Issue #14 Should Companies Build or Buy Their AI?
A debate that isn't new
As the artificial intelligence arms race accelerates, corporate boards across industries are facing a fundamental question.
Should they build proprietary AI capabilities in-house or purchase prebuilt solutions from vendors?
While the debate isn’t new, the stakes have never been higher.
The allure of building is easy to understand. If an organization holds proprietary data that can power superior predictive models, creating its own AI systems may offer a competitive edge. In finance, healthcare, or logistics, such differentiation can redefine market leadership.
EY’s recent insights echo that sentiment. Lacking access to unique datasets or cutting-edge internal capabilities could leave companies vulnerable to rivals deploying vendor tools with faster iteration cycles.
Yet, the economics of building from scratch remain daunting. Training a large language model like ChatGPT costs upwards of $10 million, a scale of investment out of reach for most firms.
To buy or not?
Even smaller models demand sustained infrastructure, DevOps support, and dedicated data science teams. Ongoing costs—maintenance, upgrades, compliance—can snowball quickly, according to analysts at Neptune.ai.
This is where buying shines. Off-the-shelf AI products typically offer fast deployment, lower upfront costs, and access to cloud-based infrastructure maintained by vendors. Nearly half of all companies pursuing AI adoption today favor this route for its speed and predictability, as noted by European Business Magazine. These prebuilt systems often integrate easily with existing tech stacks and come with support for model monitoring, drift detection, and performance updates.
But speed comes at a price. Buying introduces the risk of vendor lock-in, less control over data governance, and potential misalignment with evolving business models. Regulatory concerns—particularly under frameworks like the EU AI Act—also pose a dilemma.
Companies must trust that vendors have the processes and transparency needed to meet increasingly strict compliance demands. EY cautions that this may require a shift in procurement rigor: from evaluating technical specs to assessing ethical safeguards and accountability standards.
Operationally, the decision has long-term consequences. A bespoke model gives organizations full control over deployment, retraining, and performance tuning—but only if the internal talent exists to sustain it. Without mature MLOps pipelines, monitoring tools, or a governance framework, even the most promising internal model risks underperforming.
Strategically, companies must weigh whether the AI capability is core to their value proposition or a peripheral tool. Marty Cagan, product thought leader at the Silicon Valley Product Group, suggests that if a capability is central to market differentiation, it warrants investment. If not, attempting to replicate existing vendor offerings is a costly distraction.
Ultimately, the choice should align with the company’s digital architecture. For AI features embedded deeply in customer-facing products or core business processes, building offers flexibility and futureproofing. For one-off solutions say, automating a single internal task, buying is often more pragmatic.
Governance and ethics are fast becoming make-or-break factors. With regulations tightening globally, the ability to manage bias, ensure transparency, and document AI decisions is no longer optional. While building allows for tighter ethical control, it also means bearing the full compliance burden. Buying shifts some accountability to the vendor, but only if the right due diligence is done.
The final piece of the puzzle: success metrics. Whether a firm is chasing ROI, cost savings, or customer impact, leaders must ask which path (build or buy) is more likely to meet those goals within the company’s risk appetite and budget. For some, the predictability and proven value of vendor solutions will be hard to beat. For others, building will be justified by long-term gains and market distinction.
So, what is the solution?
Either way, the decision isn’t binary. Hybrid approaches, where companies buy foundational models and build proprietary layers atop them are emerging as a viable middle ground.
To conclude, the era of experimenting with AI in the shadows is over. As capital tightens and accountability rises, build-vs-buy decisions will separate the pragmatic from the performative.
Sources: EY, European Business Magazine, Neptune.ai, SVPG, Contus