Definition
The MarketingOps Maturity Model is a three-stage framework that describes how marketing operations has changed in what it does, how it works, and where it sits in the organization:
- Marketing Ops 1.0 — a tactical cost center run on spreadsheets and manual administration.
- Marketing Ops 2.0 — an integration and automation function built on CRM, marketing automation, and rules-based (“if/then”) workflows.
- MarketingOps 3.0 — an orchestration layer for the whole commercial system, in which humans set goals and guardrails and AI agents carry out work within defined limits.
Greg Kihlström lays out the model (the book calls it the “MarketingOps Maturity Curve”) in Chapter 4 of Marketing Operations 3.0 (De Gruyter Brill). The stages differ less in tooling than in operating logic: manual execution, then deterministic automation, then governed autonomy.
See also: Marketing Operations · Marketing Automation · Revenue Operations (RevOps) · Experience Maturity
Why it matters for marketing
Most marketing ops teams today work in some version of 2.0. They have a CRM, an automation platform, lead scoring, attribution, and a stack of integrations. The 2.0 model was a real step up from 1.0, but it hits a ceiling. Rules only cover situations someone thought of in advance, and dashboards are built for people to read, not for machines to act on.
That ceiling matters more as AI agents show up on both sides of the transaction. A customer’s AI assistant may compare options and narrow the field in seconds. A rules-based nurture sequence that waits on a weekly data sync can’t respond at that pace, and the brand drops out of consideration before anyone looks at a report.
The model gives marketing ops leaders a shared vocabulary for the gap. It’s useful for explaining to a CMO or CFO why buying another tool won’t fix a 2.0 problem, and for deciding what has to change first.
How the stages differ
The book’s maturity curve compares the three stages across more than a dozen dimensions. The core ones:
| Dimension | Marketing Ops 1.0 | Marketing Ops 2.0 | MarketingOps 3.0 |
|---|---|---|---|
| Core identity | Tactical cost center | Integration and automation function | Orchestration layer for the commercial system |
| Operating logic | Manual execution | Deterministic “if/then” rules | Probabilistic, context-aware, governed autonomy |
| Typical tools | Spreadsheets, manual reports | CRM, marketing automation, lead scoring, attribution | Shared truth layer, orchestration layer, human-agent squads, Next Best Action systems |
| Human role | Doer | Workflow builder, systems operator | Orchestrator, agent manager, policy setter, escalation owner |
| System speed | Human speed | Faster, but limited by rules and human review | Market speed, with agents sensing and responding |
| View of the journey | Linear, campaign-driven | Still mostly linear, better instrumented | Non-linear, real-time, increasingly machine-mediated |
| Data orientation | Human-readable spreadsheets | Dashboards and platform fields | Machine-usable truth and shared customer state |
| Source of scale | More people | More tools and more rules | Better orchestration of humans and agents |
| Main constraint | Human bandwidth | Static playbooks and “Frankenstack” complexity | Governance, data quality, clarity of decision rights |
| Measurement focus | Task completion | Efficiency, attribution, lead flow | System performance, Return on Agent (ROA), business outcomes |
| Org shape | Support team inside marketing | Mature ops team, still bounded by function | Cross-functional layer (Commercial Operations Orchestration) |
| Why it breaks | Can’t move faster than people work | Can’t adapt beyond what humans explicitly coded | Depends on shared truth, governance, and role design to scale |
Marketing Ops 1.0: the tactical cost center
In 1.0, operations handled budgets, schedules, list management, and report production. It was measured by task completion, so it was classified as overhead. Output grew only by adding headcount.
Marketing Ops 2.0: integration and automation
In 2.0, operations moved onto platforms. CRM and marketing automation made lead scoring, email triggers, and attribution possible at scale. The tradeoff was complexity. Many teams ended up with what the book calls a “Frankenstack” of 30-plus loosely connected tools, spending more time maintaining integrations than improving results. For a sense of scale, Scott Brinker’s 2025 Marketing Technology Landscape counted 15,384 solutions, and Gartner’s 2023 Marketing Technology Survey found marketers were using only 33% of their martech stack’s capabilities.
MarketingOps 3.0: orchestration
In 3.0, the unit of work changes from campaigns and workflows to decisions and governed actions. Humans define the “what” and “why” plus the boundaries. Agents work out the “how” from live context. Operations also stops being a marketing-only function and becomes the coordination layer across marketing, sales, service, and product.
How to assess your stage
The book doesn’t prescribe a scoring method. A simple self-assessment works:
- List the dimensions from the table above.
- For each one, mark which stage best describes how your team actually operates today (not what’s on the roadmap).
- Note where the answers cluster. Most organizations land unevenly, for example 2.0 tools with 1.0 measurement, or 3.0 pilots running on 2.0 data.
- Treat the lowest-scoring dimensions as the constraint. Agents running on dashboard-grade data or with no defined decision rights will struggle no matter how advanced the tooling is.
How to utilize the model
- Building the business case. Show executives that the limiting factor is operating logic, not tool count, before approving more spend.
- Roadmap sequencing. Fix data structure and decision rights before expanding agent autonomy.
- Role and hiring design. Move job descriptions from “workflow builder” toward agent oversight, policy design, and escalation ownership.
- Vendor evaluation. Ask whether a new platform helps the team act from shared context or just adds another rules engine to the Frankenstack.
- Diagnosing failure. When an AI pilot underperforms, check whether it was dropped into a 2.0 environment with siloed data and linear playbooks.
Comparison with other maturity models
| Model | What it measures | Stages | Focus |
|---|---|---|---|
| MarketingOps Maturity Model (1.0/2.0/3.0) | Operating logic of the marketing ops function | 3 | How work, decisions, and roles change as AI agents take on execution |
| Capability Maturity Model Integration (CMMI) | Process discipline in an organization | 5 (Initial to Optimizing) | How repeatable and measured processes are |
| Generic martech maturity models | Breadth and adoption of a technology stack | Usually 4–5 | Tool coverage and integration |
| Experience Maturity | How well an organization delivers customer experience | Varies | CX capability and culture |
The MarketingOps model can sit alongside a process model such as CMMI. A team could run highly disciplined, well-documented 2.0 processes and still be held back by 2.0 operating logic.
Best practices
- Don’t treat 3.0 as a software purchase. The book is explicit that the shift is in operating logic, accountability, and speed.
- Clean up data structure early. Product details, pricing, and eligibility rules need to be machine-readable before agents can use them.
- Keep the 2.0 foundations that work. Integrated CRM data and documented processes are prerequisites for 3.0, not things to throw away.
- Define where humans stay in charge before granting autonomy, especially for pricing exceptions, renewal disputes, and sensitive customer replies.
- Measure at the system level. Departmental efficiency metrics can hide friction that customers still feel.
- Expect uneven maturity across dimensions and plan for it rather than declaring a single stage.
Future trends
- Agent management as a core skill. Marketing ops roles are moving toward supervising AI agents, auditing their logic, and setting escalation thresholds.
- Machine-mediated buying. As customer-side AI agents handle more research and comparison, 2.0-style push campaigns lose reach.
- Governance built into systems. Policies and authority limits are moving into machine-readable rules that are checked continuously.
- Stack consolidation. Pressure to reduce Frankenstack complexity is likely to grow as teams need consistent data for agents.
- New metrics. Expect system-level measures such as Return on Agent to show up next to, or in place of, campaign and channel metrics.
FAQs
What is Marketing Ops 3.0? It’s the third stage in the MarketingOps Maturity Model. Marketing operations acts as an orchestration layer across the commercial system, with humans setting goals and guardrails and AI agents executing within defined boundaries.
Who created the MarketingOps Maturity Model? Greg Kihlström describes it in Marketing Operations 3.0 (De Gruyter Brill), where it’s called the MarketingOps Maturity Curve.
Is Marketing Ops 2.0 the same as marketing automation? Marketing automation is the defining technology of 2.0, but 2.0 also covers CRM integration, lead scoring, attribution, and the integration work needed to connect them.
Can a team skip from 1.0 to 3.0? Not really. 3.0 depends on integrated data and documented processes, which are 2.0 accomplishments. A team can move through 2.0 quickly, though, especially if it builds data for machine use from the start.
What is a “Frankenstack”? An informal term for a martech stack made of many loosely connected tools, where the team spends more effort maintaining the tools than the tools save.
Why does rules-based automation fall short? Rules only handle situations someone anticipated. They also assume a predictable, linear journey, which is less common as customers use AI assistants to research and compare.
Does 3.0 mean replacing marketing ops staff with AI? No. The book frames it as moving human work “upward,” toward goal setting, policy, oversight, and judgment, while agents handle repetitive execution.
How does this model relate to CO2? Commercial Operations Orchestration (CO2) is the organizational structure that the 3.0 stage leads to. It extends operations beyond marketing into sales, service, and product.
What’s the first step toward 3.0? Usually data. Get core product, pricing, and customer-state information into a consistent, machine-usable form, and define who holds decision rights over agent behavior.
Related Terms
- Marketing Operations
- Marketing Automation
- Customer Relationship Management (CRM)
- Lead Scoring
- Revenue Operations (RevOps)
- Next Best Action
- Technical Debt
- Digital Transformation
- Commercial Operations Orchestration (CO2)
- Return on Agent (ROA)
Listen: related episodes of The Agile Brand
- MarketingOps CEO Mike Rizzo on marketing operations as a strategic driver of growth
- Integrating AI into enterprise marketing operations with Adam Brotman, Forum3
- Asana CMO Prachi Gore on realizing AI’s true potential for marketing operations
- Marketing operations as a driver of growth with Mike Rizzo, MarketingOps.com
Sources
- Kihlström, G. Marketing Operations 3.0. De Gruyter Brill. https://www.degruyterbrill.com/document/doi/10.1515/9783112218839/html
- Gartner. “Gartner Survey Finds 63% of Marketing Leaders Plan to Invest in Generative AI in the Next 24 Months.” August 23, 2023. https://www.gartner.com/en/newsroom/press-releases/2023-08-23-gartner-survey-finds-63-percent-of-marketing-leaders-plan-to-invest-in-generative-ai-in-the-next-24-months
- Brinker, S. “2025 Marketing Technology Landscape Supergraphic: 100X growth since 2011, but now with AI.” chiefmartec, May 2025. https://chiefmartec.com/2025/05/2025-marketing-technology-landscape/
- CMMI Institute / ISACA. “CMMI.” https://cmmiinstitute.com/