Artificial intelligence
Developing an artificial intelligence solution raises a strategic dilemma: should you build your own AI or use a solution offered by a supplier? The choice has consequences for costs, time-to-market, quality, compliance and competitive advantage. This 1500+ word article compares the two approaches (build vs. buy), drawing on recent studies including a report by Menlo Ventures and analyses by HP and Aisera.
According to Menlo Ventures’ “State of Generative AI 2025” report, the proportion of purchased AI solutions has risen from 53% in 2024 to 76% in 2025. The same report indicates that purchased AI products convert at a rate of 47%, compared with 25% for traditional software, because they provide immediate value. At the same time, Aisera estimates that 90% of enterprise use cases are better served by a purchased solution rather than one developed in-house. These figures reflect the rapid rise of mature offerings, and the difficulty companies have in building effective models within reasonable timescales.
The time required to create in-house AI is considerable. HP estimates that building a custom solution typically takes 12 to 24 months, due to talent recruitment, model development, validation and testing. By contrast, purchasing an off-the-shelf solution enables deployment in 3 to 9 months. Forethought adds that in-house development time is offset by finer customization, while purchasing drastically reduces time to production.
Costs are not limited to the initial investment. HP distinguishes several items: recruitment and training of specialists, purchase of infrastructure, scaling costs, maintenance and security. For its part, Forethought notes that the cost of solving a problem using in-house AI can be as high as US$12, compared with US$8 for an outsourced solution. Buy” solutions involve subscription costs, but avoid unforeseen expenses linked to breakdowns or technological evolutions.
Building AI requires rare skills (data scientists, ML engineers, security managers) and an intense coordination effort. Companies often struggle to recruit and retain these profiles. A “buy” solution integrates the expertise of the supplier, who ensures the maintenance and evolution of the product, freeing up internal teams for high value-added tasks.
The main advantage of in-house development lies in total control of the model, its functionalities and its data. Companies can create AI tailored to their processes, culture and strategy, and retain ownership of the intellectual property. However, the model needs to be continually optimized and retrained. Conversely, buying a tool implies pre-defined functionality and less customization; nevertheless, many suppliers offer configurable options and APIs for integration into existing systems.
Legal (RGPD, AI Act, BCBS 239) and industry requirements force compliance with strict standards. When AI is built in-house, the company has full control over the data and can implement appropriate security measures. HP notes that in-house development is better suited to scenarios requiring maximum security. Aisera points out that outsourced solutions generally incorporate security certifications, governance tools and faster compliance.
AI provider platforms are designed to scale rapidly and absorb peak loads. They benefit from resilient cloud infrastructure and automated management tools. An in-house solution can be optimized for specific volumes, but will require investment to scale up, particularly in the event of rapid product success.
| Criteria | In-house development (Build) | Outsourcing (Buy) |
|---|---|---|
| Time to production | 12 to 24 months | 3 to 9 months |
| Cost per resolution (customer service) | ~12 USD | ~8 USD |
| Initial cost | High: recruitment, infrastructure, licenses | Subscription or license, staggered costs |
| Customization | Total: made-to-measure solution | Limited but configurable |
| Intellectual property | Company owns IP and model | Dependent on contract; risk of supplier dependence |
| Expertise required | Experienced ML team; recruit and train | Supplier expertise, maintenance assured |
| Security | Complete data and security control | Vendor-certified security (ISO 27001, SOC 2, etc.) |
| RGPD/AI Act compliance | Internal management: long audit processes | Suppliers offer integrated compliance tools |
| Scalability | Must be designed; heavy investment | Native via cloud and multi-tenant architectures |
| Risk of failure | Depends on in-house competence; high failure rate – 95% of generative AI pilots fail | Depends on supplier sustainability; consolidation trend |
AI projects built in-house often fail due to lack of expertise, long lead times and uncontrolled costs. MIT Technology Review reports that 95% of generative AI pilots fail to deliver value. As a result, more and more companies are opting for turnkey solutions. Menlo Ventures observes that customers buy AI to benefit from immediate “time-to-value” and a robust product. Nevertheless, some organizations continue to build when they see AI as a major differentiator, or when they are dealing with extremely sensitive data.
Finance and banking: stringent regulations (Basel III, BCBS 239) demand strong governance and absolute control of data. Banks often favor a hybrid approach: they buy model orchestration platforms and develop proprietary algorithms in-house.
Healthcare: protecting healthcare data requires a high level of security and explicability. Hospitals can outsource generic services (e.g. imaging analysis) while maintaining specific models in-house.
Customer services and marketing: outsourcing is common. Forethought notes that AI solutions for customer support reduce the cost per resolution and improve satisfaction, with a rapid ROI.
A middle way is to combine the best of both worlds: buy a configurable AI platform and develop specific modules. This approach offers a compromise between customization and speed. HP mentions the possibility of using APIs to integrate external services, while retaining certain internal building blocks. It also enables solutions to be tested rapidly, and certain functions to be re-internalised as they mature.
Evaluate use cases: determine whether AI represents a competitive advantage. If the differentiating value lies in the algorithm, internal construction can be justified.
Analyze in-house resources: having an experienced AI team and a suitable infrastructure is essential for building. Otherwise, outsourcing is more realistic.
Measure cost and time: compare the total cost of ownership over 3 to 5 years, taking into account the risk of drift. External solutions enable OpEx cash management, while in-house solutions require significant CapEx.
Check compliance and security: ensure that the supplier complies with regulatory requirements (RGPD, AI Act, industry standards) and offers contractual guarantees. Internally, implement strong governance and regular audits.
Plan ahead: anticipate future needs and choose a modular or flexible solution. A hybrid approach can be a good compromise.
Frequently asked questions :
Should you build or buy an AI? In-house construction offers complete customization and control, but requires significant investment (12 to 24 months’ development, high costs and scarce expertise). Purchasing enables rapid deployment (3 to 9 months) and lower cost per resolution, but limits customization and creates supplier dependency.
What are the hidden costs of in-house construction? In addition to development, these costs include recruitment, training, infrastructure, maintenance, testing and regulatory compliance. Talent retention and scalability can increase expenses.
Why are many companies outsourcing AI? Out-of-the-box solutions offer an immediate return on investment, and draw on the expertise of specialized suppliers. Menlo Ventures observes that 76% of use cases were outsourced in 2025.
What is a hybrid approach? This involves combining external components (platforms, pre-trained models) with in-house developments to customize certain functions. This approach balances speed and differentiation.
The choice between building or buying an artificial intelligence solution depends on a company’s strategy, resources, regulatory constraints and ambition for innovation. Current trends show a shift in favor of outsourcing, but in-house development remains relevant when it comes to a key competitive advantage or highly sensitive data. A hybrid approach can offer a happy medium.