Capital Allocation in the AI Era: Weighing Build, Partner, and Buy

Building world-class AI capability internally is not trivial. It requires rare technical talent, extensive data infrastructure, and significant computing resources. Even for well-capitalised enterprises, assembling these ingredients can take years.

Instead of building algorithms, hiring engineers and experimenting with models over multiple development cycles, a company can acquire an organisation where those capabilities already exist—often alongside intellectual property, datasets and specialized teams.

From a board perspective, this can be a compelling strategic acceleration tool. But it is also where governance discipline becomes critical.

AI-related acquisitions present risks that are not always obvious in conventional M&A.

The first is valuation discipline. AI startups often command premium valuations driven by technological promise rather than predictable cash flows. Boards must ensure that enthusiasm for emerging technologies does not weaken capital allocation rigor.

The second is talent dependency. In many AI companies, a small group of researchers or engineers represents a significant portion of the enterprise value. Retention structures, equity incentives, research autonomy, leadership roles therefore become a key board discussion point.

The third is the CAPEX–OPEX equation. The acquisition price may only be the beginning. AI platforms require ongoing investment in computing power, data storage and model training. Boards should evaluate the long-term operating expenditure implications alongside the upfront capital outlay.

Then comes data governance—a risk increasingly scrutinised by regulators. AI systems depend heavily on data integrity, provenance and compliance with privacy regulations. Thorough due diligence on datasets, model training sources and intellectual property rights is essential.

For most boards today, the debate is no longer whether AI will reshape their industry. The question is how quickly their organisation can build credible capability without compromising capital discipline.

In practice, boards tend to evaluate three strategic paths.

Internal development  works best when AI capability is expected to become a core competitive differentiator. Financial institutions building proprietary credit models or manufacturing companies optimising complex supply chains may find long-term value in owning the intellectual property. But boards must recognise that this path requires patience, sustained R&D investment and a willingness to absorb experimentation costs.

Strategic partnerships are often the most pragmatic starting point. Collaborations with cloud providers, research labs or specialised AI firms allow organisations to access advanced technology while preserving capital flexibility. For many companies, particularly those still early in their AI journey, partnerships offer a way to learn quickly without committing to large balance-sheet decisions.

Mergers and acquisitions, however, become compelling when three conditions converge: the capability is strategically critical, time-to-market matters, and the organisation has the balance sheet to support long-term investment. In such cases, acquisition may be the only realistic way to secure scarce talent, proprietary models and technological leadership.

If there is one principle boards may wish to keep in mind, it is this: AI capability should not be acquired simply because the technology is fashionable. It should be acquired when it clearly strengthens the company’s strategic moat.

Internal development builds depth.

Partnerships provide flexibility.

Acquisitions deliver speed.

The role of the board is to ensure management chooses the path that aligns not with the latest technology cycle, but with the organisation’s long-term strategic advantage.

Santanu Bhattacherjee is an industry veteran with 20 years of experience, currently at the forefront of  trust and safety at Google (Search and Gemini). A certified Independent Director, he specializes in bridging technical innovation with boardroom strategy. He is also a reputed author and recently published his second book The Alchemy of Strategy and writes extensively in LinkedIn on the intersection of deep tech and corporate governance.