Marketers today are overwhelmed by the blistering pace of change they’re dealing with on a daily basis. AI is only a part of the challenge, but it’s also key to addressing it.
Learning to effectively test AI as a marketer unlocks a world of possibilities. It enables you to learn how to integrate AI into your workflows, saving you time and making you more effective.
But the word testing is key here. You need a rigorous process for determining whether particular AI tools and applications are actually creating value. To help you develop that process for yourself, we’ll take you through some best practice and real use cases from our own experience.
Best Practices for Testing AI in Marketing
Test AI Around Your Strategic Goals
We know AI is already revolutionizing every aspect of our work. For marketers, it’s changing how strategies are developed, improving efficiency, enhancing customer engagement, and on and on.
But chances are you’re hearing about all the ways AI can benefit your marketing more than you’re actually experiencing those benefits.
Surveys show that most marketers are implementing AI in some capacity, but it’s still mostly experimental and scattershot.
In order to determine whether AI is actually delivering value to your marketing, you need some metrics to judge its performance.
Instead of choosing an AI tool and then coming up with metrics for it, start with your own strategic priorities. The main question you’re trying to answer here is whether AI can help drive performance where it really counts, not whether a particular tool you’ve heard of can improve any metrics at all.
Start Small
Instead of beginning with the aim of finding transformative and highly impactful ways to implement AI, start with small and achievable goals. Define or test an idea designed for a very specific use case and then run with it. For example, building a bot to analyze website metrics.
If you’re able to make progress on that small idea, you can then build on your success. By breaking up AI testing into smaller pieces, you’re able to learn faster and with less risk.
Try Many Tools
With the number of AI tools (not to mention their capabilities) increasing weekly, getting overly attached to a single tool is a mistake. Effective AI testing requires being tool agnostic. Be willing to test many tools quickly and abandon any that don’t work for you even if you’ve spent a few days or weeks on them.
This is another reason to start small, it minimizes the investment made in specific tools so it’s easier to move on if you need to. This approach also makes it easier to test many tools quickly, uncovering which ones will get you the most value.
Structure Your Experiments
Instructing a team to begin using a new AI tool and checking in later to see what they think the results were is not a recipe for success. If you’re going to properly access the value a particular AI solution brings, you need a structured experiment.
That requires deciding what metrics you’re testing for, setting a time limit, and limiting other factors that might impact the results. For many teams, this means testing during a sprint, but you can also simply decide how many weeks you’d like your experiment to run. Don’t forget to ensure your results are statistically significant as well!
Encourage Creativity
The fact is, nearly all marketing teams operating today have access to the same AI tools. Translating those tools into real competitive advantages requires getting creative with how you use them.
But being creative is easier said than done. Here, it begins with encouraging marketers to try testing AI ideas they may have. Even when those tests don’t produce the results you want, encouragement is crucial if marketers are going to feel supported and safe enough to really let their creative juices flow.
Put another way, if you want creativity, begin by creating psychological safety.
Don’t Stop Testing!
The last best practice you need to take away is to embrace continuous improvement. AI’s capabilities and the challenges marketers face are both evolving faster than ever. So even if your testing produces some fantastic applications for AI, you can’t just sit on your laurels!
Creating a cadence of brainstorming tests, running them, evaluating the results, and repeating the process is the best way to ensure your testing doesn’t stop. It should become an integral part of how your marketing teams operate.
AI Use Cases for Marketing
Here are five use cases that integrate Agile marketing practices with AI tools to enhance customer journey mapping. You can use them as starting points for developing your own AI tests.
1. AI-Powered Persona Development & Segmentation
Why it’s Valuable:
Persona development and segmentation is a key AI use case for marketing because AI enables deeper customer insights by analyzing behavioral data, preferences, and engagement patterns. This helps create dynamic personas that evolve over time, improving personalization and targeting.
How to Implement It:
- Use AI-powered analytics tools (e.g., HubSpot, Segment, Adobe Sensei) to analyze customer interactions.
- Cluster similar customer behaviors using machine learning models.
- Integrate AI-generated personas into Agile sprint planning for targeted content and campaigns.
- Continuously refine personas through sprint retrospectives and A/B testing.
Mistakes to Avoid:
- Relying solely on AI without validating insights through qualitative customer feedback. After all, if you don’t validate then you’re not really running a test!
- Overcomplicating segmentation, leading to excessive micro-targeting.
- Ignoring real-time persona updates, which can result in outdated strategies.
2. Predictive Content Personalization in the Customer Journey
Why it’s Valuable:
AI can predict the type of content that will engage customers at different journey stages, ensuring relevant and timely messaging, which improves conversion rates.
How to Implement It:
- Deploy AI-driven recommendation engines (e.g., Persado, Pathmatics, Optimizely).
- Use NLP-powered tools to analyze customer responses and adjust messaging accordingly.
- Implement Agile sprint cycles to test AI-suggested content variations.
- Measure content performance in real-time and adjust strategies based on AI recommendations.
Mistakes to Avoid:
- Over-reliance on AI-generated content without human oversight, which can lead to tone-deaf messaging.
- Ignoring cross-channel consistency when AI suggests different content for each touchpoint.
- Not integrating AI insights with Agile retrospectives to improve future content strategies.
3. AI-Driven Customer Sentiment Analysis for Sprint Prioritization
Why it’s Valuable:
By continuously analyzing customer sentiment across social media, emails, and chat interactions, marketing teams can prioritize campaigns based on emerging trends and pain points.
How to Implement It:
- Use AI-powered sentiment analysis tools (e.g., Brandwatch, MonkeyLearn, Qualtrics).
- Integrate sentiment insights into Agile backlog grooming sessions to prioritize marketing initiatives.
- Conduct rapid experimentation based on sentiment shifts and adjust messaging accordingly.
- Share insights with cross-functional teams to align product, sales, and customer support strategies.
Mistakes to Avoid:
- Treating sentiment analysis as a one-time project instead of an ongoing Agile process.
- Misinterpreting AI-generated sentiment scores without context (e.g., sarcasm detection limitations).
- Failing to act on sentiment insights in a timely manner, leading to missed opportunities.
4. AI-Enhanced Customer Journey Mapping for Hyper-Personalization
Why it’s Valuable:
AI allows real-time, data-driven journey mapping that adapts to customer behaviors, ensuring highly personalized experiences.
How to Implement It:
- Leverage AI-powered customer journey mapping tools (e.g., Thunderhead, Salesforce Einstein, Segment).
- Continuously update journey maps based on AI-driven insights rather than static assumptions.
- Use Agile stand-ups to assess AI-generated journey insights and iterate on campaign strategies.
- Create automated triggers for personalized experiences at key journey touchpoints.
Mistakes to Avoid:
- Rigidly adhering to predefined journey maps without real-time optimization.
- Ignoring qualitative customer insights when interpreting AI-generated journey data.
- Overloading customers with excessive personalization, leading to fatigue or creepiness.
5. AI-Powered Predictive Lead Scoring & Nurturing
Why it’s Valuable:
AI helps prioritize high-value leads and automates personalized nurturing sequences, improving conversion rates and marketing efficiency.
How to Implement It:
- Implement AI-driven lead scoring tools (e.g., 6sense, Drift, Infer) to rank prospects.
- Integrate AI-generated lead scores into Agile marketing sprints for targeted nurturing campaigns.
- Use AI chatbots and automated email sequences to guide leads through the funnel.
- Continuously test and refine lead scoring models based on Agile sprint retrospectives.
Mistakes to Avoid:
- Relying on AI lead scores without aligning with sales team feedback.
- Over-automating nurturing sequences, making interactions feel robotic.
- Ignoring AI bias in lead prioritization models, which may favor certain demographics disproportionately.
Taking Steps to Full AI Implementation
By integrating AI with Agile marketing, you can create a data-driven, adaptive approach to customer journey mapping that continuously evolves with customer behavior. The key is keeping strategic goals in mind, avoiding scope creep, creating lots of small structured experiments, getting creative, and ultimately balancing AI automation with human insights to ensure strategic alignment and authenticity.
Just remember this isn’t a process you’re going to ‘complete’ at some point. AI’s evolution isn’t going to stop and so the ways marketers use it will need to constantly evolve as well. Ultimately, the benefits, greater efficiency, more time and tools to aid in creativity, and higher quality marketing are all worth the effort.