Teams move quickly to implement it, see early promise, and then encounter challenges they didn’t anticipate. Costs increase faster than expected, compliance questions surface, workflows become more complex, and in some cases, brand or customer trust is put at risk. Becoming AI ready is not about moving faster. It’s about making better decisions earlier. A useful way to think about this is through these ten practical steps, a set of considerations that shape how AI is introduced, scaled and governed over time.
Many organisations don’t fully interrogate vendor terms before implementation, which can lead to campaign content, messaging and intellectual property being used to train external models. Over time, that can dilute differentiation and erode competitive advantage. AI readiness starts with understanding what happens to your data, where it goes, and how it is used.
Many AI tools operate as black boxes, making it difficult to understand how decisions are made or where value is coming from. Without clear measurement frameworks, organisations can struggle to justify investment or optimise performance. Establishing how success will be measured upfront creates clarity and accountability.
AI tools often operate across multiple geographies and data environments, but not all of them align with regulatory expectations. Whether it is data being stored outside approved regions or sensitive information being processed incorrectly, the risks are significant. These issues are rarely visible during procurement, but they become very visible when something goes wrong.
AI pricing is often tied to usage, processing or API calls, which means costs can increase rapidly as adoption grows. What looks like a modest investment can quickly become a major expense. A more mature approach stress tests pricing against real usage scenarios and ensures it aligns with how the business actually operates.
Many AI solutions are positioned as easy additions to an existing stack, but in reality they can introduce complexity. Data inconsistencies and incompatible systems often create manual workarounds that undermine efficiency. Instead of saving time, teams end up managing the technology. Understanding how systems connect in real-world conditions is critical.
AI systems rarely fail quietly. When issues arise, they tend to appear at the worst possible time, such as during peak campaign periods. Without proper validation, organisations are left reacting to problems rather than preventing them. Building resilience into your marketing ecosystem is just as important as introducing new capability.
There is an assumption that AI will automatically improve productivity, but poorly implemented tools can have the opposite effect. Additional steps, complex interfaces and unclear outputs can slow teams down and reduce adoption. Assess whether the technology makes day-to-day work simpler and improves your team’s Marketing Experience (MX).
As organisations build processes and customer journeys around a specific platform, their flexibility reduces. If pricing changes or the vendor evolves in a different direction, the impact can be significant. Planning for portability and keeping alternative options viable protects against this risk.
AI introduces new vulnerabilities, particularly in shared or multi-tenant environments. Sensitive information can be exposed through poor controls or unclear processes. Understanding how data is stored, processed and accessed, and ensuring appropriate safeguards are in place, is essential to maintaining trust.
AI changes how teams operate. Without clear ownership and alignment, initiatives can stall or create friction between functions. In some cases, the introduction of AI removes elements of strategic work while increasing administrative effort. For a more effective approach focus on how roles evolve, ensuring technology enhances expertise rather than diminishing it.
What connects all ten steps is the need for joined-up thinking. AI is often introduced into one part of the marketing function without considering the wider impact. That is why AI readiness is not simply a technology question. It is a Marketing Experience question.
At Intermedia Global, this is addressed through a connected view of data, technology, process and content. Data needs to be governed and protected. Technology needs to integrate and scale effectively. Processes need to support how teams actually work. Content needs to remain controlled, compliant and on brand. When these elements are aligned, AI becomes an accelerator rather than a risk.
The organisations that succeed with AI will be those who approach it with clarity. They understand where it fits, what it changes and how to manage it over time. AI is a powerful capability, but only when it is implemented with intent.
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