Build vs. Buy in the Age of AI: A CEO’s Perspective

The “build vs. buy ” debate has been a defining question in IT procurement for decades, but it takes on new urgency in the age of artificial intelligence (AI). Historically, this decision becomes critical only when a technology reaches a certain level of maturity—when its capabilities are no longer experimental but essential to achieving competitive advantage, and the product and feature set have normalized. AI may or may not have reached that point, and many organizations are now grappling with whether to invest in building bespoke solutions or buying off-the-shelf tools in the traditional IT procurement methodologies. That is a risky endeavor. While the decision may seem straightforward, AI introduces unique challenges that go beyond traditional cost-benefit analyses or feature comparison. In this context, three factors dominate the discussion: quality of results, privacy of your data, and—critically—the ability to derive real ROI from AI investments . Many organizations are finding that while buying off the shelf AI solutions may seem like the faster path to value, it often fails to deliver meaningful returns. 1. Quality of Results AI is not a one-size-fits-all solution. Off-the-shelf tools are often built for broad use cases and lack the precision required for specialized applications. For example: • A generic AI model might perform adequately for tasks like sentiment analysis or document summarization or replacing Google search but fail to meet the nuanced needs of industries like financial services , healthcare, manufacturing, or legal services . There is an entire ecosystem of companies emerging that are building specific models for specific markets – and companies are reaping rewards by focusing on those niche players. • Building custom solutions allows organizations to fine-tune models for specific tasks, combining multiple specialized models to achieve higher accuracy and relevance. However, this is largely a question of strategy and the ability to execute on said strategy. Organizations must assess whether they have the internal capacity—or patience—to develop these systems or if they can adapt purchased solutions through customization. Partner development organizations can accelerate this – but building AI is not yet at the same level as traditional IT systems. 2. Privacy of Your Data AI’s reliance on data raises serious privacy concerns. When you buy an AI solution, your data often flows into third-party systems for processing, introducing risks such as unauthorized access or misuse. Many organizations do not have a clear picture of how their data transits different multiparty cloud environments creating security concerns that are difficult to overcome. For example, if using a shared model, could your prompts leak outside of your control giving your competitors visibility to your IP? Asking yourself the right questions can prevent raising artificial barriers to entry. Key questions include: • How is your data stored and processed by the vendor? Are there guarantees it won’t be used to train their models? When the vendor is simply a wrapper around OpenAI’s ChatGPT, Anthropic’s Claude or others, can you be sure that your data isn’t leaking out? Published reports indicate that this is happening. • Does the vendor comply with critical regulations like GDPR, HIPAA, or industry-specific privacy standards? • If you operate in a highly regulated environment, are your regulatory partners ok with this data leakage or the lack of ability for you to assert that this is not happening? Share Building your own AI solution gives you full control over how data is handled and processed. It allows you to implement stringent privacy measures aligned with your organization’s needs and values. However, this approach demands robust internal governance frameworks and expertise in secure data management—resources that not every organization possesses – but if you have them already, you can fit AI into that seamlessly. 3. Deriving Real ROI from AI Investments This is where the “buy” approach often falters. Many organizations are drawn to off-the-shelf AI solutions built on top of cloud-based models because they promise rapid deployment and lower upfront costs. On paper, these tools seem like an easy win: they’re pre-built, pre-trained, and ready to integrate into existing workflows. But here’s the catch—most off-the-shelf solutions fail to deliver meaningful ROI. Why? Because these tools are designed for generic use cases that may not align with your organization’s specific goals or workflows: • They often produce outputs that require significant manual intervention or interpretation before they can be actionable. You end up having to build the last 20-40% of the solution yourself anyway • These vendors are at the whim of the organizations that build and run those models, so you have all of the complexity of a multi-cloud enterprise software environment out of the box, including the governance and management of said environments. • Integration challenges can limit their ability to work seamlessly with your existing systems, and you must build that as well • Worse still, many organizations lack the internal expertise to effectively implement and operationalize these tools, leading to underwhelming results despite significant investment. The result? Companies spend heavily on software licenses and implementation costs but struggle to extract real business value from their AI investments. The promise of quick wins turns into frustration as leaders realize that generic solutions don’t address their unique pain points or deliver insights that drive strategic decisions. In contrast, building custom AI solutions offers the potential for far greater ROI over time. By tailoring models to your specific needs, you can generate insights that are directly actionable and aligned with your business objectives. However, this requires a long-term commitment to development and maintenance. Recent developments have also radically decreased the costs in the ‘build’ side of the equation to the point that the difference between the two is negligible. The Build vs. Buy Decision in Context Ultimately, the decision boils down to balancing speed, cost, and control: • Buy: While buying off-the-shelf solutions may seem like a fast track to value creation, too often these tools fail to deliver real ROI because they don’t align closely enough with organizational needs or workflows. They may work well for generic tasks but fall short when it comes to driving strategic outcomes. 3rd party vendor risk is higher than most organizations realize. • Build: Building custom AI solutions offers greater potential for ROI by delivering tailored insights and preserving data privacy—but this approach requires significant time, resources, and expertise. As a privacy-first AI firm CEO, I have seen that decisions around building or buying AI should focus less on initial costs and more on long-term value creation. The quality of results delivered by the system, the protection of sensitive data, and—most importantly—the ability to derive real ROI must take center stage in this debate. Buying an off-the-shelf tool might seem like an easy win today—but if it doesn’t integrate seamlessly into your workflows or generate actionable insights aligned with your goals, it’s not a win at all. Building custom solutions may take longer and cost more upfront, but it offers the potential for transformative results that more than justify the investment. In an era where trust in AI is paramount—and where organizations must demonstrate tangible returns on their technology investments—the build vs. buy decision isn’t just about technology; it’s about strategy. Choose wisely. Connect with us: LinkedIn , Bluesky , X , Website To learn more about the services we offer, please visit our product page. This article was written by Sultan Meghji, CEO of Frontier Foundry. Visit his LinkedIn here . This post was edited by Thomas Morin, Marketing Analyst at Frontier Foundry. View his Substack here and his LinkedIn here . Subscribe now Leave a comment