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Marketing

AI Transformation for Marketing

Use AI to improve customer understanding, marketing execution and growth while protecting the consistency and value of the brand.

Marketing leaders are under pressure to deliver profitable growth across an increasingly fragmented environment. They need to understand customers and markets, build long-term demand, allocate budgets, produce more content, activate across channels and demonstrate the return on marketing investment.

AI changes the economics of many of these activities. Research can be synthesised faster, creative variations can be produced at scale, campaigns can react more quickly to performance, and customer interactions can become more relevant.

The challenge is to use those capabilities without creating more noise, inconsistent brand execution or a volume of content that is easier to produce than it is to value.

I work with CMOs and Marketing leaders to identify where AI can improve the marketing operating model, redesign the relevant workflows and define how customer data, AI, automation, creative judgment and brand governance should work together.

Explore AI Opportunities Discuss Your Marketing Priorities

The Marketing agenda

Marketing must balance horizons that do not always move together: brand building and short-term demand, customer relevance and consistency, speed and quality, experimentation and control, creative ambition and financial accountability.

AI is useful when it improves that balance.

The strongest opportunities are not limited to content generation. They span insight, planning, creative operations, activation, optimisation, measurement and the way brands are represented in AI-mediated discovery.

Where AI can improve Marketing

Customer and market intelligence

AI can help combine research, customer feedback, behavioural data, campaign performance, competitive activity and public information into a more current view of the market.

Applications include:

  • market research;
  • customer and audience insight;
  • segmentation;
  • trend identification;
  • competitor monitoring;
  • voice-of-customer analysis;
  • synthesis of qualitative research.

The output should strengthen the brief and the decision, not replace customer research with synthetic assumptions.

Marketing strategy and planning

AI can support planning by helping teams compare market signals, previous performance, audience priorities, channel roles and budget scenarios.

Applications include:

  • annual and quarterly planning;
  • audience prioritisation;
  • campaign strategy;
  • channel planning;
  • scenario preparation;
  • budget options;
  • launch planning.

The CMO and Marketing team remain responsible for the choices about brand, positioning, investment and growth priorities.

Content and creative operations

AI can make the content supply chain faster by assisting with:

  • briefs;
  • first drafts;
  • adaptation by format;
  • localisation;
  • asset tagging;
  • content reuse;
  • versioning;
  • production coordination.

The more content production scales, the more important brand systems, approval rules, rights management and creative direction become.

AI can accelerate execution. It should not flatten the distinctiveness of the brand.

Campaign activation and personalisation

Customer and campaign context can be used to make communications more relevant across channels.

Applications include:

  • audience-specific messaging;
  • personalised recommendations;
  • triggered communications;
  • next-best content;
  • lifecycle marketing;
  • campaign localisation;
  • channel adaptation.

Personalisation should operate within consent, brand and frequency rules. Relevance is useful only when the customer experience remains coherent.

Media and budget optimisation

AI can help Marketing teams compare performance, identify changes in response and prepare budget reallocation scenarios.

Applications include:

  • media performance analysis;
  • budget pacing;
  • channel mix analysis;
  • creative performance;
  • incremental-response analysis;
  • scenario modelling.

The final allocation decision should reflect more than the last click. Brand objectives, market context and longer-term demand still matter.

Measurement and marketing performance

AI can reduce the time required to connect data, explain movements and prepare performance reviews.

Applications include:

  • campaign reporting;
  • MROI analysis;
  • funnel performance;
  • customer acquisition analysis;
  • cohort comparisons;
  • experimentation summaries;
  • management commentary.

The objective is a clearer view of what changed, why it changed and what Marketing should do next.

How Marketing workflows can change

Insight to brief

Research, customer feedback, performance data and competitive information can be synthesised into a structured view of the opportunity.

Marketing reviews the evidence, defines the audience and business objective, and produces the final brief.

Campaign development and localisation

An approved brief, brand system, product facts and creative assets can feed a workflow that produces initial copy, formats and local adaptations.

Creative and brand teams review the work before release. Rights, claims and market requirements remain controlled.

Campaign performance review

Media, creative and conversion data can be reviewed continuously to surface material changes and potential explanations.

AI can prepare optimisation options; Marketing decides what to change, how much to invest and whether short-term performance justifies altering the plan.

Brand and product knowledge for AI-mediated discovery

As customers increasingly use AI interfaces to research and compare offers, product and brand information must be accurate, structured and consistent.

Marketing has a role in ensuring that the brand can be interpreted correctly across both human and AI-mediated channels, while maintaining approved claims, context and brand meaning.

Decision rights and brand governance

For each workflow, define:

approved customer dataconsent and purposeapproved brand and product sourcescontent rightsclaimsapproval thresholdslocalisation ruleschannel permissionsmodel accesslogging and review

Human control should remain explicit for:

positioningbrand strategymaterial claimssensitive customer segmentsmajor budget reallocationshigh-profile creativereputational issues

How I work with Marketing leaders

01

Review the marketing operating model

Map planning, insight, creative, content, media, campaign and measurement workflows.

02

Identify high-value friction

Look for slow research, fragmented customer context, repetitive content operations, manual reporting and inconsistent handoffs.

03

Prioritise the workflows

Assess business value, brand exposure, customer-data requirements, implementation complexity and scalability.

04

Redesign the process

Define AI roles, automation, human approvals, data inputs and brand controls.

05

Connect the environment

Customer data, CRM, marketing automation, DAM/PIM, analytics, media platforms, research, content tools and approved knowledge.

06

Measure and scale

Track the measures relevant to each workflow: campaign cycle, content throughput, MROI, conversion, creative effectiveness, data quality and brand consistency.

AI Opportunity Assessment

AI Opportunity Assessment for Marketing

The assessment covers:

  • customer and market intelligence;
  • strategy and planning;
  • content operations;
  • campaign activation;
  • personalisation;
  • media and budget;
  • measurement;
  • customer-data and brand governance.

The result is a prioritised Marketing AI roadmap focused on where the operating model should change first.

Discuss an AI Opportunity Assessment

Based in Geneva

My approach combines executive leadership experience, financial discipline, practical brand and e-commerce experience, operating-model design and AI.

I start with the growth objective, the customer and the economics of the workflow. Technology comes after those choices.