Build vs. buy: should M&A teams build their own AI or buy a platform?

The same conversation is happening in most M&A and corporate development teams right now: do we build our own AI tooling, or do we buy a platform? It usually starts after someone wires a language model to a data feed over a weekend and produces something that looks convincing in a demo.

The demo is the easy part. What separates a weekend prototype from a tool a deal team can put in front of an investment committee is data, evaluation and maintenance, and that is where the real cost sits. This article breaks the decision down the way a practitioner should look at it.

In this article you'll learn:

• Which parts of an AI deal sourcing and analysis stack are genuinely buildable in-house, and which are not
• Why most enterprise AI pilots never reach production, and what that pattern means for deal teams
• The cost components that rarely appear in a build business case, from data licensing to model evaluation
• The three situations in which building your own tooling is the better decision
• A vendor test that separates a good demo from a platform your team can defend in a review
• How to run the build vs. buy decision in a week instead of a quarter

What you would actually be building

Building AI for M&A is rarely one project. It is four, and they have very different difficulty curves.

1. **The data layer.** Verified company data at scale: financials, ownership structures, products and contacts, across public and private companies in every market you care about. This is licensing and pipeline engineering, not machine learning.
2. **The retrieval and mapping layer.** Turning a search intent such as European suppliers of precision-machined components for medical devices into a defensible set of companies, independent of rigid industry codes.
3. **The analytics layer.** Valuation, peer group construction, benchmarking, forecasting. Anything that produces a number a partner has to sign off on.
4. **The output and governance layer.** Documented sources, reproducible results, board-ready output and an audit trail that survives review.

Most in-house builds succeed at a thin version of layer 2 and stop there. The reason is not a lack of talent. It is that layers 1, 3 and 4 are permanent operational commitments, not projects with an end date.

Why most in-house AI builds never reach production

The pattern is well documented outside M&A too. MIT's NANDA initiative found in its 2025 report The GenAI Divide: State of AI in Business that roughly 95% of enterprise generative AI pilots produced no measurable business return, and that the failures were mostly organisational rather than technical: tools that did not retain feedback, did not adapt to the workflow and were never integrated deeply enough to be used under deadline pressure (reported by Forbes).

Deal teams face an additional constraint that a general enterprise AI project does not: the output has to be defensible. A longlist that misses an entire category of targets is not a minor quality issue, it is a finding in a post-deal review. That raises the bar from useful to auditable, and auditability is the expensive part.

A related version of this problem is covered in why ChatGPT is not the right tool for M&A longlists: a general model without a verified company universe behind it produces plausible names, not complete coverage.

The cost components a build case usually misses

The compliance line has become heavier than it used to be. Where a firm falls in scope, documentation is a standing obligation rather than a one-off exercise. What that looks like in practice is covered in EU AI Act compliance for audit firms.

When building is the right call

Building is defensible in three situations.

• **You hold proprietary data nobody can sell you.** A decade of your own deal history, pipeline outcomes or operating data from portfolio companies is a real edge, and only you can build on it.
• **The workflow is idiosyncratic and central to your strategy.** If your sourcing thesis depends on a screen nobody else runs, a bought tool will always be an approximation.
• **You already run a production data and ML function.** Not a team that builds dashboards. A team that ships, monitors and is accountable for models in production.

If two of those three do not apply, building is usually a slower and more expensive route to the same result.

When buying is the right call

Buying wins when the bottleneck is coverage and speed rather than differentiation. Almost no corporate development team creates competitive advantage by owning a company database, in the same way that almost none creates advantage by running its own mail server. The advantage sits in the judgement applied to the output.

Buying also front-loads what a build defers. Someone else has already paid for the data licences, the entity resolution and the years of quality work, and spreads that cost across every client rather than across your one deal team.

Because information is available faster and at higher quality, our analyses are more meaningful. Whether it's a longlist or a multiple-based valuation, I can rely on the data foundation and stand behind the results.

Nikolai Üstündağ, Senior Manager at WTS Advisory

The hybrid path most teams end up on

In practice the answer is rarely absolute. The pattern that works is to buy the data and analysis layer and build the thin layer on top that is genuinely specific to you: exports into your own CRM or pipeline model, your own scoring on top of a bought longlist, your own template logic on top of standard output.

That keeps the expensive, undifferentiated work on someone else's balance sheet and points your engineering effort at the part that is actually yours.

How to evaluate a platform before you sign

If you decide to buy, the scripted demo is the least informative part of the process. Test these instead.

1. **Bring your own case.** Ask for a live run on a niche you know well, not a prepared example. You will see immediately whether coverage holds up outside the obvious names.
2. **Check the misses, not the hits.** Ask which known companies the tool did not return, and why. A vendor that can explain its gaps understands its own data.
3. **Ask how the numbers are produced.** Anything financial should come from deterministic logic, not from a language model generating plausible figures.
4. **Ask where your data goes.** Processing location, isolation, and whether your inputs can end up in the training of public models.
5. **Ask for the audit trail.** Sources, dates and reproducibility. A result you cannot reproduce next quarter cannot go into a board pack.
6. **Look at renewals, not logos.** Renewal rates tell you whether teams still use the tool once the novelty has worn off.

Where StrategyBridgeAI fits

StrategyBridgeAI is the buy side of this decision, built so that deal teams do not have to construct layers 1, 3 and 4 themselves. The platform covers around 50 million public and private companies in more than 100 countries and combines four things in one workflow: a global company database, best-in-class longlists, Outside-In business analysis, and on-demand niche market reports.

The architecture reflects the auditability point above. Financial work, such as estimating P&L figures from balance sheet data or running valuations, uses proprietary deterministic machine learning models, because a valuation cannot be allowed to hallucinate. Language models are used where language and semantics are the actual problem, such as niche search queries and company mapping. All data processing runs through European server infrastructure, data is processed in isolation, and no client data flows back into the training of public models.

Development started more than five years ago, well before commercial language models had their breakthrough. Across customers the average time saving is about 80%: work that used to take 20 to 120 hours is completed in under five. StrategyBridgeAI is recommended by the Institut der Wirtschaftsprüfer (IDW), and the annual customer renewal rate is 97%, against a B2B SaaS median of 91% (SaaS Capital, 2026).

Running the decision in a week

Build vs. buy does not need a quarter of analysis. Write down the one workflow that costs your team the most hours this year. Ask whether the bottleneck in that workflow is data coverage, analysis speed or something only you could build. Then run the same real case through a platform and through your own team, and compare the output, not the effort.

If the platform gets you to a defensible answer faster, buy it and spend your engineering budget on the part nobody can sell you.

The fastest way to test the buy side of the decision is on your own case. Book a demo and bring a niche where you already know the right answer.