7 steps to build real AI readiness in your CRM

Marketing means constant reinvention. We launch, measure, tweak and sometimes scrap entire systems when new insights surface. I’ve managed more than a dozen platforms, rebuilt the same lifecycle journeys hundreds of times and relaunched campaigns in new forms.

That same iterative discipline now defines how we approach AI. The question isn’t how to bolt it on — it’s which jobs actually need to be done and how they should be done now.

AI readiness in marketing, especially in CRM, demands a closer look at the processes and roles behind every output. Instead of asking whether AI can write an email, ask whether that step still needs to exist — and who or what should own it.

The goal is simple: expose the bottlenecks, redundancies and misalignments that AI can finally fix so humans can focus on the work that truly needs them.

Step zero: The real win comes before the AI

More than automation, the biggest impact of AI is alignment. Before any model, prompt or workflow touches your CRM, your first job is to force the conversations that have been quietly avoided for years:

• Why do we do it this way?
• What is the actual outcome we’re trying to achieve?
• Who owns that outcome?

Most marketing inefficiency lives in the translation between teams. AI surfaces errors immediately because machines require precision where humans can tolerate ambiguity. That’s why your step zero has nothing to do with AI. It’s about separating jobs from tasks:

• Job: The outcome that must happen (e.g., secure legal approval for campaign messaging).
• Task: The steps or rituals we currently perform to make that job happen (e.g., write draft → email legal → wait for edits → update doc → resend → upload to platform).

When you examine the job, you find flexibility. Maybe legal guidance could live inside a retrieval-augmented generation (RAG) system trained on your company’s approved language and claims. Maybe a model context protocol (MCP) client could house current T&Cs and automatically flag outdated phrasing before it ever hits a legal’s inbox.

Agents helped reveal where workflows were unclear, overlapping or outdated. Humans are great at navigating messy middle grounds — but when you try to codify that mess for AI, you finally see where your process actually breaks.

Step 1: Define jobs to be done

This is about separating the what from the how — the immutable results your process must achieve versus the ways you currently get there. Once you strip away who does what and how it’s done, you start seeing opportunities for simplification and automation.

• Identify the core jobs: The non-negotiable outcomes every campaign or workflow must deliver (e.g., secure legal approval, personalize content, validate data accuracy).
• Avoid naming steps or roles: Focus only on the end conditions that signal done.
• Anchor AI discussions in outcomes: Ask “what must be accomplished for this process to move forward, regardless of who or how?”

Dig deeper: 4 marketing ‘jobs to be done’ 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 interacts with them differently. Before you can automate or redesign anything, you need to understand those realities at scale. 

The goal is to collect real, unfiltered insights on how work actually gets done. Uncover the friction points and the emotional cues — what frustrates people, what energizes them and where they’ve built personal workarounds that hide systemic gaps. 

• Engage ~10% of your process stakeholders: Enough coverage to capture variance without bogging down.
• Skip surveys and group workshops: Instead, have them record voice notes answering structured prompts. This captures context and emotion and avoids meeting fatigue. Grab the transcript and put it through an AI tool to give you a first draft of the process, gaps and pain points. 
• Ask the right questions:
  • Walk me through the steps you take to get X done.
  • What outcomes must you validate along the way?
  • Which steps feel most manual, repetitive or frustrating?
  • Which steps do you actually enjoy or see value in doing yourself?

Dig deeper: Is your marketing team AI-ready? 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 draw it. When you force every task into a single visual or spreadsheet, blind spots emerge: duplicate steps, outdated checks, unnecessary dependencies. 

• Document every distinct step: Global CRM orgs can easily hit 80–100 steps from brief to activation. These should fall into core jobs (defining strategy, legal approval, content creation, audience creation, UAT/QA, etc.). If a step changes hands or tools, then you need to write it down. 
• Capture:
  • Purpose (job to be done).
  • Current owner.
  • Human desire/enjoyment of the step.
  • Resource intensity.
  • Repetitiveness.
  • Difficulty.
  • Current state (manual/semi-automated/automated).

Build this into a living spreadsheet, which becomes your baseline for understanding AI use cases. 

Step 4: Score and prioritize for AI fit

Once the process is visible, patterns start talking back to you. Scoring forces objectivity. Not every pain point deserves AI and not every beloved ritual should stay manual. This step quantifies human sentiment and operational strain so you can focus on impact, not novelty. Think of it as triage for your automation roadmap — where effort meets opportunity.

Apply a scoring system across three lenses:

• Human desire: Do people want to do this?
• Resource load: How costly/time-consuming is it?
• Repetitiveness: How often is it repeated and how similar each time?
• Task difficulty: Is it hard to do? Highly technical? Nuanced?

Patterns will then emerge:

• High desire + creative tasks: Candidates for AI enrichment (AI as thinking partner, not replacement).
• Low desire + high resource + repetitive: Prime automation and AI workflow candidates.
• Low desire + manual but not repetitive: Simple automation may be enough — AI not required.

Step 5: Differentiate automation vs. AI vs. agentic AI

When teams say “we’ll use AI,” they often mean entirely different things. Some problems need simple scripts. Others need generative reasoning. A few require fully agentic orchestration, which requires tooling, contextual architecture and solid data. Mislabeling these leads to wasted investment and unrealistic expectations. 

• Automation: Replace repetitive mechanics (e.g., routing briefs, logging approvals).
• AI (assistive/generative): Enrich, transpose, accelerate creative or analytical work (e.g., draft copy variations, flag anomalies).
• Agentic AI (early stage): Give an AI agent a toolkit and autonomy to decide how to complete multi-step jobs with guardrails. Powerful, but still maturing for enterprise CRM.

Focus less on the grand AI vision and more on matching solutions to the right jobs.

Dig deeper: Agentic AI is about to transform the martech stack — and the way marketers work

Step 6: Select your top 5 use cases

You now have data on what’s broken, repetitive or joyless. Instead of declaring 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 teach you far more than fifty half-finished ones.

From your readiness map:

• Identify the five most impactful candidates across cost, time and employee sentiment.
• Frame them as jobs to be done (not tasks). 
• Design pilot initiatives. Some may need simple automation, others AI workflows, a few AI enrichment.

Step 7: Iterate like a marketer, build like an architect

AI maturity starts with pilots — and grows by learning not just what works but where each solution belongs. As you learn, start mapping your discoveries to the right layer of ownership. 

If the problem is narrow — say, a CRM-specific use case or an agent to speed audience testing — that might live within your own team’s budget and sandbox. If the challenge touches shared infrastructure, like metadata, asset libraries or localization systems, you’re now talking about a platform problem, not a CRM one.

The discipline here is to match scale to scope:

• Local problems → local tools: Fund and build what’s uniquely yours.
• Enterprise problems → enterprise platforms: Elevate the need to the teams that own content systems, data governance or translation pipelines.
• Hybrid problems → partnerships: Co-develop where your use case reveals a gap everyone will eventually face.

AI isn’t one layer on top of marketing. It’s a web of intelligence built to mirror your business architecture. If you can remap your CRM jobs into outcomes instead of legacy steps, you’ll uncover where AI actually belongs. 

Some jobs will stay human, others will become AI-assisted and a few will disappear into automation entirely. The companies that win won’t be the ones with the most AI. They’ll be the ones with the smartest division of labor between humans, machines and process design.

Dig deeper: How to unlock the true potential of AI with adaptive structure

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