Introduction
Let’s start by acknowledging that Artificial Intelligence in content production, much like creative automation, is not coming… it’s here.
The rise of AI has reshaped various industries, and content production is no exception. As in-house agencies, brands, and agencies continue to experience growing pressure to deliver high-quality, fast-turnaround content at scale, AI offers a compelling solution to meet these demands. Yet, despite its potential, many in-house teams, brands and agencies are either slow to adopt AI or uncertain of where it fits within their production processes.
This, coupled with the sheer volume of tools now available that are specific to AI content generation, makes it an increasingly complex challenge. It’s also important to note that AI is not automation and vice versa, yet they are often confused because they go hand-in-hand.
So, where do we currently stand?
There is a broad spectrum of AI adoption in content production. The nature of which varies based on appetite. Some teams have embraced AI tooling with enthusiasm while others remain cautious, unclear on if AI can deliver without compromising the quality of creative output and often worried about the impact it will have on their teams. This is then overlayed by additional concerns relating to intellectual property and copyright, as well as data accuracy.
From our perspective, the contrast between the organizations we work with can be stark. Beyond the obvious, there are so many additional factors which exist within organizations that also influence the uptake of AI. Business appetite, internal politics, budget availability, and skills deficits on both the source and selection side as well as the implementation and day-to-day usage of AI, to name a few.
There is clearly no one-size-fits-all solution on the ground, even though there can be a certain amount of commonality in the overarching strategic approach one might have in helping one’s clients. To start, they all have varying degrees of maturity on their AI journey, with some only at inception and looking to start, while others are charging ahead and already looking at their next evolution.
Most commonly we see three categories:
- Early Adopters – who are boldly going where innovators have opened doors;
- Early Majority – who are cautious experimenters who have seen the light; and
- Outright Sceptics – who are framing up to be laggards in the innovation lifecycle.
1. Early Adopters
These organisations have recognised that there are clear benefits with teams leveraging AI for ideation, content optimisation, and localisation. For example, one of our clients has recognised the areas where AI excels in speeding up translation tasks without sacrificing creativity or the nuances of localisation. They are currently building a bespoke platform to manage their production workflows that will lean heavily on AI to optimise their operational speed and capacity.
2. Early Majority
The folk are exploring AI through small pilot projects. For example, one of our clients, an in-house agency, has started using AI-driven tools like Jasper AI to generate first drafts of blog posts, product descriptions, and social media copy. They don’t fully rely on AI for final content, but they’re discovering how much time can be saved by using AI to handle initial drafts, which their human writers can then refine and polish.
3. Outright Sceptics
Hesitant to embrace AI, they typically object to the wave that needs to be caught. They find barriers-to-entry, mostly internally, and tend to fear the impact it may have on their current status quo. Particularly in relation to resourcing and the potential impact it may have on their finalised output, as well as their jobs. They worry that AI-generated content may lack the authenticity and emotional depth that their brands are known for.
While this is a diminishing category it still exists, but the growing recognition of AI on a global level is encouraging them to view AI as an augmentative tool. Which is why we end up speaking to them.
What are some potential use cases for AI in content production?
Realistically, there are a multitude of touchpoints in the creative production space where AI can supercharge a team’s ability to deliver high quality content at scale.
However, we see four distinct pillars where AI is actively being used to support this:
- Content Ideation – where AI tools are leveraged for brainstorming creative ideas and generating draft content;
- Content Creation – where AI powers design tools, video creation platforms, and text generators;
- Content Optimization – where AI tools speed up the briefing process, or SEO audits and gap analysis, ensuring content production meets several criteria and tailored for both search engines and audiences alike; and
- Localization & Translation – where AI-enabled tools adapt content across languages and regions efficiently.
1. Content Ideation:
Whether it’s blog posts, marketing copy, or even video scripts, tools like ChatGPT, Jasper AI, Claude, and Copy.ai are fully capable of assisting in generating initial ideas and content drafts. This essentially helps overcome white paper syndrome.
These tools essentially analyse and repurpose existing data, identifying patterns in the public domain as well as from previous work (and whatever else you feed them) to produce creative ideas and text, generating multiple content options quickly. This gives creative teams the ability to ideate rapidly and spin through creative concepts until worthy ideas reveal themselves.
While AI-generated content often lacks the nuance that human creatives bring, it provides a solid starting point that can be refined and tailored.
2. Content Creation:
When it comes to generating creative assets, AI has made remarkable progress in generating content. From copy to custom images and videos based on text prompts. These are particularly useful for rapid prototyping visual elements, saving time and money in the early stages of production.
Again, tools like ChatGPT, Jasper AI, and Copy.ai are generating copy content that is virtually indistinguishable from human outputs. Dall-E, Midjourney, and Adobe Firefly are great for generating images from text prompts. And automation tools like Celtra and Chili Publish go a step further in giving teams the ability to tweak and scale approved concepts for different applications, based on things like size, format, location, and language.
Generative AI video tools like Runway and Lum AI are revolutionising video from text prompts, paving the way for virtual shoots and automated scene creation. Synthesia allows teams to create professional-quality videos using AI avatars and natural-sounding voiceovers, eliminating the need for costly studio shoots.
AI is particularly beneficial for creating marketing videos and instructional content at scale, where personalisation is key and regular production costs are prohibitive. AI has a definitive role to play throughout the creative and production process.
3. Content Optimization:
Need to optimize content for SEO? Look to MarketMuse and Clearscope assist. These tools analyze existing content to identify gaps, suggest relevant keywords, and provide insights on how to improve search visibility. They are invaluable for ensuring that content is both discoverable and aligned with what users are searching for.
When it comes to speeding up your briefs we’ve found that ChatGPT works well, but can be laborious in feeding it the right prompts and data. MarketMuse looks to have the potential. You also need to be careful as a lot of AI tools claiming to deliver briefs really only deliver content outlines. This is an area of AI that probably needs a bit more time to reach maturity.
4. Localization & Translation:
AI is becoming invaluable in supporting the localization and translation process, ensuring that content resonates with different cultural contexts while maintaining brand consistency. These days personalization at scale is fundamentally important.
For this kind of functionality, look to players like iLangL, Jacquard, Persado, and Xbench. They all use AI to tailor messaging based on user data, helping brands deliver personalized content that improves engagement and conversions.
What’s the verdict?
Our view remains that AI is more augmentative than anything else. For now.
While it brings significant value across production, the creative process, and the ability to scale outputs… it’s not quite an all-encompassing solution. The real key to success lies in your ability to understand how your AI tools augment your current processes to solve bottlenecks and provide better quality outputs for your brand, to free up creatives to be creative. It’s not a solve-all.
While investing in AI is critical, we still have a lot more to look forward to. Continuing to evolve with the right strategy and investment will ultimately offer significant value improvements that can and will grow. That much is certain.
