With AI on the minds of everyone, managing the data it uses and produces has become more important than ever.
As AI adoption accelerates, managing its underlying data is critical for data sovereignty and security. Successful integration weaves AI into end-to-end enterprise management rather than simply layering it onto applications. Because effective AI relies on high-quality, governed data, heavily regulated industries are uniquely positioned to lead enterprise AI innovation. Furthermore, AI will not replace employees; instead, humans will elevate to validate decisions and drive strategy. Meaningful enterprise transformation requires adopting AI with clear, outcome-driven business cases.
It is a common trend, and maybe even a human flaw, that we only seem to scrutinise technology once the thrill of using it has worn off.
You only have to look at the current state of social media, the current state of network infrastructure, or the ascension of any of the “big tech” companies to see that it’s all sunshine and rainbows on the way up. Yet, once a couple of questions are asked, it’s clear that there are drawbacks to the technology being used.
Although cloud technologies have obviously been around for a long time, the convenience of being able to start and run a business for a fraction of the cost has made it an all too easy technology to invest in without considering all the issues at play.
With cloud computing embedded into most modern business, data sovereignty has become a topic of interest for many, so they are able to answer any questions customers might have, and stay on the right side of punitive regulations.
“For years, enterprises have been thinking about where they put their public and private data,” said Shannon Bell, Chief Digital Officer and CIO at OpenText. “We’ve seen this in the EU over many years with data protection regulation, people are concerned about what data is being stored, where is is, how is it being used and who’s operating on it, so it’s really not a new concept.
“With that being said, the discussion about sovereignty has gained pace with geopolitics and what’s happening in the world. For me, the sovereignty discussion is about knowing what you’re deploying, where it is, who’s using it, how you want it to be used. But more importantly, in the age of AI, where is that AI living? How is it using your data? And what are you doing to protect your enterprise?
“If you think about public and private data sets, 80% of the data most enterprises create could logically sit in public hyperscalers, in the public domain. There’s probably 10 to 20% of their data that truly needs to be protected. For years people have made a choice about deploying on-premises or protecting that data, now with sovereignty, there are options around how you’re protecting that data in a public hyperscaler, as an example.”
Heart of the Matter
The question of AI is one we can not get away from. But while the providers pitch it as a great tool and ‘experts’ warn it will take over the jobs market, the anecdotal evidence I hear from those who try to use AI said it’s underwhelming.
Of course, these Generative AI models rely on data to make the most informed decision it can. If the prevailing usecase for AI is agents to assist employees and customers alike, Bell said that managing the data AI has access to is crucial to the success of these agents.
“It’s important to recognise that AI starts with data, so we’ve always looked at the data as the critical piece. It’s not about the application, SaaS or deployment model, it’s really about what is being managed. At the end of the day, we’re managing content, we’re managing machine-generated content, human-generated content, all different types of transactional content, all different types of content that have to be provided in context for AI to work.
“How AI is layed on top of that becomes the critical differentiator. At the heart of it is providing access to that content. I think it’s important to think that through when you’re looking at your AI strategy, because you have to know the value that you’re bringing to AI.
“It’s not about layering AI on any application or on any part of your enterprise. It’s about weaving AI into all parts of how you manage your enterprise end to end.”
Looking After the Agents
One of the fundamental parts of the enterpise is the people who work inside it. Speaking at the OpenText Summit in April, Ball said that they are focussed on enabling the people within the company to be more efficient.
“What we’ve done in the context of the AI genome is look across the enterprise and business processes to understand what is the role of the human in the loop, what is the role of the agent, and where can the agent accelerate those business processes?”
Speaking about the role of AI with employees, Bell’s perspective was largely consistent with the rest of the B2B industry, that operational efficiencies will be the first gains for the average business. However she did say “the role of the human” is going to elevate to a purveyor of discipline.
“Looking at how you weave agentic across the enterprise is critical. The role of the human is in helping to validate and understand decisions, evaluate the AI, and make sure that it’s making the right decisions and the right guardrails are in place.
“Ultimately, humans play an evolving role in the age of AI, which is to drive the outcomes for the enterprise. While the humans in the loop, it may transition to being on the loop, where they have some autonomous flows.
“Humans will be driving the innovation and the strategy around AI. It’s a mistake to think about AI as a replacement technology, because then you’re not really understanding the value that your humans bring to the enterprise. The subject matter expertise, the domain knowledge, the understanding of the business, are things that may be unique to your business, your enterprise, your differentiation and AI is not going to replace that.”
Rabbit and the Hare
While AI may not replace the employees for now, it is obviously in high demand.
But with AI throwing up so many questions around security and data sovereignty, could those in strictly regulated industries be in the best place to innovate?
“We see a lot of acceleration of AI in consumer space where there’s no guardrails, so consumers have the ability to take advantage of different AI capabilities,” said Bell.
“I actually think regulated industries are in a perfect position to drive innovation, because foundational to AI is good quality data that’s governed and secure.
“Being in regulated industries, these organisations have largely invested in secure information management, and that has put them in a great position to actually start to drive AI adoption on top of that data and content that they’ve invested in already.
“I see it as they have an advantage on accelerating innovation over other enterprises in the private sector. Obviously, the consumer speed and pace of innovation is outpacing everyone, but they don’t have the same guardrails, and they don’t have the same concerns about protecting intellectual property and the enterprise, but I think that investment in secure information management positions regulated industries well to add AI, and a lot of businesses are trying to catch up.”
Bell finished by saying that the enterprises who have taken their time to adopt AI are in a stronger position than those who were quick to embrace AI when it was first introduced.
Citing studies from MIT, she said that those who have adopted AI for the sake of it have not seen the same value as those who took their time and had an outcome in mind from the outset.
“The barrier to entry with AI is low, but it doesn’t mean that it’s operating in the safe constraints that you want inside your enterprise. Taking time to build in the business case, understand the outcomes, and then apply AI to drive those outcomes is what will actually drive more material benefits.
“Then we will take AI from just being a technology for technology’s sake project, into something that’s truly transforming the enterprise.”
