The Easy Way vs The Hard Way: Building Reliable AI That Actually Works
After building over 20 commercial AI projects, we've discovered there are two distinct approaches to AI development.
The Easy Way treats AI like traditional software engineering - fast to build, impressive in demos, but fragile in production. The Hard Way treats AI as fundamentally a data problem, requiring weeks or months of careful Policy creation and domain expert collaboration.
This isn't another "5 quick AI wins" post. Instead, we'll explore when each approach works, why most projects eventually need to abandon The Easy Way, and how to recognise when it's time to make the switch to building AI that actually works in production.
Why Policy is the Key to Building Trustworthy AI
Most enterprise AI projects struggle to move from concept to reality. The root cause is the lack of clear, unambiguous definitions of what success looks like for a particular business.
This article explains why traditional AI development often fails to earn trust, how our approach of “Policy” offers a solution, and how businesses can use this approach to confidently deploy AI systems that deliver real-world results.
The Key to Successful AI Implementation for Businesses
Everyone knows AI solutions for businesses could transform their companies, but often nobody on their team actually knows how to make it work properly.
The gap between what AI promises and what actually gets delivered is massive.
In this guide, you'll learn why most AI projects fail, how our approach to custom AI development services tackles real problems instead of theoretical ones, and why companies choose us.