Author: cvjepsen

Claus Jepsen is a technology expert who has been fascinated by the micro-computer revolution ever since he received a Tandy TRS model 1 at the age of 14. Since then, Claus has spent the last few decades developing and architecting software solutions, most recently at Unit4, where he is the CTO leading the ERP vendor’s focus on enabling the post-modern enterprise. At Unit4, Claus is building cloud-based, super-scalable solutions and bringing innovative technologies such as AI, chatbots, and predictive analytics to ERP. Claus is a strong believer that having access to vast amounts of data allows us to construct better, non-invasive and pervasive solutions to improve our experiences, relieve us from tedious chores, and allow us to focus on what we as individuals really love doing.

Sustainable Velocity: Why Clarity Is Making a Comeback

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Ever notice how ideas in software development tend to come full circle? There was a time when we put a lot of effort into documenting everything up front. It wasn’t perfect, but many of us believed that gaining clarity early would lead to better decisions later on.

Then the world sped up. Markets moved faster, customers expected continuous improvement, and the industry responded. Agile methodologies emerged as a way to shorten feedback loops and stay closely aligned with real user needs. “Working software over comprehensive documentation” became a guiding mantra, and it made sense. It kept teams focused on delivering value rather than writing paperwork for its own sake.

But in practice, some teams took that mantra a bit too literally, almost as if “documentation doesn’t matter as long as we ship.” The Agile Manifesto never said documentation has no value. It simply warned against letting documents slow us down. Skipping documentation entirely might reduce overhead in the moment, yet it often creates a different kind of waste: confusion, rework, and lost context. A lack of shared understanding doesn’t remove cost; it only postpones it.

Today there’s a quiet return to writing things down for clarity. Techniques like Architecture Decision Records, specification by example, and lightweight design docs are gaining traction again. Not as bureaucracy or thick binders on a shelf, but as living tools that help teams move quickly without losing meaning. Documentation is returning not to slow us down, but to make sure our speed is sustainable.

This shift even aligns with the tools we’re beginning to rely on. Code generation, model-driven systems, and AI-assisted development all depend on precision. You cannot feed a vague idea into a generation engine and expect reliable software to emerge. A clear specification, however, can be turned into code. The more we ask machines to help us build, the more disciplined our thinking needs to become. That kind of discipline starts to look a lot like the documentation we thought we could neglect.

Maybe that’s the lesson behind these cycles. Writing down what we mean isn’t a step backward. It’s part of moving forward. Speed still matters, but clarity is what makes speed repeatable and trustworthy. If both humans and machines can clearly understand what we intend to build, then we can move fast with purpose.

In that sense, the renewed interest in documentation isn’t about going back to the past. It’s an investment in sustainable velocity, the kind of speed we can maintain with confidence over time.

Five Ways To Decide If Disruptive Tech Is For You

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Disruptive technologies can drive a frenzy in boardrooms, with executives seeing dollar signs—or doom—ahead. So, what’s the best way to stay centered and make logical choices when radical change is in the air?

The term “disruptive innovation” was popularized in the 1990s by Harvard Business School Professor Clayton Christensen. Much has been written about what it means exactly—and it’s not my intention to debate this here.

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AI and the post-modern, modern data stack

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of AI-powered applications and agentic AI is dramatically redefining the demands placed on data stacks. Rather than becoming irrelevant, the modern data stack is a major consideration as organisations attempt to embrace AI to increase decision velocity.

Organisations must think carefully about how their data stacks are structured so that their AI agents can operate effectively and deliver accurate, insightful information as quickly as possible.

First, it’s important to qualify what we mean by the data stack. At its core, it is (according to IBM): “…integrated, cloud-based tools and technologies that enable the collection, ingestion, storage, cleaning, transformation, analysis and governance of data.”

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Want to become a CTO? Here’s the hard truth

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For anyone aiming to become a Chief Technology Officer (CTO) that means working out how to build the technical, strategic, and leadership skills to stay relevant and in demand throughout their career. If technology is constantly changing, and one innovation is replacing another, how do you know where to place the right bets in terms of what to learn?

Having been in the technology industry for some time now, I believe there are some core truths that will help you to achieve a durable career even in a field where change and uncertainty go hand in hand.

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AI For Business: Focus On The Steak, Not The Sizzle

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Many of us love sharing amusing AI-generated images with our friends. But are you willing to trust AI with your core business? More people, I’ve observed, are waking up to the costs and risks.

Whoops And Shares

Imagine coming across a big crowd in the street. At the center, there’s a magician mesmerizing everyone with a parlor trick. The crowd whoops, and people are sharing video clips of the trick with their friends.

At the end of the magician’s performance, a smartly-dressed business executive steps forward—and asks the street performer to join his company, with the idea that she’ll soon replace several departments.

This seems bizarre, right? But in my view, similar thinking is seeping into the corporate world with AI.

Although AI isn’t controlling companies, it’s dominating conversations. Across business networks and the IT marketplace, you’ll encounter futuristic visions, hear sweeping claims and sense that everyone’s on board, leaving you behind.

No wonder many in the C-suite, as well as tech providers, feel under pressure to talk up their strategies and capabilities.

But do people’s ideas and expectations match reality? 

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Ambient ERP – breaking free of the enterprise software triad

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Imagine a future where your back-office applications are context-aware, operating in the background, only surfacing what matters when it matters. Thanks to progress being made in artificial intelligence (AI) this is no longer a fanciful dream. It requires a complete shift in the mental model for delivering business applications, from viewing it as software that humans are heavily involved in operating to tools that either act for us autonomously (within parameters designated by us) or support us by providing relevant analysis and context in real-time. Essentially, this means automating what can be automated and augmenting human judgement in other situations. For me, this is the best marker of when software becomes pervasive. It does not disappear entirely from the user’s orbit, but it reduces how often they must interact with it. A shift from invasive to ambient business applications. 

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Self-driving ERP systems will end digital drudgery, halving the number of tasks that need manual interventions

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Agentic AI is attracting a lot of attention for its ability to behave autonomously and render complex, multi-step processes into seamless wholes by joining up data from multiple sources. For ERP, agentic AI holds particularly spectacular promise, offering the orchestration and automation capacity to accelerate actions, dispense with dull chores and inform smarter decisions. To put a number on the scale of change that AI and associated technologies will afford, we believe that half of ERP tasks will be automated. That will be a game-changing development that liberates humans from digital drudgery and creates the sorts of process efficiencies and accuracy gains that CIOs have long dreamed of.  

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Surfing the Void: A Code Meditation

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Sometimes, coding isn’t about solving a problem. Sometimes it’s just a quiet way to sit with the absurd, sip your coffee, and let the rain do its thing. This one’s for those Sunday afternoons when the void isn’t threatening, just kind of hanging out.

# Import cosmic futility
import universe
import death
import coffee

# Set awareness level (1 = blissful ignorance, 10 = full Marvin)
awareness = 8

def main():
    try:
        # Accept the void
        universe.meaning = None
        print("Everything is meaningless.")
        
        # But also, go surfing
        if weather.sunny and body.not_in_pain:
            go_surfing()
        else:
            vibe_gently()
        
        # Eat snacks anyway
        eat_ice_cream(flavor="whatever", mood="existential")

    except Death:
        print("Well... that’s it.")
    finally:
        print("Wasn’t much, but it was ours.")

def go_surfing():
    print("Paddling into the abyss... feeling the breeze of nothingness... cowabunga.")

def vibe_gently():
    print("Sitting in quiet awe of entropy... scrolling memes... being okay with it.")

main()

The Philosophy of Self-Driving Enterprise Software – Rethinking How We Build Software

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At its core, the philosophy is simple and something I have always believed in and promoted: enterprise systems should support people, not vice versa. Technology should extend human capability, not consume it. For too long, enterprise software has demanded that people conform to its predefined structure, based on legacy workflows, accommodating the ever-increasing amount of functionality. Over time, this has turned enterprise software from a valuable productivity tool to a burden that can best be described as digital bureaucracy.

I believe in building context-sensitive, adaptive systems aligned with operational intent. Systems that reduce cognitive load by providing relevant insight when needed to improve decision-making. This is not just about productivity. It’s about respecting people’s time by giving people back their time, focus, and ability to do work that matters.

The philosophy of AI-supported enterprise systems is rooted in utility, ethics, and design that handles complexity while minimizing user burden by removing digital bureaucracy. Software should no longer be the center of attention. People should.

The De-Flowchartification of Enterprise Software

For decades, enterprise software has been defined by structure and control. We created systems to record, to enforce, to comply. They reflected the world as we thought it should be: organized, linear, deterministic.

We took paper-based processes, turned them into flowcharts, and then imprisoned users within the rigidity of those flowcharts. But the problem didn’t stop there.

Screens were built to reflect not just the workflows, but the underlying database tables themselves. In many enterprise systems today, you can look at a screen and nearly reverse-engineer the data model behind it. A tightly coupled triangle emerged: flowcharts dictating processes, databases shaping screens, and UIs enforcing structure and workflow.

This triad has constrained enterprise software into a narrow path, where the user’s experience is predefined by systems designed for data accuracy and compliance, not for real-world flexibility. The result is an inflexible, stepwise interaction model that reflects system architecture more than human need. The system we constructed stripped users of their agency, reducing them to transactional processing entities.

Over time, we slowly lost track of how users actually experience these systems. Enterprise software began to serve its own abstractions, tables, forms, and flows rather than the people using it.

In the AI-supported era, deterministic structures may still exist in the background to safeguard data fidelity and transactional integrity, but the visible rigidity dissolves. What emerges instead is a more adaptive, event-driven interaction where action is based on context, not sequence. Users are guided, not constrained.

At the heart of this shift lies a subtle but profound transformation: the databasification of enterprise systems. What used to be an application-centric model is now turning inside out, where the structured data, not the software UI, becomes the primary foundation. We’re moving toward systems where data is becoming truly decoupled from interfaces and behavior, allowing algorithms to interpret, through data and meta-information, state, process, and intent more adaptively.

This transformation requires more than just capturing data; it requires contextualizing it. The traditional record-based view is being replaced by semantic context, made possible through ontologies, state models, and defined relationships, the meta-information layer. It’s how we turn enterprise data from a passive ledger into a dynamic structure for inference, enabling automation systems to respond based on context, not just rules.

We’re entering a new era where algorithmic systems, powered by real-time data, probabilistic models, and embedded inference, are reshaping the very foundations of how we think about enterprise software.

This isn’t just a technological shift. It’s a foundational one.

From Control to Context

Traditional enterprise systems were built around control, predictable flows, predefined fields, and rigid, rules-based outcomes. Users had to conform to the system’s logic, navigating experiences shaped more by internal structures than real-world needs. In the process, they lost agency and were reduced to functionaries within systems designed more for control than contribution. What was meant to support work ended up dictating it.. Rather than being helpful or enabling, these systems were constraining by design.

AI-supported systems can invert the old paradigm. Where traditional systems dictated steps, these systems adapt. Instead of requiring users to follow rigid flows, they respond to context, surfacing what’s needed and when it’s needed. Rather than demanding conformity, they provide assistance. This puts the user in the center rather than the system, making the system the operator of the user, and not the other way around.

This marks a fundamental shift. Enterprise software moves from enforcing structure to enabling flow, from predefined workflows to dynamic events, from system-driven usage to purpose-driven relevance.

From UI to Inference

One of the core ideas I believe in, and always have, is that the best software becomes pervasive, not by disappearing but by reducing the need for users to engage with it actively. In traditional systems, every action required a corresponding screen. Each step was tightly coupled to a screen, a transaction, or a workflow. As a result, the user experience was designed around the software’s internal structure, rather than focusing on delivering a relevant outcome.

The ability to run machine learning algorithms at scale gives us a unique opportunity to change this. This, finally, allows us to move from interface-driven interactions to inference-driven assistance. Through predictive algorithms, the system “learns” to anticipate, recommend, and act, surfacing relevant information or completing routine steps without requiring the user to navigate menus or remember where to go next. The burden of knowing how to use the system shifts from the user to the system itself.

This is the shift from invasive to ambient. Traditional enterprise software forced users into transactional UIs. Context-aware enterprise software operates in the background, surfacing what matters when it matters.

It’s not just about reducing steps. It’s about changing the mental model entirely, from software as something we operate to software that either acts on our behalf or provides relevant information and context when needed. It automates what can be automated and augments when human judgment is needed.

Data as Living Context

To make this possible, we need to think differently about data, not just as structured records but as a living context. Traditional enterprise data has been fragmented across various software areas, often stripped of meaning beyond its immediate functional use to satisfy the system’s fundamentals. In an AI-first model, data must be elevated: organized not just by schema but, more importantly, by purpose, relationships, and state.

This is where the meta-information layer becomes critical. By introducing ontologies, state models, and defined relationships, we can construct algorithms that interpret data in context, not just what something is, but why it matters and how it connects to the broader flow of work. This is what enables predictive logic, relevance-based assistance, and automation that goes beyond coded scripting.

In AI-supported systems, the data model evolves from a passive record-keeping structure to a dynamic, semantic framework that reflects real-world complexity. The goal is to create a foundation that allows the system to draw connections, track transitions, and operate with context-driven logic.

Data is no longer just for reporting. It becomes the foundation for context-driven computation and algorithmic execution.

Enterprise Systems as Operational Infrastructure

The databasification of enterprise systems marks a fundamental shift. What used to be centered around coded business logic leading to predefined screens and database tables will increasingly be modeled through a combination of ontologies, state models, and process descriptions.

This shift allows algorithms to operate across data, state, and events without the user guiding every step. Instead of burying business logic in screens and interfaces, we make it accessible and available as computational tools that algorithms can use to adapt and optimize. Execution logic is derived in context, based on detected patterns and statistical correlations, not hardcoded instructions.

Enterprise software has always shaped how organizations operate, but now, it’s beginning to influence how they adapt to real-time conditions. From constraints and exceptions to shifting priorities, systems are starting to guide action instead of just enforcing structure.

AI-supported systems affect how decisions are framed, priorities are surfaced, and operational knowledge is applied. In this sense, they become operational infrastructure, scaffolding for operations and improvement.

This is what I mean by databasification. Databases transitioned from terminal-based interaction to a supporting infrastructure. Similarly, enterprise software is evolving, no longer just the “system where you go to work,” but now the pervasive engine that records changes, interprets data, provides insights, and triggers actions, based on analysis, across the workplace.

This is where the real shift lies. We’re no longer just building tools. We’re enabling systemic responsiveness.

A Belief System, Not a Roadmap

What I’m trying to describe isn’t just a strategy. It’s a belief system, a philosophy for constructing systems that, through thoughtful application of technology, reduces complexity and increases clarity to help people do more of the work that matters.

I fundamentally believe:

  • That software should empower, not encumber.
  • That system should adapt to circumstances, not require people to adjust to rigid flows.
  • That automation should decrease complexity and increase clarity.
  • That enterprise software can evolve from a back-office burden to forward-looking engines of innovation.

We have the tools. We have the data. What we need now is the discipline to rethink the foundations.

Because the future isn’t just about building better features. It’s about building better systems of interaction.

The Enterprise Software Promise

An ethical commitment for building AI-supported systems that serve human purpose.

As creators of enterprise systems in the age of adaptive automation, let’s hold ourselves to the following principles:

1. Build systems that support people, not systems that people serve.
The highest obligation is to assist human agency and purpose, giving people time back to work on what matters.

2. Amplify human judgment, don’t override it.
Enterprise systems should support decision-making, not dictate it. Software should be assistive, not authoritative, tools that extend human capability without displacing responsibility or control.

3. Design for relevance, not just execution.
Surface relevant information in context when needed.

4. Automate only what should be automated.
Recognize the difference between repetitive operations and human judgment.

5. Respect the dignity of work.
Enhance creativity, collaboration, and insight, not replace them.

6. Treat data not as exhaust, but as actionable input.
Give data meaning through meta-information to create systems that can respond.

7. Respect time and cognitive load.
Prioritize relevance, reduce friction, and eliminate wasteful complexity. Build systems that work for people, not the other way around.

Why Agentic AI Should Be an Evolution, Not a Revolution

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U.S. private AI investment grew to $109.1 billion in 2024 – leading to an “AI arms race” in which many businesses scrambled to be the first to announce shiny new capabilities. But did some tech vendors get so caught up in the AI FOMO (fear of missing out) that they lost sight of the user experience?

Instead of approaching agentic AI as a revolution, we should see it for what it is: an evolution. The ERP industry has been on a decades-long mission to automate processes, and agentic AI is an important part of this progression. As with any innovation, the most valuable applications often aren’t immediately apparent, but emerge over time. After all, before it was popularized as a children’s toy, the Slinky was invented as a naval instrument during World War II.

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