Build vs Buy: The Shift in Enterprise AI Strategy
If you’ve been in any boardroom discussion about AI over the past eighteen months, you’ve likely heard the same debate playing out repeatedly. Should we build our own models or buy off-the-shelf solutions? It’s a question that’s consumed countless strategy sessions, burned through consultant fees, and kept CTOs awake at night. Here’s the thing, though. The market has already answered this question, and the answer might surprise you.
In 2024, enterprises were split roughly 50/50 between building their own AI solutions and purchasing them from vendors. Fast forward to 2025, and that balance has shifted dramatically. Now, 76% of AI solutions are being purchased rather than built in-house. That’s not a trend, that’s a seismic shift in how businesses approach artificial intelligence.
The Case for Building (Historical Context)
To understand where we are, we need to understand where we’ve been. The appeal of building proprietary AI models was never difficult to grasp. If you were sitting in a Fortune 500 company in 2023, the pitch practically wrote itself.
Building your own models meant complete control over your most valuable asset, your data. You weren’t sending customer information to third-party vendors, you weren’t relying on someone else’s infrastructure, and you certainly weren’t sharing your competitive insights with companies that might be working with your rivals. The data stayed inside your walls, processed by your systems, governed by your policies.
Beyond data sovereignty, there was the promise of customisation. Generic solutions might work for generic problems, but your business wasn’t generic. You had unique workflows, specific industry requirements, and particular customer needs that off-the-shelf products simply couldn’t address. Building in-house meant you could tailor every aspect of the model to your exact specifications, creating something that fit your organisation like a bespoke suit rather than something pulled off the rack.
Perhaps most enticingly, building your own AI held out the promise of genuine competitive differentiation. If everyone else was using the same vendor solutions, how could you possibly get ahead? The logic seemed sound. Proprietary models would give you capabilities your competitors couldn’t replicate, creating a moat around your business that would be difficult to breach.
For technology companies especially, this reasoning felt particularly compelling. If you were already employing world-class engineers and data scientists, why not put them to work on AI? The infrastructure was there, the talent was there, and the potential upside seemed limitless.
The Reality Check: Why Building Failed to Scale
Then reality intervened, as it tends to do.
The first problem was talent, or more accurately, the complete absence of it. Everyone wanted to hire AI engineers and ML specialists. The universities weren’t producing them fast enough, and the few who were available commanded salaries that made investment bankers look modestly compensated. Even when companies managed to recruit top talent, keeping them proved nearly impossible. The allure of startups, the appeal of working on cutting-edge research at AI labs, and the aggressive poaching by competitors meant that retention became a full-time battle.
Infrastructure costs turned out to be far more substantial than initial projections suggested. Those early proof-of-concept projects ran on modest compute resources, but scaling to production was an entirely different proposition. GPU clusters, data pipelines, MLOps platforms, and all the supporting infrastructure quickly consumed budgets. What started as a manageable investment ballooned into ongoing expenditure that required constant justification.
The timeline problem was perhaps even more frustrating. Initial estimates suggested that models could be built and deployed within six to nine months. In practice, most projects were still in development a year later, and many never made it to production at all. Meanwhile, the competitive landscape was evolving rapidly, and the problems these models were meant to solve were either changing or being addressed by faster-moving vendors.
Model maintenance proved to be its own headache. Unlike traditional software, ML models degrade over time. Data drift, changing user behaviour, and evolving business conditions all meant that models required constant retraining and adjustment. What seemed like a one-time development effort turned into a perpetual maintenance obligation that required dedicated teams and ongoing resources.
Perhaps most challengingly, the pace of improvement in foundation models made it nearly impossible for internal teams to keep up. OpenAI, Anthropic, Google, and others were releasing better models every few months, each one more capable than the last. Internal teams found themselves constantly playing catch-up, trying to match capabilities that were already available through API calls.
The Case for Buying in 2026
The shift toward purchasing solutions wasn’t born from defeatism, it came from pragmatism. Vendor solutions were reaching production faster, often within weeks rather than months. The models were already trained, the APIs were already built, and the integration patterns were already documented. For businesses trying to capture value quickly, the math was straightforward.
Vendor expertise turned out to matter more than initially appreciated. These companies weren’t distributing generic models, they were providing ongoing improvements, security updates, and feature enhancements. When GPT-4 became available, existing customers got access automatically. When safety improvements were implemented, they benefitted everyone. The continuous improvement cycle happened without any additional effort from the purchasing organisation.
The resource equation changed dramatically as well. Instead of maintaining teams of ML engineers, data scientists, and infrastructure specialists, companies could redeploy those resources toward problems that were genuinely unique to their business. The question shifted from “can we build this?” to “should we build this, or focus our talent on something only we can do?”
Enterprise-grade security and compliance, often cited as reasons to build in-house, turned out to be table stakes for serious vendors. SOC 2 compliance, GDPR adherence, industry-specific certifications, these were all built into vendor offerings. For many companies, vendor security actually exceeded what they could achieve internally, particularly for organisations outside the technology sector.
Total cost of ownership became impossible to ignore. When accounting for salaries, infrastructure, opportunity cost, and the risk of project failure, purchased solutions frequently came in cheaper. The predictable subscription model, while sometimes expensive, was easier to budget for than the uncertain costs of internal development.
Implementation Considerations
The 76% figure doesn’t mean building is always wrong, it means the calculus has shifted. There are still scenarios where building makes sense. If your use case is genuinely unique, involves proprietary data that cannot leave your infrastructure, or represents a core competitive advantage, building might still be justified. Defence contractors, financial institutions with highly specialised trading algorithms, and companies with truly novel applications might still find building worthwhile.
The more common approach now involves hybrid strategies. Many organisations are purchasing foundation models but building specialised layers on top. They’re using vendor APIs for general capabilities whilst developing proprietary fine-tuning, retrieval systems, or agent frameworks that leverage their specific data and workflows. This approach captures the benefits of both worlds, vendor speed and improvement cycles combined with customisation where it genuinely matters.
Vendor selection has become a critical competency. The questions have shifted from “can we build this?” to “which vendor best fits our needs?” Evaluation criteria now include not only model performance but also API reliability, pricing predictability, data handling policies, integration capabilities, and the vendor’s financial stability. Locking into a vendor that goes out of business or pivots away from your use case creates its own set of problems.
Integration with existing systems deserves particular attention. The best AI model in the world provides little value if it can’t connect to your CRM, ERP, or data warehouse. Organisations are discovering that integration expertise, understanding how to connect AI capabilities to existing workflows, matters as much as the AI capabilities themselves.
Strategic Implications
This shift is changing the composition of AI teams across enterprises. The demand for ML researchers and model developers is declining, whilst the need for integration specialists, prompt engineers, and AI product managers is growing. Companies are realising they need people who understand both the technology and the business, who can identify where AI adds value and implement it effectively.
Budget allocation is following suit. Money that would have gone toward R&D is being redirected to procurement and implementation. Finance teams are developing new frameworks for evaluating subscription costs against build costs, and procurement departments are learning to negotiate with AI vendors in ways that differ substantially from traditional software purchases.
Partnership and vendor management has become a core competency. Unlike traditional software relationships, AI vendors are often releasing updates monthly or even weekly. Managing these relationships, understanding roadmaps, providing feedback, and ensuring alignment requires dedicated attention. Some organisations are creating specific roles focused entirely on AI vendor relationships.
The risk profile has shifted as well. Building carried the risk of project failure, wasted resources, and opportunity cost. Buying carries the risk of vendor dependency, pricing changes, and loss of control. Neither risk is inherently better or worse, but they require different management approaches.
What This Tells Us
The movement from 50/50 to 76% represents more than changing procurement preferences. It signals the maturation of the enterprise AI market. The experimental phase, where every company felt compelled to build their own models, is giving way to a more rational phase where organisations focus on value creation rather than technology development for its own sake.
This evolution mirrors what happened with cloud infrastructure a decade ago. Initially, many companies insisted on building their own data centres and infrastructure. Over time, most realised that AWS, Azure, and Google Cloud could provide better, cheaper, more reliable infrastructure than they could build themselves. The companies that thrived weren’t the ones running the best data centres, they were the ones building the best products and services on top of cloud infrastructure.
The same dynamic is playing out with AI. The winners won’t necessarily be the companies with the best models, they’ll be the companies that most effectively apply AI to create customer value, improve operations, or enable new business models. For most organisations, that means buying the foundation and building where it truly matters.
Will the pendulum swing back? Perhaps, though likely not to the same degree. As open-source models continue improving and as tooling makes model development more accessible, some organisations might bring certain capabilities back in-house. But the fundamental economics, the speed of vendor improvement, the talent scarcity, and the total cost of ownership, these factors aren’t likely to change dramatically.
The 76% figure tells us something important about where we are in the AI adoption curve. We’re past the phase where every company needs to be an AI company in the sense of building models. We’re entering the phase where every company needs to be an AI-enabled company, using these tools effectively to serve customers and run operations. That’s a subtle but crucial distinction, and it’s reshaping enterprise strategy across industries.
For business leaders still debating build versus buy, the market has offered its verdict. The question now is whether you’re listening.
