AI marketing automation can save immense time and resources while improving the ability of marketing teams to perform vital tasks like campaign management and data analysis. But unlocking these benefits requires overcoming major challenges like compliance and resistance to change. Fortunately, authoritative data validates that Agile ways of working are an effective enabler for implementing AI in marketing and continuing to improve it in time.
It can feel like Large Language Models (LLMs) like ChatGPT get all the attention when it comes to AI marketing. They’re quite easy to use for basic tasks, so marketers can quickly start implementing them to help with things like improving email copy or brainstorming.
However, one area where AI is set to transform marketing in more profound ways is through automation. Much in the same way technology has enabled entire factories to operate more efficiently with a fraction of the workforce, AI holds the promise of enabling fewer marketers to do more.
This might mean taking things like optimizing email subject lines, triggering email sequences, personalizing content, or doing sentiment analysis. Taking that last one as an example, it can take a marketer hours of reading comments, emails, articles, etc., to get a sense of how customers are responding to something like a product release. Many AI tools can do this in just a few minutes.
Add up all the AI marketing automation use cases and you have something that can transform your marketing. But unlocking those benefits requires really understanding how AI marketing automation works.
What Is AI Marketing Automation?
There’s always been some kind of automation in modern marketing. Often, this might be something like using Zapier to migrate data from one tool to another instead of doing it manually. Or it might mean creating rules for an email sequence. So, how does AI marketing automation differ?
In short, AI is creating new possibilities in the world of marketing automation by being “smarter.” It uses AI tools to automatically manage, optimize, personalize, etc., various tasks at scale. That can be done by answering questions via an AI chatbot or recommending products for customers based on their past purchases. Automating these types of tasks at scale, particularly in larger enterprises, was just not feasible in the past.
Even as these capabilities became realistic, compliance issues and the typical challenges of implementing major changes at large organizations all made such automation difficult to achieve.
Core Components of AI Marketing Automation
It’s worth taking a moment to break down more specific examples of AI marketing automation to understand its most impactful use cases.
Predictive Analytics
One of the great ironies of modern marketing is that, despite the availability of vast amounts of useful data, actually turning that data into useful insights is exceptionally time-intensive and difficult. AI-driven predictive analytics offer a solution.
AI tools can bring together enormous amounts of data from a wide range of sources, like on-site data, information from sentiment analysis, and various information on consumers, to spot trends. This can enable marketers to uncover insights about how consumer trends are evolving faster than ever before.
Intelligent Content Personalization
Personalized content typically translates into a 10-15% increase in revenue. In today’s hyper-competitive landscape where marketers struggle to squeeze every bit of ROI out of their campaigns, that increase is transformative. The challenge has always been providing that kind of personalization at scale.
AI marketing automation offers a way to do just that. By leveraging customer data, AI tools can offer everything from personalized offers to recommendations. Automation enables this to happen at scale. At the same time, AI tools offer an effective way to analyze the resulting data and further hone the strategy.
Chatbots and Conversational AI
AI chatbots offer more than simply a way to streamline the answering of basic customer questions. Sure, they can replace the need for humans to perform these roles, but more than that, they can analyze the resulting conversations to extract more customer insights. By understanding the questions customers are asking, marketers can, for example, better hone their customer journeys and create more useful content.
Automated Media Buying and Optimization
While companies like Google have long offered AI-powered tools for managing paid ads, putting such tools in the hands of regular companies marks a major shift. Previously, even larger enterprises had to rely on (and trust) platforms to do such automation. Now, independent AI tools offer far greater control, flexibility, and functionality around how your campaigns are optimized and managed.
Lead Scoring and Nurturing Automation
As any marketer who’s done it will tell you, lead scoring is time-consuming, difficult, but exceptionally important. Figuring out what behaviors reliably indicate that leads are at a particular stage is always a challenge. But AI marketing automation tools can automate this, analyzing vast quantities of data to uncover patterns that humans may miss. This results in time savings and, typically, improved performance.
That same data can then be used for automation nurturing and fine tuning. AI tools can use data about a prospect to select the right content for that moment, analyzing results and continuously improving.
Why Enterprise Organizations Need AI-Driven Marketing Now
The impact of fully integrating AI into your marketing, including AI marketing automation, is enormous. The latest State of Agile Marketing Report found that full implementation of AI made marketers far more successful overall.
It’s not hard to understand why.
When implemented strategically, AI unlocks entirely new capabilities for individual marketers and their teams. More complex customer journeys can be built, the personalization customers increasingly demand can be created at scale, and staggeringly large data sets can be analyzed in minutes instead of days.
This is particularly true for larger enterprises, where the scale of such automations can lead to enormous savings. Business leaders today know that if they aren’t implementing AI in their marketing, their competitors surely are. It’s no wonder CMOs, CTOs, and such organizations are feeling the pressure to make AI marketing automation a reality ASAP.
The Human Side of AI Marketing Automation
When it comes to customers, marketers need to remember that AI tools can only go so far. Authenticity, human interactions, etc., still play an important role in building lasting customer relationships. It may be easy to let that slip when implementing AI marketing automation.
But ideally, these AI tools will instead free up marketers to spend more time on high value adding activities that AI tools aren’t well-suited for. This may mean focusing on creativity in developing ideas for campaigns or performing direct outreach to specific customers.
Then there’s the question of ethics and AI. Organizations need to be transparent about when AI is being used in customer-facing roles. Even if this isn’t explicitly required by regulations, it goes a long way towards building trust with customers while demonstrating a commitment to transparency.
How AI Integrates with Agile Marketing Practices
For any marketing leader looking to implement AI marketing automation, Agile ways of working need to be top of mind. Data from the latest State of Agile Marketing Report clearly shows that fully Agile teams are dramatically more likely to successfully fully integrate AI into their processes.
In fact, while more than a third of fully Agile teams had already fully integrated AI, the report was unable to find a single non-Agile marketer who could say the same. But what explains Agile marketers being so far ahead of their peers in areas like AI marketing automation? It comes down to a few key ways Agile teams operate.
First, Agile marketing teams are built on experimentation and continuous improvement. Their cultures are centered around regularly examining what’s working or not, brainstorming new ways to improve processes, and rigorously testing those ideas. This culture is ideally suited for implementing AI marketing automation.
The reason is simple: there are hundreds of AI tools available and thousands of ways to use them for marketing automation. Figuring out which tool is used in which way is going to bring your organization the greatest value is a huge challenge that requires plenty of testing and iteration. That same report found that Agile teams had more autonomy alongside greater prioritization and focus. This enables them to sort through that haystack of AI use cases to find the needle they need to really have an impact.
Agile team cultures also emphasize creating psychological safety. This feeling of safety is crucial for truly successful experimentation. If team members don’t feel comfortable making unorthodox suggestions (or any suggestions at all) around how AI tools might be used, you’re losing out on potentially innovative ideas.
Overcoming the Challenges of Implementing AI in Enterprise Marketing
While the data clearly shows that Agile ways of working go a long way towards making AI marketing automation easier to implement, challenges still exist.
Organizational Resistance
A massive challenge that can easily go under the radar in a large enterprise is organizational resistance. Often marketers themselves will resist efforts to implement AI marketing automation, either because they don’t enjoy or feel comfortable with AI, or simply because they fear it may replace them. This kind of resistance can easily create setbacks around AI implementation that may not even be evident at first.
So what should leaders do to overcome this resistance?
Start with clear communication. Marketers understandably have a lot of anxiety around AI and when they understand exactly:
- What organizational goals will AI help them achieve
- What rules should govern how they use AI
- That they will be given autonomy to figure out how best to use AI
- That this will be a back-and-forth process, with leaders providing active support
When marketers don’t feel that they’re simply receiving a mandate from above to go figure out AI marketing automation, they’re more likely to embrace the process. Ensuring they have the autonomy, resources, and voice necessary to figure out this difficult challenge is vital.
Compliance and Regulatory Issues
Compliance concerns represent a serious barrier to enterprises fully embracing AI marketing automation. While there’s no silver bullet in an area that’s still rapidly changing, the best approach is to treat compliance or legal teams as key stakeholders.
This means bringing them into strategic planning so they can offer their input on how AI is implemented early on. By getting this feedback earlier in the process, marketing can have compliance in mind throughout their AI implementation. From that point, input from such teams should be sought on a regular basis, enabling marketing and compliance to operate as partners.
Measurement and Attribution Difficulties
Few things are more frustrating than learning that after testing an AI marketing automation tool for the past few weeks, a team isn’t able to quantify its impact. Without real data, it’s difficult to know whether AI implementation is really moving the needle, whether it warrants more investment, etc.
Avoiding this problem begins with proper experimental design. Setting a specific test period, deciding what metrics will be used, and establishing a baseline will all make measurement and attribution far easier. If your marketing teams aren’t well-versed in this kind of experimentation, it may be worth investing in some training before you begin AI implementation.
Lacking Sufficient Talent
While using an LLM like ChatGPT to help think through a question is fairly straightforward even for the least technically able, AI marketing automation is a different story. Connecting tools so they can share data, troubleshooting technical errors, etc., can require very specific technical abilities most marketers don’t possess.
On the other hand, hiring marketers who are already highly adept at things like AI marketing automation can be expensive and time-consuming. That’s why it typically makes sense to invest in the talent you have. This can be AI implementation-specific and technical, but should also include cultural training around Agile ways of working to help with experimentation.
Best Practices for Implementing AI Marketing Automation
If you’re feeling ready to lead AI implementation at your organization, here are some best practices to follow. They’re based on our internal experience implementing AI in our marketing as well as client work and surveys.
Begin by Understanding Your Goals
Asking a marketing team to design a campaign to achieve 10 goals is a bit pointless. Trying to do everything typically results in few, if any, real gains. The same principle applies to implementing AI marketing automation. You first need to identify what you actually want to achieve. Ideally, these goals will come from strategic planning and be tied to broader organizational goals.
This is particularly important when marketers are struggling to choose between dozens or hundreds of different AI tools and use cases. Targeted goals help make prioritization and focus easier. So instead of feeling overwhelmed and wondering where to start, marketers feel confident diving in and getting to work.
Create Structured, Focused Tests
The importance of focus continues through the testing phase. A well-structured experiment should be focused. Instead of asking “how can AI marketing automation improve our campaign performance?” try asking “how can AI marketing automation improve our time to market by reducing the hours needed to execute a new campaign?”
It can help to start small. Starting with a large and complex automation task creates unnecessary risks because it may take longer to implement, delaying your ability to learn insights from the test. Instead, try automating simpler things like analyzing campaign analytics before attempting to build a complex lead scoring system. This also offers a chance for marketers to hone their experimentation and attribution skills over time.
Expand and Iterate
It can’t be said enough that AI is evolving at an unprecedented pace. This means that even when a team has successfully implemented some AI marketing automation, they can’t simply stop there. Regular testing, iteration, and improvement on existing automation systems is what helps ensure marketing remains relevant and competitive.
Again, having an Agile culture built around continuous improvement helps a lot here.
AI Marketing Automation FAQs
Can AI Be Used for Automation?
AI excels at automation, as it can turn repetitive and time-consuming tasks into automated ones that are completed quickly and affordably. The trick is identifying what tasks AI can automate and which it can’t. For example, data analysis is ideal for AI, while developing creative marketing campaign ideas is something it can only assist with.
What Cannot Be Automated by AI?
While AI is great at answering simple questions like “when was my order shipped?” it lacks emotional understanding to tackle more difficult and complex customer interactions. AI is also not likely to automate creative work that requires strategic thinking. Lastly, AI struggles to make decisions without data, so any work where you cannot create a dataset will prove extremely difficult to automate.
What is AI automation in marketing?
This involves using AI tools to automate things like predictive analytics, simple customer interactions through chatbots, lead scoring and nurturing, campaign optimization, and more. These automations can be simple or extremely complex, use a single tool or connect multiple tools, etc. However, it’s important to note that AI marketing automation is evolving rapidly, so what’s possible is likely to change in the future.
Will marketing be automated by AI?
While AI is already automating many things in marketing like lead scoring, campaign optimization, and data analysis, it will never automate everything. The reason comes down to competition. The vast majority of AI tools are available to everyone, making it difficult to achieve a competitive advantage with them. The human element will always be important to supplement AI’s capabilities.
How is AI used in marketing?
Today, marketers use AI to do research, help generate content, handle customer questions, analyze large amounts of data, and automate things like campaign management. However, with new AI marketing tools being released daily, the ways AI is used in marketing are only set to increase.
Taking The Next Step Towards AI Marketing Automation
What should be clear by now is that AI marketing automation has enormous potential, particularly for larger enterprises. The savings that can be achieved by reducing the costs and inputs needed for marketing to function alongside the benefits of improving things like data analysis make for a powerful one-two punch. But frankly, most marketing leaders already know that (there’s a reason only 7% reported they weren’t considering AI at all in our latest annual survey).
The real challenge comes with figuring out how to train marketing teams to be ready and able to make AI marketing automation a reality. Our data shows that the single best way to do that is through Agile ways of working. Fortunately, we’ve been developing educational resources and courses designed specifically around that challenge.
So if you’re ready to get serious about AI marketing automation, check out our resources on AI marketing and look for potential next steps at the bottom of the page.
Oh, and before you leave, why don't you take a second to get our latest State of Agile Marketing Report and explore why Agile marketers are 3x more likely to be successful in integrating AI into their processes?
