Is “Buy” still better than “Build” in an AI world?

Vinay Vaswani
Oct 14
4 min read

Updated: Oct 28

For years, the logic was relatively clear: build when software is integral to your competitive edge; buy for everything else - because buying is usually faster, cheaper, and carries lower execution risk.

The SaaS prevalence made that logic even stronger: mature products could be deployed in weeks instead of months, came with pre-built integrations, and benefited from vendor-funded R&D that no single company could match.

But AI is changing the equation. Generative AI, low-code platforms, and code copilots are reshaping the economics of building. Across studies, the evidence points to meaningful efficiency gains rather than a full-scale revolution. McKinsey, BCG, and Google DeepMind estimate that AI can reduce software development time by 20-40 %, while GitHub and Microsoft report that Copilot users complete coding tasks up to 55 % faster. Deloitte and Forrester place overall cost savings in the 20-40 % range, mainly from fewer engineering hours and shorter delivery cycles.

If AI can help teams build in weeks what once took months - does the “buy is better” logic still hold?

Why “Buy” still makes sense (more often than not)

AI may make coding faster, but the broader economics of enterprise software go far beyond writing code. Building is not just writing software - it’s signing up to maintain, secure, and evolve it indefinitely.

Here are some real-world considerations that keep “Buy” relevant for most companies:

Technical debt grows faster than you think Every custom build starts clean but ages quickly. Business rules evolve, teams change, frameworks get deprecated, and integrations break. Maintaining, testing, and upgrading an internal product consumes ongoing capacity. Many organizations underestimate these lifecycle costs - studies suggest maintenance can account for 50–70% of total software spend over its life. Buying from a vendor externalizes much of that debt.
Engineering talent is scarce and expensive Even with AI tools, developers remain the most constrained resource in most enterprises. Every hour spent rebuilding something that already exists is an hour not spent on innovation. In Gartner’s 2024 CIO survey, leaders cited talent availability as their top digital execution risk. The strategic use of engineering time is often more valuable than marginal cost savings from in-house builds.
Security, compliance, and continuity risk Enterprise software is rarely just about features - it’s about robustness, certifications, and accountability. Vendors spend millions annually on compliance (SOC 2, ISO 27001, GDPR), penetration testing, and uptime guarantees. Replicating that rigor internally can be prohibitively expensive. A vendor outage may cause disruption, but an internal failure without redundancy or security oversight can be catastrophic.
Integration and adoption complexity Building an internal system doesn’t remove integration headaches - it amplifies them. Data pipelines, APIs, single sign-on, analytics layers, and user onboarding still need to work seamlessly. Established SaaS platforms have spent years refining these interconnections. In contrast, internal tools often struggle to reach that level of reliability or user experience, especially at scale.
Vendors have taken over the custom/specialised aspect of build Modern SaaS products are no longer static. Vendors themselves are adopting AI to make their solutions more adaptive and customisable - from embedded copilots to domain-specific large language models. That means the “buy” option is increasingly flexible, and the old complaint of “one size fits all” is steadily fading. Today’s enterprise buyer can often tailor a platform almost as deeply as if it were built in-house.

Put together, these factors highlight that the true cost of building extends well beyond the sprint timeline.

The rise of “Boost”: a pragmatic middle path

MIT Sloan called it the “Buy-Boost-Build continuum,” where enterprises start from proven foundations, layer proprietary logic, and build only the final 10–20 % that defines their competitive edge. (MIT Sloan, 2025)

Many leading organizations are adopting the third route: “Boost.” Buy the reliable core platform, then extend, train, or fine-tune it using your own data, models, and workflows.

This hybrid approach blends the speed and stability of buying with the flexibility and differentiation of building.

Where the balance might land

AI will continue to blur the lines. The economics of “Build” are improving - not just in speed but in flexibility. Yet the economics of “Buy” are strengthening too, as vendors evolve faster, modular architectures mature, and ecosystems deepen.

The smarter question isn’t whether to build or buy - it’s where to build and why.

AI makes the “Build” option more accessible, but it doesn’t erase integration, maintenance, or governance complexity.

In most cases, the optimal answer will remain hybrid:

Buy for speed, scale, and reliability
Boost for tailored differentiation
Build where it truly creates advantage

AI will shift the boundaries, but not the fundamentals. Or to put it differently, “Buy for efficiency. Build for identity.”

If you would like to share your opinion on this topic or clarify any questions, please feel free to write to vinayvaswani@strivo.nl

Written by Vinay Vaswani for Strivo B.V (with the help of AI)

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