THE STATE OF AI
The ICP Approach:
Consultancy, testing, and embedding the right technology
ICP supports organisations through two connected strands of work: evaluating the creative operation as it exists today, and embedding technology so it improves delivery in practice
On the consultancy side, we look at how work actually flows through the organisation: briefing, creation, collaboration, approvals, versioning, delivery, and performance feedback. We identify where friction appears, where rework is being generated, and where scale requirements create bottlenecks. The output is a practical view of what to fix first and what success should look like, grounded in the realities of production rather than in tool features.
Crucially, we also focus on the changes required to make AI viable in day to day delivery. That includes strengthening briefing discipline, defining ownership and roles, designing review cycles for faster iteration, and setting quality and governance standards that teams can apply consistently.
On the implementation side, we remain tech agnostic. We evaluate options across ecosystems and we emphasise tool testing before purchase. That typically means designing a short pilot around real use cases, representative asset types, and the stakeholders who will be involved in review and approvals. The objective is to understand fit, adoption likelihood, governance implications, and operational performance before making a commitment.
A unique part of ICP is our Content Studios team. Because we also produce content day to day, we can test and learn in real production conditions with experts in production and content creation. That gives us a practical perspective on what will genuinely reduce effort, what will create unnecessary complexity, and what needs process support to be successful. It also means we can translate tooling decisions into working practices, training, and documentation that help teams adopt changes without disrupting delivery.
Embedding technology is not only about configuration. It is about implementation that sticks: working practices, success measures, integration across tools where required, and a delivery model that keeps outputs editable, handovers straightforward, and feedback loops usable.ble.
