For the last twelve months, we’ve been banging on about how the rise of AI copywriting is more tentative than you might expect, with fewer than 14% of UK businesses currently using LLMs to generate written content.
One thing we have learned, from our friends at Brightful, who specialise in AI-integrated creative workflows, is that successful AI adoption is often hampered by inadequate orchestration – that’s the people-led process of harmonising tech solutions with human skills to create effective workflows.
So how can marketing teams achieve successful orchestration? Firstly, by not being too seduced by AI – or at least by the surrounding hype.
The early excitement around GenAI back in 2023 focused on the limitless possibilities of AI-powered automation. It was hard not to be seduced by the idea that tools alone could unlock dramatic productivity gains. In practice, GenAI without process, structure and governance rarely delivers on that promise.
When AI is bolted onto existing processes, it often magnifies their weaknesses. Unclear briefs lead to unclear prompts, which in turn lead to inconsistent outputs. Likewise, poorly-defined review stages actually increase internal rework. Disconnected processes create new silos rather than removing old ones.
In this context, AI can feel like more to manage, not less. In many cases, outsourced talent may be needed to bail out overworked internal teams.
This is what gives organisations pause.
It’s not that AI lacks value. It’s that the value is not immediately accessible without rethinking workflows.
Creating an AI workflow isn’t just about selecting the right tools and hoping for the best. It’s about using AI to support teams by automating repetitive, rules-based tasks.
This is the essence of orchestration. When done right, it can help teams produce consistently high-quality content at scale. Moreover, it can make their working days less stressful and more creatively rewarding.
As Brightful founder Richard Coope puts it, “Brands are under pressure to deliver increasingly personalised content quickly and at scale. Legacy processes typically aren’t up to the challenge. Scale often comes at the expense of consistency. When they have the right orchestration in place, teams can create once and adapt many times. AI automation manages repetitive production tasks, freeing creative teams to focus on impact and storytelling.”
How is successful orchestration achieved? Here are the key strategies that teams need to meaningfully integrate AI. When done right they should enhance rather than dilute everyone’s creative potential.
The first step is to map the content supply chain – the multi-stage process that covers the entire content production cycle.
It includes:
In many organisations, this chain evolves organically. Tech tools and processes are added over time by necessity rather than design. As teams scale, the chain stretches and strains.
The aim of mapping is to identify points of friction where AI-powered automation could ease the flow of content.
Once the ‘where’ has been established, teams can focus on the ‘how’. At the core of successful orchestration is the use of toolkits to create content.
Toolkits ensure that AI-assisted content is matched to your brand’s standards and your client’s expectations. They are an essential guardrail that ensures quality while reducing the risk of task duplication.
Examples of toolkit content include:
LLMs are trained on vast datasets. This enables them to generate an answer to any prompt. Unfortunately, they’re unable to discern the quality of the sources they scour. A Reddit post carries the same legitimacy as a book by an accomplished expert. This accounts for much of their inaccuracy and hallucination.
Custom AIs allow users to supplement LLMs with client resources like ToV guidelines, brand bibles and campaign overviews. This helps AI-generated content feel more authentic – especially when paired with human editing. Custom AIs can also ingest a client’s proprietary data, research and keystone content. This allows them to incorporate information not readily available on the web into content with brand authority.
Teams build up large collections of digital assets over time. Repurposing these assets can be a real boon to automated workflows. However, large asset libraries can easily become unwieldy. Being able to access this content quickly allows workflows to stay fluid.
Tasks like asset tagging, versioning and metadata management for shared digital assets can be automated using AI. This makes digital resources readily accessible and frees teams up for more important and enjoyable work.
Meaningful GenAI adoption doesn’t start with choosing a platform. It starts with orchestrated workflows that make life easier and more creatively fulfilling for teams. When that happens, integration stops feeling like a leap of faith and starts feeling like a natural next step.
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