Navigating AI in social media marketing – top models, efficiency gains, brand integrity and the future role of agencies

As AI language models become integral to businesses across Australia, the focus has shifted towards understanding their practical applications.

Entrepreneurs, senior leaders and marketing teams alike are exploring how these technologies can drive efficiency, boost productivity and maintain brand integrity. One sector where this transformation is particularly evident is social media marketing – a vital part of modern marketing strategies.

The adoption of AI for tasks like refining post copy or generating visually compelling content is already widespread. However, the question remains – how will marketing agencies and teams leverage this technology moving forward? What are the critical considerations and safeguards needed to ensure that AI-generated content aligns with a brand’s unique voice and stands out in an increasingly crowded digital space?

This blog aims to answer these questions. You might wonder why a marketing agency would openly discuss strategies that could potentially reduce traditional service fees. The answer is straightforward – like businesses that fail to embrace AI, agencies that do not adapt will miss out on the efficiency and productivity gains AI offers.

Here at Sketch Corp, are we worried AI will replace human roles in marketing? Not at all. While AI might render some traditional services obsolete, it can’t replace the nuanced understanding of a brand, its audience or the dynamic shifts in market trends. Herein lies the crucial role of marketing agencies and teams in 2025’s social media landscape – they are the drivers, ensuring that AI’s output aligns with strategic brand objectives while maintaining quality and uniqueness.

Here’s what we’ll cover in this blog:

• • The top AI models and their uses. What model should you use for your social content, when and why?
  • Achieving brand consistency when using AI tools for social media content creation.
  • Ensuring brand differentiation with AI in social media content creation.
  • The role of marketing agencies in the era of AI advancements in social media marketing.

The top AI models and their uses

As of January 1 2025, there are over 1,328 large language models (LLMs) available globally. China accounts for approximately 36% of these models, with the United States leading in the number of LLMs. The global LLM market has experienced significant growth, with projections indicating an increase from $1.59 billion in 2023 to $259.8 billion by 2030, reflecting a compound annual growth rate (CAGR) of 79.8%.

In our humble opinion, there are five models at the forefront of the AI language model offering which stand to benefit marketing teams and content creators heading into 2025. We do, however, add the disclaimer that the environment is ever-changing and it’s highly likely that by this time next year, there will be new entrants in the mix. These are:

GPT-4 by OpenAI

• • Description – An advanced version of the Generative Pre-trained Transformer series, ChatGPT is known for its versatility in handling a wide range of tasks from text generation to complex problem-solving. It’s multimodal, capable of processing both text and images, and can learn a brand’s preferences and even tone of voice.
  • Usage – Widely used in applications like content creation, as customer service bots and as an enhancement to various software tools like Microsoft’s Bing search engine.

Claude 3 by Anthropic

• • Description – Claude is noted for its safe AI practices, aiming to produce less harmful or biased content. It’s designed for tasks requiring nuanced understanding and ethical considerations.
  • Usage – Employed in business settings for document summarisation, analysis and as an assistant in coding or research tasks.

Google Gemini

• • Description – Google’s latest iteration in AI language models focuses on improving multilingual capabilities and integration with Google’s broader ecosystem.
  • Usage – Powers Google’s Bard chatbot, enhancing functionalities like text generation, translation and aiding in complex queries across Google services.

Grok by xAI

• • Description – Known for its unique approach to AI with elements of humour and sarcasm, Grok aims to provide answers with a distinct personality, leveraging real-time data from X (formerly Twitter).
  • Usage – Used for engaging, conversational AI experiences, with potential applications in social media interactions and customer engagement.

Llama by Meta

• • Description – Llama is an open-source model, which has been fine-tuned for various applications, providing a cost-effective alternative for organisations looking to implement LLMs without high costs.
  • Usage – Utilised in academic and commercial settings for research, content generation and as a base for further model customisation.

Achieving efficiency gains in social media content creation

Leveraging the top AI language models for content creation and social media management involves strategic use of each model’s unique strengths, while ensuring content remains on-brand and effective.

1. GPT-4

• • Content creation – Use GPT-4 to generate diverse content types like blog posts, product descriptions or social media copy. It can adapt to different tones and styles, making it excellent for maintaining brand voice across various platforms.
  • Strategy
    • Drafting: Start with broad prompts to generate initial drafts, then refine for tone, style and brand specifics.
    • A/B testing: Utilise its capabilities for generating multiple variations of posts to test which work best.
    • Scheduled posts: Automate the drafting of regular posts, ensuring they align with current marketing campaigns or trends.
    • Engagement: Create response templates for common customer interactions, reducing response time while keeping messages on-brand.

2. Claude 3

• • Content creation – Ideal for content that requires a nuanced understanding or ethical considerations, like sustainability reports or sensitive consumer interactions.
  • Strategy
    • Content review: Use Claude for reviewing content for ethical alignment or potential biases before publishing.
    • Educational content: Leverage for creating detailed, accurate and responsible educational content.
    • Crisis management: Prepare responses for potential PR issues with a focus on empathy and ethical considerations.
    • Community guidelines: Help in drafting or explaining community guidelines in a user-friendly, ethical manner.

3. Google Gemini

• • Content creation – Its strength in multilingual capabilities makes it perfect for global campaigns or localising content.
  • Strategy
    • Multilingual content: Generate content in multiple languages, ensuring cultural nuances are respected.
    • SEO optimisation: Use for keyword research and content optimisation to improve search visibility.
    • Global engagement: Manage social media for different regions by adapting content to local languages and customs.
    • Trend analysis: Integrate with Google’s tools for trend analysis to time posts for maximum impact.

4. Grok by xAI

• • Content creation – Grok’s unique personality can be harnessed for engaging, humorous content that stands out.
  • Strategy
    • Engagement content: Use for creating memes, witty responses or light-hearted posts that can increase shareability.
    • Brand personality: Define a playful or quirky brand voice that Grok can enhance.
    • Real-time interaction: Its real-time data capability can help in crafting timely, relevant posts or responses.
    • Community-building: Foster a community vibe through humorous, engaging interactions.

5. Llama by Meta

• • Content creation – As an open-source model, it’s cost-effective for creating bulk content like social media updates or basic FAQ responses.
  • Strategy
    • Customisation: Fine-tune the model with brand-specific data for more personalised content creation.
    • Volume: Use for high-volume content needs, like daily social media posts or newsletters.
    • Social media management: Use it for ongoing social media updates.
    • Automation: Automate routine posts, and free up human resources for more strategic tasks.
    • Analytics: If integrated with analytics tools, use it to generate insights or automated reports on performance metrics.

General tips for all models

• • Brand guidelines – Always program or prompt these models with detailed brand guidelines to ensure content consistency.
  • Human oversight – Maintain a human review process to catch nuances AI might miss, ensuring brand voice and accuracy.
  • Integration – Use these models within a broader social media management toolset for scheduling, analytics and multi-platform posting.
  • Feedback loop – Continuously feed back successful content types or phrases into your AI’s learning model for improvement.

Achieving brand consistency when using AI tools for social media content creation

AI language models can be effectively utilised to create on-brand content for social media by integrating brand-specific guidelines into their training or operational prompts. Initially, these models can be trained or fine-tuned on a dataset that includes the brand’s historical content, style guides and voice documents. This training involves feeding the AI with examples of past successful posts, brand messaging, tone of voice and even specific vocabulary or jargon that the brand employs. By doing so, the AI learns to mimic the brand’s unique style, ensuring that generated content resonates with the brand’s established identity.

Additionally, continuous feedback from human moderators or brand managers can be used to refine the AI’s outputs, correcting deviations from the brand voice or style, which helps in maintaining consistency over time.

To ensure the AI consistently produces on-brand content, marketing teams can employ several strategies post-training. One effective method is to use prompt engineering, where specific instructions or questions are framed to guide the AI to produce content within the desired parameters. For instance, prompts could include directives like “generate a tweet in our brand’s playful tone” or “create a caption that reflects our commitment to sustainability.”

Moreover, integrating real-time feedback mechanisms allows for immediate adjustments. Tools can be set up to flag content that might not align with brand guidelines, such as checking for tone, sentiment or even cultural sensitivities. This involves human-in-the-loop processes where AI drafts are reviewed before publication, ensuring they meet the brand’s standards. Over time, as the AI processes more brand-aligned feedback, its performance improves, leading to more accurate and brand-consistent content generation for social media platforms.

How marketing teams and content creators can ensure differentiation when it comes to AI and social media

Marketing teams and content creators can ensure brand differentiation in AI-generated content by infusing unique elements that reflect the brand’s identity into the AI’s training and operation. This starts with embedding distinctive brand attributes, such as specific storytelling methods, unique humour or proprietary insights, into the AI’s learning data. For example, if a brand is known for its quirky humour, the AI can be trained with examples of past humorous posts or scripts from brand’s ad campaigns. Customising prompts to include these unique elements when generating new content ensures that the output maintains the brand’s distinctive voice, even amidst a sea of generic AI content. Additionally, integrating niche-specific or industry-related terminologies that are unique to the brand can help in creating content that feels bespoke and tailored.

Further differentiation can be achieved by regularly updating the AI with new data points that reflect current brand activities, campaigns or cultural trends relevant to the brand’s audience. This dynamic approach ensures the AI does not just replicate past content but evolves with the brand. Marketing teams should also use AI in conjunction with human creativity for the final touches – perhaps adding a personal anecdote, a timely reference or a visual element that only human insight can perfectly align with the brand’s ethos. By combining AI efficiency with human creativity, marketers can produce content that not only stands out but also carries the unmistakable signature of the brand, making it less likely to blend into the generic AI-generated landscape on social media.

What’s the role of a marketing agency, given the AI advancements in social media content creation?

In the era of AI advancements in social media content creation, the role of a marketing agency has evolved significantly but remains indispensable. Traditionally, agencies have been the architects of brand strategy, content creation and campaign management. With AI tools now capable of automating much of the content generation process, agencies shift towards a more strategic, oversight role. They leverage AI to enhance efficiency, ensuring that the content produced aligns with broader marketing goals while maintaining brand consistency. Agencies are now responsible for curating AI outputs, selecting the best content for brand voice, and integrating these with human-crafted elements where AI might fall short in creativity or nuanced understanding.

Marketing agencies serve as the guardians of brand identity in an AI-driven landscape. They train and fine-tune AI models with brand-specific data, ensuring that every piece of content reflects the unique personality and ethos of the brand. This involves not just the initial setup but ongoing management, where agencies refine AI models based on performance metrics, audience feedback and market trends. Agencies also play a critical role in differentiating content from the generic AI output seen across platforms. They do this by adding layers of creativity, strategic insight and human touch that AI alone cannot replicate, such as developing unique campaigns, storytelling or engaging directly with the community through personalised interactions.

Finally, with AI’s capabilities, agencies can focus on higher-level strategic planning, such as understanding demographic shifts, predicting consumer behaviour with AI analytics, and crafting multi-channel strategies that leverage AI’s strengths across different platforms. They become the strategists who not only use AI for content but also for deep analytics, campaign optimisation and personalised marketing. This evolution allows agencies to offer more value through strategic foresight, ensuring that AI advancements amplify, rather than replace, the nuanced, human-driven aspects of marketing.