You can’t automate brand voice, but you can train AI to respect it

Your marketing team is under pressure. You need content that converts leads and stays true to your brand. Digital experience platforms (DXPs) promise to streamline this, but many CMOs find their teams bogged down by manual processes or inconsistent outputs. 

AI can help if used strategically, with 35% of marketers listing content creation as a top AI application. Most AI-generated content feels generic because marketers often treat it like a magic button, rather than a tool that requires direction. Let’s break down how to use AI effectively.

Start with your brand’s voice

A DXP with generative AI can produce text, images or videos in seconds, but they often sound generic. Your brand has a specific voice (witty, authoritative or empathetic) and your audience notices when the content doesn’t match.

Document your brand voice in a style guide. Conversational brands ban formal phrases like “furthermore” and use contractions like “you’re.” Technical brands prioritize precision and avoid slang. Feed these rules into your DXP’s tone and style settings.

Your team needs to review every AI output against this guide. Say your brand avoids hype. A line like “revolutionary content creation” doesn’t match your voice. Cut it and replace it with “content that converts” instead. AI will drift from your voice unless someone’s checking.

Dig deeper: How to scale content without losing your brand voice

If your martech stack doesn’t have a DXP, you still need to maintain brand consistency across content. Custom AI assistants can help.

Tools like Custom GPTs (OpenAI), Gemini Gems (Google), Claude Projects (Anthropic) or Grok (xAI) let you create a branded AI assistant that remembers your guidelines. Feed it your brand voice guide, three examples of on-brand content, your keyword list and preferred CTAs—the more specific your examples, the better your outputs. The assistant stores these instructions and applies them every time you use it.

Custom AI assistants work because they maintain persistent memory. Unlike one-off prompts, they don’t forget your brand voice between sessions. Ask for a product launch email, and it applies your documented style automatically. That reduces editing time and ensures consistent outputs.

The limitation? You still need a strategy and clean data. A custom assistant can’t fix unclear goals or messy customer segments. If you can’t articulate what converts your leads, AI won’t be able to figure it out for you.

Test every output against your brand guide. If your assistant starts straying from your voice, refine its instructions with specific corrections. Review 10 pieces of AI content, mark what works and what doesn’t, then refine the prompts. Consistency improves with feedback, not hope.

Use AI to scale your strategy

AI excels at repetitive tasks, letting your team focus on strategy. Up to 79% of businesses report an increase in content quality when using AI tools. Pair AI drafts with human oversight to align with your content goals. 

Map content to business outcomes. To reduce customer acquisition costs, use AI to generate personalized email subject lines based on user behavior data. To boost CLV, have AI draft upsell messages for high-value segments. Conversational keywords that mirror natural search patterns are crucial for achieving visibility in 2025. These tasks work because they tie to revenue.

Configure your DXP to limit word count, enforce brand keywords and prioritize your CTAs. Test outputs in small batches (10 emails or one landing page) before scaling. Measure click-through rates and conversions. If the numbers don’t improve, adjust the prompts or drop the approach.

Dig deeper: My AI marketing team has a professor, a writer and a slick salesperson. Yours can, too.

Clean your data for better output

Many teams struggle with fragmented or outdated customer data, and AI often draws from these same sources.

Start with customer behavior data. Your DXP needs to unify data from your CRM, website, and email platform. AI generating personalized web content requires accurate data on user preferences and past interactions. Miss key details like cart abandonment, and your AI will serve a generic ad instead of a targeted discount.

Assign someone to verify that your DXP’s data connectors are pulling accurate, real-time information. If your AI uses outdated segments, you’re wasting resources. Hold vendors accountable for integration issues and demand SLAs that guarantee data syncs work.

Train your team to focus on results

Your team may be familiar with your DXP’s interface, but can they effectively tie AI-generated content to revenue? Most marketers get stuck mastering tools instead of driving results. Teach content creators to ask, “Does this piece drive conversions?” If AI suggests a blog post on industry trends, check if it addresses a customer pain point. If not, cut it.

Pair AI with human oversight. Strategists should define goals, like increasing lead quality by 10% and let AI handle drafting variations. Analysts should measure performance, not compile reports. If your DXP’s AI generates 20 social posts, pick the five that best align with your goals and test them.

Marketers should be cautious of vendor claims regarding capabilities such as real-time personalization. When your CEO asks why leads aren’t converting, “AI-powered” isn’t an answer. Demand proof that their AI delivers measurable outcomes.

Ask vendors for case studies from companies like yours. If their AI claims better email open rates, run a 30-day pilot against your current approach. Measure open rates and conversions. Negotiate SLAs tied to your results, like improved click-through rates. Focus on what their tool does today, not future promises.

Make AI work or watch it fail

Too many teams treat AI like a fix-all. It’s just a tool that magnifies whatever you feed it. Bad strategy and messy data produce bad content faster, while clear direction and clean systems produce results that scale.

Four areas determine whether AI helps or hurts:

• Brand voice: Document your tone and style before AI touches anything. Without this, every output needs heavy editing.
• Business outcomes: Connect AI tasks to acquisition costs, lifetime value or conversion rates. Generic content creates generic results.
• Data quality: Clean up fragmented customer data. AI amplifies whatever information it receives, including garbage.
• Team capabilities: Train people to measure results, not operate software. Hold vendors accountable with performance data.

Your DXP’s AI can generate content all day. Your job is to ensure it drives revenue and aligns with your brand. Most marketing teams won’t do this work. They’ll chase features, trust vendor promises and wonder why their AI-generated content underperforms. That’s your opening.

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