Stop Overthinking AI. Just Start.

Every media organisation knows AI is reshaping the industry. Yet many remain stuck in a cycle of observation rather than action. The fear is understandable: what if we get it wrong? What if the tools hallucinate? What if our audience loses trust? These concerns are valid. But the organisations that will fall behind are not the ones that make mistakes with AI. They are the ones that never start.

Fear Breeds Inaction

The psychology of AI anxiety is well documented. When a tool feels opaque and its outputs unpredictable, the rational response is caution. But in fast-moving industries like media, caution can quickly become stagnation. The gap between those experimenting with AI today and those planning to start next year is already widening. By the time hesitant organisations feel ready, the early movers will have built workflows, trained teams and established competitive advantages that are difficult to replicate.

Start Small, Learn Fast

The most effective approach to AI adoption is structured experimentation. This does not mean giving every journalist access to every tool and hoping for the best. It means identifying low-risk, high-value use cases and building confidence from there. Start with transcription. Move to headline testing. Experiment with social repurposing. Each small win reduces fear and builds institutional knowledge that compounds over time.

At Other Labs, our accelerator programs are built on this principle. We do not lecture about AI capabilities in the abstract. We put tools in people's hands and guide them through real use cases drawn from their own work. The transformation happens when someone who was sceptical two hours ago watches AI cut a repetitive task from forty minutes to four.

Building a Culture of Permission

Experimentation requires permission. Not just from leadership but culturally across the organisation. Teams need to know that trying an AI tool and deciding it does not work is a valid outcome, not a failure. The most innovative newsrooms we work with have created explicit space for experimentation: dedicated time, sandbox environments and shared documentation of what works and what does not. This normalises the process and removes the stigma of imperfection.

The Cost of Waiting

Every month spent debating AI strategy without hands-on testing is a month of learning lost. The media companies that emerge strongest from this transition will not be the ones with the most sophisticated AI policies. They will be the ones whose teams have the deepest practical experience. Experimentation is not reckless. It is the most responsible path forward. Start today, start small and let the work teach you what no whitepaper ever could.