The New AI Stack: Part 2
Why 73% of AI Projects Fail And How to Fix It
Part 2 of a 5-part series on building enterprise infrastructure for autonomous AI
The Hidden Cost of Getting AI Strategy Wrong from the Start
Here's what I see repeatedly: organisations launch AI initiatives with ambitious visions, but without clarity on how these systems will actually integrate with their existing business operations. The result? Research shows that 60% of enterprise AI project budgets end up going towards integration work rather than AI capabilities.
It's not that integration is inherently expensive—it's that most organisations approach it backwards. They build the AI solution first, then discover the complex reality of connecting it to their Salesforce instance, SAP systems, data warehouses, and the inevitable collection of legacy systems that somehow still run critical business processes.
By the time they realise the integration challenge, they're already committed to an architecture that makes everything harder than it needs to be. What should have been a six-month deployment becomes an eighteen-month integration nightmare, with budgets spiralling and stakeholder confidence evaporating.
Sound familiar?
This integration bottleneck is why 73% of AI projects never make it to production. But here's the thing: this problem is finally solvable. Organisations that understand the Model Context Protocol (MCP) shift from Part 1 are breaking free from integration hell.
