Sustainable Velocity: Why Clarity Is Making a Comeback

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

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

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

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

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

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

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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Why Agentic AI Should Be an Evolution, Not a Revolution

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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Business Decisions: Does Your Data Deserve A Seat At The Table?CFOs: Are you ready to let go and trust AI?

What should your data be telling you about ways to improve your business? Seams of golden insight might exist within mountains of stored information. But what’s the best way to find it?

It’s estimated that “64% of organizations already manage at least 1 petabyte of data.” In physical terms, that’s like filling 20 million tall filing cabinets, according to calculations.

These are just the foothills, though. It’s thought that “41% [of organizations] manage at least 500 PB of data.”

Enterprises need to store data for compliance, governance and accounting reasons. But today, there’s sometimes a vague assumption that vast amounts of seemingly random business data will come in useful somehow, so it’s kept for years at huge expense.

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