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The GenAI race isn’t just about adoption, which tends to get alot of attention. It’s also about aligning capabilities with ambition.
Organizations have been successful in deploying models, introducing copilots, and securing boardroom backing. However, new pressure points are surfacing as costs climb, vendor limitations set in, control gaps widen, and questions about long-term scalability grow louder.
At the center of it all is a decision that now carries more weight than ever: whether to build GenAI capabilities in-house or buy them from the outside.
This buy vs. build dilemma comes with important tradeoffs. Buying gets you moving fast, but it often means bending to someone else’s roadmap. Building gives you more control, but it takes serious time, talent, and conviction. As GenAI initiatives move from pilots and experimentation to real-world deployment, this decision is becoming even more critical.
At first glance, the choice can feel straightforward: build if you want more control, buy if you need to move fast. However, the reality is more complicated.
Factors like cost, data privacy, model interoperability, internal talent, competitive pressure, and time-to-value all could play a role in the decision. What works for one team might not work for another. For example, a solution that fits an e-commerce giant could fall short for a government agency with strict compliance needs.
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For many teams, buying is the easier way to start. It allows you to get something up and running quickly without building everything from scratch. With so much competitive pressure to get on with GenAI, it’s a quick path to getting started. Off-the-shelf tools often plug into your existing systems, and you don’t need a dedicated AI team to get value from them. For organizations that are still early in their GenAI journey, this approach can feel both practical and low risk.
However, buying comes with its own set of challenges. You’re often tied to what the vendor offers, which means you may not get the features or flexibility you need. If your business evolves or your use case becomes more complex, the solution might not keep up. While upfront costs can seem manageable, they can rise over time, especially if you start layering on multiple tools or scaling usage.
Switching vendors later or moving to a custom setup may end up being more difficult than expected. Buying also allows teams to focus on business-specific tasks rather than the complexities of building AI.
Still, that hasn’t slowed demand. Gartner study reveals organizations are expected to spend $14.2 billion on GenAI models in 2025, which is more than double what they spent in 2023. That kind of momentum shows just how eager companies are to turn GenAI into something tangible. While the benefits are clear, the rush to demonstrate progress may lead some teams to adopt tools that address immediate needs but constrain future flexibility.
According to an IDC blog published earlier this year, “The ‘buy’ approach is suitable for enterprises wanting quick access to GenAI benefits, especially those with low maturity around enterprise data management and AI. It can kickstart the GenAI journey while establishing a foundation for data management, governance, and the skills needed for further GenAI development.”
Not every organization wants to be limited by what’s already on the shelf. For those with complex workflows, specialized data, or ambitions that don’t fit neatly into pre-built templates, building GenAI capabilities in-house can offer a stronger long-term payoff. It allows for deeper customization and greater control over model performance and data governance.
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That control comes at a cost. Building means investing in infrastructure, assembling a highly skilled technical team, and staying ahead of a fast-moving field. It requires clarity of purpose and the ability to evolve as the technology does.
Even with the right foundations, there’s no guarantee of success. In-house systems must be maintained, refreshed, and monitored constantly to keep pace with changing business needs and the rapid evolution of GenAI itself.
That’s why, as EY puts it, the real question isn’t just about speed or control. It’s about what fits. Every organization has different needs, operating models, and levels of readiness. A pre-built solution might get you to value faster, but it could also create new challenges, especially if your team doesn’t yet have the processes or governance to manage it properly.
Building in-house can give you more flexibility and the chance to create something truly tailored. But that only works if the right foundations are in place: solid data, the right talent, and enough time to build and iterate.
To help leaders think through the pros and cons, EY recommends asking a few practical questions: What’s the real cost of building and running your own model versus buying one off the shelf? Do you have the skills, data, and time to build something better than what’s available? How might new AI regulations shift the risks either way?
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They also advise considering how each path fits your current operating model. Will buying create data privacy issues? Could you get locked into a vendor and lose flexibility later? There’s no one-size-fits-all answer, but working through these questions can bring you closer to the one that’s right for your team.
The right answer for build vs buy depends on where you are today and where you’re trying to go. Whether you build, buy, or blend the two, the best path is the one that works for your team and your strategy.
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