If the average employee saves 2.5 hours a day using AI it’s criminal for marketing leaders to make basic AI mistakes.
AI can save marketers time and money, but mistakes can cost more, waste time AND severely damage team morale.
Note: This post is for marketing teams at least dabbling in AI. If AI is nothing but a “we really need to look into that,” then start here.
Here are the top AI mistakes in marketing with clear examples and how to fix them.
1. Lack of a Clear Strategy
- Mistake: Implementing AI without a defined purpose or alignment with business goals.
- Example: A team deploys an AI tool to generate content without knowing which part of the funnel it supports, leading to disconnected messaging and poor ROI.
- Fix: Start with specific, measurable goals (e.g., increase lead conversion, improve customer segmentation, automate personalization) and identify where AI can help. See my post about identifying AI capabilities across marketing technology platforms.
2. Overestimating AI Capabilities (and Insulting Your Team in the Process)
- Mistake: Assuming AI can make autonomous decisions or “think” like a marketer.
- Example: A brand uses AI to auto-generate social media posts without review, resulting in off-brand or tone-deaf messages.
- Fix: Treat AI as a powerful assistant, not a strategist or replacement for humans. Human oversight is still critical for brand tone, ethics, and context.
Important note: Be careful when suggesting AI that takes over a person’s complete role.
For example, if you have a great copywriter and you suggest using AI for all copywriting it immediately devalues the copywriter. Instead, suggest language and solutions that positions AI as augmentation of the writer vs a replacement.
3. Poor Data Quality and Management
- Mistake: Using AI with incomplete, outdated, or biased data sets.
- Example: An AI tool trained on outdated customer data recommends irrelevant product offers, hurting engagement rates.
- Fix: Clean, structure, and audit your data regularly. Ensure diverse data inputs to avoid bias and ensure better training and predictions.
4. Ignoring Customer Privacy and Compliance
- Mistake: Violating privacy laws (e.g., GDPR, CCPA) with AI-driven personalization or data use.
- Example: A marketing team uses AI to retarget users based on personal behavior without consent, leading to a legal complaint or fine.
- Fix: Ensure compliance is baked into AI processes, including data consent management and anonymization techniques.
5. Automating Everything Too Quickly
- Mistake: Over-automating workflows without testing or understanding the customer journey.
- Example: A company launches an AI chatbot for all support queries, but it fails to handle complex questions, frustrating customers.
- Fix: Start small with pilot projects. Gradually scale AI where it adds real value, such as email optimization or chatbots for common queries. See my post about using AI to increase ROI with truly individual email personalization.
6. Failure to Train Teams
- Mistake: Assuming marketing teams can “just do it with AI” without training.
- Example: A team misinterprets AI insights from a predictive tool and targets the wrong customer segment, wasting ad spend.
- Fix: Invest in upskilling your team to understand AI’s capabilities, interpret outputs, and recognize when human input is necessary.
7. Siloed Implementation
- Mistake: Deploying AI in isolated campaigns or channels without integration into the broader marketing ecosystem.
- Example: A team uses AI for email personalization, but the insights aren’t shared with the paid ads or content teams, leading to inconsistent messaging.
- Fix: Ensure AI tools are interoperable and feed into a centralized marketing strategy or customer data platform (CDP).
8. No Performance Monitoring or KPIs
- Mistake: Not tracking the performance of AI-driven initiatives.
- Example: A company launches AI-generated landing pages but doesn’t measure conversion rates—so poor performance goes unnoticed.
- Fix: Set clear KPIs (e.g., engagement rate uplift, time saved on segmentation) and continuously optimize based on data.
9. Vendor Lock-in Without Flexibility
- Mistake: Committing to one AI vendor or platform without flexibility or future-proofing.
- Example: A marketing team builds workflows around a proprietary AI tool that later increases pricing or restricts access, making migration costly and difficult.
- Fix: Choose platforms that allow integration with other tools and exportability of data and models.
Important note about cost: all of the “free” and cheap AI solutions in 2025 remind me of the early days of Uber when rides were mostly free.
Everyone had free promo rides to share with friends. For years, Ubers were WAY cheaper than traditional cabs. But who was actually paying for this?
Uber was funded by Venture Capital (VCs) for years, but eventually it had to start making a profit. Take an Uber today and it’s certainly no guarantee it’s cheaper, and in fact it’s often more expensive than traditional cabs!
All the signs point to AI following a similar trajectory. Do not assume that an AI feature, platform or service will always be free or cheap. AI is enormously expensive to run and maintain, and it’s just getting started.
Conclusion
Start with a plan. Start small. It’s still a relative wild west with AI and there is a lot of runway for experimentation and planning a solid foundation for your marketing team.