Build vs. Buy in the AI Era: Where Differentiating Software Still Matters
Most organizations do not struggle to find software. They struggle to find the right software.
If you need accounting, payroll, or a standard CRM workflow, you can typically purchase a mature product that is proven, supported, and constantly improved. That’s a good thing. Packaged software exists for a reason, and for many capabilities it is the quickest way to get value.
The more challenging questions arise when the software you need doesn’t exist, or when every off-the-shelf option pushes you into the same patterns as everyone else. That is when “build vs. buy” becomes less about procurement and more about strategic choice.
Commodity software is everywhere. Differentiating software is not.
A straightforward way to look at it is this: there is software you can purchase, and there is software you cannot. Commodity software helps you run the business. Differentiating software helps you be the business.
Differentiating software is what encodes the thing that makes your organization distinct. It might be a proprietary workflow that has been refined for years. It might be the way you price, underwrite, schedule, route, detect risk, or personalize service. It might be the internal platform that enables teams to operate faster and with more confidence than competitors. In many cases, it is the connective tissue between your data, your processes, and your customer experience.
When that connective tissue is missing, teams end up compensating with manual steps, spreadsheets, workarounds, and tribal knowledge. When it is done well, it becomes a durable advantage that compounds over time. (For more on how SPR thinks about specialized, proprietary, mission-critical custom software, see Application Development Services.)
AI changes the economics, but it does not eliminate the need
Generative AI has changed what is possible in software delivery. It can accelerate research, drafting, prototyping, testing, and even parts of implementation. It can also help teams unlock value from unstructured information like documents, emails, call notes, and policies.
But AI does not magically turn a unique business into a generic one.
The most valuable applications of AI still depend on context. Your workflows, your data, your risk tolerance, your regulatory environment, your customers, and your operating model all matter. AI can help you move faster, but it does not remove the need to make intentional choices about how work should happen and how decisions should be made.
This is why the “build” side of the equation still matters. In the AI era, the advantage often goes to organizations that use AI to accelerate the creation of differentiated capabilities, not to those who simply bolt a chatbot onto existing tools. If you want a practical companion read on the data side of this, AI Ready Data: What it Means and How to Get There is a useful starting point.
A practical way to decide what to buy and what to build
The best outcomes are rarely “all buy” or “all build.” Most organizations land on a hybrid approach, and the real work is drawing the line in the right place.
Buy when the capability is broadly available, widely standardized, and not central to how you compete. If a process is mostly the same across your industry, you are often better served by a proven product that can be configured and integrated. You get speed, vendor support, and a clear upgrade path.
Build when the capability is proprietary, mission-critical, or directly tied to differentiation. This is especially true when off-the-shelf tools force compromises you can feel in customer experience, operational efficiency, or data quality. Building can also be the right move when you need ownership and control over the roadmap, the integrations, and the security posture, or when you are creating something that is truly new.
A good signal that building is worth exploring is when leaders can clearly articulate the uniqueness they are trying to protect. If the goal is “we need a system like everyone else,” buying is usually fine. If the goal is “we need a system that reflects how we uniquely create value,” buying may not get you there.
Differentiation shows up in the seams
A lot of the advantage does not live in a single tool. It lives in the seams between tools.
That is where custom development often delivers the highest leverage. Integration, workflow orchestration, data unification, and automation are not glamorous, but they are frequently where time is lost and errors are introduced. These seams also tend to be where AI becomes most useful, because AI can help interpret messy inputs, summarize context, route work intelligently, and generate structured outputs that downstream systems can use.
In practice, this might look like connecting customer communications, operational data, and policy rules to produce faster, higher-quality decisions. It might look like automating the mechanical steps that slow teams down, while keeping humans focused on the judgment calls that require experience and accountability.
Keep humans in the loop on purpose
One of the most important mindset shifts with AI is recognizing that AI is not something happening to your organization. It is something your organization chooses to create and deploy.
That matters because the design choices are consequential. Where AI sits in a workflow, what it is allowed to decide, how it explains its outputs, how it is monitored, and when a human must review or override are all decisions that shape trust and outcomes.
AI can amplify unique human talents when it is used to remove the repetitive and mechanical parts of work, surface better context, and give teams more time for judgment, creativity, relationship-building, and problem-solving. When AI is used carelessly, it can create new friction, reduce transparency, and damage customer experience.
If you are looking for a concise description of how to frame the path from identifying opportunities to building production-grade systems, Our Approach lays out a practical model.
Or take a deeper look at a security-focused approach.
Where to start
If “differentiating software” feels like a big concept, start small and specific. Pick a workflow that is both high-impact and chronically constrained. Look for processes where teams are repeating the same manual steps, where decisions are made with incomplete context, or where customer experience suffers because systems do not connect cleanly. Then ask a simple question: is this workflow core to how we compete?
If the answer is no, buying and integrating may be the best move. If the answer is yes, that is where building can generate compounding returns, especially when AI is used to accelerate delivery and improve workflow.
Modernization decisions often sit underneath this question as well. If you are weighing how to evolve legacy systems without a full rip-and-replace, Future-Ready, Without Starting Over: AI’s Role in Legacy Modernization is a helpful perspective.
The AI era is moving fast, but the fundamentals have not changed. Commodity tools help you keep up. Differentiating software helps you stand out.
