While we’re expecting some big announcements from Knowledge this month, ServiceNow’s regional AI Summits have provided some compelling AI-fuelled insights ahead of the flagship conference. I went along to the London event back in March to learn more.
With AI baked in at every level, ServiceNow is making some impressive moves, but it is not immune to that universally acknowledged challenge faced by every large company – the challenge of adoption. As part of the remedy to this age-old conundrum, a short and simple phrase emerged as perhaps the most important takeaway of the day: ‘data readiness’.
We’re no strangers to the concept of ‘rubbish in, rubbish out’, and this reality certainly isn’t unique to Big Tech, but it is where data best practices set the tone – especially when it comes to the fast and vast world of AI.
So, if agentic AI is the brain, data is the backbone supporting it. And, to stretch out the metaphor just a little more, data also acts as the feet grounding AI. If we want meaningful adoption, teams are going to need to follow those feet with confidence and autonomy. Data can provide us with solid foundations to stand on, or it can trip us up.
Okay, this metaphor has almost certainly run its course, so let’s jump into some key highlights from the summit, plus some practical guidance for setting yourself (and your teams) up for success.
What Is Data Readiness?
First up, what do we even mean by data readiness? Words like quality, connectivity, and governance may spring to mind. And it’s also worth flipping this question on its head – what makes data bad?
Messy, incomplete, inaccurate, adrift…
Inevitably, it can be any and all of the above! But it’s also data without a strategy. Data without direction. Data without guardrails.
To quote Dale Cheeseman (EMEA Director AI, ServiceNow), who presented at the AI Summit in London, “AI isn’t Harry Potter”.
It isn’t magic, nor should we expect it to be. It’s a tool to be wielded with purpose and strategy by humans – individuals, teams, and especially leaders responsible for setting the tone when it comes to meaningful adoption of AI.
Dale Cheeseman’s article about AI governance is well worth a read.
Is Your Data Ready for AI?
It’s okay if it’s not… yet. The key thing here is acknowledging and understanding where you are on the path to readiness. Once you have the lay of the land, you can take actionable steps.
“The main thing for me is to deliver data, data strategy, data readiness. AI then just becomes the breeze, and AI is not the problem. AI is the solution.”
Dale Cheeseman – EMEA Director AI, ServiceNow
So, we’ve established that data is important and that it needs to be kept in good shape, but a steady and measured approach is also vital. Cheeseman spoke about adoption speed, especially in relation to ‘shadow IT’, which has the potential to create unnecessary urgency, ‘breed nervousness’, and ultimately slow things down as teams question which (and indeed if) processes need to be changed or fixed.
“I always say the term shadow IT, because you’ve got what I refer to as the change control team, or work prevention team – they can slow the process down. So you end up with shadow IT organizations that are deploying as fast as they can. So a lot of organizations rush at it, and then they stand back and go, is this the right use case? Is this where we need to go?”
While it’s understandable to want to harness AI to foresee and fix issues (sometimes before they even exist), data has to come first.
As the saying goes, ‘slow and steady wins the race’.
A Success Story of Culture and Collaboration
The AI Summit in London showcased some serious success stories, including Rolls-Royce, which has managed that tricky transition from reactive to proactive by focusing on building a solid data foundation.
“We did a lot of work on data foundation… We continue to do work on data foundations. It doesn’t stop. It’s that continual improvement of maturing our data.”
Rachel Cameron – VP of Performance and Improvement, Rolls-Royce
With a 99.7% success rate and 54% deflection away from the service desk, AI-powered virtual agent Merlin has been staggeringly effective, with Cameron describing it as “really powerful”.
Cameron also talked at length about the importance of learning and collaboration, especially in relation to the launch of their ideas portal, which has been integral to adoption.
“It is the first time we’ve been able to collaborate on improvement ideas across the enterprise. It’s a huge step forward for us. We have a 236% engagement on improvement ideas… It’s been really helpful in driving that cultural shift. So it’s not just the technology, it’s how you drive that culture of data-driven insights and continuous improvement.”
When done well, Cameron said, “people can also be your biggest accelerator”.
Final Thoughts
So, what’s more important than AI technology? By now, hopefully it’s clear that having a solid strategy for your data isn’t just a nice-to-have – it’s essential. Only with this key part of the puzzle in place can you expect real adoption and investment from teams, with a culture of ‘data readiness’ filtering down from leadership.
Have a clear definition of data readiness. Build on a solid data platform. Invest in your people. And keep doing the work. After all, AI needs to be constantly fed, and it’s only as good as the last meal you served.
“Data is as important as AI, if not more.”
Dale Cheeseman – EMEA Director AI, ServiceNow