May 16, 2023

CMOs, generative AI just disrupted your MarTech stack

Varun Sarin

Varun Sarin

CMOs, generative AI just disrupted your MarTech stack

On 8th March 2023, Salesforce announced an integration with ChatGPT which allows you to converse with your Customer Relationship Management tool (CRM).  Adobe made their own announcement at Summit, by introducing Firefly on 23rd March, which can generate art using AI and is built directly within their Creative Cloud product suite. 

In the meantime, Microsoft, Google, Amazon, and OpenAI are all battling it out for chatbot supremacy.  

Is this a fad, or has AI finally reached a tipping point?  

In this article, we will deep dive into MarTech challenges facing CMOs in 2023, and how AI could address them. Crucially, we'll talk about the risks, separate the noise from what you actually need to know, and discuss where to start on your own AI journey. 

Read next: The Savvy CMO: Data Privacy - Dilemmas & Remedies

What is generative AI and why is it in the news? 

In more general terms, AI (or artificial intelligence) refers to computer systems that can perform tasks which normally require human intelligence. AI can be used for a variety of applications, including image recognition, natural language processing, decision-making, and more. 

You've probably heard, read, or used ChatGPT by now. It is an example of an AI-powered chatbot technology that uses natural language processing (NLP) to understand customer inquiries and generate responses. It can generate content based on input from users, including articles, blog posts, emails, and more.  

This type of generative AI is becoming increasingly popular in marketing, as it allows marketers to automate parts of their workflow and increase efficiency. 

 With ChatGPT, we are only just beginning to scratch the surface of how AI will transform a marketer's job. However, that doesn't mean much, unless we can connect the technology to the real challenges CMOs are facing in 2023.  

MarTech challenges facing CMOs in 2023 

Too much information, not enough insight 

There are more than 10,000 MarTech solutions available today. An average enterprise deals with about 90 cloud services and marketing software products, many of them designed to deliver a single message. Marketers need to be well versed in CRM, CMS, DAM, CDP, and a variety of other specialist products that slice the customer journey into manageable chunks.  

Each product contains a ton of data, and nuggets of insight. These insights don't necessarily work across the enterprise stack, and their scope is generally the system that you currently find yourself in. 

The paradox here is that while we are drowning in data - we are also starved for valuable insight, because much of it is fragmented.   

MarTech stacks are increasingly bloated 

Another major hurdle CMOs need to overcome is dealing with vendors that push for operating only within their proprietary ecosystem. The pressure on marketers to deliver results has never been greater, and as such, there is often a temptation to take the easy route by relying on such off-the-shelf solutions.  

To stay ahead of the curve in 2023, CMOs will need to strike a delicate balance between different types of vendor solutions while preventing stack bloat. The need for speed must be managed with procurement risk, by working with multiple partners and ecosystems. 

There is a technical skill gap in the market 

One in three CMOs admit their marketing team does not have the skills and experience to get the best out of [their] technology. 

With technology advancing at an unprecedented pace, it is becoming increasingly difficult for marketing professionals to keep up with the latest trends and tools. This means that CMOs must invest in training programs or recruit new talent who possess these essential skills. 

In addition, keeping up with technological advancements requires ongoing learning and development initiatives so that marketing teams can remain knowledgeable about new tools and techniques as they emerge on the market. Without this continuous investment in education and development opportunities for employees, organisations risk falling behind their competitors who are more agile and adaptable when it comes to leveraging cutting-edge technology. 

How AI solves these three challenges 

AI is different because it isn't just a technology - it also changes our relationship with technology.  

For a marketer, this could a fundamental shift in what you do and what skills you need to develop. The shift could look like this:  

Skill shift blog

Let’s explore each of the transition points in more detail. 

A natural language interface unlocks insight.  

Generative AI can remove the bottleneck to insight, because now your reports can be based on natural questions instead of loaded data queries. Regardless of your technical skill level, you can easily see whether the question is being phrased in a way to drive a particular point of view or bias. 

With the rise of AI, your marketing experts can not only do what they do best, but they now have a common currency of communication with each other - natural language. 

A CRM expert can now say, ‘Show me the most successful email headers that were sent to my most profitable audience segments’ and instantly cut across silos. No dependencies, no waiting, no humans. 

Using these tools, non-technical marketers can automate reporting tasks, gather data from various sources, and generate reports on performance metrics. This finally democratises data, unlocking insights that may not have been possible with more traditional methods. 

AI models hide the messy tech stack underneath 

Why are all the biggest tech firms investing heavily in AI? Because they know a fundamental truth: if they don't, then their platforms are in danger of becoming abstracted behind an AI layer. Funnily enough, that's exactly what tech did to other industries, like banking. 

As marketing technology (MarTech) continues to evolve, the underlying tech stack can become increasingly complex and difficult to manage. This can be a nightmare for Chief Marketing Officers (CMOs) who are responsible for overseeing MarTech implementations, as they must navigate a fragmented landscape of software solutions, data sources, and analytics tools. 

However, with the emergence of generative AI technologies that can automate many of these tasks and processes, CMOs no longer need to worry about the complexities of their MarTech stack. The AI layer effectively wraps around the entire system and handles all the leg work on behalf of humans. 

This means that CMOs can focus on higher-level strategic initiatives rather than getting bogged down in technical details. They don't have to spend time managing multiple vendors or troubleshooting software integrations – instead, they simply rely on their AI-powered systems to do it for them. 

Prompt engineering will be valued over deep technical skills 

Knowing the right answer used to be the coveted skill. With generative AI, it’s - can you ask the right question?  

There is a term for this - prompt engineering. According to Wikipedia, "Prompt engineering is a concept in artificial intelligence, particularly natural language processing. In prompt engineering, the description of the task that the AI is supposed to accomplish is embedded in the input, e.g. as a question, instead of it being implicitly given." 

The CRM question earlier in the article is one example. 

Prompt engineering brings together the three essential skills that the marketer of the future will need to have:

  1. The ability to react to insights, which means understanding the customer.

  2. The ability to show creative leadership and tie a powerful story to a customer need.

  3. The ability to apply context and remove biases.

Risks in Adopting AI 

We're not going to sugar coat this – As with any new technology, AI comes with its own unique risks. 

Generative AI relies on collecting data from customers to generate insights and make decisions. Although this can be incredibly beneficial for businesses, there is also a risk of data privacy breaches if proper security measures are not taken when collecting or storing customer data. It is important that companies ensure they have the necessary procedures in place to protect customer information, such as encrypting sensitive information and restricting access to certain areas of the system. 

Additionally, it is important for companies to remain up to date with changing regulations surrounding customer data protection so they can adjust their practices accordingly. They should also provide clear communication about how they use AI and give customers control over what happens with their personal information. Taking these steps will help minimise any potential risks associated with using generative AI in a MarTech stack. 

There is also an inherent risk of bias if the data used to train the algorithm has been collected in a biased manner. This could be due to sampling errors or other factors that lead to inaccurate representation of certain demographics or groups. As CMOs are responsible for making sure their marketing messages and campaigns are inclusive, understanding any potential biases in their datasets should be taken seriously and addressed promptly. If not accounted for, these biases can have far-reaching consequences on how people perceive your brand and its messaging. 

How to get started with generative AI 

Marketers are delivering more campaigns and content across more channels than ever. As a CMO, you need to decide where AI can free up the most time for your time, and which are the highest value tasks where their expertise is needed.  

There are three areas which are prime candidates for testing out generative AI.

  • Run a content creation pilot: Give your copywriters and campaign managers the ability to supplement their copy with the use of a generative AI tool. A/B test this content against human-generated content to see what it does to effectiveness. Measure this against an email or social media campaign.


  • Run a reporting pilot: Take extracts of test data, as you must be extremely careful about not loading commercially sensitive data, and see what insights generative AI can provide. The job of actually connecting live data sources is a longer-term roadmap, but this exercise can help your team learn what the right types of questions to ask are and which prompts generate the most valuable insight. and


  • Run a design pilot: Give your creatives the ability to generate designs, artwork, and imagery using generative AI. As with the above, test this against completely human-generated content and measure incremental effectiveness. This is a great test to run on your website, or specific landing pages .


CMOs, you've got your work cut out 

Generative AI has opened the door to a future that not many of us can imagine today. But it is inevitably the future we find ourselves barrelling towards. At this stage, the forward-thinking CMO needs to plan for the skillsets, people, and technical capabilities that will be valuable in the future. This will open up the marketing talent pool, allowing your team to recruit from industries and areas that you never considered before. 

However, it comes with its own risks and challenges that CMOs need to be aware of to remain competitive in their industry. The CMOs of the future will be the ones who are able to ask the best questions about how they can leverage generative AI to optimise their marketing strategies and platforms. 

What next? 

Our  Modern Marketing Transformation practice  at Credera is comprised of industry experts with decades of combined experience across all facets of marketing and technology. 

If you’re investigating how to utilise the latest tech to invest your marketing budget more strategically, our team can help you assess your current infrastructure and identify opportunities to evolve, with commercially stunning results.  

To learn more, please  get in touch  with a member of our team. 

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