Expertise / AI in Marketing & Automation
AI in Marketing & AutomationAI should improve marketing decisions. Not just speed up production.
I use AI to structure marketing analysis, review complex CRM and advertising setups, and turn data into prioritised improvements. My approach combines marketing experience, systematic access to data and quality assurance.
AI does not create value simply because a process can be automated. It creates value when it helps identify relevant problems, ask better questions and establish a clearer basis for action.
I develop and use AI-supported audits of CRM, email marketing and paid advertising performance. That includes how data is accessed, which marketing criteria are applied and how observations become practical recommendations. I work with existing marketing platforms and the commercial context rather than treating AI as an isolated discipline.
Get in touchWhat I work with
Journeys, segments, campaigns, triggers, data and governance.
Campaign structure, audiences, creative, tracking and acquisition economics.
MCP connections where permitted by the client; otherwise controlled exports.
Marketing assessment criteria, source validation and human approval.
Documented processes for analysis, reporting and improvements.
From complex CRM setups to prioritised improvements
A CRM setup can contain many journeys, segments, campaigns, data sources and contact rules. Without an integrated view, it becomes difficult to assess relevance, how effectively customer data is used and where improvements matter most.
I have developed AI-supported audit workflows that review CRM and email activity through a consistent marketing framework. Depending on the account, I examine journey architecture, triggers, segmentation, data models and events, campaign practices, deliverability, contact policy, documentation and governance.
The goal is not to produce the longest possible list of findings. It is to distinguish real problems, uncertainties and opportunities, then prioritise them against the customer journey and the organisation's capacity to act.
Advertising performance is more than a single KPI
An ad account may show acceptable ROAS or CPL while still having weaknesses in audience strategy, creative, tracking or the connection between paid acquisition and the rest of the customer journey.
I also use AI-supported workflows to review paid advertising performance. They combine a structured audit process with my hands-on background in paid social, paid search, acquisition strategy and marketing leadership.
I examine whether campaign objectives fit the business model, whether the structure supports meaningful measurement, how creative aligns with the intended outcome and how performance should be interpreted across acquisition and the subsequent customer relationship.
Data access must work within the organisation's constraints
Where a client's policies and permissions allow it, I use MCP connections to access data for the audit process. Where direct access is not possible or appropriate, I use controlled data exports instead.
Both approaches must answer the same underlying questions: Which data is relevant, what does it actually demonstrate, and how does it relate to decisions the marketing team needs to make?
I prioritise traceability, consistent definitions and clear boundaries. An observation in a dashboard is not automatically an explanation, and a recommendation does not in itself prove a subsequent business result.
An audit only matters if the findings can be used
The method can be summarised as:
AI can help create consistency across a complex review. But prioritisation still requires context about the company's objectives, resources, data quality and ability to implement change.
I therefore focus on making the reasoning behind findings reviewable and open to challenge by people. This reflects the same emphasis on governance and marketing operations that runs through my wider work with CRM and marketing technology.
Principle illustration: Data access → Analysis → Marketing judgement → Prioritisation → QA
Designing controlled multi-agent workflows
I am also working on the design of workflows in which different AI models take on defined roles within a shared process. The architecture work covers responsibilities, permissions, task control, versioning, quality checks, budget constraints and human approval points.
This is currently a design and pilot-planning initiative. The objective is to explore how AI-supported processes can become more controlled and useful in practical knowledge and marketing work.
AI is a method, not a replacement for marketing expertise
My core work remains CRM, lifecycle, customer data, acquisition, marketing operations and commercial decision-making. AI makes it possible to structure and document more of that work, but value still depends on having the right data, choosing the right problems and being able to act on the findings.
That is why I treat AI as an integrated part of marketing, rather than a separate strategy detached from the business.
Want a closer look at your CRM or marketing setup?
I can help define the questions and data a focused audit should start with, and how findings can become a practical, prioritised plan.
Senior roles · Interim · Selected projects