Do you build custom AI tools or agents?

No. I apply existing AI tooling inside marketing workflows — creative production, qualification, lifecycle, and analysis. If your requirement is bespoke software or an engineered agent, you need an engineer, and I will tell you that rather than take the work.

Will AI-generated creative hurt performance?

It can, if volume replaces judgement. The point is not to ship more output — it is to get more concepts into test while a human still controls what is approved. Without that review standard the result is just faster mediocrity.

Where does AI genuinely not help in marketing?

Positioning, offer design, deciding what to measure, and judging whether a campaign should exist at all. Those are reasoning problems, not throughput problems. AI is useful once the direction is set, not for setting it.

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Consulting

Use AI where it removes a bottleneck. Ignore it everywhere else.

Most marketing teams are either avoiding AI entirely or using it to produce more mediocre output faster. Neither helps. I work through where it genuinely compresses effort in your operation — creative volume, qualification, and reporting — and where it is a distraction from the actual constraint.

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Who This Is For

For teams that want a practical read on where AI fits into their marketing operation, from someone who uses it in delivery rather than selling it as a category.

Typical Stage

Any stage with an active marketing operation and real workflow load

What You Get

A clear map of where AI earns its place in your workflow, what to automate, what to leave alone, and how to keep quality control on anything it touches.

Teams whose creative testing is limited by production capacity
Funnels where qualification and follow-up are still fully manual
Operations drowning in reporting time rather than analysis time

Core Deliverables

01Workflow audit identifying where AI removes a genuine bottleneck
02Creative production framework for higher testing velocity
03Qualification, enrichment, and lifecycle automation design
04Reporting and analysis workflow using AI to interrogate campaign data
05Quality-control standards so nothing ships unreviewed

Strategic Outcomes

Creative volume that is no longer capped by production hours
Less manual triage between lead capture and sales contact
Faster interpretation of campaign data, not just faster reports

The Result

More testing throughput, less manual handling in the funnel, and faster reads on performance — with a clear boundary around what stays human.

How I Approach It

Find the actual bottleneck

I map where time and capacity are genuinely lost in your marketing operation. If the constraint is strategic rather than operational, AI is not the answer and I will say so.

Creative throughput

I set up a production approach that raises the number of concepts and variants you can put into test, with a review standard so the volume stays usable.

Qualification and lifecycle

I design where automated scoring, enrichment, routing, and follow-up sit in the funnel, and where a human still needs to make the call.

Analysis, not just reporting

I build a workflow that uses AI to interrogate campaign data and surface what changed and why, so the review meeting starts from a hypothesis rather than a dashboard.

Why This Is Different

I use these workflows in delivery rather than selling AI as a category. The recommendations come from what has actually held up in client work.

I will tell you where AI does not help. Strategy, positioning, offer design, and judgement about what to build are not throughput problems, and treating them as such produces confident nonsense.

Quality control is part of the scope. Volume without a review standard just multiplies whatever was already wrong.

Engagement Models

Workflow audit and AI adoption roadmap
Implementation support alongside an existing engagement
Ongoing advisory as the operation scales

Good Fit When

Best for teams already running campaigns who want more output without more headcount.
Useful when leadership is being pushed toward AI adoption and needs an honest read on where it applies.
Strong fit alongside a performance or systems engagement rather than as a standalone experiment.

Related Case Studies

No published case study for this track yet. The other engagements show how the surrounding systems were built.

Common Questions

Do you build custom AI tools or agents?: No. I apply existing AI tooling inside marketing workflows — creative production, qualification, lifecycle, and analysis. If your requirement is bespoke software or an engineered agent, you need an engineer, and I will tell you that rather than take the work.
Will AI-generated creative hurt performance?: It can, if volume replaces judgement. The point is not to ship more output — it is to get more concepts into test while a human still controls what is approved. Without that review standard the result is just faster mediocrity.
Where does AI genuinely not help in marketing?: Positioning, offer design, deciding what to measure, and judging whether a campaign should exist at all. Those are reasoning problems, not throughput problems. AI is useful once the direction is set, not for setting it.

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Strategic Advisory / Consulting