Expert analysis, trends, and practical applications of artificial intelligence in business.
AI in logistics is a $12B+ market in 2026. Discover the 5 highest-value use cases — from demand forecasting to agentic AI — why most projects stall, and how logistics companies can build AI products that actually scale.
You can build an AI MVP in 30 days. Not a polished, production-ready product — but a working, testable version that proves (or disproves) your core idea before you invest serious time and money. The key is ruthless prioritisation: one problem, one user, one core AI capability. With the right process, a small team (or…
AI in healthcare is no longer a future-state promise. It’s a $37 billion market in 2026, growing at a CAGR of more than 44% toward a projected $613 billion by 2034. Sixty-six percent of physicians now use health AI tools — a 78% jump from just 38% in 2023. And the average ROI for AI…
Five major trends are reshaping AI product development in 2026 — from agentic AI moving to production, to the collapse of inference costs and the rise of AI governance as a product requirement.
What Does “Build vs. Buy AI” Actually Mean in 2026? Build vs. buy AI is the strategic decision every business leader faces when adopting artificial intelligence: do you invest in developing a custom AI solution from scratch, or do you purchase an off-the-shelf platform and adapt it to your needs? In 2026, the answer is…
AI in fintech is a $45B market in 2026 with 92% of financial firms investing. Here are the use cases that work, why most products stall, and the governance-first playbook we use at Neomeric.
Standard websites and traditional SEO are losing ground fast. AI Overviews have cut organic CTRs by 61%, while AI-augmented websites convert 4.4x better. Here is why businesses need to shift to AI search optimisation and intelligent web experiences.
AI in healthcare is delivering real results in 2026 — from clinical documentation and diagnostics to drug discovery and clinical trials. Here is where the technology is making the biggest impact, what adoption really looks like, and what organisations need to get right when building AI-powered health products.
AI products fail to scale because teams optimise for model performance while neglecting infrastructure, data pipelines, and operations. Here are the 7 most common failures and how to fix them.
Scaling an AI product means moving from a working prototype to a system that handles thousands or millions of users reliably. This guide covers infrastructure architecture, model optimization, cost management, and the operational disciplines needed to scale successfully.