AI workflows - Storyblok: Delivering AI impact in the digital experience
This is a guest post for the Computer Weekly Developer Network written by Facundo Giuliana, solutions engineering team manager at Storyblok – the company is known for its headless content management system (CMS) designed to allow organisations to create, manage and deliver digital content across websites, apps and other platforms.
Reminding us that the digital experience world is undergoing a seismic shift and AI is at the heart of it, Giuliana says that – quite suddenly – things that used to take endless hours of manual work (such as hyper-personalisation at scale, localisation or customer forecasting) – can now be done almost instantly with smart automation and adaptive algorithms.
Still, he advises, while it can be tempting to get swept up in the excitement of AI, it’s crucial to first consider how to actually weave these new capabilities into existing systems and ways of working. That’s where the real challenge (and opportunity) lies, especially if you want to stay ahead in such a fast-moving market.
Giuliana explains in full and writes as follows…
AI workflow platforms: basics
When evaluating the AI workflow technology market, integration is everything.
Enterprises run on complex stacks – CMSs, CRMs, analytics tools – and any AI solution needs to fit in seamlessly, not create new silos. Features such as native integrations, robust APIs and pre-built components are essential for ease of deployment.
Transparency, security and compliance must be accounted for too. As we’ve seen too many times before, poor AI governance can lead to all manner of reputational damage, financial losses and legal risks. Avoiding this requires clear visibility into how AI models make decisions, along with audit trails, access controls and adherence to regulations like GDPR. A platform that lacks these safeguards risks eroding trust and slowing adoption.
Scalability and interoperability are also key. Modular, interoperable, composable software lets developers break free from monolithic systems, cut costs, speed up development and build solutions that actually fit their needs.
It’s becoming less of a nice-to-have and more of a strategy priority.
By 2027, Gartner predicts 60% of organisations will make composability a core part of their digital strategy. The most effective AI workflow platforms, therefore, are not just intelligent – they are connective, secure and flexible, designed to support enterprises as they grow, innovate and adapt to evolving digital landscapes.
Delivering on AI impact: Use cases
With the basics covered, the next big question is – where can AI deliver the biggest difference?
In our experience, AI is really changing the game when it comes to managing content workflows, particularly repetitive, time-consuming tasks such as tagging content, localisation, rephrasing and personalisation at scale. This automation significantly reduces manual effort and accelerates time-to-market.
Perhaps, though, the most notable impact is AI’s ability to bridge structured and unstructured data to make content more discoverable, contextually relevant and easier to find. This is a big win in terms of helping companies to get the right message to the right people quickly.
Inside an AI workflow
In terms of the type of data elements that go into AI workflow platforms and tools in the content process, it can vary a lot depending on the type of business.
However, generally speaking, these types of workflows will combine structured content models (such as schemas, taxonomies, metadata) with unstructured inputs like text, images, or video. For example, when a new entry is added to a headless CMS, it can automatically trigger an AI-driven pipeline that enriches metadata, generates translations, applies personalisation rules and then publishes the enhanced content smoothly across different channels.
Silos to synergy: tackling barriers
Like any powerful tool, AI workflows come with their own set of challenges. One of the biggest of these is fragmentation. Too often, AI tools exist in isolated silos that make it difficult to connect workflows end-to-end. This disjointed setup can slow down processes, create inefficiencies and lead to inconsistent results.
The way forward lies in embracing open APIs, adopting shared data standards and maintaining clear human oversight. This approach ensures AI outputs are consistent, explainable and trusted throughout the organisation.
The good news, too, is that this is changing. More recently, we are seeing AI workflows evolve from isolated automations to multi-agent ecosystems where specialised models collaborate in real-time. With standards like Modern Context Protocol (MCP), these agents can share context, coordinate tasks and plug into composable stacks, enabling orchestration that feels less like scripts and more like adaptive, collaborative systems.
Measuring success
Lastly, when evaluating the business value of AI workflow platforms for content management, metrics such as time-to-publish, content reuse and engagement uplift are critical. Equally important are operational efficiency metrics like reduced manual effort, lower error rates and measurable ROI from personalisation or localisation improvements.
Together, these metrics can provide a clear picture of how AI is improving both speed and quality, helping organisations deliver more relevant content faster while reducing costs.
