Bankim Chandra is Director & CEO of Dotsquares. Always committed to innovative solutions and mentoring the next generation in the industry.
These days, most business decisions are, in one way or another, technology decisions. And few come up as often or carry as much long-term impact as whether to build a solution in-house or buy one off the shelf.
It’s a question I’ve seen play out countless times across organizations of different sizes. On the surface, it feels like a logical, almost mathematical decision. With access to better tools, data and AI-driven insights, leaders can now compare costs, timelines and capabilities faster than ever before.
And yet, despite all this progress, many are still getting it wrong. Part of the challenge is that the decision often appears clearer than it really is. Buying a solution is typically associated with speed and efficiency. It gets you to market faster, reduces initial investment and comes with pre-built functionality. Building, on the other hand, is seen as slower and more resource-intensive but offers greater flexibility and control.
If you rely purely on data, the answer can seem obvious. But in practice, the outcome is rarely that straightforward. What I’ve observed is that data tends to favor what’s immediate and measurable. It highlights cost savings, deployment speed and feature comparisons. What it doesn’t fully capture is how a solution will evolve with the business, how well it fits within existing teams or how it supports long-term strategy.
For example, a platform might look like the perfect fit today, but over time, it can introduce constraints that weren’t initially visible. Teams may find themselves working around the system rather than with it. Similarly, a custom-built solution may require more upfront investment but can become a key enabler for innovation as the business grows.
These aren’t decisions that can be made in isolation or purely through analysis. They require context, experience and, importantly, a clear understanding of what the business is trying to achieve beyond the immediate need. There’s also a tendency to underestimate the trade-offs on both sides. Buying can create dependency on vendors, on predefined roadmaps and on limitations that may only surface later. Building, meanwhile, carries its own risks. Without clarity and discipline, projects can expand beyond their original scope, consuming more time and resources than planned.
In reality, the most effective approach I’ve seen isn’t choosing between build or buy but understanding where each makes sense. Certain areas of a business benefit from speed and standardization, where existing solutions work well. Others require a more tailored approach, especially where differentiation or customer experience plays a critical role.
Making that distinction is where leadership becomes important.
It’s also where collaboration matters. The best decisions tend to come from conversations that bring together technical expertise, business priorities and an outside perspective when needed. This helps challenge assumptions and ensures that decisions aren’t driven purely by short-term pressures.
As AI continues to play a larger role in decision making, it will undoubtedly make these comparisons even easier. But ease shouldn’t be mistaken for accuracy. The role of leadership isn’t just to interpret data but to question it, especially when the stakes are high. If there’s one takeaway, it’s this: The build versus buy decision is less about choosing the faster or cheaper option and more about choosing what will still make sense two or three years down the line.
Because in the end, the decisions that shape a business are rarely the ones that look best on paper. They’re the ones that continue to deliver value long after they’ve been made.
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