Salesforce’s State of Marketing - AI dominates the agenda, but data still holds Marketers back

Salesforce’s latest State of Marketing confirms what most marketers already know: marketing is undergoing major changes driven by rising customer expectations, rapid advances in AI, and expanding data ecosystems. But just because they know things are changing doesn’t mean they are prepared, or are even sure what to do.

The research, based on a double-blind survey of 4,450 Marketing professionals across 26 countries, shows that Marketing faces a dual challenge of innovating with AI and meeting customer needs. These two things actually go hand in hand because, when done right, it’s AI that can help improve customer experiences.

Looking at the top challenges and priorities for Marketers, AI takes the top two spots: implementing and operationalizing AI, and adapting to AI’s impact on Marketing.

It seems the priority is first improving operations and helping Marketers be more efficient, particularly through AI tools and technology. 76% of respondentd use at least one form of AI (ie, predictive, generative AI, agentic AI), such as personalizing content, predicting campaign performance, or generating visuals. Currently, only 13% use agentic AI, and the 82% who use or plan to use agents expect major or moderate improvements in ROI.

High performers (the report breaks responses down into three groups: high performers, moderate performers, and underperformers) noted that using AI agents enabled them to reclaim 8 hours a week and boost return on investment (ROI).

Other notable stats when AI is deployed:

• 20% increase in ROI
• 20% in customer satisfaction
• 19% increase in conversion rates
• 19% decrease in costs.

The promise of AI is to take on much of the daily operational workload for marketers, allowing them to reinvest their time in the more creative aspects of Marketing, such as experimenting with new channels and tactics, digging deeper into customer insights, and doing more strategic work overall.

But...

But just because it sounds like AI is the answer to all Marketing challenges, doesn’t mean everyone is integrating different AI tools into their processes right away. For 61% of survey respondents, AI adoption is high, but full integration remains a work in progress. Part of the reason for this lack of integration relates to privacy and data security, as well as concerns around AI accuracy.

Among those who have yet to adopt AI, several blockers were noted, including a lack of expertise to use, support, and maintain it, as well as the maturity of the tools and technology. Many have also still not identified the right use cases or strategy.

The top use cases listed for AI include content personalization, predicting campaign performance/ROI, followed by generating visuals and copy. Predicting customer behavior wrapped up the top five use cases.

All of these use cases involve the customer at some level, which means traditional approaches to customer engagement are no longer working, and marketers need to evolve their strategies to incorporate AI.

Re-thinking customer engagement

Adapting to changing customer expectations and behaviors ranked fifth on the list of priorities, with 64% saying they struggle to keep up with changing customer and prospect behaviors, and 69% saying new customer acquisition is getting harder.

There is a belief that AI can help improve customer engagement; however, 51% say campaigns still feel generic, and 37% are dealing with inconsistent messaging. What this tells us is that Marketers are still caught in the trap of thinking only about AI technology, not AI-driven use cases. Over half haven’t figured out how to adapt their strategies for the broad use of AI.

One area Marketers are focused on is SEO (search engine optimization) and AEO (answer engine optimization), with 85% saying they are re-shaping SEO strategies and 88% in the process of optimizing for AI-driven search experiences like ChatGPT and Google’s AI Overviews. Both of these are important considerations, but it’s still not clear how well AEO works.

Data and personalization continue to bring challenges

Data continues to be the one piece that organizations struggle the most to address. The average marketing organization has seven data sources to integrate to support agentic Marketing (the implementation of AI agents that act autonomously with little human intervention to perform Marketing activities), and only a little over a half have access to additional data they need, such as Service, Sales, and Commerce data.

Despite the number of data sources and challenges in connecting this additional data, 71% are satisfied with their ability to connect them. Marketers currently using agentic AI are more satisfied, but that’s because they have already done the work to connect the data for their agents to work properly.

Connecting all the right data is no small task. It should be at the top of the list when working on new AI initiatives, especially agentic AI programs. At the same time, a strong data foundation should work for all AI and non-AI initiatives, not just one-off agents.

Data is also the key to personalization, but 46% said they lack the customer preference data needed to provide relevant content. 98% of Marketing teams using AI reported at least one data-related barrier to personalization (e.g., data silos, too much data, poor-quality data).

There’s a bit of a contradiction here to consider. Why are so many marketers still struggling with personalization if they are confident in their ability to connect the data needed to personalize? If they can connect the necessary data, they can implement AI to improve personalization efforts. For example, predictive analytics are more accurate when there’s a lot of customer data to analyze, and generative AI can help generate more variations of content personalized to each customer.

My take

The scope of the Chief Marketing Officer (CMO) is growing, expanding into six functional areas, including traditional brand and product marketing, analytics, data strategy, and revenue operations. It’s not the first time the CMO role has expanded, but this time feels different because expectations seem much higher for both connecting data and contributing a qualified pipeline. CMOs who define an AI strategy that lays a foundation for learning and growth are essential, and leadership needs to get behind them to create the right strategy.

One other interesting point in the survey is that many Marketers are looking to up-skill in technical and non-technical areas, including:

• Data analysis and interpretation
• AI tool management
• Strategic and creative thinking
• Content strategy and curation
• Privacy and compliance management

This makes sense, as AI will continue to take on more of Marketers' daily work tasks. The areas noted are where marketers can lean into their creative and strategic capabilities to ensure they remain relevant.

I think the key takeaway from this study is that Marketing still has a long way to go in re-designing its strategy to incorporate AI effectively. Because it’s not about implementing AI tech for the sake of it; it needs to be in the service of creating better customer experiences.