Greg Schroder, head of AI applied engineering at Entelect, shares why disciplined adoption beats AI enthusiasm every time.
Ask most executives why their company is investing in AI, and the answer comes quickly: cost reduction, productivity gains, faster time-to-market. Greg Schroder has a more nuanced answer – and it is one he believes the industry has not fully absorbed yet.
“People think AI is all about saving time and money,” says Greg, who has spent 18 years across multiple technology leadership roles. “It’s really about raising the bar – on quality of delivery and what can reach the end-users.”
That reframe matters. Productivity as the primary lens creates pressure to automate quickly and broadly, often without the governance and reliability safeguards that make AI adoption sustainable. However, productivity doesn’t mean quantity and efficiency of delivery alone: it’s also quality. And that demands something more: experimentation, discipline and a clear-eyed vision of where AI genuinely moves the needle long-term.
The AI vendor landscape has made that guidance both more valuable and more difficult to deliver. Every major software toolchain now has an AI layer. Every boardroom conversation includes AI. And every client, Greg says, is driving some degree of AI initiative, but not all of them are doing it well.
“The challenge is dispelling what’s hype and what’s actually feasible and practical,” he says. “A lot of clients and executives know they need AI, but they don’t always know where to start. That’s where we provide guidance and execution.”
Using AI across the software delivery lifecycle
One of Greg’s first priorities as head of AI applied engineering was to get a view of how Entelect’s teams use AI to improve their delivery of solutions. This formed the starting point for organisational knowledge.
He draws a sharp line between two distinct plays that are frequently conflated.
The first is AI as part of the product itself: conversational banking interfaces, fraud detection engines, automated claims assessment. The second is using AI to build – deploying it across the software development lifecycle (SDLC) to improve how teams design, develop, test and deliver software. This is where Greg and his team spend most of their focus.
“Our positioning is to help our clients understand what’s most practical and where they should start,” he explains. “You can’t go from zero to 100 miles per hour without keeping quality, governance and reliability in mind. Our expertise is guiding clients to do it with IP and security factors at the forefront.”
He observes two distinct groups among Entelect’s client base. Early adopters are bullish, eager to apply AI broadly and move fast. A second group is more cautious – they’re focused on steering effectively, avoiding the pitfalls that could generate costly regrets further down the line. Both are reasonable positions. Entelect’s role is to give each the same thing: a balanced approach, with the right tool choices for their environment.
Greg is clear about what that approach demands: a solid understanding of which AI tools are required, and what safety nets, security controls and reliability safeguards must be in place before deployment at scale. “AI does require guidance and stewardship,” he says. “It’s not as autonomous or easy to gain long term, quality results from it as many think.”
Building a pioneer culture
Greg’s model for keeping pace with a fast-moving field is structured around a team he calls AI pioneers: engineers and specialists living and breathing AI on a daily basis, running experiments, testing tools and surfacing what actually works. Their findings feed back into the broader organisation, giving teams across Entelect access to hard-won, practical knowledge rather than theoretical frameworks.
“It’s really about learning through doing – and then applying and adapting that to the given environment,” he says. “You can do a course and learn some of the fundamentals, but AI is so fast-paced that by the time you apply what you’ve learnt, the landscape has already changed. You can’t outsource that learning – you need to be practical and apply judgement as you go.”
That culture of experimentation is reinforced by a deliberate knowledge-sharing initiative. Entelect has launched an internal forum specifically designed to keep the wider team up-to-date, share techniques and convert individual lessons into collective capability.
The discipline behind the excitement
What Greg describes is, in essence, a case for AI maturity over AI enthusiasm – a framework that matches ambition with structure, speed with governance and adoption with a genuine understanding of what you are deploying and why.
“With AI being everywhere, it’s exciting to understand its abilities and capabilities,” he says. “But using it optimally requires guidance. The organisations that will come out ahead in the long run are the ones that get that right from the start.”
For Entelect’s clients, getting it right starts with a clear-eyed conversation about where they are, what they actually need, and what a sustainable path to AI value looks like. Greg brings 18 years of experience building technology solutions and the intellectual rigour to separate signal from noise. In an AI landscape full of both, that combination is increasingly rare - and increasingly in demand.
Greg Schroder is Head of AI Applied Engineering at Entelect, an end-to-end technology services and solutions company. He leads Entelect’s AI Applied Engineering practice, helping clients navigate practical AI adoption across the software development lifecycle.
