Category: Databases and storage

The AI coding dilemma Automating coding with AI seem like a breakthrough. You might ask why pay more for human work when AI can do it at a lower cost. However, this introduces a fundamental tension: as coding becomes more...

Facing the challenges of a competitive telecommunication industry Telecommunication companies face numerous challenges daily, from massive traffic growth to dissatisfied customers. Moreover, companies need to modernize their networks and integrate new technologies such as 5G, AI, machine learning, edge computing,...

Why speed is redefining automotive leadership The benchmarks in the automotive industry was German engineering, Detroit scale or Japanese reliability. Nowadays, a new rival has emerged: China and their speed. Once regarded as a low-cost hub for copycat engineering, China...

From AI experiments to business value: what matters in 2026 From curiosity to competitive instrument Artificial intelligence has moved beyond experimentation. According to McKinsey’s 2025 State of AI report, 88% of organisations now use AI in at least one business...

Digital transformation requires more than technology Why do most digital transformations fail, even with advanced AI technology? Digital transformation often involves advanced technologies like artificial intelligence, automation, and data analytics. Focusing only on technology overlooks a critical reality: transformation succeeds...

Enterprise AI in 2026 Artificial intelligence is no longer an experimental technology used only by research teams or large tech companies. Recently, AI has become a common tool in business operations, helping companies analyze data, automate tasks, and support decision-making....

Software supply chain attacks are exploding: how to protect your business in 2026 Details The rising risk of software supply chain attacks Over the past year, software supply chain attacks hit record levels. Malicious open-source packages increased dramatically year-on-year, and more...

Key takeaways Define first, deliver better Clarifying the core problem, users, and success metrics upfront prevents scope creep and misaligned expectations later in the project. Reality-driven planning Being realistic about budget, timeline, and MVP forces better trade-offs and reduces the...