Abstract: Big tech has been promising next-level ROI from AI adoption, but the results tell a different story. While hallucinations are still a problem, the main culprit for marketing organizations is their own disorganized content supply chains. Marketers cannot effectively unlock and scale AI-driven efficiencies without addressing their content plumbing first. A clean, centralized, and organized content operation is no longer just a productivity play; it’s a prerequisite for AI-powered transformation.
AI is everywhere, but we must ensure it delivers as intended.
Across industries, companies have embraced AI as the next frontier, hoping that by building AI capabilities of their own, they’ll unlock value to help scale their business for years to come. In fact, about 90% of companies report using AI or say they want to use it in the future. And AI has the potential to help businesses garner real success –– whether companies leverage it to help automate time consuming tasks, generate content, or accelerate other workflows, there’s a clear opportunity in smart AI adoption.
But by now, you know the story: 95% of GenAI offerings have failed to deliver a significant positive impact or return on investment –– thanks to issues like change management challenges, technical bugs impacting tech systems, scaling roadblocks tied to workforce adoption, and AI models hallucinating inaccurate outputs.
It’s easy to point fingers. But there’s one key issue no one’s talking about that is standing in the way of AI tools’ success: internal, disorganized content supply chains. Just like a freight train cannot reach its destination without tracks, AI cannot reach its full potential and help businesses scale without a clear process. And to understand the significance of organized, easily accessible content at scale, we can look to a transformative technology of the early 2000s: Amazon Web Services.
AI has the potential to provide AWS-level transformation.
Amazon developed AWS to build a common platform that freed software engineers and project teams from repeating the same frustrating, time-consuming, and repetitive tasks time and time again. AWS’s goal was to democratize IT infrastructure, helping organize the massive amount of information on the internet into an easily accessible repository of information.
Content marketing platforms have a similar goal: streamline workflows to create a centralized project management tool for individual users and their teams at scale. Now, AI can help marketing teams move from idea to published experience faster with more streamlined content workflows and collaboration.
But in order to reap the benefits of AI, models must be able to easily access data. AWS successfully provided the foundation for data to flow through the internet with organized purpose. The same approach with centralized, well-organized content operations will give AI tools the launching pad required to achieve real, scalable business results for marketing teams.
In order for AI to function without human hand-holding, content inputs need to be organized.
Think about building a house. In order to get a sturdy and safe structure, you must first ensure that all of the building materials are organized and the foundation is sound. If pipes fail to connect the kitchen sink to a water source, or if electrical wires get crossed, it would be a disaster. Similarly, AWS provides an example of a centralized platform constructed not only with a strong foundation and long-term growth in mind from day one, but also a clear roadmap for scaling with as many customers and integration partners as possible.
We can look at AI adoption in the same way. AI is not a miracle technology. Flying solo, it can’t convert bad inputs into good outcomes. Messy tech stacks and disorganized processes impact the quality of work, contributing to the “workslop” issue. In fact, 40% of workers report frustrations with AI-generated content that lacks substance and slows productivity.
In order for companies to realize the benefits of AI devoid of workslop, AI models must be able to easily access an organization’s input data. And without a centralized system to manage content in an organized way, it’s nearly impossible for the AI model to leverage the data we need when we need it. Disorganization not only increases risks of falling behind competitors, it threatens workslop outputs, governance issues, off-brand content, and an abandoned holistic strategy –– and all preventing the AI model from delivering scalable value.
As the AI race turns into a fierce final stretch, companies that fail to prioritize content operations will be left behind.
Today, with a breakneck pace of enterprise-grade innovation, relying on subpar tech systems is a critical flaw blocking companies from achieving true, AWS-grade digital transformation –– and not to mention, generating a return on a costly investment. When building AI capabilities, companies need to prioritize carefully building the framework AI needs to succeed.
That starts with content organization. From managing deadlines, campaigns tasks, and review processes, to storing assets for maximum reuse and governance, CMPs allow AI agents and humans to pull from all relevant materials when completing a task. Having a clear, visible content creation and storage process helps AI integrate into company workflows and instantly access relevant data, powering long-term ROI at scale instead of fragmented moments of success.
