Build, Buy, or Borrow? How Indian CTOs Should Decide on Their AI Team

Most CTOs are under the same pressure right now. The board wants an AI roadmap, competitors announce new features every few weeks, and the people who can build it are hard to find. India has a deep talent pool, but it is also the most fought-over one. A good ML or GenAI engineer usually has several offers on the table, counteroffers arrive within hours, and salary expectations climb with every hiring cycle.

That is why a wrong call here costs more than it looks. Spend six months hiring a team with no clear use case, or sign a vendor contract you can't get out of, and you lose money and a quarter or two of a roadmap along with it.

AI is a team sport. The most successful machine learning teams comprise software engineers, data scientists, product managers, business analysts, cybersecurity experts, compliance officers, and domain-specific specialists, although the structure may vary across different organisations. Each member brings a different perspective, resulting in better solutions while also mitigating implementation and adoption risks.

Cross-functional teams are the new normal as they enable faster time-to-market by allowing business stakeholders to collaborate with technical implementers throughout the AI lifecycle. This approach reduces the risk of misaligned expectations while also accelerating AI adoption. For HR leaders, this is an important consideration, as organisations are likely to prioritise more collaborative and multidisciplinary hiring approaches in 2026.

Before you decide, it helps to be clear about what each path means in practice.

Build - Means hiring a full in-house team. You own the code, the models, and the know-how. Nobody understands your product better than your own people. The catch is time. Hiring, onboarding, and getting a team to ship can easily take six to nine months, and you carry the attrition risk the whole way.

Buy - Means using off-the-shelf AI tools or handing the work to a vendor. You move fast and skip the hiring headache. In return, you give up control, your differentiation is thinner, and you depend on someone else's roadmap and pricing.

Borrow - Sits in between. You bring in contract engineers, a dedicated team, or work with a hiring partner like us, to place specialists quickly. You get skilled people without a long-term commitment, and you can scale up or down as the work changes. Pay us a visit!

There is no single right answer to build versus buy. What works is matching the model to your stage, your data, and your timeline, and being willing to change it as you grow. Start with the use case, be honest about what you can manage, and bring in help where speed matters.

If you are planning to build or scale your AI team, we at Hiringhood can help you find the right people faster. Talk to us about your hiring needs.