The Buy vs. Build Dilemma; What the Enterprise Needs to Consider

The AI market is expanding rapidly, with projections estimating it will reach $407 billion by 2027. For companies looking to harness AI’s potential, this growth brings both opportunity and complexity. Among the first critical decisions is whether to build custom AI solutions or buy pre-existing ones. Making the right “build vs. buy” choice is essential to avoid wasted resources and ensure the company’s investment translates into real, measurable value. This strategic decision impacts the speed, cost, and effectiveness of AI implementation and ultimately determines how quickly companies can leverage AI to drive innovation and optimize operations.

Enterprises face a significant decision in choosing whether to build or buy AI solutions—a choice that shouldn’t be taken lightly. The wrong approach can result in lost opportunities and wasted resources. Gartner predicts that by the end of 2025, at least 30% of Generative AI projects will be abandoned post-proof of concept, often due to issues like poor data quality, insufficient risk controls, escalating costs, or lack of clear business objectives. Pursuing poorly scoped AI projects can create substantial setbacks and deter future technology investments that could otherwise optimize business operations.

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The International Data Corporation (IDC) recommends a “buy” approach for companies aiming to quickly implement AI and achieve faster results. Purchasing a proven AI solution delivers immediate value without the extensive time commitment required for in-house development, making it a strategic choice not only for organizations with limited AI expertise but also for those seeking to configure and expand on an established foundation. While building a custom solution offers tailored functionality, it demands significant machine learning expertise, specialized data science resources, and incurs high costs for data acquisition and infrastructure.

By choosing the buy approach, companies can validate user adoption within months rather than the year or more that custom builds typically require. This allows for early testing and scalability without the heavy lift of internal development resources. With pre-configured platforms, organizations can quickly adjust to evolving regulatory demands while benefiting from ongoing vendor support for compliance, maintenance, and updates. Buying also delivers instant access to critical features. For example, in industries like pharma, where Generative AI holds great promise but must navigate complex regulatory frameworks, buying ensures immediate access to essential capabilities such as GxP compliance, audit trails, and secure data governance.

To decide whether to build or buy an AI solution, companies should first evaluate their data readiness and clarify business objectives. Successful AI deployment depends on well-structured, machine-readable data to enable effective testing, validation, and optimization. Without these preparations, companies may invest in solutions that fall short of their needs, resulting in costly, time-intensive adjustments post-deployment. Standardizing data sources, harmonizing document templates, and adopting lean content practices are essential steps to ensure seamless integration and efficient performance, regardless of the build or buy approach. This groundwork helps organizations align AI solutions with their goals while mitigating potential costs.

Generative AI is most effective when tailored to specific workflows, enhancing productivity and operational efficiency. For industries like pharma, building an in-house AI solution may be preferable for strategic applications like drug discovery, where customized, proprietary tools create a competitive advantage. However, for tasks like regulatory documentation, which benefit from cross-industry collaboration, the buy approach offers distinct advantages. This path allows companies to adopt best practices and stay updated with vendor-driven innovations. Additionally, many underestimate the long-term maintenance costs of Generative AI, including the expenses of tuning, updating, and managing language models. Building in-house can also lock companies into a specific Large Language Model (LLM), reducing flexibility in a field that is advancing rapidly.

To Build or To Buy?

Choosing the build approach for AI requires enterprises to thoroughly assess the considerable resource demands involved. Building in-house solutions necessitates a dedicated team—data scientists, developers, and project managers—who will manage, update, and refine the AI platform on an ongoing basis. In addition to talent, the build approach demands significant infrastructure investments, robust data collection, and strict adherence to regulatory standards, which can place a heavy burden on internal IT teams if adequate support is not in place. Developing a fully custom AI solution is often both time-intensive and costly, particularly when ongoing feature updates are required to keep pace with changing needs. These considerations highlight the importance of a clearly defined strategy and sufficient resources to support the long-term demands of AI development.

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Alternatively, choosing the buy approach gives organizations immediate access to pre-built AI solutions without requiring an in-house development team. This option is ideal for companies seeking rapid results, as it offers pre-configured models that streamline data management and governance. Additionally, purchased solutions include dedicated vendor support, which reduces the implementation and maintenance burden on internal teams. By outsourcing AI management to specialized providers, companies can lower ownership costs while benefiting from ongoing improvements and updates driven by industry advancements. This approach allows enterprises to focus on strategic applications of AI, leveraging external expertise for continuous innovation without the challenges of internal technical upkeep.

Generative AI providers continually refine their products based on insights from past implementations, giving companies a solution that’s both tested and improved. These vendors typically offer onboarding assistance, comprehensive documentation, and customization options, enabling organizations to tailor the technology to their specific needs without prolonged development cycles. This streamlined approach accelerates deployment, minimizes reliance on internal resources, and ultimately allows companies to focus on achieving value quickly. However, the decision to build or buy should still reflect each organization’s unique use case, resource availability, and speed-to-value objectives.

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