As quickly as AI became a go-to tool for marketing teams, new problems began to surface. While efficiency is up, so are confusion, generic content, and unnecessary costs.
In this article, we break down seven common AI challenges marketers face—and how to solve them.
1. Your content sounds too generic
The challenge:
AI tools can generate a high volume of content in record time. Great for efficiency—less great for brand distinctiveness. The result? Generic copy, bland messaging, and a diluted brand experience. Your audience loses interest.
The fix:
Define your brand voice. What personality traits set you apart? How do you sound? Use that as a foundation when crafting prompts. Don’t be vague—avoid asking for “an article about challenges in marketing teams.” Instead, describe your target audience, outline the structure you want, or paste previous content as a reference.
Always refine your output. AI is a fast producer, not a final editor. You’ll still need to polish, adjust, and make sure the final version aligns with your brand identity.
2. Your AI tools cost more than they deliver
The challenge:
It started with one tool to save time. Then came another. And another. Now your team’s tech stack is bloated, and what was meant to boost efficiency is hurting your bottom line.
The fix:
Take inventory. What tools are you paying for, and what are they actually solving? Chances are, you’re using multiple tools for the same job. Audit your stack—and trim the excess.
3. You’re automating the wrong things
The challenge:
Many teams automate what's easiest to automate—not what would save the most time. So you skip manual steps where it’s quick, but keep doing high-effort tasks manually where automation would make a real difference.
The fix:
Start with a time-value audit. Identify which tasks take up too much time while offering low strategic value. Prioritize automation there first.
4. You’re doing more—but not growing
The challenge:
AI often helps teams do more of the same—but not better. It ends up rephrasing old ideas instead of unlocking new ones. That means missed opportunities for customer insights, innovation, and business growth.
The fix:
Use AI as an analysis tool—not just a content machine. Ask smart questions, feed it data, request insights. What’s missing in the market? What are competitors doing? What do customers actually care about? Use the answers to drive smarter, more strategic decisions.
5. You don’t know what’s working
The challenge:
AI is used in many places, but few know what impact it actually has. Without clear goals or metrics, it's hard to tell what's working—and what's just noise.
The fix:
Set clear KPIs before you test a new AI tool. Is the goal to save time? Improve conversions? Boost quality? Track, evaluate, and adjust. That’s how you separate tools that deliver real value from the ones that don’t.
6. AI acts in ways your brand can’t stand behind
The challenge:
AI models draw from large datasets but don’t always grasp nuance, culture, or brand values. This can result in inappropriate, confusing, or off-brand messaging.
The fix:
Create clear AI usage guidelines. Communicate that AI is a support tool—not a standalone communicator. Always review the final output through the lens of brand tone, context, and ethics.
7. Everyone’s testing—without a shared direction
The challenge:
One team uses AI to boost production speed. Another uses it to analyse campaign data. Everyone’s experimenting—but without a shared goal, it's hard to build momentum.
The fix:
Align on what AI should help you achieve. Faster production? Smarter customer experiences? Reduced costs? Once goals are clear, it’s easier to prioritise efforts and measure success together.
Get more value from AI
Using AI isn’t enough. You need to understand why, how, and what it’s helping you achieve. Done right, AI can help you work smarter, produce better content, and make more insight-driven decisions.
Curious about how to get more from your AI efforts?
Let’s talk. We’ll help you find the right way forward
