Does Your AI Speak Your Brand Voice?

Is your AI brand voice recognizable?

Statistics indicate that 61.4% of marketers have used AI in their marketing activities, with 44.4% saying it’s most used for content creation. Not only does generative AI promise added speed and ease of content creation, it’s also possible to tailor the way your content speaks to different target audiences, offering an unprecedented level of personalization. But while it’s possible to personalize the brand voice for each individual customer, companies need to retain the essence of what makes their brand recognizable to everyone. Personalization doesn’t negate the need for a consistent brand voice.

That’s why we’re asking, does your AI speak your brand voice? And if it doesn’t, should it?

The components of a successful brand voice

A brand voice is the consistent and distinctive way your brand communicates with your audience. It’s your brand’s unique personality and style reflected in language at every stage of the customer journey.  

An established and maintained voice prevents mixed messaging from ruining the customer experience. It also offers your audience an authentic way to understand  your company’s core values and brand personality, giving them even more reason to trust you.

The components of a successful brand voice include:

• Consistent expression of voice, style, tone, and terminology.
• A unique personality reflected in all communication with your audience.
• Clear content strategy and governance. 

Grounding your own private LLM on your company style guide requires intimate knowledge of the language elements that make up your brand identity. Without that, LLMs may produce content that contains mixed messaging, forms of humor that alienate your audience, or the wrong technical terminology. All that makes a brand appear unsure of its identity and unreliable. Not to mention it requires extensive editing to make LLM-content compliant with your style guide, negating some of the initial time-saving benefits. 

AI-generated content that sounds like your brand

Many enterprises are opting to deploy a private LLM or a private account of a public LLM (like OpenAI or Azure) because they have sensitive enterprise content that needs to remain private and accurate. Grounding your model on quality content reduces the chances of it creating content that’s off-brand, is non-compliant, or uses discriminatory language. 

The recipe for success? Making sure LLM-generated content sounds like your brand starts with high-quality source content. And then maintaining it across the enterprise with content governance, implementing AI guardrails at different stages of your content supply chain. 

Here are five suggestions, ranging from easy to more technical, to make sure LLM-generated content aligns with your brand’s style guide and maintains a unified brand voice. 

Easy	Medium	Technical
Curate a collection of existing content that captures your brand voice and style for different content types and use them to help the LLM understand the tone and formats that are already working well for your brand.	Create content filtering options so you can exclude outputs that don’t meet your brand voice or standards. For example, filter out highly technical words for a brand targeting a non-technical audience.	Fine-tuning adjusts a pre-trained LLM to a specific task by further training the model on a narrower dataset for a specific application – like a customer service chatbot or medical research. This process refines the model’s weights and biases to better perform on the target task.
When prompting your LLM to generate content, include keywords and phrases that reflect your brand’s style guide. For example, if your brand is playful, use words like “fun” or “conversational” in your prompt.	Provide the LLM with feedback on its generated content. Point out the specific issues and offer preferred words and phrases to help your LLM improve over time. Alternatively, some LLMs allow you to upload your style guide as a PDF.	RAG is the most effective technique for LLM grounding. RAG enriches LLMs with your trusted, up-to-date business data by referencing an authoritative knowledge base outside of its training data sources before generating a response. This improves the relevance and reliability of LLM responses.

Acrolinx helps regulated industries govern AI-generated content

All companies face the same challenge: The content your LLM learns from must meet even your most complex writing standards, of which brand voice is just one. Acrolinx is your enterprise content insurance policy. Our AI-powered content governance software captures and digitizes your style guide to make your writing standards, standard. By using Acrolinx you’ll never have to worry about “garbage in, garbage out” again. 

Acrolinx can automatically check 100% of your content to make sure your editorial review process scales at the same speed as AI-generated content creation. You can even set up quality control standards that automatically block content that’s off-brand from ever-being published, and send it back to writers for review. 

There, writers can review how well your content speaks your brand voice. Acrolinx offers improvement suggestions — based on your standards —  to writers in their favorite authoring environment. For ultimate writer productivity, they can use generative AI safely and effectively with our AI Assistant and Get Suggestions, where over 87% of tips to improve content are solvable with just one click. 

Ready to teach your LLM how to speak your brand voice? Download our checklist on how to prepare your content to ground your LLM today!