How Companies Know When To Build vs. Buy AI Solutions
The decision of whether to build custom AI or buy off-the-shelf solutions is one that keeps executives up at night. There’s no right answer, and what might fit perfectly for one company could be an utter disaster for another. Misfire in either direction and budgets are flushed down the toilet, competitive opportunities are squandered, and teams abandon the notion of AI entirely.
Companies enter this debate from a wrong starting point, however, too optimistic about what building AI entails. They see the success stories from the most powerful companies and assume they can take the same approach. But those companies have the internal talent, timeframes, and tech sophistication that most institutions lack.
The Cost of Building Custom AI
Building AI custom means an upfront investment but also one that extends far into the future. Companies need access to top-level talent, data scientists, machine learning engineers, AI researchers – and their salaries alone add up quickly to over $200,000 per year. This doesn’t account for the data processing capabilities and storage needs (computational investment) that are needed to successfully craft models, data sets, and applications for systems testing.
Companies need time, too. General expectations for building custom AI is that 6-18 months will need to pass before any meaningful results can be realized, not to mention businesses will pay full price for salaries + infrastructure costs with no return during this necessary delay. Many systems run into technical challenges unexpected, extending timelines further so what could have been a calculated risk becomes a budgetary oversite.
There’s also long-term expected liability. Companies that build custom AI models must assume they won’t be hands-off projects. Enterprises need to relearn models when data sets fluctuate, systems need upgrading when expectations change, security shortcomings become internal responsibility. Building custom AI over time offers ongoing technical debt liability that demands staffing support and expected budgetary coverage.
When Buying Makes More Sense
Commercial AI products have advanced significantly in recent years. For generalized business applications like customer service chatbots, document processing, and sales forecasting – buy it – there’s a proven product to get in place within weeks instead of months. These pre-established solutions come with responsible teams for ongoing support and guidance, updates along the way, and recommended best practices to avoid implementation pitfalls.
The economics favor standard use case purchase as well, a SaaS vendor solution comes with costs between $50,000-$200,000 per year which seems extreme until one calculates the need for custom building. The estimated cost of development and maintenance comes to far over $500,000 without even factoring in opportunity cost against resources time due time spent without full implementation.
When working with experienced genai consulting partners, typically 60-70% of a company’s needs can be met through established solutions with custom development reserved for more unique determinations.
Purchased solutions come with accountability. If something fails or doesn’t achieve promised results, someone else is on the hook. Companies who don’t have enough manpower or resolve get trapped in their own red tape with minimal recourse.
The Unseen Benefit of Building Custom Solutions
Yet despite the time and investment involved, building custom AI makes sense under certain circumstances. When companies operate with uniquely proprietary data or processes, off-the-shelf tools fail to provide the precision necessary. A manufacturer operating for decades with intimate knowledge of data processing may build superior predictive maintenance programs than any generic systems can offer.
Competitive advantage also warrants development. When certain capabilities make critical parts of businesses succeed in differentiation efforts, custom development ensures that competitors will not have access to the same tools. A logistics company needing routing algorithms powered by AI needs to ensure competitors cannot access the same features.
Data risk also warrants a build scenario. Organizations who process highly sensitive data, healthcare companies, financial institutions, defense contractors, face compliance issues that commercial real-world AI may not sufficiently cover. Building custom AI solutions allows ultimate transparency with data use, system operations, and security measures.
The Questions That Actually Matter
Assessments start with understanding internal capabilities. Can enterprises attract specialized talent sufficient for customs AI? Does leadership have the patience for 12+ month collaboration before seeing ROI? Are companies in it for the long haul as maintenance will now rely on internal support?
Uniqueness also requires investigation. Few companies recognize how not unique their challenges actually are relative to generalizations of customer service inquiries, inventory recommendations, or financial forecasting options. The assessment should discern whether existing options ultimately fail to meet standards rather than presumed uniqueness fails to consider areas where companies are not special.
ROI must be measured fairly at both extremes. Custom development needs to encompass not just initial costs but maintenance costs projected for 3-5 years + opportunity costs relative to time diverted from operational potentials. Purchased solutions must assess licensing fees, integration fees, and potential vendor lock-in. The true opportunity comparison is long-term ownership associated with the determined lifespan of any solution as opposed to operating within confines of Year 1 expenses.
The Hybrid Solution Nobody Wants to Admit Existed
Sometimes the question itself is misleading; there are many successful implementations using blends, commercial products purchased to handle generalized processes with custom development for competitive need-specific developments. A retail company may buy off-the-shelf customer service chatbots but custom builds recommendation engines based on proprietary purchasing data.
This allows companies to access proven use cases quickly while diverting developmental efforts where they create true differentiation. This allows flexibility as understanding develops; what’s needed today as a custom development may emerge tomorrow with exceptional commercial alternatives down the line.
Making The Call
Ultimately the build versus buy decision comes down to strategic fit instead of absolute technical realities. Companies should build when they know AI capabilities are driving competitive advantage; they should buy when it’s solving generalized operational issues; they should build when talents or qualities are uniquely their own; they should buy when speed of value matters or internal capabilities aren’t aligned with developmental requirements.
No answer is worse than indecision, constantly debating a solution while competitors build or buy at will. Either solution can prove effective when aligned with business realities; it’s a matter of an honest assessment of what a company realistically can do versus what it hopes it could accomplish.