Stop building AI—you’re already years behind. Why smart CIOs buy vs build
Your board wants AI transformation. Your engineers want to build it. Your CFO wanted ROI yesterday. Here's why the smartest chief information officers are buying, not building.
The temptation is understandable. You've got brilliant engineers who built your core systems. You know they can create a custom AI solution perfectly tailored to your needs. You’re seeing open-source models that have full control. You’re wrong—not about their capabilities, but about the economics and velocity of AI development.
The brutal truth is that by the time your team finishes building v1, commercial platforms will be on v10. And those platforms will cost less than your infrastructure alone.
The hidden iceberg of AI development costs
When teams pitch building AI internally, they show you the tip of the iceberg: model selection, fine-tuning, basic infrastructure. What they don't show you—because they don't know yet—is everything beneath the surface.
The real costs your spreadsheet is missing
Infrastructure alone is shocking. Training and running models requires specialized hardware that makes your current data center look quaint. That’s millions in GPUs that depreciate faster than smartphones. Cloud costs that scale exponentially with usage. Redundancy requirements that double everything.
But infrastructure is the easy part. Talent is where it gets hard. ML engineers command giant salaries. You'll need at least a dozen to start—that's millions annually before benefits. And that assumes you can find them. There are roughly 10 open positions for every qualified ML engineer.
Then comes the timeline tax. An 18-month build plan is fantasy. Real enterprises average 2-3 years to production-ready AI. During those years, companies burn cash with zero return while competitors who bought solutions are already optimizing their third-generation implementations.
Security and compliance add another layer. Each area requires specialized expertise. One compliance failure can cost millions in fines and destroy customer trust. In turn, commercial platforms have already spent years getting this right.
AI CX platform comparison matrix of solutions and key capabilities
Let’s think about velocity
OpenAI releases major updates monthly. Anthropic pushes improvements weekly. Google's team includes literally thousands of researchers. Your team of a few engineers—no matter how brilliant—cannot match this pace.
Consider what happened to companies that built their own search instead of using Google, or their own cloud instead of AWS. They spent years building infrastructure while competitors spent those years building differentiators.
The AI market moves even faster. Today's state-of-the-art model is tomorrow's legacy system. Commercial providers obsess over staying current because their survival depends on it. Your internal team will always be playing catch-up.
And integrations on the backend
Building AI isn't just about the model. It's about making AI work with everything else. Your CRM, ERP, customer service platform, knowledge base—they all need deep integration.
Then there's the ecosystem. Modern AI requires multiple models for different tasks. Language understanding, image processing, speech recognition, specialized analytics—each needs different capabilities.
Maintenance becomes a permanent tax. Every API change, every security patch, every version upgrade requires engineering resources. Commercial platforms handle this transparently. Your team becomes permanently tied up in keeping lights on instead of driving innovation.
Finally, talent retention
Even if you successfully recruit an ML team, there is no guarantee of them staying with you long-term. Tech companies want experienced ML engineers, and competing recruiters will call them daily with offers you can't match.
Just as they become productive on your systems, they might leave for companies where AI is the core business, not a side project. Your institutional knowledge could walk out the door any moment.
Commercial platforms employ hundreds of ML engineers. Losing a few doesn't impact service. Losing two from your team of ten cripples development. You become perpetually stuck in recruiting and onboarding instead of advancing capabilities.
The opportunity cost that kills companies
Every dollar spent building commodity AI infrastructure is a dollar not spent on your actual differentiators. Every engineer building chatbot frameworks isn't improving your core product. Every month delayed is market share lost to faster competitors.
Successful digital transformation isn't about owning every piece of technology. It's about combining best-in-class platforms to create unique value. The companies winning with AI aren't the ones building it from scratch, they're the ones applying it creatively to their specific challenges.
Your competitive advantage isn't in having AI. Everyone will have AI. Your advantage is in how you apply AI to your unique data, processes, and customer relationships. That requires your engineers focused on application, not infrastructure.
When building does makes sense
There are exactly three scenarios where building AI makes sense…
First, if AI is your core product. If you're competing with OpenAI, obviously build your own. But if you're a retailer, bank, or manufacturer, AI is an enabler, not your product.
Second, if you have truly unique requirements that no commercial platform addresses. This is rarer than teams believe. Most "unique" requirements are just familiar problems described differently.
Third, if you have the right resources and long timelines. A handful of companies focused on AI-systems can afford to build foundational technology. Everyone else should focus on application.
The path forward
Smart CIOs are treating AI like they treat cloud infrastructure—buy the platform, build the differentiators. Use commercial AI for foundation capabilities. Focus internal resources on proprietary applications that leverage your unique data and domain expertise.
Start with proven platforms that can scale with you. Look for vendors investing millions in R&D, not new-age startups with impressive demos but uncertain futures. Evaluate based on integration capabilities, not just model performance.
Most importantly, move fast. The AI window is now. Companies that spend three years building will find themselves three generations behind. The question isn't whether to adopt AI—it's whether you'll be applying it while competitors are still building it.
CX platform buyer's checklist
Recommended Blogs
How is the CXO role changing?
See how the role of the CXO is transforming and how it can drive revenue.
Before you get AI-powered support, get executive buy-in
Get executives on board for your support team's AI rollout. Gladly walks you through how to state a compelling case for implementing effective AI.
The impact of leadership on customer service success
Discover actionable tips for customer service leaders to enhance CX by empowering their teams through effective leadership.