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Onboarding is not an HR process

Every organization talks about Customer Experience. Increasingly, they talk about Employee Experience too. There are conferences dedicated to it. Dashboards measuring it. Entire software platforms promising to improve it. And yet, I continue to see organizations where a new employee spends the first weeks chasing a laptop, waiting for system access, wondering who to ask about a lease car, or discovering that nobody seems entirely sure what should happen next. That isn't an HR problem. It is an organizational one. The first experience shapes everything We often assume culture is something employees discover over time. I don't think that's true. Culture starts on day one. Not during a presentation about company values. Not during an all-hands meeting. Not because someone tells you what the organization stands for. Culture emerges from dozens of seemingly insignificant moments. Was someone expecting me? Was my manager prepared? Did my accounts work? Did I know where to go for help? Did different departments seem connected, or did I become the person connecting them? None of those moments appear in an annual report. Yet together they answer a much bigger question: "Do these people have their organization under control?" Every small interaction builds operational trust Trust is often discussed as something leaders earn over months or years. But there is another kind of trust. Operational trust. It has nothing to do with charisma. It comes from consistency. Every smooth handover, every proactive update and every well-prepared first day tells a new employee the same thing: "Someone thought this through." The opposite is equally powerful. Every missing approval. Every unanswered question. Every process that requires the employee to coordinate departments that should already be working together. Those moments don't just create frustration. They quietly undermine confidence in the organization itself. Onboarding is not an HR process This is perhaps the biggest misconception. Organizations often divide onboarding into responsibilities.HR prepares the contract. IT provisions the laptop. Facilities arranges a desk. Procurement orders the phone. The hiring manager schedules introductions.Individually, each team may perform perfectly. Collectively, the experience can still fail. Because onboarding isn't a collection of departmental tasks. It is the first end-to-end process an employee experiences. The employee doesn't care where HR ends and IT begins. They experience one company. Which means onboarding is not an HR process. It is one of the clearest demonstrations of operational excellence a company will ever give. Or fail to give. Culture is experienced before it is explained Organizations spend enormous effort defining culture. Mission statements. Leadership principles. Core values. Internal campaigns. Most of them are well intended. But people don't believe culture because they read it. They believe culture because they experience it. If your organization says people matter, but nobody notices a new colleague waiting three days for access to essential systems, the employee remembers the experience. Not the PowerPoint. Culture is never communicated as effectively as it is demonstrated. Different people need different beginnings One of the mistakes organizations make is assuming everyone wants the same onboarding experience. Some people want structure. Others want autonomy. Some appreciate detailed guidance. Others would rather receive a laptop, a login and the freedom to explore. Neither approach is right. Neither is wrong. The real challenge is recognizing that equality does not always mean uniformity. Good organizations don't standardize people. They standardize quality while allowing room for individual needs. AI isn't replacing onboarding Every technology conference seems to ask the same question: "What's our AI strategy?" Perhaps a better question is: "Which problems are we still asking people to solve manually?" Ironically, many onboarding activities have already been automated for years.HR-driven provisioning creates accounts automatically. Identity platforms assign access. Workflow engines trigger approvals.The technology already exists. Yet the employee experience often remains fragmented. Not because automation is missing. But because the process itself was never designed as a single experience. That is where AI becomes genuinely interesting. Not as another chatbot. But as an orchestration layer. An assistant that notices a laptop hasn't been delivered before the employee does. That reminds managers of conversations they should have already scheduled. That recognizes dependencies across HR, IT, Facilities and Procurement before they become delays. That answers questions before someone has to ask them. The real opportunity isn't replacing people. It is removing unnecessary friction between the people who are already involved. Why CEOs should care Too often, onboarding is delegated. HR owns part of it. IT owns another. Facilities owns something else. Everyone has responsibilities. Nobody owns the experience. That should concern every CEO. Because onboarding is rarely remembered for a single event. It is remembered as a pattern. A pattern that answers one simple question: "Is this an organization that operates deliberately, or one that reacts continuously?" That first impression influences trust. Trust influences engagement. Engagement influences retention. And retention ultimately influences business performance. This is no longer an HR conversation. It is a leadership conversation. Final reflection Organizations often say that people are their greatest asset. I believe most leaders genuinely mean it. But beliefs become visible through design. The first weeks of employment are not simply about receiving a laptop, signing policies or collecting access rights. They are the first demonstration of how an organization thinks, collaborates and executes. Customers experience your products. Employees experience your organization. Both form opinions remarkably quickly. The difference is that customers can walk away. Employees first decide whether they believe your culture. Only afterwards do they decide whether they want to become part of it.

When intelligence becomes something we outsource

We are slowly doing something unusual with intelligence. Not replacing it. Not augmenting it. But outsourcing it. One prompt at a time. Artificial Intelligence tools like ChatGPT, Gemini, and Claude are often framed as productivity multipliers. And they are. They compress time, reduce friction, and make complex outputs accessible at almost no cost. But there is a quieter shift underneath that narrative. We are starting to separate thinking from understanding. And that is where things become fragile. The illusion of competence A well-written prompt can produce a remarkably convincing answer. Structured, fluent, confident, even nuanced. But confidence is not the same as correctness. And fluency is not the same as comprehension. The problem is not that AI produces wrong answers. It is that it produces answers that feel right often enough that we stop checking. And once that habit sets in, something subtle changes: We stop being the system that verifies. We become the system that accepts. Intelligence without ownership There is a difference between using a tool and depending on it for cognition. A calculator never made anyone worse at math. But it also never asked them to understand what it was doing. Modern AI tools sit in a different category. They don’t just compute. They interpret, summarize, explain, reason. Which means they don’t just extend intelligence. They can replace the experience of thinking. And once that replacement becomes comfortable, it becomes structural. The cloud problem, repeated at a cognitive level We have seen this pattern before. Cloud computing abstracted infrastructure:servers became services systems became APIs operations became dashboards complexity became someone else’s responsibilityIt worked brilliantly—until it didn’t. Because abstraction has a hidden cost: distance from reality. And with each layer of abstraction, fewer people understand what is actually happening underneath. Now we are doing the same thing with intelligence itself. From understanding systems to trusting outputs In earlier generations of engineering, you were forced to understand what you built. If a system slowed down, you needed to understand IOPS, memory pressure, CPU scheduling, network latency. Today, many engineers start at the top layer: Deployments, pipelines, managed services, black-box scaling. Even education has adapted. We teach how to use systems, not how they fundamentally behave. And that works—until something breaks outside the abstraction layer. Then the question becomes uncomfortable: Who still understands what is actually happening underneath? The real risk is not AI becoming too smart The real risk is humans becoming too comfortable. Because when AI works well, it removes friction. And friction is often where understanding is formed. If everything just works, there is no need to dig deeper. If nothing requires repair, there is no need to understand cause and effect. If answers are always available, the discipline of reasoning slowly erodes. Not dramatically. Not visibly. But cumulatively. “Just ask AI” is not a strategy There is a growing cultural reflex:Don’t know it? Ask AI. Need it explained? Ask AI. Need a decision? Ask AI.And most of the time, that is fine. Until it becomes the only mechanism. Because AI does not create accountability for truth. It produces plausible synthesis based on patterns. Which means it inherits one critical dependency: There must still be human intelligence capable of questioning it. Not superficially. But structurally. What happens when the underlying knowledge disappears? This is the uncomfortable edge of the argument. What if we gradually lose the ability to independently validate what AI produces? Not because we are incapable. But because we stopped practicing. Then we reach a point where:the system produces an answer nobody truly understands how it was derived and nobody can confidently say whether it is correctAnd at that point, intelligence is no longer something we use. It is something we receive. The paradox of progress We are building systems that make us more capable than ever before. And at the same time, potentially less resilient than ever before. Because resilience is not measured by output. It is measured by what remains when the system is not available. Or when the abstraction fails. Or when the data is missing. Or when the model is wrong. Closing thought AI is not the end of thinking. But it might become the end of careless thinking, if we are intentional. The real question is not whether AI can do the work. It is whether we are still willing to understand the work that is being done on our behalf. Because at some point, the dependency becomes invisible. And when that happens, the most important system we have is no longer artificial intelligence. It is human understanding. And if we outsource that too far, we may eventually discover that we still have answers— but no longer know how to question them.

AI didn't replace engineering. We just stopped talking about it.

Artificial Intelligence has become impossible to ignore. Open Gartner. AI. Read CIO.com. AI. Attend Microsoft Build, Google I/O or AWS Summit. AI. Scroll through LinkedIn for five minutes and you'll quickly get the impression that every meaningful conversation in technology now begins and ends with large language models, autonomous agents and AI-assisted development. I understand the excitement. AI is a remarkable technological breakthrough, and its impact will be difficult to overstate. But I've started wondering about something else. Not what we're talking about. What we've stopped talking about. The conversations that quietly disappeared A few years ago, our industry spent enormous amounts of time discussing operating models, governance, architecture, automation, platform engineering and cloud operating practices. Those conversations weren't glamorous. They rarely filled conference halls. They certainly didn't dominate social media. But they mattered. Because they determined whether technology actually worked once the keynote was over. Today those disciplines seem strangely absent from the conversation, as though AI somehow made them less relevant. It didn't. If anything, it made them significantly more important. Engineering never disappeared One of the more curious assumptions behind today's AI enthusiasm is that intelligence somehow compensates for engineering. That if an AI model can generate code, architecture becomes less important. That governance becomes something you can add later. That operational excellence is simply another problem AI will eventually solve. I'm not convinced. Software has never failed because people lacked ideas. It usually fails because complexity quietly grows beyond anyone's ability to understand or control it. AI doesn't remove that complexity. It introduces an entirely new category of it. Unlike traditional software, these systems are probabilistic. They don't always behave the same way twice. They require validation instead of assumption, observation instead of certainty. That doesn't reduce the need for engineering discipline. It raises the standard. Demonstrations have an unfair advantage One reason the current conversation feels so optimistic is that most of what we see are demonstrations. Someone builds an agent in twenty minutes. Another team generates an application from a prompt. A startup orchestrates half a dozen AI services into something that looks almost magical. And genuinely—it often is impressive. But demonstrations have an unfair advantage. They don't have to survive production. They don't have to operate for three years. They don't have to pass security reviews. They don't have to explain themselves during an audit. They don't wake someone up at three o'clock in the morning because an automated decision suddenly affected thousands of customers. Production has always been where technology stops being exciting and starts becoming accountable. That hasn't changed. Abstraction is a wonderful servant The cloud taught us an important lesson: Abstraction is incredibly powerful. We no longer think about physical servers before deploying an application. Kubernetes allows developers to focus on workloads instead of individual machines. Managed services remove enormous amounts of operational burden. Those are extraordinary achievements. But abstraction has always come with an implicit agreement. Someone still needs to understand what happens underneath. Every abstraction layer increases productivity for thousands of people while simultaneously reducing the number of people who understand the foundation beneath it. That trade-off is acceptable. Until the abstraction breaks. Then expertise suddenly becomes scarce. I wonder what we're teaching the next generation When I speak to younger engineers, I'm often impressed by how quickly they adopt new technologies. Many can build sophisticated cloud-native applications long before they have ever managed a physical server. Increasingly, many can also build AI-powered applications before they've fully understood distributed systems, identity, networking or storage. None of that is their fault. We teach what the industry rewards. And right now, the industry rewards speed of adoption far more visibly than depth of understanding. I sometimes wonder what happens twenty years from now. Not when AI becomes more capable. But when the people responsible for critical systems have never needed to understand the layers beneath the abstractions they inherited. The question that interests me most Perhaps this isn't really an article about Artificial Intelligence. Perhaps it's about attention. Technology has always moved in waves. Every few years we collectively decide what deserves our attention, and everything else quietly disappears into the background. Today, AI occupies almost all of that space. Meanwhile, architecture, governance, operational excellence and systems thinking continue doing what they have always done. Quietly determining whether ambitious ideas become reliable systems. Or expensive experiments. Final reflection I have no doubt that Artificial Intelligence will transform our industry. I also have no doubt that most organizations are underestimating what it takes to operationalize it responsibly. Because intelligence alone has never been enough. Not in software. Not in leadership. Not in engineering. Perhaps that is what concerns me most. We celebrate every new abstraction as progress, while paying remarkably little attention to the knowledge it slowly replaces. Every generation of technology asks us to understand a little less of what happens underneath. AI simply accelerates that trend. Maybe that is inevitable. But history has rarely been kind to civilizations that confuse convenience with understanding. The industry is celebrating intelligence while quietly abandoning wisdom. And history has never been particularly kind to civilizations that confused the two.

The future of managed services is letting go of control

For decades, managed services have been built around a simple idea: The provider builds. The customer consumes. We standardized desktops. We standardized servers. We standardized networks. We defined what users were allowed to do, locked everything else down, and called it governance. It made perfect sense. Technology was complex. Expertise was scarce. Standardization created stability. But if I look at the direction our industry has taken over the past fifteen years, I don't see a story about better infrastructure. I see a story about increasing autonomy. And I don't think we've fully realized what that means for the future of managed services. This didn't start with AI AI is getting all the attention. But the shift started long before large language models. Think about what we've introduced over the last decade. Infrastructure as Code allowed engineers to describe infrastructure instead of manually configuring it. Cloud platforms removed the need to provision hardware. The modern workplace allowed users to work from anywhere, on almost any device. Power Platform enabled business users to automate processes without waiting for IT. Platform engineering is giving development teams self-service platforms instead of ticket queues. These aren't isolated innovations. They all move in exactly the same direction. Every generation of technology removes another dependency on central IT. Every generation gives more capability directly to the people creating value. AI simply accelerates that trend. Customers don't want fewer capabilities They want fewer dependencies. That's an important difference. Organizations don't want to submit tickets to deploy an application. They want to deploy it themselves. They don't want to wait three weeks for an environment. They want it in three minutes. They don't want IT departments approving every workflow. They want to automate their own. For years, many managed service providers viewed this as a threat. I think it's exactly the opposite. Because customers aren't trying to eliminate the MSP. They're trying to eliminate unnecessary friction. The MSP is no longer the builder Imagine a product team in three years. A product owner describes a new customer portal. An AI engineering team generates the application. Another agent provisions infrastructure. Security agents validate policies. Test agents perform functional and performance testing. Deployment agents roll everything into production. None of that feels unrealistic anymore. The interesting question isn't whether this will happen. It's what role the MSP still plays. I don't believe the answer is "building the platform." Because increasingly, customers will do that themselves. Or rather, their AI agents will. The foundation becomes the product If customers can build, deploy and operate faster than ever before, then the value of the MSP shifts underneath the visible work. The platform becomes the product. Not the portal. Not the virtual machine. Not the Kubernetes cluster. The invisible foundation beneath all of it. The landing zones. Identity. Networking. Compliance. Policies. Guardrails. Observability. Knowledge. Recovery. Customers won't ask an MSP to deploy an application. They'll expect an environment where deploying applications is safe by default. That's a fundamentally different business. Governance stops saying "no" Many organizations still think governance means restricting users. Removing permissions. Blocking installations. Limiting change. That approach worked when IT was responsible for every change. It breaks down completely when hundreds of developers, business users and AI agents are continuously creating new workloads. The answer cannot be to review every deployment. It cannot be to manually approve every prompt. And it certainly cannot be to lock everything down. Governance has to evolve from permission to policy. Instead of deciding who may build, we decide the conditions under which anything may be built. Instead of reviewing every change, we continuously validate every outcome. Instead of configuring environments manually, we enforce compliance automatically. Control doesn't disappear. It simply moves to a different layer. The MSP becomes an enabler of autonomy This may be the biggest mindset shift our industry has ever faced. For years, success was measured by how much work the provider performed. Tomorrow, success may be measured by how little intervention is required. The best managed service providers won't be the ones operating every workload. They'll be the ones enabling thousands of safe deployments that never required them in the first place. Their customers will move faster. Developers will have more freedom. Business teams will automate more processes. AI agents will continuously improve solutions. And underneath all of it, the MSP quietly ensures that security, compliance and operational resilience remain intact. Invisible when everything works. Essential when it doesn't. Expertise doesn't disappear Some people interpret AI as the end of expertise. History suggests otherwise. Every abstraction has increased demand for people who understand the layer beneath it. Cloud didn't eliminate infrastructure expertise. Infrastructure as Code didn't eliminate architects. Platform engineering didn't eliminate operations. It simply changed where expertise creates value. AI will do exactly the same. The future MSP won't spend its days deploying resources. It will design the ecosystems in which autonomous systems can safely deploy themselves. Closing thought I don't believe the future of managed services is about doing more work for customers. I think it's about making customers capable of doing more themselves. Not because the MSP becomes less relevant. But because relevance is moving. From operating technology... ...to enabling autonomy. The organizations that understand this will stop asking how AI fits into managed services. They'll realize managed services are being redefined by the same force that is reshaping every other part of IT: giving more control to the people closest to the problem, while ensuring the platform beneath them remains secure, compliant and resilient. That, to me, is what the next generation of managed services looks like.