Agent Automation Job Map
Map customer jobs, rank automation potential, and deploy agents progressively
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 6
- Confidence
- 98%
The method begins with the customer journey rather than an AI tool. The team decomposes every journey stage into specific jobs to be done, then evaluates which jobs an agent can perform reliably with current models. Candidates are stack-ranked using practical factors such as feasibility, business value, available context, and required oversight. The team implements the strongest opportunities first while retaining a backlog of jobs that remain beyond current model capabilities. As models and integrations improve, the backlog is reviewed and progressively automated. This creates a capability-aware adoption path: agents take over suitable repeatable work, while people concentrate on judgment, creativity, relationships, and other higher-value responsibilities.
Origin
Extracted from Marketing Against The Grain during Kieran Flanagan's recommendation for preparing a business to adopt AI agents.
Core principles
- 01Break journeys into discrete outcomes before choosing technology
- 02Match each job to current model capabilities
- 03Automate feasible jobs before speculative ones
- 04Reassess deferred jobs as models improve
- 05Redirect saved time toward higher-value work
How to run it
- 1
Map the customer journey
Document the stages through which a prospect or customer moves, from discovery to post-purchase support. Include important handoffs and decision points.
Pro tip Start with one product or customer type to keep the first map manageable.
Watch out A journey described only at a high level will conceal the actual work an agent might perform.
- 2
Identify the jobs
Break each journey stage into discrete jobs to be done with a clear input, action, and desired outcome.
Pro tip Phrase each job as an observable task rather than a department or broad responsibility.
Watch out Do not combine several unrelated actions into one oversized job.
- 3
Assess agent feasibility
Determine whether present-day models can complete each job with the available data, tools, permissions, and acceptable oversight.
Pro tip Use a small real-world test to distinguish demonstrated capability from vendor claims.
Watch out Do not automate tasks whose errors would create unacceptable legal, financial, or reputational risk.
- 4
Stack-rank the opportunities
Prioritize jobs according to feasibility, expected value, frequency, and the effort required to deploy and supervise an agent.
Pro tip Favor frequent, bounded jobs with verifiable outputs for the first deployment.
Watch out High theoretical value does not compensate for unreliable execution or missing context.
- 5
Automate progressively
Deploy agents on the jobs that are achievable now, monitor their results, and retain human review where necessary.
Pro tip Define success and escalation criteria before an agent begins operating.
Watch out Avoid treating initial automation as permanently autonomous.
- 6
Revisit the backlog
Regularly reassess deferred jobs as models, integrations, and internal data access improve. Move newly feasible jobs into implementation.
Pro tip Review the ranked backlog after meaningful model or platform releases.
Watch out Do not assume a task that failed once will always remain unsuitable.
In the wild
A marketing team maps the path from webinar registration to sales qualification. It identifies reminder emails, attendance classification, resource delivery, and routine follow-up as bounded jobs. The team tests an agent on resource delivery and personalized follow-up first, while keeping pricing negotiations and complex qualification with humans. More jobs are added after the agent demonstrates reliable performance.
→ The team shortens response times and frees marketers to focus on campaign strategy and high-value conversations.
Common mistakes
Starting with the tool
Choosing an agent platform before mapping the work encourages novelty-driven projects that may not solve a valuable customer or business problem.
Automating beyond current capability
A valuable job can still be a poor initial candidate if the model lacks the context, reliability, or integrations required to perform it safely.
Ignoring the deferred backlog
Model capabilities change quickly, so jobs rejected during the first assessment should be reviewed rather than forgotten.
Is it for you?
Best for
Marketing and operations teams with a defined customer journey and several repetitive digital tasks.
Not ideal for
Teams that have not yet documented their customer journey or cannot safely grant agents the required data and system access.
From the transcript
“I would map out your customer Journey break it into jobs to be done and start to see what jobs to be done you think…”
“have those stack rank prioritize and start to work on them as the models get better”
“do the ones that are doable today and then over time start to do the ones that the models where where the models get better”
From the episode
Ai Agents: The Future Marketers You Can't Afford to Ignore