As organizations continue embracing AI-driven approaches, marketing leaders should be empowered to push the boundaries of innovation and drive better results with less manual, time-consuming effort — but it requires thinking beyond basic applications. 

AI tools and large language models (LLMs) are changing the content marketing process, impacting how we conceive, produce and distribute content. Enhancing the creative approach to improving existing processes leaves us in a position to focus more on creativity and optimization. 

Below, I delve into the content marketing lifecycle to show how content teams are incorporating generative AI at different stages. 

1. Insights and research

Typically, an early phase of the content marketing lifecycle involves gathering data and conducting trend research to inform your content strategy. It’s a foundational step that ensures the cornerstone content your team publishes is relevant and engaging to your target audience and aligned with strategic goals, market dynamics and industry trends. 

Many major marketing tools already use AI models and machine learning to work behind the scenes. These days, they’re adding more AI features to help retrieve detailed insights more easily. At the same time, we’re seeing new marketing tools emerge that focus more on AI to more deeply understand what people like, how they behave and how they interact with content.

Tools for content marketing research and insights: 

  • Consensus AI, billed as “ChatGPT for Research.”
  • Brandwatch for consumer research on social media and influencer insights.
  • Perplexity AI, a real-time AI-powered search engine for the web.
  • Trendup AI for finding trending topics on social media.
  • for trending keywords in search.

Use these tools to power up your content research, enabling your team to build more effective content strategies that deliver impact and without draining time or quality. 

Dig deeper: 9 best practices for AI tools in content creation

2. Ideation: Using AI to expand content themes and plan for future activations

Every great piece of content starts with a compelling idea, but generating those ideas consistently is where creative marketing teams can easily run into bottlenecks. In these situations, AI is a brainstorming partner that never lacks suggestions. However, only 33% of marketers using AI task it with generating ideas or content inspiration, according to a recent survey from HubSpot.

  • GPT-4 via ChatGPT Plus, Bing or an API.
  • Google’s Gemini product suite.
  • Anthropic’s Claude models.
  • Meta AI’s LLaMa models.
  • StoryLab AI.
  • TweetHunter.
  • HypeFury.

These tools can leverage trending data, industry insights and even competitor analysis to suggest themes, expand ideas and refine existing unfinished ideas to resonate with your target audience. Many popular language models like GPT (and even open-source models like Meta’s) are gradually being integrated into enterprise-level content marketing software. 

Be sure to check the tools your marketing department is already using, especially since platforms like Salesforce, HubSpot, and Pipedrive are rapidly integrating AI features to improve user experiences. 

Real-life application: I worked with a client whose brand was constantly up against creative blocks when planning content, with not enough time or resources to spend on deep-dive content research. By feeding GPT-4 the baseline content strategy, target audience and important campaign themes, we were able to fine-tune a list of engaging content ideas that seemed feasible and aligned with the bottom-line goal. 

The interplay between AI’s critical reasoning ability and creative human nuance can easily extend to other areas that require strategic conceptualization and ideation, from creating ways to repurpose an ebook to curating a mood board for a social media activation. 

Tools that go beyond LLMs and use AI in a visual context: 

  • MidJourney.
  • OpenAI’s DALL-E 3. 
  • Adobe Firefly.
  • Google Gemini.
  • Ideogram.
  • Stable Diffusion.
  • Runway ML.
  • OpenAI’s Sora. 

Dig deeper: The new frontier of visual content: A marketer’s guide to AI

3. Creation: AI-assisted content execution and design

This phase of the content marketing lifecycle involves production. While roles vary between organizations, content production is often the responsibility of copywriters, designers and managers. 

Although AI writing assistants and image generation tools aren’t a replacement for human creativity, they have a lot of value in enhancing the creative production process.

Combining AI and creativity

Magic happens when human creativity can be leveled up with some help — which is why I recommend using AI’s efficiency. For instance, writers can refine AI-generated drafts to inject brand personality, and AI-suggested designs can be customized to perfectly match a campaign’s aesthetic or brand’s visual identity. This blend ensures that content is produced quickly and maintains the high-quality and unique touch that audiences crave.

From using AI’s support to develop visual assets to integrating deeper, more valuable messaging with the help of an LLM, here are a few ways content teams are already leaning on AI through the production process: 

  • Curating mood boards and visual themes. 
  • Producing varied character descriptions for storytelling.
  • Creating unique visuals or graphic elements.
  • Generating backgrounds or landscapes for visual projects.
  • Simulating different lighting or atmospheric conditions in scenes.
  • Providing language translations for global campaigns.

Dig deeper: How AI enhances multimedia content creation

4. Reporting and analysis

The final piece of the content marketing lifecycle involves understanding the impact of content to inform future strategies. AI tools can help make sense of these insights, providing richer context and more meaningful data points for subsequent content executions. 

Sentiment analysis

With AI able to quickly ingest vast amounts of online and proprietary campaign data, it can more easily pinpoint how people feel about a brand or topic across different websites. This translates to more informed decision-making around what people currently think about your brand, what’s trending and what your audience is interested in. AI-enabled sentiment analysis, in turn, can impact your content output and strategic messaging. 

Informed strategy adjustments

With budget cutbacks at an all-time high, campaign optimization is key to making every dollar count. By tapping AI (whether built-in to the tools you already use or added on top of the martech stack), you can approach content optimization more easily and efficiently, focusing efforts where they’ll have the most impact. 

  • Sprout Social.
  • Twilio’s Segment.
  • Crayon AI.
  • GPT-4 custom APIs. 

Dig deeper: AI and machine learning in marketing analytics: A revenue-driven approach

The AI transformation is here — and it’s changing content marketing

Integrating AI tooling across the content lifecycle offers a clear path to more efficient, targeted and impactful marketing operations, empowering marketers — especially those focused on content — to achieve targeted success in their campaigns.

With AI in the fold, you can adapt, learn and glean increasingly sophisticated insights that open new avenues for creativity and engagement. 

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