FMI

CMO Digest

How can CMOs use AI to help drive revenues? Insights from the CapGemini Research Institute

In today’s business landscape, CMOs are no longer limited to brand-building and campaign oversight. They are now recognised as being central to driving revenue and profit, navigating a rapidly changing environment that demands a fusion of creativity, data, and technology. With generative AI emerging as a transformative force, CMOs have a powerful ally to meet these elevated expectations. This article explores how AI can help CMOs prioritise revenue growth and operational efficiency, outlining actionable strategies for leveraging this technology, alongside effective use cases to influence the task force.

This Digest is distilled from an excellent research paper from the CapGemini Research Institute (see link below), at 84 pages in total, we’ve read it all and condensed their AI adoption guidance into a topic which we know our community cares most about: how to drive revenue, and demonstrate it.

Click here for a helpful free one-page download we have pulled together: Marketing Leaders’ AI Roadmap

How Expectations on CMOs Have Shifted: In all sectors

Over the last decade, the role of CMOs has expanded dramatically:

• From Campaign to Revenue Driver: Increasingly, CMOs are held accountable for tangible business outcomes, with nearly 49% directly responsible for revenue growth and 44% sharing accountability for profit.

• Cross-Functional Influence: CMOs now oversee more than marketing. They collaborate with sales, customer experience, and technology teams to deliver measurable value.

• Data-Driven Mandate: Marketing decisions are expected to be rooted in analytics, requiring CMOs to interpret vast datasets and translate insights into strategies that drive growth.

Generative AI is uniquely positioned to address these new demands, equipping CMOs with the tools to drive revenue and optimise performance across channels.

And be sure, if you’re not doing it – your competitors are:


How AI Can Drive Revenue Growth

In our industry, the uptake of these tools is rapid - with nearly 50% of marketing teams reporting dedicated, allocated budgets for generative AI implementation. Deploying that investment effectively, will be crucial – especially as a tool to drive revenue across multiple dimensions:

1. Data-Driven Customer Insights

  • Why it Matters: Customer segmentation and personalisation are critical to driving conversions. Generative AI enables CMOs to analyse customer behaviour, predict preferences, and create hyper-targeted campaigns.

  • Example: Across financial services companies, AI-driven predictive analytics can identify upselling opportunities or customers at risk of churn, enabling timely interventions that boost lifetime value. Success requires great analytics functions in marketing and/or effective partnerships with data science.

2. Personalisation at Scale

  • Why it Matters: Modern consumers expect personalised experiences – and that includes individuals working in B2B. Generic marketing efforts often fail to capture attention or build loyalty.

  • How AI Helps: Generative AI can create bespoke email content, dynamic website experiences, and customised product recommendations, fostering deeper engagement.

  • Example: Banks can leverage AI to generate tailored financial advice based on customer profiles, enhancing cross-sell opportunities.

3. Enhanced Efficiency and Cost Optimisation

  • Why it Matters: Time and cost savings enable teams to focus on high-value activities.

  • How AI Helps: By automating content creation, campaign optimisation, and A/B testing, generative AI reduces manual effort while improving outcomes.

  • Example: AI can generate marketing copy variations, test them, and identify the most effective versions – all within hours – enabling your team to focus more on market dynamics and event-driven campaigns for real relevancy.

4. Improved Decision-Making with Predictive Modelling

  • Why it Matters: Strategic decisions grounded in data minimise risks and maximise ROI.

  • How AI Helps: Generative AI tools analyse historical data and market trends to forecast campaign performance or identify new market opportunities.

  • Example: Predictive models can simulate how a new pricing strategy might impact revenue before it’s rolled out, and help prioritise your team’s time and effort on the products which will drive results.

5. Strengthening Team Collaboration

  • Why it Matters: Revenue growth often depends on seamless collaboration between marketing, sales, and customer experience teams.

  • How AI Helps: Generative AI integrates insights from various sources, creating a unified view of customer journeys and aligning team efforts.

  • Example: Opportunity to create a genuine customer 360 dashboard – from acquisition to retention and everything in between.

Practical Steps for Implementation

To maximise the revenue-driving potential of generative AI, CMOs should consider the following steps:

1. Set Clear Revenue-Focused Goals

  • Identify specific revenue metrics to influence, such as customer acquisition cost (CAC), lifetime value (LTV), or conversion rates.

  • Develop KPIs to measure the impact of AI-driven initiatives.

2. Prioritise High-Impact Use Cases

  • Start with areas that directly impact revenue, such as:

    o Campaign optimisation

    o Lead nurturing and scoring

    o Personalised product recommendations

3. Upskill Teams for AI Adoption

  • Invest in training marketers to work with AI tools effectively, focusing on skills like data interpretation and prompt engineering.

  • Encourage collaboration with data scientists and IT teams.

4. Build or Integrate AI Tools

  • Leverage external platforms for quick deployment while exploring customised in-house solutions for long-term scalability.

  • Ensure integration with existing CRM and analytics platforms to create a seamless workflow.

5. Monitor and Iterate

  • Regularly assess the performance of AI-driven initiatives, using insights to refine strategies.

  • Stay agile, adapting to evolving market conditions and consumer preferences.

Long-Term Benefits for CMOs

Generative AI not only addresses immediate revenue challenges but also offers long-term strategic benefits:

  • Enhanced Agility: AI enables CMOs to respond quickly to market changes, from customer behaviour shifts to competitive pressures.

  • Innovation Opportunities: By freeing teams from repetitive tasks, AI allows for greater focus on creative problem-solving and innovation.

  • Stronger Customer Relationships: AI-powered personalisation fosters loyalty and trust, critical to sustaining revenue growth in competitive markets.

And crucially, as expectations evolve, it’s imperative to meet them: CMOs anticipate AI will dramatically enhance the quality of content and reduce marketing costs by up to 14% over the next 2–3 years. This dual promise of better quality and efficiency makes AI a must-have tool rather than a mere experiment.

When to Start? If you haven’t already… now

Generative AI adoption is no longer optional for CMOs seeking to drive revenue. With 58% of organisations already leveraging AI in marketing, smart adoption ensures a competitive edge. Start small with focused initiatives, build momentum, and scale for broader impact.

In a world where CMOs are expected to deliver measurable business outcomes, generative AI is the key to staying ahead. By integrating AI strategically, CMOs can not only meet but exceed revenue expectations, positioning themselves as indispensable drivers of organisational growth.

To read the full CapGemini report ‘Generative AI and the evolving role of marketing: A CMO’s playbook‘, please visit their website here: https://www.capgemini.com/insights/research-library/cmo-playbook-gen-ai/