The build vs buy software ERP decision used to be easier to frame: Buy what already exists, customize only when necessary, and avoid building from scratch unless the business case was obvious.
That tradeoff is changing.
Cloud has already reduced much of the infrastructure burden.
AI is now reducing the time and cost required to build.
For organizations evaluating their options, the question is no longer whether custom development is automatically too expensive or too risky. It’s whether a tailored solution creates enough value to justify building.
In this article, I’ll look at how AI is reshaping the build vs buy software ERP conversation, why traditional add-on models are being challenged, what still belongs inside Microsoft Dynamics 365 Business Central, and how business leaders can make smarter technology decisions without creating unnecessary complexity.
How cloud changed the baseline
Before AI entered the picture, cloud had already started changing the build vs buy software ERP equation.
Historically, the biggest barrier to customization wasn’t building; it was maintaining it.
Infrastructure, upgrades, and support made custom solutions difficult to justify, which is why many organizations relied on standard ERP functionality or third-party add-ons.
Cloud changed that cost model.
It reduced maintenance overhead, simplified updates, and shifted effort away from keeping systems running and toward improving how the business operates.
For organizations moving from GP or NAV to Business Central, that shift creates more flexibility in how systems are designed and where customization makes sense.
How is AI changing the cost and timeline of building custom business applications?
If cloud reduced the cost of maintaining software, AI is now reducing the cost of creating it.
According to McKinsey, generative AI could create significant productivity value across business functions, including software engineering. That matters because AI-assisted tools are accelerating development cycles, making it possible to move from concept to working solutions much faster.
This is especially relevant for operational workflows: approvals, handoffs, reporting steps, and day-to-day processes that don’t always fit neatly into packaged systems.
This shift is also reshaping the broader AI impact on software development decisions, particularly for organizations evaluating how quickly they can move from idea to execution.
Instead of configuring systems to approximate their processes, teams can begin building solutions that reflect how the business actually operates.
That’s where the conversation around AI custom software vs SaaS becomes more practical.
AI doesn’t remove the need for structure, but it does lower the barrier to experimentation. Prototypes can be developed quickly, adjusted as needed, and aligned more closely with real-world operations.
Why traditional add-on models are being disrupted
For years, third-party add-ons filled the gap between what ERP systems could do and what businesses truly needed. But as development becomes faster and more accessible, that model is starting to shift.
Organizations are questioning whether it still makes sense to adapt their processes to fit packaged tools, or to build solutions that better reflect how they operate.
This doesn’t mean add-ons disappear. Standardized functions like finance, HR, and compliance are still strong candidates for packaged solutions. But for processes that differentiate the business, the balance is changing.
We’re also seeing a shift away from the assumption that scaling requires adding more systems. In many cases, a more integrated platform approach provides the flexibility organizations need without introducing that additional complexity.
Should companies still build custom software in the age of AI, or is buying SaaS still better?
The answer isn’t one or the other. AI makes custom builds more viable than before, but SaaS still makes sense for standardized processes where reliability, compliance, and support matter more than differentiation.
That means the build vs buy software decision is becoming more selective.
Organizations should buy what is common, stable, and non-differentiating. They should consider building where the process creates a competitive advantage, improves customer experience, or supports a way of working that packaged software can’t easily match.
This is where leaders need a clearer framework for when to build vs buy software. The question is not “Can we build it?” AI is making that answer easier.
The better question is, “Should this capability be unique to how we operate?”
Evaluating the build vs buy custom software pros and cons now requires a different lens: speed, adaptability, ownership, governance, and long-term maintainability.
How the build vs buy software ERP decision affects what belongs in the ERP
This shift is also forcing organizations to rethink where functionality should live.
Business Central should remain the system of record for financial integrity, transactions, inventory, reporting, and core operational data.
But not every workflow needs to sit inside ERP.
In many cases, surrounding tools such as Power Platform, Azure, or custom applications can support more dynamic processes without over-customizing the core system.
That separation matters. It allows the ERP to remain stable while the business builds around it where flexibility is needed.
The goal is not to turn ERP into a junk drawer for every process. It’s to use Business Central as the operational core and build intelligently around it.
What are the risks of replacing third-party software with AI-built custom solutions?
AI-generated solutions still require governance, oversight, and alignment with core systems. Without that, organizations risk creating fragmented, unsupported tools.
The growing shift toward custom software vs SaaS AI also introduces new risks if governance and system alignment are not clearly defined.
The ability to build faster doesn’t remove the need for discipline. Custom solutions can introduce new challenges if they’re not properly managed, including:
- Lack of long-term support or ownership
- Security and compliance risks
- Inconsistent data across systems
- Fragmented processes
Frameworks like the NIST AI Risk Management Framework highlight the importance of governance when deploying AI-driven solutions. The goal is not to replace SaaS indiscriminately. It’s to make more informed decisions about where building creates value and where it introduces unnecessary risk.
Without that balance, organizations can recreate the same complexity they were trying to avoid.
Implications for business leaders
For business leaders, the build vs buy software ERP conversation is no longer just a technology selection exercise. It’s a strategy decision.
AI makes it easier to build, but that doesn’t mean everything should be built. The real opportunity is to decide which capabilities should be standardized, which should be differentiated, and which should be kept close to the operational core.
That requires clear ownership, strong data strategy, and a realistic understanding of how the business actually works. It also requires resisting the urge to buy another add-on every time a process feels inconvenient.
The organizations that get this right won’t simply modernize their systems. They’ll create a more adaptable technology foundation—one that supports growth without forcing the business into unnecessary complexity.
If your organization is evaluating how AI changes your build vs buy strategy, Kopis can help you assess what belongs in Business Central, what should be built around it, and where a tailored solution could create the most value.
Schedule a discovery call to start the conversation.
About the Author
Adam Drewes is the Chief Technology Officer at Kopis, where he helps companies make smarter software decisions that align with their business goals, whether that means deploying proven tools or building custom solutions that protect their competitive edge.
With more than two decades in the software services space, Adam brings a rare mix of technical depth and business insight to every conversation. He’s endlessly curious about how companies operate, what drives their success, and how the right technology choices can accelerate their growth.
