Marketing means constant reinvention. We launch, measure, tweak and sometimes scrap entire systems as new insights emerge. I’ve managed over a dozen platforms, rebuilt the same lifecycle paths hundreds of times, and relaunched campaigns in new forms.
That same iterative discipline now defines how we approach AI. The question is not how to secure it; it’s about which jobs actually need to be done and how they need to be done now.
AI readiness in marketing, especially in CRM, requires a closer look at the processes and roles behind each output. Instead of asking if AI can write an email, ask if that step should still exist – and who or what should own it.
The goal is simple: expose the bottlenecks, redundancies, and misalignments that AI can finally solve, so people can focus on the work they really need to do.
Step Zero: The real victory comes before the AI
The biggest impact of AI is not just automation, but also coordination. Before any model, prompt, or workflow touches your CRM, your first task is to force the conversations that have been quietly avoided for years:
- Why do we do it this way?
- What is the actual result we are trying to achieve?
- Who owns that outcome?
Most marketing inefficiencies lie in translation between teams. AI reveals errors immediately because machines require precision where humans can tolerate ambiguity. Therefore, your step zero has nothing to do with AI. It’s about separating duties and tasks:
- Function: The outcome that needs to happen (for example, securing regulatory approval for campaign messages).
- Task: The steps or rituals we currently perform to get that job done (e.g. draft writing → legal email → wait for edits → update document → resend → upload to platform).
When you research the job, you discover flexibility. Perhaps legal guidance could include a Retrieval-Augmented Generation (RAG) system trained in your company’s approved language and claims. Perhaps a model context protocol (MCP) client could house current terms and conditions and automatically flag outdated wording before it ever hits a legal advisor’s inbox.
Agents helped reveal where workflows were unclear, overlapping, or outdated. Humans are good at navigating messy middle ground, but when you try to codify that mess for AI, you finally see where your process actually breaks.
Step 1: Define the tasks to be performed
This is about separating the What of the How – the unchangeable outcomes your process needs to achieve versus the ways you currently get there. Once you no longer know who does what and how to do it, you start to see opportunities for simplification and automation.
- Identify the core tasks: The non-negotiable outcomes that each campaign or workflow must deliver (e.g., securing regulatory approval, personalizing content, validating data accuracy).
- Avoid naming steps or roles: Focus only on the end conditions that indicate it’s done.
- Anchor AI discussions in the outcomes: Question: “What needs to be accomplished for this process to move forward, regardless of who or how?”
Dig deeper: 4 must-do marketing tasks that are being transformed by genAI
Step 2: Capture stakeholder perspectives at scale
AI readiness starts with people, not data models. Your CRM processes are lived experiences. Every marketer, analyst and copywriter deals with them differently. Before you can automate or redesign anything, you need to understand these realities at scale.
The goal is to gather real, unfiltered insights into how work is actually done. Discover the friction points and the emotional signals – what frustrates people, what energizes them and where they have developed personal solutions that hide systemic gaps.
- Involve ~10% of your process stakeholders: Enough coverage to capture variance without getting stuck.
- Skip surveys and group workshops: Instead, have them record voice notes and answer structured prompts. This captures context and emotion and avoids meeting fatigue. Take the transcript and run it through an AI tool to give you an initial outline of the process, gaps, and pain points.
- Ask the right questions:
- Walk me through the steps you take to get X done.
- Which outcomes do you need to validate along the way?
- Which steps feel the most manual, repetitive or frustrating?
- Which steps do you actually like or do you see as valuable?
Dig Deeper: Is Your Marketing Team Ready for AI? 8 steps to strategic AI adoption
Step 3: Map the end-to-end CRM process
Most teams think they know their process until they have to sign it. When you force every task into one visual or spreadsheet, blind spots arise: duplicate steps, outdated checks, unnecessary dependencies.
- Document every single step: Global CRM organizations can easily go through 80 to 100 steps from command to activation. These should fall under the core tasks (defining the strategy, regulatory approval, content creation, audience creation, UAT/QA, etc.). If a step changes ownership or tools, write it down.
- Commitment:
- Goal (task to be performed).
- Current owner.
- Human desire/pleasure of the step.
- Resource intensity.
- Repetition.
- Difficulty.
- Current status (manual/semi-automatic/automated).
Build this into a living spreadsheet, which becomes your foundation for understanding AI use cases.
Step 4: Score and prioritize for AI fit
Once the process is visible, patterns start talking to you. Scoring forces objectivity. Not every pain point deserves AI and not every beloved ritual should remain manual. This step quantifies human sentiment and operational pressure so you can focus on impact, not novelty. Think of it as a triage for your automation roadmap, bringing together effort and opportunity.
Apply a scoring system to three lenses:
- Human desire: Do people want to do this?
- Withholding tax: How expensive/time-consuming is it?
- Repetition: How many times is it repeated and how similar is it each time?
- Difficulty level of the task: Is it difficult to do? Very technical? Nuanced?
Patterns will then emerge:
- High desire + creative tasks: Candidates for AI enrichment (AI as a thinking partner, not as a replacement).
- Low desire + high resources + repetitive: Prime candidates for automation and AI workflow.
- Low desire + manual but not repetitive: Simple automation can be enough – AI is not required.
Energize yourself with free marketing insights.
Step 5: Distinguish between automation and AI versus agentic AI
When teams say ‘we’re going to use AI’, they often mean very different things. Some problems require simple scripts. Others need generative reasoning. Some require fully agentic orchestration, which requires tooling, contextual architecture and solid data. Mislabeling this leads to wasted investments and unrealistic expectations.
- Automation: Replace repetitive mechanisms (e.g. directions, logging approvals).
- AI (assistive/generative): Enriching, transposing, accelerating creative or analytical work (e.g. drafts of copies, highlighting deviations).
- Agentic AI (early stage): Give an AI agent a toolkit and autonomy to decide how to complete multi-step tasks with guardrails. Powerful, but still mature for enterprise CRM.
Focus less on the big AI vision and more on matching solutions with the right jobs.
Dig deeper: Agentic AI is poised to transform the martech stack – and the way marketers work
Step 6: Select your top 5 use cases
You now have data on what is broken, repetitive, or joyless. Rather than announcing an AI transformation, select a small, diverse portfolio of pilots that test automation, enrichment, and orchestration in different contexts. The discipline here is restraint: five great use cases will teach you much more than fifty half-finished applications.
From your readiness map:
- Identify the five most impactful candidates in terms of cost, time and employee sentiment.
- Frame them as tasks that need to be done (not tasks).
- Design pilot initiatives. Some might need simple automation, some AI workflows, some AI enrichment.
Step 7: Repeat like a marketer, build like an architect
AI maturity starts with pilots – and grows by learning not only what works, but where each solution belongs. As you learn, you can map your discoveries into the appropriate layer of ownership.
If the issue is narrow (for example, a CRM-specific use case or an agent to accelerate audience testing) it may be within your own team’s budget and sandbox. If the challenge involves shared infrastructure, such as metadata, asset libraries, or localization systems, you are now talking about a platform problem, not a CRM problem.
The discipline here is to match scale and scope:
- Local issues → local tools: Finance and build what is uniquely yours.
- Enterprise Issues → Enterprise Platforms: Shift the need to the teams that own content systems, data management, or translation pipelines.
- Hybrid issues → partnerships: Help evolve where your use case reveals a gap that everyone will eventually face.
AI is not one layer on top of marketing. It’s a web of intelligence built to reflect your business architecture. If you can remap your CRM tasks to results instead of existing steps, you’ll discover where AI actually belongs.
Some jobs will remain human, others will be AI-enabled, and a few will disappear completely into automation. The companies that win won’t be the companies with the most AI. They will be the ones with the smartest division of labor between people, machines and process design.
Dig Deeper: How to Unlock AI’s True Potential with an Adaptive Structure
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