Agentic AI and Servicenow

Yokohama’s Core Feature: Adoption not Experimentation

AI adoption isn’t complicated. Stop overthinking it.

Every week, I see more AI tenders hitting the market. It’s a sign that momentum is building. But it is also a sign of hesitation. Too many of these tenders focus on low-risk, low-value AI applications like transcription, summarisation, or sentiment analysis. Useful? Sure. Transformative? Not even close.

And let’s be honest. Many of these tenders aren’t really about AI adoption at all. They’re Trojan Horses for broader platform rollouts. Once a vendor gets embedded, dislodging them becomes nearly impossible. That’s not necessarily a bad thing if that’s the strategy. But if not, organisations need to recognise the long-term implications of the AI choices they’re making today.

The buyer behaviour is pretty clear. It is far easier to buy point solutions than it is to commit to an ecosystem that will shape your AI strategy for years.

Right now, ServiceNow is the company showing what AI adoption actually looks like. The Yokohama release isn’t about AI-powered assistants that automate small tasks. It is about AI operating at scale. Purpose-built AI agents for different functions. An Agent Orchestrator to coordinate them across departments. This is the real problem statement every organisation is trying to solve even if they haven’t fully articulated it yet. And to do it while rationalising your application footprint and introducing enterprise-grade AI at the same time? That’s worth an hour of anyone’s time to investigate further.

At the business platform level, ServiceNow continues to show the world what’s next in AI. But innovation alone isn’t enough. The real challenge becomes adoption.

I’m hearing a lot about AI Centers of Excellence (COEs) as the go-to strategy. And sure, they sound great to boards, and they make sense in theory. But in practice, they often create silos instead of embedding AI into how the entire organisation works. Or they are a blueprint for the future not today.

My advice? Don’t task your core team with building an AI CoE just so the organisation can buy itself time to “figure it out”, especially if figuring it out requires a front to back technology refit. That’s not how transformation happens. If AI remains the domain of a single team, or a new function, it just won’t scale.

Organisations seem to be focusing on governance and architecture way too much. Probably because it feels familiar. These are well-trodden paths. But they are also convenient ways to avoid the real work of embedding AI into everyday operations.

The difference between a true AI transformation and just another pilot project isn’t a CoE. It’s good old-fashioned change management.

AI adoption priorities can’t just be about better models or better governance. Those will get pushed aside or relegated to IT soon enough.

The real priority is structured adoption of AI at scale. And that doesn’t have to be complicated. PaaS provides a clear, practical pathway.

And that means it is not just an IT discussion or a boardroom decision. It is a business transformation conversation that corporate services directors, operational leaders, and department heads should be having today.

The surge in AI tenders proves that momentum is definitely here. But it also seems that too many organisations are still playing it safe. Treating AI as a simple task assistant rather than a true operational force multiplier is a distraction.

AI at scale, like that available in the ServiceNow Yokohama release, can be embedded into operations within weeks. It doesn’t need a CoE or wasted months of experimenting.

Stop putting up roadblocks. Go and see for yourself.