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Framework v2.0

The AI Marketing Ascendancy Framework.

I built this framework because I couldn't find one that met my needs – so I made what I wished existed. It's here to help you and your team move from AI-curious to AI-confident. It walks through the challenges (inside the team and outside it), maps practical use cases across the marketing value chain, and lays out a maturity model across People, Processes, and Technology. There's also a role-based growth framework – what AI fluency should look like as a marketer moves from junior to lead.

The challenge

Why AI adoption isn't a plug-and-play move.

AI changes what marketing actually is – not just how we do it. That's a bigger claim than it sounds, and most teams feel it before they can name it.

Content is suddenly cheap to produce, so producing more of it stops being an edge. Anyone can generate a decent-looking campaign in an afternoon, which means a decent-looking campaign is worth less than it used to be. The hard part moves – away from making things, and toward judgment, taste, and knowing what's worth saying in the first place.

That asks something real of a team: new skills, new ways of working, and a clear line on where the ethics sit. And it lands at the same time as a shift we don't control – how people discover, trust, and engage with brands in an AI-mediated world.

Those who adapt will amplify their impact. Those who don't risk becoming invisible.

How to use this

Five ways in.

  1. № 01 Start with the challenges. The internal and external sections lay out what AI is actually changing – inside your team, and in the market around it.
  2. № 02 Take the use cases that fit. The library shows where AI can help across strategy, planning, execution, and optimization. Keep what maps to your team and campaigns; ignore the rest.
  3. № 03 Place your team on the maturity model. People, Processes, and Technology – so you can see where you stand and what the next step is.
  4. № 04 Use the growth framework for role clarity. What AI fluency should look like from junior to lead.
  5. № 05 Let it start the strategic conversations. Ethics, tooling, governance, creative standards. That's where the real alignment gets done.

The challenges, up close

What AI is actually changing.

The same pressures show up in two places – inside the team, and out in the market. Switch between them.

Skill disparity

Not everyone is equally comfortable with AI tools, so adoption is uneven.

Forces you toRethink how you hire, onboard, and keep training people.

Quality risks

GenAI generates fast, but not always well. Poor output erodes credibility.

Forces you toDefine quality standards and reintroduce human curation as a checkpoint.

Ethical concerns

Using GenAI without understanding its provenance can backfire.

Forces you toEducate teams on responsible use; know where human sensitivity still matters.

Tool chaos

A growing stack fragments the work and creates decision fatigue.

Forces you toCentralize guidance, approve standard tools, embed them in workflows.

Fear of irrelevance

For some, AI feels like a threat to their creative identity or role.

Forces you toCreate safety to experiment; frame AI as an enhancer, not a replacement.

Lack of governance

Without guidelines, AI use spirals into inconsistency and reputational risk.

Forces you toDocument rules of engagement, approval flows, and content policies.

Autonomy vs standardization

As more people generate content, tone and brand coherence get harder.

Forces you toProvide toolkits and brand playbooks that protect brand integrity.

Use-case library

Where AI actually helps.

A working list of where AI actually helps, grouped by function. It's not exhaustive – it's here to spark ideas about what's possible, and just as often to flag where a human still has to make the call.

One thing to keep in mind: models aren't interchangeable. Picking the right one for the task – research, drafting, optimization – matters as much as the decision to use AI at all.

Strategy & Insights 6 use cases
  • Persona research
  • Competitive analysis
  • Audience prediction
  • A/B recommendations
  • Trend ID
  • Segmentation
AI works well for

Spotting early trends and summarizing large research – social listening, persona inputs.

Use with caution

Can reinforce bias or oversimplify segments when the data is incomplete or skewed.

Campaign Planning 5 use cases
  • Journey mapping
  • Channel mix
  • Timelines
  • Workback plans
  • Forecasting
AI works well for

Optimizing channel mix and suggesting schedules from historical patterns.

Use with caution

May ignore brand timing, internal dependencies, or key market moments.

Sales & Enablement 5 use cases
  • Battlecards
  • Deck co-creation
  • Objection scripts
  • Training
  • Enablement
AI works well for

Drafting battlecards and objection handlers from call transcripts or FAQs.

Use with caution

Tone and context get lost in high-stakes, nuanced conversations.

Analytics & Optimization 4 use cases
  • Report summaries
  • Insight narratives
  • Ad-spend
  • Anomaly detection
AI works well for

Summarizing performance data into plain-language insights.

Use with caution

Anomalies flagged too late or misread without human validation.

Content & Creative 6 use cases
  • Copy drafting
  • Visual generation
  • Video editing
  • Repurposing
  • SEO
  • Tone
AI works well for

Speeding up copy ideation and generating visual prototypes.

Use with caution

Off-brand or generic output without proper curation.

CRM & Personalization 4 use cases
  • Subject-line tests
  • Personalized messages
  • Lead scoring
  • Journey tailoring
AI works well for

Testing subject lines and generating personalized variants.

Use with caution

Over-personalization can feel invasive and erode trust.

Personal Productivity 5 use cases
  • Meeting summaries
  • To-do generation
  • Brief writers
  • Prompt libraries
  • Time management
AI works well for

Summarizing long meetings and turning action points into to-dos.

Use with caution

Summaries drop emotional cues or strategic subtext.

The economics

AI feels free. It isn't.

Cheap and fast is the first impression, and it's misleading. Every token costs something. Per prompt it's small enough that nobody notices – until a workflow runs ten thousand times a month, or someone points the most expensive model at a job the cheapest one could have done.

Replacing a person with AI isn't automatically cheaper, either. Quality aside, a human doing a task well once can beat a model doing it badly three times – plus the review, plus the rework. So there's a discipline forming that most teams don't have yet: knowing which model to use for which job, and what each one costs. The frontier model for the thinking that needs it; a small, cheap one for routine volume. Matching the tool to the task isn't only a quality question anymore. It's a budget one.

  1. A token isn't free

    The per-use cost hides at small scale and shows up hard at volume. Know the unit cost before you automate something that runs thousands of times a month.

  2. Cheaper than a person? Do the math

    Sometimes AI wins on cost, sometimes a person does it right the first time. It's a calculation, not an assumption – and it changes per task.

  3. The budget is moving

    AI is shifting from a central experimentation budget to each function's P&L. When it lands on your line, the cost is yours to own – so understand it now.

Maturity model

Where you stand.

Five stages for people, processes and tech each.

Stage 01Aware

People

Teams hear about AI tools but don't engage. No skills mapping or training.

Processes

Aware AI could affect processes, but no changes made yet.

Technology

Aware of GenAI tools but don't use them meaningfully. No official stack.

Inside each pillar

AI runs on people, not just platforms. This pillar is about whether the team has the skills, the literacy, and the judgment to use it well – and the ethical compass to go with it.

Skills Mapping & Training

Inventory AI skill levels; plan upskilling paths.

Maturity indicatorA clear skill taxonomy (e.g. prompt design, tool fluency).

Metrics to consider% of team assessed, % trained to target, AI fluency benchmarks.

Hiring Standards

Define AI expectations in job descriptions, like languages or software.

Maturity indicatorJD templates include AI proficiency levels per role.

Metrics to consider% of new hires at target AI level.

Ethical Awareness

Literacy around AI, IP, inclusivity, and brand integrity.

Maturity indicatorTeam-wide alignment on ethical content use.

Metrics to consider% of team completing the AI ethics module.

Creative Judgment

When to value human creation, curation, or augmentation.

Maturity indicatorCase-based training on quality thresholds and context.

Metrics to considerInternal audit: % of content hitting quality standards.

Employee Governance

Clear do's and don'ts for GenAI, in and outside Marketing.

Maturity indicatorInternal policy and training on creative responsibility.

Metrics to consider# of reported violations; engagement in guidelines training.

Marketer growth framework

What AI fluency looks like at your level.

Pick a level and see how the four dimensions shift. Adapted from the Developer Growth Framework by Tamara Buckland (@LadyGalaxyNZ), updated for AI.

build

high-quality work, rooted in customer empathy

You confidently manage large-scale campaigns and budgets, with a strong grasp of KPIs and performance targets. You understand your audience's pain points and craft messaging that lands. You can define multi-tactic strategies, allocate budget effectively, and incorporate best practices and emerging trends – and you proactively form your own point of view to guide decision-making.

+ AI skills

You select the most appropriate AI tools for the task at hand, and tailor AI-generated outputs to align with brand tone, campaign objectives, and audience expectations.

deliver

quality projects, on time, communicated well

You lead and contribute to the successful delivery of complex projects alongside your team. You proactively share information, seek and give feedback, and ensure clear communication across multiple stakeholders. You help others deliver high-quality work, set the tone and pace for execution, and model a resilient, solution-oriented mindset for junior team members.

+ AI skills

You lead projects that strategically apply AI to accelerate execution, enhance personalization, or enable faster iteration – keeping outcomes aligned with brand and business goals.

connect

culture, community, sharing knowledge

You actively share your expertise with other marketers and contribute to the company's internal knowledge base. You work to enhance the organization's reputation externally – attracting talent and positioning the company as a thought leader – and you foster a culture of openness, curiosity, and continuous learning.

+ AI skills

You help integrate AI tools into cross-functional workflows and act as a bridge – demystifying AI for non-experts and enabling more confident, effective adoption across teams.

lead

helping others succeed; a safe environment

You inspire and support a small group of teammates, encouraging them to grow beyond their comfort zones. You create repeatable processes and tools for ongoing challenges, actively promote psychological safety and open conversations about well-being, and help managers navigate performance challenges with courageous, constructive conversations. You consistently role-model empathy, compassion, and emotional intelligence.

+ AI skills

You coach others on how to use AI responsibly – prompt design, ethical boundaries, real-world use cases – and help shape team norms around AI adoption, raising awareness of where human judgment and creative oversight are essential.

Cross-cutting

What runs across every pillar.

A few things don't sit inside a single pillar – they run across all of them.

  1. Ethics & Sensitivity

    Built into training, tools, and workflows – e.g. flagging AI images that mimic artists without consent.

  2. Content Quality Matrix

    What's OK at each stage: a rough GenAI sketch for internal alignment is fine; a public post isn't, unless curated.

  3. Creative Role Taxonomy

    When to use human-only, AI-assisted, or AI-only creation, based on purpose and audience.

  4. Governance

    Clear policies for internal and external content use, accountability models, and documentation.

Doing this with your team?

The framework is the map. The hard part is the walk.

If you're moving a marketing team from AI-curious to AI-confident and want a senior operator in the room for it, that's the kind of work I do.

Read some writing first →