You do not have a technology problem. You have a "it lives in Janet's brain and she is on holiday" problem.

Most organisations have cracked the technology problem. The tools exist, the infrastructure is there, and AI is more accessible than ever. The real bottleneck is something far less glamorous: the knowledge that lives in documents nobody reads, systems nobody connects, and the head of the one person who always seems to be in a meeting.

You might already have your best innovation resource sitting in a Tuesday morning meeting, slightly frustrated that no one is asking for their ideas. This is about the people inside companies who are wired to improve things and what happens when leaders actually pay attention to them.

AI governance sounds like a compliance headache. It does not have to be. At its core, it is simply about making sure the right people have a say in how AI works inside your business and that is a much more interesting conversation than it sounds.

When the map stops matching the terrain, most organisations respond by moving faster. But speed without clarity is just a faster way to drift.

The businesses getting the most from AI are not the ones with the best tools. They are the ones who understood their business model and operating model clearly enough to know exactly where technology should go, and where it should not.

Success in AI and digital transformation is not just about finding the perfect technology or the most capable team. It is about creating the conditions where change can actually happen.

When the right people are involved, when alignment happens before execution, and when leaders focus on the wider benefit rather than departmental boundaries, technology becomes an enabler rather than a source of frustration.

AI can now turn decades of operational experience into structured intelligence. But before we celebrate the efficiency gains, we need to ask a harder question: how do we automate in a way that strengthens the people and communities connected to the work, rather than quietly replacing them?

Many SMBs and scale-ups reach a point where the business has matured faster than the systems supporting it. The technology still works, but not as smoothly, and the team is doing their best without the senior oversight needed to guide the next stage. Here's what's really happening, and how to move from reactive to intentional

When teams focus on shared understanding rather than assumptions, communication improves naturally. Misalignment stops being personal. Decisions become clearer. Rework decreases. And progress accelerates because everyone is finally moving in the same direction.

 Because the best time to fix your data strategy was six months ago, the second-best time is today.

Andy Jassy, the CEO of Amazon, recently shared an important message with his employees that serves as a powerful signal for all business leaders. It's a clear vision of how Generative AI isn't just an emerging technology; it's a fundamental force that will redefine how companies operate, innovate, and manage their talent. For CEOs and founders, this isn't about distant tech trends; it's about making critical strategic decisions that will define your company's future.

In today's technology landscape, the ability to balance speed and sustainability isn't just a nice-to-have; it's a competitive advantage. Companies that burn through development teams chasing unrealistic deadlines ultimately move slower than those that maintain a sustainable pace with stable teams who deeply understand their product.

When it comes to problem-solving and daily tasks, working in a startup is vastly different from navigating the corporate world. Startups require agility, quick pivots, and wearing many hats, while larger companies offer structure, stability, and often complex layers of decision-making. In this blog, we explore the key differences between these environments and share insights on how each impacts your work and strategy in tech. Whether you thrive in fast-paced ambiguity or structured processes, understanding these contrasts can help you navigate both worlds more effectively.

Tech leadership can often feel overwhelming, especially with how fast technology evolves. It’s easy to think you need to know it all—but the truth is, no one does, and no one should.

If you are planning on outsourcing the development of your application or system start with these 6 things to avoid making the same mistakes non-technical founders make.

When working with machine learning models, where can you get labeled data to train it? Even better, how can you get people to create it for you, for free, in a fun and collaborative way?

Do you feel like there are so many jobs in tech out there that you don’t even know exist but wish you would? Are you exploring starting a career in tech but have no idea where to start?

After hearing many misconceptions about tech, I decided to create a guide to help people considering a career change or younger people figuring out what path to take, to get an idea of what jobs actually exist in tech and what skills are required.

The struggles and the adventure of learning a new language. My tico face

My colorful inconsistencies in the tech world