The build-vs-buy decision is not just about cost or control. It’s about aligning an approach to AI with an organization’s long-term digital ambitions, organizational capabilities, and operational realities.
The use of AI agents in industrial operations has become a critical component of digital transformation. Such agents are often autonomous or semi-autonomous systems capable of perceiving data, making decisions, and taking action. Their use in managing complexity, improving safety, boosting productivity, and enabling predictive maintenance is on the rise.
What makes this moment particularly pivotal is the convergence of edge computing, the explosion of industrial sensor data, and advances in AI/ML modeling. Together, these forces are unlocking a new wave of automation, empowering machines and systems to act intelligently at scale.
As industrial enterprises embrace this shift, organizations face a defining strategic decision: should they build AI agents in-house or buy/adopt them from technology vendors?
The Strategic Decision: Build vs. Buy
When evaluating any technology solution, organizations routinely face the classic build vs. buy dilemma. The decision often hinges on factors like cost, control, speed, and scalability.
Building a solution in-house offers maximum customization and control, allowing organizations to tailor features to their exact operational needs and retain full ownership of their data and intellectual property. However, this route often demands significant upfront investment, long development cycles, and access to scarce technical talent. Maintenance and upgrades also become ongoing internal responsibilities, which can strain resources over time.
On the other hand, buying a commercial solution typically enables faster time to value, access to vendor expertise, and regular updates without the overhead of in-house development. It’s often the preferred choice when time, budget, or internal capabilities are constrained. But buying comes with trade-offs, including limited customization, potential vendor lock-in, and reduced internal knowledge development.
Those issues must be considered as organizations focus on AI agents.
Building AI Agents In-House
For some organizations, building AI agents from scratch offers appealing advantages. Chief among them is deep customization. That often includes the ability to tailor AI behavior to highly specific workflows, proprietary equipment, or unique environmental conditions. Organizations that operate in highly specialized domains may view this as essential.
There is also the matter of data sovereignty and control. Developing in-house allows organizations to keep sensitive operational data within their infrastructure, minimizing exposure and ensuring compliance with strict regulations. Additionally, companies can potentially gain competitive differentiation by developing proprietary AI capabilities that others can’t easily replicate.
However, these benefits come at a steep cost. R&D investment is high, particularly when it comes to recruiting and retaining the necessary talent in AI, ML, and industrial operations. An additional factor to keep in mind is that building robust, reliable agents is not just about coding. Organizations must also handle deep systems integration, domain-specific training, and ongoing iterations of their agents. That translates into long development timelines and a constant need to maintain and upgrade systems internally. For many industrial companies, this level of commitment may not be feasible.
Buying or Adopting AI Agents from the Marketplace
On the other end of the spectrum is the buy/adopt approach. Technology vendors now offer AI agents embedded within broader industrial platforms. They are ready for deployment with minimal ramp-up time. The key advantage here is speed to value. With prebuilt agents and vendor support, companies can pilot, test, and scale AI-driven processes in weeks or months instead of years.
Vendor-provided solutions also bring built-in expertise. These platforms are typically developed by teams with deep knowledge of both AI technologies and industrial operations. They may also come with integrations into IoT, asset management, and ERP systems, helping ensure that AI agents are not operating in a silo.
That said, there are trade-offs. Customization can be limited, especially if the vendor solution is designed for a broad market rather than a specific vertical. Data privacy and security may be a concern, especially if data must leave the company’s firewalls. There’s also the risk of vendor lock-in, where critical capabilities are tied to a single provider. That would make future changes expensive or disruptive. And finally, relying too heavily on external platforms may limit internal learning and innovation.
Emerging Hybrid Models
The good news is that the build-vs-buy dichotomy is evolving. Hybrid models are emerging to give industrial organizations more flexible options.
Some vendors now offer modular platforms that let companies combine prebuilt agents with custom components. Others provide open-source frameworks or development toolkits that enable in-house teams to build atop a proven core. Increasingly, configurable AI agents are becoming available that can be tuned for a specific organizational need without requiring a full development cycle.
The success of emerging hybrid approaches is dependent on interoperability. In a modern industrial environment, no AI system exists in isolation. The ability to integrate via APIs, connect to third-party data sources, and work with existing infrastructure is critical. Platforms that enable this kind of composability give organizations the best of both worlds: the speed of buying with the flexibility of building.
See also: 5 Real Ways AI is Transforming Day-to-Day Industrial Operations
Teaming with a Technology Partner: What to Look For
Whether you choose to build, buy, or employ a hybrid approach, the right technology partner can make all the difference. That is where Cognite comes in.
Cognite supports both ends of the spectrum. For organizations looking to build, its open, API-driven architecture and industrial data foundation enable rapid prototyping and development. For those preferring ready-to-go solutions, Cognite offers prebuilt AI agents integrated with its platform, Cognite Data Fusion, that help automate and streamline production, root cause analysis, planning, and other workflows.
Additionally, Cognite offers the ability to scale, integrating across legacy systems and data silos while abstracting the inherent complexity of industrial data. Its teams bring a combination of industrial domain knowledge and AI expertise, enabling organizations to execute with confidence and accelerate their AI roadmaps.
Perhaps most importantly, Cognite is built for collaboration. Its platforms don’t force an organization into a binary build-or-buy choice. Instead, it supports a continuum, empowering teams while accelerating time to value.
A Final Word
As industrial enterprises navigate the next decade, AI agents will become foundational to building long-term competitive advantages. They will drive faster decisions, safer operations, and smarter use of resources.
But realizing that vision requires more than adopting the latest tech. It requires strategic clarity. The build-vs-buy decision is not just about cost or control. It’s about aligning an approach to AI with an organization’s long-term digital ambitions, organizational capabilities, and operational realities.
In the end, the best approach may not be to ask, “Should we build or buy?” but rather, “How can we build what matters—and buy what accelerates our journey?”
