Every company is now an AI company, or trying to be. But with hundreds of new tools entering the market each month, many leaders face the same question: Should we build our own AI solution or buy one off the shelf?

At Maxwell Bond’s AI & Data Leaders Roundtable, this debate split the room. Some argued for control and IP protection through in-house builds. Others championed speed and scalability through SaaS. The emerging consensus? The best route is often a blend of both.

 

1.    Building In-House: Control and Customisation

The strongest argument for building AI internally is control. When you build, you own your data, your IP, and your roadmap. This is critical in sectors like legal, finance, and health, where data sensitivity and compliance are paramount.

Pros:
•    Total ownership of data and privacy
•    Customisation for your exact processes
•    Competitive differentiation
•    Easier to embed into existing systems

Cons:
•    High upfront cost
•    Requires niche technical talent (data engineers, MLOps, AI leads)
•    Slower to implement

As one Roundtable participant shared, “We built our own chatbot, and it’s been transformational, but it took six months and several specialists to get there.”

 

2.    Buying Off-the-Shelf: Speed and Scale

Buying AI tools from established vendors can give you immediate results. From sales enablement to analytics and HR automation, SaaS-based AI platforms (like Gong, Fireflies, or Copilot) are plug-and-play and integrate quickly with your tech stack.

Pros:
•    Fast implementation
•    Regular updates and vendor support
•    Predictable subscription cost

Cons:
•    Risk of vendor lock-in
•    Limited flexibility
•    Potential data exposure

As one leader put it, “Every SaaS provider now sells AI as part of the package, but knowing which one is right for you is a minefield.”

 

3.    The Hybrid Strategy: The Best of Both Worlds

Most forward-thinking organisations are adopting a hybrid strategy; buy for utility, build for differentiation.

•    Buy for common tools (automation, CRM, analytics).
•    Build for areas tied to your unique IP or customer experience.

This approach lets you move fast without losing control. It also supports scalability, as your internal AI team can later refine and expand what works.

 

Data Governance and Security Come First

Regardless of your approach, good data governance is non-negotiable. From GDPR compliance to model auditability, secure data foundations underpin every successful AI strategy. One Roundtable participant put it best: “AI will only ever be as good as the discipline behind it.”

 

The Leadership Imperative

AI adoption is no longer just an IT initiative, it’s a leadership one. The most successful organisations appoint dedicated AI or Innovation Leads to drive the strategy forward and bridge the gap between tech and business.

Leaders must ask:

•    What’s our biggest problem AI can solve?
•    What data do we need?
•    How will we measure success?
•    Do we have the right people to deliver it?

 

How Maxwell Bond Can Help

Whether you’re building, buying, or blending, Maxwell Bond can help you succeed. We connect organisations across the UK, EU, and US with exceptional AI, Robotics, and Data talent, permanent and contract.

Our specialists can help you assemble the right mix of engineers, analysts, and strategists to build scalable, secure AI capabilities.

We also host AI Leaders Roundtables (in-person and virtual) for executives across Tech, Legal, Operations, Sales, and HR.

👉 Contact Maxwell Bond to discuss your 2026 AI talent and hiring strategy.
👉 Contact Andy Holt to join our next AI Leadership Roundtable.