What gen AI means for martech and how to benefit in 2024

Gen AI seems to promise and be a disruptive presence in the future of so many areas of our professional lives. But what does Gen AI mean for managing and running a martech stack. Is the future dystopian or bright? According to Gen AI engines themselves they unsurprisingly believe the future, or certainly their view of their own future, is amazing. For martech, they believe the three main arenas of change that they will impact are:

• Content creation Gen AI can automate the creation of a wide variety of marketing content, such as blog posts, social media posts, email campaigns, and ad copy.
• Personalization at scale generate personalized product recommendations, email newsletters, and even landing pages for individual customers to improve customer engagement and conversions.
• Service delivery create chatbots and other AI-powered customer service tools that can answer customer questions and resolve issues quickly and efficiently.

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But as a CMO, marketing or martech leader, how do we position ourselves for this seemingly dazzling future?

1. Execute the brilliant basics

Marketing is an iterative discipline with each advancement and improvement being developed and built upon existing knowledge and processes. You cannot execute advanced capabilities without having the basics in place, and this self-evident truth applies to bringing in advanced technologies as well as processes. As you look to bring in Gen AI, your marketing basics will need to first be in place and functioning with a high degree of confidence. Think of this as meaning the processes, results and iteration are almost being run on auto-pilot. The performance is consistent and your teams are focusing their energies and development efforts elsewhere. This needs to be in place because implementing any LLMs from Gen AI will accelerate automation, so your basic processes and decisioning must be rock solid. Automation puts significant pressure on any arbitration, or human in the loop operations. For Gen AI to scale your base models and processes must be brilliant to minimize the need for manual intervention. Such 'brilliant basics' include:

1. Identifying customers with deterministic or probabilistic processes.
2. Understanding behaviors of individuals, cohorts and developing confidence in recommending the next best action and its value, or lack thereof.
3. Linking customer actions, behaviors and engagement across channels.
4. Documenting the activities and outcomes or audiences to create the data for LLMs to improve.

2. Develop your teams through upskilling, not just augmenting

To deliver foundational improvement you need people who understand existing engines, systems and constraints i.e. practitioners who know your business. Simply adding AI experts, data scientists and the such, will only create a fractured organization. While these skills are important, your existing teams needs investment and training to use their institutional expertise and knowledge to guide, accelerate and actually execute. Without this investment you will create analytical models based exclusively on academic insights with no application of business capabilities. When this happens you create significant organizational strain as the analytics team moves faster than the organizational capacity to operationalize, measure and iterate their models.

The result of this approach is generic and lo-value models that quickly decay as they don't leverage the nuance and insights from within your organization to deliver sustainable value to your users. To avoid this you must upskill your marketing practitioners to be able to engage with the data scientists working on training your next gen analytics system.

3. Plan additional investment and don't position it as a cost saving exercise, yet

One of the most common questions I get asked is "What can I eliminate if I bring in Gen AI?". This often driven by marketers looking to offset costs as they have no additional budget or justify Gen AI as part of a budget reduction exercise. Gartner states the 75% of marketers report being under pressure to cut martech spend this year to deliver better ROI(1) so this is a legitimate concern and approach. Unfortunately it is neither a practical nor an effective one.

You need to learn how Gen AI will aid and advance your capabilities and so help you achieve your objectives. As with all new opportunities this must be done through a considered series of test and learn exercises. Gen AI holds significant promise but as evidenced almost daily in the news there are also many risks. As a marketing leader you must approach this opportunity in a structured and objective manner. First learn how it works, how it supports your needs and what can acceptably be implement. When you know these, look for efficiencies. All of this seems blindingly obvious and perhaps even trite but orgs both large and small are already swapping out systems for Gen AI without doing their due diligence. A small number may achieve first mover advantages but many more will leak data and IP, and do significant harm to their long term growth, perhaps even existence.?

4. Make investment a shared, not a marketing, responsibility

To do all this you have to invest and grow your budget. Gen AI is not a panacea to use and offset cutbacks in the org. Overtime it should definitely allow you to upskill jobs and migrate workers into new opportunities but this is not an immediate or short term solution. Budgeting the introduction of Gen AI as such will create a false narrative with unachievable expectations.

But this is not exclusively a marketing investment. Gen AI is a multi-discipline opportunity that needs enterprise assets to run and work. Implementing, operating and governing Gen AI properly is an organizational deliverable and needs expertise and resource from many domains, not just marketing. The funding of any Gen AI initiative is an enterprise wide responsibility, not solely a marketing one.

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Conclusion

Gen AI holds a great deal of promise but for the next few years marketers must not lose sight of delivering the basics while they look at the expansive opportunities and acceleration, that this and other technologies can present. To do this they will need to be able to:

• Succinctly structure and process their data and insights.
• Invest in developing the skills of in-house talent, and not just augment with new hires.
• Test, learn and iterate. Gen AI is an opportunity to expand and operate in new ways. It is not an instant panacea to existing issues.
• Make this an enterprise, not marketing, investment to realize the true opportunity.

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1 Source: ?Gartner 2023 CMO Spend and Strategy Survey