10 Ways Agentic AI is Supporting Marketing Teams [Backed by New Data]

This year, there is no need for any grand introduction to AI. It’s now part of most marketers’ tech stack. However, there are still major shifts happening beneath the surface, particularly with the rise of AI agents and their impact on how marketing teams operate.

To understand what that looks like in practice, Salesforce surveyed nearly 4,450 global marketers about their challenges and where they’re investing next. The findings show a clear shift from light AI experimentation to operationalising it across the marketing department and beyond, driven heavily by AI agents.

This article explores that shift and breaks down the 10 ways AI agents are supporting marketing teams in 2026, backed by the latest Salesforce State of Marketing Report (10th Edition) and related research.

1. Using AI for everyday marketing tasks
2. Using autonomous agents to handle complex tasks
3. Shifting SEO strategies for AI-generated answers
4. Turning on real-time, two-way customer conversations
5. Connecting data to build personalised campaigns
6. Closing the gap between AI ambition and execution
7. Managing risk, privacy, and data security
8. Using AI to free up time to focus on strategy and creativity
9. Keeping up with rising customer expectations
10. Using historical targeting to predict customer behaviour

In Salesforce’s State of Marketing Report (10th Edition) , AI adoption continues to accelerate, with 75% of marketing organisations now using it in at least one form. However, while adoption is high, full integration remains a work in progress for 61% of marketing teams.

This highlights that AI is the ultimate double-edged sword. It’s the number one priority for marketers, but it’s also one of the hardest things to implement properly.

Despite the perceived challenge, the ongoing level of investment reflects AI’s potential to streamline nearly every aspect of a marketing strategy when implemented effectively.

Teams are willing to put in the extra work to do things like use generative AI to power personalised AI-driven content generation, segment audiences for more efficient targeting, analyse campaign performance to recommend optimisations, and provide personalised customer service at scale.

One example of how powerful AI can be once implemented can be seen from the luxury appliance brand Fisher & Paykel. With the help of Agentforce, they deployed an AI agent to handle customer queries 24/7 via live chat. This tool now draws on a knowledge base of more than 10,000 articles to diagnose and help resolve complex issues faster.

As of 2026, top-performing marketing teams have gone beyond basic AI tools and are using agentic AI. For the uninitiated, agentic AI is technology that not only generates outputs, but can also act independently and learn over time.

While many top-performing teams are using these tools, there’s still a clear gap. Of the 75% of marketing organisations using AI for things like personalisation, campaign prediction, or content generation, only 13% are tapping into the power of self-directed agents.

When teams are using generative AI (compared to agentic AI) to personalise messages, they’re still doing a lot of manual work. That includes feeding in data, prompting, adding context, and moving outputs across systems. Agentic AI is the natural next step, as it does all of this for teams with full context from their customer data. In the background, it can continue to learn and optimise messaging to get them the best ROI.

TripADeal’s pop-up in Sydney gave a glimpse of what an autonomous agent can do. Visitors could plan a holiday by talking to an AI agent. It pulled from live data, adjusted as people chatted, and handled the whole process without the need for users to click through options.

SEO has always evolved as Google updates its algorithms. However, with the introduction of AI, the search landscape is now fundamentally different.

Search engines are increasingly becoming destinations that can answer questions themselves. Tools like ChatGPT and Google’s AI Overview provide users with full responses directly on the results page, reducing the need to click through to websites.

This shift has left many marketing teams questioning why traffic is declining and wondering how they can recapture this attention. Currently, 85% of marketers say AI is reshaping their SEO strategy, with 88% having already started optimising for AI-generated answers. In addition, high-performing teams are 2.2 times more likely to be ahead of this shift.

As a result, teams are shifting towards GEO (generative engine optimisation), focusing on how they can show up in AI-generated answers. There is also a move away from tracking traffic alone, with teams tracking other metrics like branded search and conversion rates as stronger indicators of performance.

No one likes being constantly talked at, and customers are no longer willing to accept one-way communication from brands. In 2026, they expect to be able to respond to emails, ask questions, and get answers.

This loss of engagement was highlighted by Steve Hammond on the Executive Conversations podcast. “Do not reply… that was the end of the line, a conversation that never really began.”

The data also reflects this shift, with 83% of marketers agreeing that customers want two-way interactions.

The disconnect here is that only 55% of marketers say they frequently reply to customer responses via email and SMS.

Part of the challenge is the vast scale of responding to everyone. In many cases, responses are still handled manually, making it difficult to keep up.

Meanwhile, high-performing teams are 1.5 times more likely to respond to customers across these channels using agentic AI. This difference shows just how much of a competitive advantage having 24/7 autonomous support can be.

Teams have more options for personalisation with AI, but if the data behind it is still fragmented, they won’t be able to get the desired results. This is the case for most teams, with only 26% of marketers reporting that they are fully satisfied with their data unification.

On a more local level, in Australia, only 59% of marketers have full access to service data, 60% to sales data, and 55% to commerce data. While in New Zealand, this sits at 63% for service and sales, and 57% for Commerce.

The problem here is that, without a connected view, teams struggle to personalise campaigns, which leads to disjointed experiences for potential customers. Ultimately, this could also lead to missed revenue.

To solve this, there is a shift towards centralised platforms like Data 360, which unify real-time data into a single, actionable view without manual effort. From there, teams can use Agentforce Marketing to create one campaign that is sliced into different segments and personalised based on the recipients' past behaviour.

For example, an ecommerce hardware platform might send an email about drill bits to a carpenter and one about sockets to a sparkie, each featuring products based on their browsing history.

Right now, many teams are still working out their AI strategy. A major challenge is that, while leadership teams have ambitious AI plans, many marketing teams don’t yet have the skills to deliver.

In fact, a “lack of capacity or skilled resources” remains one of marketing's top challenges. For slower adopters, “lack of employee expertise” is a key reason AI rollouts stall.

Without the skills to manage AI tools or interpret data, these investments often underdeliver and lead teams to fall back on doing manual work.

In response, high-performing teams are prioritising building their AI capabilities. This includes building skills in data analysis and AI tool management, and learning how to use generative AI in their content strategy while keeping a human touch.

The great part is that there are plenty of free programs recognising this need and providing accessible resources. Explore Salesforce’s Trailhead or broader initiatives like the Australian Government’s free AI upskilling programs to learn more.

It’s fair to say there is some healthy scepticism around AI. When teams start using new technology, especially alongside sensitive data, the burden is on the AI to prove itself a trustworthy ally.

That hesitation shows up in the numbers, with only 39% of marketing teams reaching full integration.

The irony is that, in trying to reduce risk, teams often end up using AI in a fragmented way, relying on disconnected tools that can expose their data to less trustworthy platforms.

With privacy and data security ranking as the top concerns, followed closely by accuracy, the focus is shifting from whether teams trust AI to who teams trust to provide their AI.

This means teams are looking to partner with an AI platform that has put in the work to build security, privacy, and compliance into their tools, rather than settling for quick-fix AI tools operating under the “move fast and break things” mantra.

Imagine how much more creative work would get done if all your marketers had their own personal assistant to handle the admin side of their role. That’s what AI agents are built for.

According to Salesforce’s latest marketing report , marketers expect to save up to eight hours per week using AI agents. This time can then be reinvested in strategy, creative thinking, and understanding their customers.

The challenge is that AI is often used in small, disconnected ways. For example, generating content here, analysing data there, but teams are still relying on manual processes to stitch everything together. With a setup like this, it’s hard to actually save time with AI.

The AFL solved this challenge by moving from spreadsheets and disconnected systems to Agentforce. This allowed them to automate campaigns, track and optimise engagement in real time, and remove the need for manual coordination. From this switch, their clubs were able to save more than 42+ hours per week through email automation alone.

Marketers are being squeezed from both sides. According to Salesforce’s State of Marketing Report , 85% of marketers say customer expectations are rising, while 69% say acquiring new customers is getting harder. On top of that, 64% struggle to keep up with changing customer and prospect behaviour.

Teams are finding that customer expectations are through the roof, but teams have less time than ever before. Customers now expect quick and relevant communication from a brand, but many campaigns are still manual.

Using Agentforce Marketing, teams can respond in real time and adjust campaigns based on live behaviour. This helps them meet growing expectations without sacrificing the time they need for strategic work.

Teams are not just looking at what has happened, but also analysing real-time data to predict next actions. Instead of relying on past actions, marketing teams are using AI to interpret real-time signals and predict what customers are likely to do next.

In fact, 38% of marketers using AI are already segmenting audiences based on predicted behaviour, compared to 30% without AI.

For example, imagine a customer who browses running shoes multiple times, reads related content, but then abandons their cart. An AI agent can use this data to predict high purchase intent and automatically trigger a personalised email, a limited-time offer, or a retargeting ad.

This approach doesn’t replace traditional data like purchase history or engagement metrics. It builds on it. AI connects these signals, adds context, and turns them into forward-looking insights, giving teams a clearer view of intent and the ability to act before the moment has passed.