The CMO's Guide to GenAI Success: A 6-Step Framework

Chief Marketing Officers (CMOs) are constantly seeking out innovative strategies that can drive substantial returns on investment (ROI). Generative AI is a clear and emerging game-changer in their sights today. 

A recent BCG report found that of 200 CMOs surveyed worldwide, over 70% were already using generative AI, while an additional 19% were testing it. Personalization, at 67%, topped the list of marketing use cases for genAI, followed by insight generation at 51% and content creation and measurement at 49%. 

Source: BCG

Most CMOs see positive results right away, with 93% reporting a positive or very positive improvement in how they organize their work and 91% reporting a positive or very positive impact on their efficiency. The company’s initial assessments suggest that GenAI’s low cost and ease of use can yield as much as 30% productivity gains.

While there is much excitement around the shiny, new technology and its impact on marketing, organizations always have to proceed systematically and with caution. As the leader of the marketing organization, it falls on the CMO to interact with technology experts to create the right infrastructure and embed robust training and usage DNA across their teams so generative AI becomes the foundation of a revenue-driving marketing engine. 

This is a guide for the CMO to navigate the implementation, adoption, and impact of genAI across their remit.

Focus on Strategic Alignment

The CMO, in consultation with other marketing leaders and the C-suite, must clearly define the role of generative AI in the strategic marketing vision of the company. For instance, one firm might find that genAI will help drive efficiency, scale, and RoI for their personalization activities, whereas another firm may find it much more useful to leverage generative AI for consumer insights and vendor relationships. This shared baseline across marketing and the wider organization must be established so there are no conflicts or wastage of resources from the start.

Enable teams to use genAI safely and effectively

Given the predominance of technology across the business world today, many people in marketing will have worked with CRM platforms, graphic design tools, analytics platforms, and other software. There is also likely to be a degree of familiarity with regular AI embedded in many marketing tools. Nevertheless, generative AI, which harnesses complex algorithms to understand patterns in structured and unstructured data, brings its own nuances and challenges.

• For example, even well-trained and tested generative AI models can ‘hallucinate’ - which is to give a completely false response but seems like a realistic, accurate solution to the problem posed. GenAI models can also respond differently each time, as that is the nature of self-learning algorithms. 

CMOs should ensure that their teams are trained not once but regularly by internal and external experts from industry and academia to use the technology safely and discerningly. This needs to begin before the genAI deployment and continue indefinitely.

• To deeply entrench safe and ethical use of genAI within their teams, CMOs should ask each team to choose a genAI ambassador who keeps themselves updated on the latest developments and trains their colleagues, keeps an eye out for new ethical and accuracy concerns in in-house genAI, and spots new use cases. The genAI ambassadors can come together to form an in-house marketing genAI CoE that stays abreast of trends and innovations and keeps the marketing organization ahead of the curve.

Institute a change management framework

Marketing teams may need help replacing current processes with generative AI or adding the technology as an additional layer for speed and creativity to existing workflows. CMOs should ensure that change management frameworks are in place in collaboration with change management experts, technology teams, and external implementation partners so that marketing teams can make a seamless transition. 

Crucially, change management initiatives should reassure marketing teams that genAI will not replace them but help them do their work better. They should include pointers on how marketing personnel can use the bandwidth and creativity unlocked by genAI to focus on enhancing strategy and optimizing execution.  

Embrace experimentation

GenAI can certainly help boost the efficiency and effectiveness of daily marketing activities. However, the CMO should encourage experimentation to draw tangible value from genAI deployments. Running pilots to do things differently using genAI’s strengths must be made a standard practice at the team and individual levels. Wherever possible, cross-functional and even cross-ecosystem collaboration must be encouraged to tap into genAI’s ability to incorporate human expertise and collective wisdom to do better at scale on the go.

Build the technology foundation

Only the CMO and the marketing teams know the ins and outs of the data in their ecosystem and clearly envision how it should be harnessed to deliver competitive advantage. Hence, they must share this expertise with the technology folks to build a genAI-powered marketing organization that stays ahead of the game.

• Accommodate data growth: During genAI deployments, data initiatives will pull in never-before volumes of data from within the organization, vendor partners, third parties, and more. CMOs and their teams must collaborate with tech experts to ensure that the right data is brought in, the appropriate vetting and cleaning mechanisms and international security and privacy compliance are in place, and the data platform has the scalability and flexibility to deliver on marketing goals.
• Optimize the tech environment: The IT teams and external partners will ensure that the underlying systems and platforms have the computing power to handle genAI scaling for marketing applications. However, the CMO and the marketing leaders should work closely with these stakeholders to direct seamless API integration with current marketing tools and technologies to unlock the power of genAI. If necessary, an audit can identify if existing solutions have to be upgraded to meet this objective.

• Risk Management: Chief security officers, compliance and ethics teams, auditors and finance leaders in large organizations will ensure that genAI deployments are optimized for risk. Zooming in on marketing, however, the CMO and marketing leaders will have a couple of core responsibilities. These are areas where the marketing genAI CoE can be a key contributor
  • Prepare a crisis response: Despite all the training, checks and balances, inappropriate genAI output might still make its way into the public domain as a piece of communication that speaks for the organization or one of its brands. CMOs, along with their colleagues in public relations and advertising, must be prepared with a clearly defined crisis response if this happens. 
  • Track unexpected costs closely: While a cost-benefit analysis will be a part of the initial evaluations, implementing a new technology always takes unexpected twists and turns. Much money will be saved by the boost in efficiency and creativity once genAI initiatives are running smoothly, but the path there might require higher-than-planned investments in technology, people, training and absorbing failed launches. If the CMO is not alert to this, it may lead to being at cross-purposes with finance and the rest of the C-suite, scuttling the objective of long-term genAI-led success for marketing.

• Leverage old and new KPIs to monitor success: Large organizations with their varied and rich expertise and access to the best partner ecosystems, will successfully set up comprehensive genAI systems for business functions to use. However, to monitor the progress and impact of these systems, the organization has to bring together existing KPIs such as NPS and RoI and new ones such as model and system quality, Google Cloud experts advise. Again, this is an effort that will need the hands-on participation of functional leaders including the CMO.
  • Business impact: These are classified into two categories:
    • Usage metrics: To track the adoption rate, abandonment rate and related information like the time per session and the length of each query for every user of the system - for an accurate idea of how effectively intended users are interacting with the system.
    • Business improvement metrics: These are efficiency and RoI-based KPIs such as time saved for a process, engagement rates for campaigns and revenue lift. It is important to monitor from the start whether genAI is creating a positive impact or will do so in the long run, so quick changes in direction can be made.
  • Model and system quality: These are no doubt niche areas that monitor how genAI models perform and how relevant and accurate the data inputs are. However, as mentioned earlier, CMOs and their teams need to share their core marketing perspectives with the experts handling this so a good job is done.

In conclusion, generative AI represents a formidable tool in the modern CMO's arsenal, offering the potential to drastically increase ROI and redefine the marketing landscape. However, this will require them to collaborate in new and challenging ways within the company and with knowledgeable partners, which by itself will push the boundaries of marketing as we know it today.

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Editorial Team
Tredence

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