How solving integration nightmares prepared us for the AI implementation challenge
The issue with implementing AI solutions isn’t new – we’ve seen it before in marketing automation. Businesses face the same choice: invest in what we termed “messiah” gold-plated solutions which on paper offers a comprehensive solution which would solve all their problems. What follows is years of paying high licence fees, sky high never ending integration challenges and costs and at the end a fair chance that the project fails , or build lightweight, specific solutions that tackle one core issue at a time.
For years, we’ve watched companies spend millions and years trying to connect every marketing platform into one comprehensive system. Meanwhile, their competitors implemented focused solutions – connecting just their CRM to email marketing, or just their ad platforms to analytics – and started seeing results in weeks.
Same fundamental choice, different technology.
The skills transfer perfectly to AI development: Marketing automation taught us to break complex workflows into manageable pieces, connect disparate systems reliably, and measure what actually matters.
We learned that the magic isn’t in comprehensive solutions: it’s in intelligent connections that solve specific business problems immediately.
When we approach AI projects, we think like marketers who’ve spent years making platforms talk to each other, not like developers who want to build impressive systems. We ask: “What’s the smallest automation that delivers the biggest impact?” Then we build that, prove it works, and expand gradually. It’s the same methodology that’s served marketing automation well, applied to a more powerful technology.
The companies succeeding with AI aren’t the ones with the most sophisticated implementations – they’re the ones treating AI like we’ve always treated marketing technology: practical tools that should improve business results, not impressive demonstrations of what’s possible.
Have you noticed similarities between current AI implementation challenges and earlier technology adoption cycles? What lessons from previous automation projects apply to today’s AI decisions?
