Artificial intelligence has entered a new phase. Over the past decade, massive investment has flowed into building increasingly large models, training on ever-larger datasets, and deploying enormous cloud infrastructure. This has delivered powerful tools, but it has also created a growing problem: AI is becoming too expensive to scale. Training large generative models costs millions of dollars. Running them continuously requires enormous energy consumption, expensive GPUs,

Artificial intelligence investment has exploded over the past five years. Billions of dollars have flowed into building massive data centers, training giant language models, and scaling cloud infrastructure. While this approach has delivered impressive breakthroughs, it has also created a serious financial problem: AI is becoming extremely expensive to build, operate, and scale. The rising costs of computing power, energy consumption, training data, and specialized hardwar

Edge computing and artificial intelligence are coming together in a way that is reshaping how digital systems work in the real world. This shift, often called Edge AI, moves intelligence away from distant cloud data centers and places it directly into devices, machines, and local environments where data is created. As we move into 2026, the opportunities created by edge computing and AI are becoming practical, measurable, and commercially meaningful. Instead of relying on

Artificial intelligence is evolving at an extraordinary pace. Over the last few years, generative AI models like GPT-4 and other large vision-language systems have amazed the world by writing, reasoning, and interpreting images and video. Yet behind the excitement, these systems still rely on a simple idea: predicting the next word or pixel, one step at a time. In late 2025, Meta’s AI research team introduced a radical alternative called VL-JEPA (Vision-Language Joint Embe

By 2025, sovereign AI has moved from a niche policy debate to a central pillar of Europe’s economic and geopolitical strategy. Artificial intelligence is no longer just another digital technology. It is fast becoming a form of national infrastructure, shaping productivity growth, public services, industrial competitiveness and national security. For Europe, the stakes are particularly high. The continent faces slow productivity growth, rising geopolitical risk, and deep re

Artificial intelligence (AI) is no longer a future concept. It is already shaping how economies grow, how businesses compete, and how governments deliver services. From finance and healthcare to public services and manufacturing, AI is becoming a core economic tool. However, not all countries are equally ready to benefit. Some nations are well positioned to turn AI into productivity gains, higher incomes, and stronger global influence. Others face a tougher transition, wit

Government agencies today face a tough challenge: the public expects fast, clear, and transparent information at a time when teams are shrinking and resources are tight. As expectations rise, agencies must respond with accuracy, speed, and consistency across many communication channels. This pressure grows even stronger as policy environments shift and communities demand higher levels of trust, accountability, and respect. At the same time, AI-driven communications tools a

Generative AI tools like ChatGPT are becoming common in government offices. They help teams write faster, explain complex ideas, and save many hours of work. But even with these benefits, public servants still face one major challenge: understanding how to draft a prompt to create a government briefing that is safe, accurate, and useful. Good prompts help AI produce clearer drafts, stronger summaries, and better structures. Poor prompts, on the other hand, can lead to mist

AI tools like ChatGPT are now part of everyday work in government. They can speed up writing, help explain complex policies, and save hours of time. But knowing when it is okay to use AI for government briefings—and when it is not—is becoming one of the most important digital skills for public sector teams. This article explains the risks, the right use cases, and the best practices for using AI safely. It also shows how AI can help governments shift to clearer, more acces

Artificial intelligence is growing at a pace the world has never experienced before. From smart assistants to advanced analytics, AI now sits behind almost every digital tool we rely on. But powering this new intelligence comes at a significant cost: AI energy demand is rising far faster than today’s electricity systems can keep up with.   Data centres, GPU clusters, and high-performance computing hubs now consume astonishing amounts of power. AI training runs for large lan

AI tools now offer teachers a wide range of support options, and these solutions can be grouped into five core areas that make the biggest difference to teaching, learning, and daily classroom practice. The follow article is a short summary of the options that teacher may wish to consider while exploring the many possibilities. You may also also want to read the following article: 'How teachers should be using AI in education'.

Artificial intelligence is changing how schools work, how teachers teach and how students learn. What once felt experimental is now widely used in classrooms, learning platforms and school systems across the world. When used well, AI in education can personalise learning, reduce teacher workload, improve accessibility and give students new tools to succeed. This article explores how AI supports teaching, learning and school management today. It blends practical examples, r

Getting roles ready for automation isn’t just about buying new technology — it’s about understanding how work actually gets done. Before automation can deliver real value, organizations need a clear, accurate view of their existing processes. This is where process mapping for automation becomes essential. Process mapping allows teams to document each step, role, and decision within a workflow. It shows how work moves between people, systems, and departments. When done prop

Organisations today face a growing imperative to make their systems and processes “AI ready” — in other words, to ensure that their workloads can harness artificial intelligence (AI) to deliver real business value. Preparing workloads for AI involves far more than simply installing a new tool or model. It requires strategic planning, data preparation, infrastructure modernisation, talent development, and strong governance. In this article, we explore the key steps to get work

Artificial intelligence is changing how we work, live, and learn—fast. But not all cities will feel these changes the same way. Some places will boom as AI-powered industries grow and new high-skill jobs appear. Other cities may face big challenges as automation replaces routine and service-based roles. This article unpacks which cities will be most impacted by AI, why some regions are more vulnerable, and what people and governments can do to prepare. We will explore glob

Every generation gives its children advice about work. When I was growing up, the message was simple: get a qualification, work hard, and you’ll be safe. That formula doesn’t work anymore. Artificial intelligence has rewritten the rules. I’ve started telling my kids something very different — “pick a job that AI can’t easily do.” The rise of AI and jobs automation is no longer science fiction. It’s reshaping companies, rewriting job descriptions, and wiping out roles

Artificial intelligence (AI) has moved far beyond the realm of early experimentation. By 2025, it has become a defining force reshaping how businesses operate, compete, and create value. The conversation has shifted from “what can AI do?” to “how do we use AI responsibly, efficiently, and at scale?” The year marks a major turning point. AI has embedded itself into everyday business life — from decision-making and automation to design, logistics, and customer engagement. Th

Artificial intelligence (AI) is transforming the world at remarkable speed. From business and healthcare to education and industry, AI is already reshaping how societies work. But as excitement grows about its potential, one key question remains: Will AI simply increase the digital divide between rich and poor countries? The digital divide refers to the growing gap between those who have access to modern digital technologies and those who do not. With AI now driving the ne

When I first started reading about artificial intelligence in finance, I’ll be honest — I felt nervous. The more I understood what AI could do, the more unsettled I became. I’ve been in finance for years. I’ve spent countless hours building models, analysing data, and writing reports. But suddenly, I was seeing headlines about AI systems that could automate half of what I did for a living. I remember sitting at my desk late one night and thinking, “If AI can do my job fast

For many organisations, extracting data from legacy systems remains one of the biggest barriers to digital transformation. Legacy platforms—often decades old—hold valuable operational data, but accessing that information can be slow, costly, and error-prone. Traditional extraction methods rely on manual coding, reverse engineering, or complex data integration projects. These are effective but time-consuming and expensive. Today, artificial intelligence (AI) and generati