The honeymoon phase with Generative AI is over.
Most organizations we speak with are stuck in the same bottleneck: they have brilliant isolated pilots and impressive internal demos, but they are hitting a wall when trying to scale these into enterprise-wide value. The challenge isn’t the technology itself—it’s the industrialization of it. Wrapping cool models in the necessary governance, security, and integration layers often turns a sprint into a slog.
There is a significant shift happening in how tech giants are solving this, and it’s a strategy every founder and CTO needs to pay attention to right now. The industry is moving away from “building” and toward “assembling.”
The Shift: Asset-Based Consulting
Traditionally, if you wanted to integrate AI into your legacy workflow, you hired consultants to build a bespoke solution. This is labor-intensive, slow, and expensive. It’s like commissioning a custom architect to design every single brick of a new house.
The new model—recently championed by major players like IBM—is asset-based consulting. Instead of starting from a blank page, businesses are now encouraged to use a catalog of pre-built software assets. This allows you to construct and govern your own AI platforms by leveraging existing architectures.
The core philosophy is simple: Don’t rip and replace. Connect your new AI agents to your old legacy systems without tearing down the core infrastructure that runs your business.
Escaping Vendor Lock-In
One of the biggest hesitations for business leaders is the fear of backing the wrong horse. If you build your entire AI strategy on one proprietary platform, what happens if pricing changes or a better model emerges elsewhere?
The modern “assemble-first” strategy mitigates this by embracing a multi-cloud reality. The best architectures today are designed to be agnostic. They support:
- Amazon Web Services (AWS)
- Google Cloud
- Microsoft Azure
- Open-source and Closed-source models
This approach respects the messy reality of enterprise IT. You don’t need a pristine, single-vendor environment to succeed. You just need an integration layer that plays nice with everyone.
The Proof is in the Productivity
Why does this matter? Because speed creates value. By utilizing pre-built delivery platforms rather than coding custom workflows for every client, firms are reporting massive internal productivity boosts—up to 50 percent in some cases.
Real-world application is already visible. Look at Pearson, the global learning giant. They didn’t just “buy AI”; they used this asset-based approach to construct a custom platform where human expertise manages AI agents for everyday decision-making. They focused on the workflow, not just the model.
The Strategic Takeaway
For founders and decision-makers, the message is clear. We need to stop obsessing over which Large Language Model (LLM) is slightly smarter this week. The conversation must shift from capability to architecture.
Success in 2024 and beyond won’t come from having the smartest chatbot. It will come from your ability to integrate AI agents into your existing ecosystem without creating new data silos. If you are still building everything from scratch, you are likely moving too slow.
