Build or Buy AI Agents for Industrial Operations: Making the Right Choice for Long-Term Success

The decision to build or buy AI agents is about more than just cost or control. It’s about matching your AI approach with your organization's long-term digital goals, skills, and real-world operations. AI agents—autonomous or semi-autonomous systems that can sense data, decide, and act—are becoming essential in industrial operations. They're helping manage complexity, improve safety, increase productivity, and enable predictive maintenance.

What makes this moment crucial is the mix of edge computing, a surge in industrial sensor data, and advances in AI and machine learning. Together, these trends enable a new level of automation where machines act intelligently at scale. As industrial companies adapt, they face a key strategic choice: build AI agents internally or adopt them from technology vendors.

The Strategic Choice: Build or Buy

Organizations often struggle with the build-versus-buy question when adopting new technology. Building offers full customization and control, letting you fit AI agents exactly to your operations and keep your data and IP in-house. But it requires heavy upfront investment, long development times, and hard-to-find AI and operations talent. Plus, your team must maintain and update the solution over time, which can stretch resources.

Buying an off-the-shelf solution usually means quicker deployment, vendor expertise, and ongoing updates without the maintenance burden. It’s often the choice when time, budget, or internal skills are limited. However, buying can limit customization, risk vendor lock-in, and reduce your team’s internal AI knowledge.

Building AI Agents In-House

Building AI agents internally allows for deep customization to handle specific workflows, unique equipment, or specialized environments. For industries with unique operational needs, this can be crucial. Keeping data inside your infrastructure also helps with security and compliance. Plus, proprietary AI capabilities can offer a competitive edge.

But these benefits come with costs. You need to invest heavily in R&D, hire and retain AI, machine learning, and industrial experts, and manage complex system integration and training. Development cycles are long, and ongoing maintenance is necessary. For many industrial firms, these demands can be too much.

Buying AI Agents from Vendors

Buying AI agents embedded in industrial platforms offers speed and convenience. Prebuilt agents and vendor support allow companies to pilot and scale AI projects in weeks or months instead of years. Vendors bring deep AI and industrial expertise and often provide integrations with IoT, asset management, and ERP systems, ensuring AI agents work smoothly with existing infrastructure.

The downsides include limited customization, possible data privacy concerns if data leaves your network, and the risk of vendor lock-in that makes future changes costly. Relying heavily on vendors may also hinder your team’s internal learning.

Emerging Hybrid Models

The strict build-or-buy choice is fading as hybrid models gain traction. Some vendors offer modular platforms that combine prebuilt agents with custom components. Others provide open-source frameworks and toolkits allowing your team to build on proven cores. Configurable AI agents can be adjusted for specific needs without full redevelopment.

Success with hybrid approaches depends on interoperability. AI systems must integrate easily with APIs, third-party data, and existing infrastructure. Platforms that support this composability offer the speed of buying and the flexibility of building.

Partnering with the Right Technology Provider

Choosing to build, buy, or blend approaches means partnering with the right technology provider. Providers like Cognite support both approaches. For builders, Cognite’s open, API-driven platform enables fast prototyping and development. For those buying, Cognite offers prebuilt AI agents within its platform, Cognite Data Fusion, helping automate production, root cause analysis, planning, and more.

Cognite can scale across legacy systems and data silos while simplifying industrial data complexity. Their teams combine domain knowledge with AI expertise, helping organizations move confidently and accelerate AI adoption. Importantly, Cognite supports a flexible approach rather than forcing a strict build-or-buy decision.

Final Thoughts

AI agents will be key to competitive advantage in industrial operations—enabling faster decisions, safer processes, and smarter resource use. But success demands clear strategy. The build-or-buy decision goes beyond cost and control; it’s about aligning AI with your organization’s digital goals, capabilities, and realities.

Instead of asking, “Should we build or buy?” it’s smarter to ask, “How can we build what matters and buy what speeds our progress?”