Enterprises use an applied AI company instead of building AI capability internally for three reasons: speed, success rate and focus. A partner arrives with a team that has already shipped AI into production, so the first working system lands in months. Partnered builds also fail less. MIT’s 2025 GenAI Divide research found that buying from specialized vendors and building with partners succeeded about 67% of the time. Internal builds succeeded only a third as often (Fortune).
The best answer is rarely build or buy. Buy what is a commodity. Choose a partner to re-build what is core. Grow your own team while the partner hands the work over.
Why do companies use an applied AI company instead of building AI capability internally?
Because an internal team has to exist before it can build, and that team is hard to hire. In a 2025 Bain & Company survey, 44% of executives said a lack of in-house expertise was slowing AI adoption. Every month spent recruiting is a month your competitors spend shipping.
The market has already moved. In 2024, enterprises built 47% of their AI solutions internally. By 2025, 76% of AI use cases were purchased rather than built, according to the 2025 enterprise report from Menlo Ventures. Essentially, buyers learned that a working system today beats a perfect platform in two years.
An applied AI company brings three things an internal team takes years to accumulate:
- Production patterns: Architectures, evaluation methods and monitoring that have survived real users in other enterprises.
- Cross-functional crews: Strategy, design, data and engineering in one team, so the system fits a workflow and gets adopted.
- Pattern recognition: A view of what failed elsewhere, which saves you from repeating it.
What does building AI capability in-house actually take?
It takes far more than data scientists. Choosing a model is the small part.
Production AI needs data pipelines, integration with your CRM and ERP, workflow design, security, access control and a way to catch drift before users do.
Additionally, each of those needs an owner. You need data engineers, software engineers, a product owner, a platform team and someone accountable for governance. And you still own the risk. The foundation model provider is not your firewall.
The common failure is a pilot that works and then stalls, usually because nobody designed it to scale. As a result, the architecture is hardwired to one model, the integration was never scoped, and the team that built it has moved on. An experienced partner designs for scale during the pilot, which is the cheapest moment to do it.
When should you build AI in-house instead?
Build in-house when the capability is your competitive advantage. One question decides it: is this a commodity, or is it core? Ask it again every year, because the answer moves as the market does.
Building your own makes sense when:
- The system runs on proprietary data no vendor can access or combine
- It encodes IP or ways of working that set you apart
- Many people use it every day, so the investment pays back
- You have the talent to run it after launch, not only to build it
Buy instead when usage is low, the user group is small or the business case doesn’t clear the bar. Don’t build the one model to rule them all. The value of a custom build sits in your data pipelines and workflow orchestration. Retraining what a foundation model already does well rarely pays.
What’s the difference between an AI consultancy and an AI studio?
An AI consultancy decides what to do with AI. An AI studio builds and delivers it. A consultancy provides strategy, use case prioritization and roadmaps. A studio delivers working products, usually with a small team of designers and engineers. (An AI studio here means a partner type. Google AI Studio, the developer tool, is something else.)
An applied AI company covers both ends: strategy through to systems running in production. Here is how the four options compare.
| Option | What you get | Best for | Watch out for |
|---|---|---|---|
| In-house team | Full control and deep business context | Core, high-use capabilities built on proprietary data | Slow hiring, and pilots that stall at scale |
| AI consultancy | Strategy, prioritization and roadmaps | Deciding where AI matters most | Plans that never become working systems |
| AI studio | Working products from a small build team | Fast prototypes and MVPs | A weak link to strategy and the operating model |
| Applied AI company | Strategy through production, with handover | Core systems that need to ship and scale | Check IP ownership and knowledge transfer terms |
For a fuller map of partner types, see our comparison of applied AI companies for large enterprises.
How do you work with an applied AI company without creating dependency?
You write ownership and handover into the deal from day one. Two terms are non-negotiable. You own the IP: models, outputs and data transformations, not only the code. And knowledge transfer is part of the plan, with your people embedded in the build team.
Then, test the details before you sign. Your data should never train the vendor’s models. There should be a clear exit path with portable data. Cost should be modeled over two to three years, because usage-based pricing can spike at scale. Our guide on how to choose an applied AI company has the full question list.
The strongest setups are hybrid. The partner builds the first systems and the shared platform underneath them. Ownership moves into the business units as each system goes live. Your central team shrinks to a hub that sets standards, while product owners in the business run the AI they depend on.
Where do you start?
Start with one workflow that matters to growth. Skip the easiest use case on the list. Decide whether it is commodity or core. Then pick who builds it, and set the date your team takes the keys.
BOI builds Applied AI with enterprises, from strategy to working systems, independent of any platform or model provider. The first system is the proof. The capability your team keeps is the point.
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Emili Castro
Marketing
