Johnny Beverton
In this episode, Laurens Ruijtenberg, Principal Consultant in Microsoft Process Automation and a university lecturer in AI, joins Empiric’s Dynamics 365 European Lead Taylor Rothwell, to discuss accountable AI, compliance, and the growing need for broad skills across the Microsoft ecosystem.
Laurens combines hands-on experience building Microsoft Power Platform and Dynamics 365 solutions with his perspective as a university lecturer in software engineering and AI - watch the video below and read the full recap, below.
The Reality of AI Adoption
The rush to integrate AI is visible everywhere, from finance to healthcare. Laurens advises caution: "If you're really beginning with AI or are really still in an experimental phase, it’s important to start small with small use cases of which you can easily measure the success or which you can easily fine tune because I wouldn't underestimate the degree of the familiarity that you need to get with the technology."
In regulated fields like finance and accountancy, Laurens stresses that AI must be traceable and explainable.
Bridging Theory and Practice: The Lecturer’s View
Laurens’s dual role provides a critical perspective on responsible and accountable AI: “I can take my practical experience from working with clients into my work as a lecturer, and being closely involved in academia also helps me stay on top of the latest developments.”
The difficulty for business leaders lies in translating academic ideas of trust and ethical deployment into technology strategies that are actionable and scalable - a key learning that can be taken from academia involves moving the focus from the AI’s output to how it operates within a rigorous system.
Strong process makes AI accountable, with clear, repeatable methods helping teams to scale responsibly. Laurens sees this mindset already in his students, who are completely open to the ideas of responsible AI and are building that into the next generation of solutions.
Explainable AI: Why Frameworks Matter
AI is only as strong as the trust people have in its decisions. Laurens stresses the need to clarify every step - especially where the consequences are real. "I think we need to come to a place where we have a framework but also technology because this is usually about a combination of people, process and technology in the end... where you can trace back all the decisions being made in the software being generated but also in the software development process itself.”
He discusses the emergence of spec-driven development - formal specifications acting as the core AI input - but is candid about progress: “I think we’re still in an infancy stage,” and highlights the ongoing need for specialist engineering talent able to develop and integrate frameworks.
Solving AI Fatigue: Focus on the Business Problem First
A common struggle for many organisations is "AI fatigue," which Laurens attributes to a mismatch in how the technology is marketed and approached.
"We are trying to sell AI as a product on and of itself, but in the end just like with traditional tools and technologies it's a tool to get to a result." He warned that focusing too much on the current limitations or features of the technology - asking "what can we do with AI right now?" - is risky because the technology keeps progressing so quickly.
The focus must shift from the technology to the business challenge. "What I rather want to talk about is what is the challenge you're trying to solve and which components of AI can we put into place to help you solve that challenge," he explains.
If the discussion starts at a problem, it becomes far simpler and more effective, focusing on solving the issue at hand rather than chasing trends - centring the discussion on specific business needs makes the wins real and measurable.
Microsoft Power Platform, Dynamics 365, and the Rise of AI as a Commodity
Laurens sees a major shift in the Microsoft ecosystem, with Power Platform, Dynamics 365, Copilot Studio and Azure delivering a more integrated experience, with Copilot for Microsoft 365 as a central hub. Laurens describes professionals starting from a central hub and effectively acting as managers of their AI agents.
The implication - teams will need broad skills across platforms, not just deep specialisation in one area. While democratisation of AI empowers more users, Laurens is clear that dedicated AI experts remain necessary for edge cases, custom models, and pushing the boundaries of what is possible. His final recommendation: "Don't just focus on LLMs... try to focus on the field of AI as a whole" and consider what comes next, beyond large language models.
Conclusion and Looking Ahead
Building strong technology teams remains challenging - taking people who understand both the platforms and the rules they operate under.
Our dedicated recruitment team connects organisations with leading professionals with proven track records who can drive your strategy forward. If you are looking to build out your team, hire Microsoft leaders, or are a technology professional seeking your next role, get in touch with our team today – we'd be happy to assist.
Be sure to connect with Taylor and Laurens on LinkedIn to continue the conversation and be sure to follow us on LinkedIn and sign up to our newsletter to stay in the loop on what’s next.
