition
A CO2 Council is a cross-functional leadership body that holds decision rights over a company’s commercial system once humans and AI agents work together across marketing, sales, service, and product. It owns the decisions that no longer fit inside one department. These include what a brand agent may offer in a negotiation, who sets escalation thresholds, and whether an agent-driven workflow should be scaled, fixed, or shut down.
The term comes from Marketing Operations 3.0 by Greg Kihlström (De Gruyter Brill), where it’s the governing body for Commercial Operations Orchestration (CO2). The book is direct about its purpose: the council isn’t there to review dashboards or discuss alignment. Its job is to decide.
See also: Decision Rights · AI Governance Board (AIGB) · Revenue Operations (RevOps) · RACI Matrix
Why it matters for marketing
In a traditional org chart, most marketing decisions stay inside marketing. That changes once AI agents start acting across the customer lifecycle. A single data agent might support campaign planning, sales prospecting, and post-purchase support. When it goes wrong, which department owns the fix?
Without a clear owner, the book describes a predictable failure pattern. One team assumes another owns the issue. Escalations sit in queues. Policy questions turn into recurring meeting topics rather than decisions. And economic results get spread across so many budgets that nobody feels responsible for them.
For CMOs and marketing ops leaders, the council is where marketing’s growth goals meet finance’s cost discipline and IT’s data and security requirements, with a mechanism to settle trade-offs. A workflow can be profitable and reputationally risky at the same time. Someone has to make that call, and the council is meant to be that someone.
How it works
Membership: built as a decision-rights map
The book argues the council shouldn’t be “a symbolic committee of familiar titles.” Each seat represents a category of consequence the business can’t ignore. Titles vary by company, but the accountabilities are consistent:
| Role | Primary accountability | Key metric |
|---|---|---|
| CMO / Chief Growth Orchestrator | Go-to-market orchestration and brand equity | Next Best Action conversion lift |
| CFO | Resource allocation and Return on Agent (ROA) | Hybrid Squad Efficiency Ratio (HSER) |
| CIO | Data veracity and agent connectivity | Data Readability Score (DRS) |
| CEO | Ethical guardrails and vision | Compliance and trust score |
| Bot Manager liaison | Squad execution and feedback loops | Learning velocity |
Source: Table 19.1, “The CO2 Council Decision Rights Matrix,” in Marketing Operations 3.0.
The book also calls the inclusion of sales and service leadership “non-negotiable,” since agents are increasingly reused across functions. The Bot Manager liaison seat is the unusual one. It brings in someone who supervises AI agents day to day, so the council hears what’s actually happening in live execution.
The charter
The council’s charter spells out what it owns, what it reviews, what it decides directly, what stays delegated, and how exceptions move between functions. Two pieces stand out.
- Agent charters. The council defines each AI agent’s role the way HR would define a human role: its mission, its non-negotiable guardrails (for example, “Do not offer a discount greater than 15% without human approval”), and a pre-defined kill switch for deactivating it if it drifts.
- Governance-as-code. The council decides which policies get turned into machine-readable rules that systems enforce at runtime, who maintains them, and how exceptions are reviewed. A policy that lives only in a PDF won’t stop an agent.
Operating cadence
- Bi-weekly pulse. A short working session to clear cross-functional blockers. The agenda has two parts: resolving data bottlenecks (for example, an agent that can’t act because CRM data is siloed) and refining Next Best Action logic where agents stall or customer-side agents reject offers.
- Lab vs. factory. The council decides which agents stay experimental (“the laboratory,” where failure is expected and useful) and which get promoted to production (“the factory,” where they run under strict governance-as-code and kill-switch protocols).
- 90-day ROA review. A deeper quarterly review of whether the hybrid system is becoming more valuable, not just more active.
From dashboard to decision
The council’s dashboard covers four areas: workforce efficiency and ROA, commercial velocity, governance and quality, and customer experience impact. Each review should end in one of four decisions:
- Scale: performance is improving and guardrails are holding.
- Fix: the use case is sound, but data, orchestration, or governance needs work.
- Reallocate: the capability works, but it’s pointed at the wrong lifecycle stage or customer problem.
- Kill: the value isn’t there, or the governance burden is too high.
The book offers a simple test. If the council can’t say what it would do differently after looking at the dashboard, the organization is still reporting, not governing.
How to utilize a CO2 Council
- Start when the first cross-functional agent goes live. One squad can be governed informally. Two or three sharing data and policy can’t.
- Map decisions before seats. List the recurring cross-functional decisions (discount authority, escalation thresholds, data ownership, scale-or-stop calls) and assign each one before deciding who attends.
- Write agent charters for every production agent. Include mission, guardrails, escalation rules, and shutdown conditions.
- Keep the pulse tight. Two agenda categories, decisions recorded, owners named.
- Tie budget to the lab/factory line. Experimental agents get learning budgets. Factory agents get funded on ROA.
- Publish decision rights internally. Teams trust a hybrid system more when they can see who decides what and how escalation works.
Comparison: CO2 Council vs. related governance bodies
| CO2 Council | AI Governance Board (AIGB) | Center of Excellence (CoE) | Change Control Board | RevOps steering committee | |
|---|---|---|---|---|---|
| Main question | Should this part of the commercial system scale, change, or stop? | Is this AI use safe, ethical, and compliant? | How do we spread expertise and standards? | Should this change be approved? | Is the revenue process aligned? |
| Scope | Marketing, sales, service, product, and agents working across them | AI use across the enterprise | One capability area | Specific systems or projects | Marketing, sales, customer success |
| Authority | Decision rights over policy, agent authority, investment | Usually approval or veto on AI risk | Advisory and enablement | Approval of individual changes | Usually coordination |
| Key metric | Return on Agent, plus guardrail metrics | Risk and compliance measures | Adoption and capability maturity | Change success rate | Pipeline and forecast accuracy |
| Cadence | Bi-weekly pulse plus quarterly ROA review | Periodic or per-use-case | Ongoing | Per change request | Weekly or monthly |
The CO2 Council and an AI governance board can coexist. A common split is for the AIGB to set enterprise-wide AI risk policy while the CO2 Council decides how agents operate inside the commercial system under that policy.
Best practices
- Give the council real authority, including budget reallocation. A body that can only recommend will be routed around.
- Keep membership small and accountability-based. Seniority alone isn’t a reason for a seat.
- Separate the fast pulse from the quarterly economic review so urgent blockers don’t crowd out strategic decisions.
- Don’t let pilots skip the factory gate. The book warns against treating experimental capability as production-ready in budget and scaling decisions.
- Treat intervention as normal stewardship. A culture that sees pausing an agent as failure will pause agents too late.
- Record every scale, fix, reallocate, or kill decision with its rationale so the next review has something to measure against.
Future trends
- More formal AI governance requirements. Frameworks such as the NIST AI Risk Management Framework put “Govern” at the center of AI risk management. Commercial teams will likely need a body that can show how agent decisions are overseen.
- Executable policy. Expect more council decisions to ship as machine-readable rules rather than memos.
- Agent portfolio management. As agent counts grow, councils will likely manage them as a portfolio, with scale and retirement decisions made on economic and risk data.
- New seats. Roles such as Bot Manager or Agent Orchestrator are likely to become standard participants in commercial leadership forums.
FAQs
What does CO2 stand for in CO2 Council? Commercial Operations Orchestration, the operating model described in Greg Kihlström’s Marketing Operations 3.0.
Who should sit on a CO2 Council? Leaders accountable for growth (CMO), economics (CFO), data and security (CIO), enterprise trade-offs and ethics (CEO), plus sales and service leadership and a Bot Manager liaison who represents the human-agent squads.
Isn’t this just another steering committee? No. Most steering committees coordinate or advise. A CO2 Council holds decision rights, including authority over agent permissions, policy, and resource reallocation.
How is it different from an AI governance board? An AI governance board focuses on enterprise AI risk, ethics, and compliance. The CO2 Council focuses on how the commercial system performs and which agent-driven capabilities should scale. The two can work together.
How often does the council meet? The book recommends a bi-weekly pulse for cross-functional blockers and a deeper 90-day review of Return on Agent.
What is an agent charter? A document the council approves for each AI agent. It covers the agent’s mission, guardrails, escalation rules, and shutdown conditions, ideally in machine-readable form so systems can enforce it.
What decisions does the council make? At each review, it decides whether each capability should be scaled, fixed, reallocated, or killed. It also sets agent authority limits, escalation thresholds, and data ownership.
Do smaller companies need one? The formal structure scales down. A smaller company might fold the role into an existing leadership meeting, as long as someone explicitly owns cross-functional decisions about AI agents.
What’s the “lab vs. factory” distinction? The lab is for experimental agents, where failure is expected. The factory is for production agents, which must meet performance benchmarks and run under strict governance before customers see them.
Related Terms
- Decision Rights
- AI Governance Board (AIGB)
- RACI Matrix
- DACI Model
- Center of Excellence (CoE)
- Change Control Board
- Revenue Operations (RevOps)
- Next Best Action
- Commercial Operations Orchestration (CO2)
- Return on Agent (ROA)
Listen: related episodes of The Agile Brand
- Expert Mode: A seat without decisions is theater, with Richard Barnett from Fusion Risk Management
- CIBC’s Stephanie Leheta on moving from silos to enterprise-scale CX
- Tying CX outcomes to commercial goals with Markus Engel and Linzi Hindle at DHL
Sources
- Kihlström, G. Marketing Operations 3.0. De Gruyter Brill. https://www.degruyterbrill.com/document/doi/10.1515/9783112218839/html
- National Institute of Standards and Technology. “AI Risk Management Framework.” https://www.nist.gov/itl/ai-risk-management-framework