Build vs. Buy: An AI Field Guide for Energy & Manufacturing Leaders
When it comes to automating and optimizing industrial, manufacturing, and energy systems, the decision to build versus buy your AI solution is bigger than a budget line.
Harnessing AI across plants, grids, and processing lines could be transformational for uptime, safety, and regulation; and yet, few are cashing in on the benefits. One study by IBM found that recent enterprise-scale AI initiatives achieved a mere 5.9% return on investment (ROI). Another study found that more than half of AI projects fail to deliver value or worse.
Curtailed success is often linked to poor integration and change-management, rather than model novelty, underscoring the idea that value comes from disciplined execution, not just deployments. To find success, start with a right-sized use case on a clean, governed data foundation and align the delivery plan to the people and resources available.
Five questions to consider when implementing AI across industrial IT and OT environments:
Is this capability a utility or a weapon?
Before moving forward with any AI project, evaluate its strategic value. Commodity capabilities (generic anomaly flags on standard motors, routine back-office automations) are seldom your moat—buy them. But if a model captures the physics of your turbine fleet, or encodes know-how competitors can’t replicate, that’s an opportunity to build or co-build.
How much will it cost? (TCO/ROI)
Look beyond the price sticker to the total cost of ownership (TCO). Builds incur ongoing model ops, retraining, and talent retention. And buys carry integration challenges, premium support, and potential switch costs. To extract meaningful ROI, prioritize measurable, near-term wins while you plant seeds for a sustainable advantage.
What is the timeline? (TTV)
If you need proof points in weeks or a quarter, buy a battle-tested platform and configure to your needs for quick, expert-led deployments that drive high time to value (TTV). But if success is tied to a foundational capability bound to asset lifecycles (such as thermal efficiency models tied to a combined-cycle fleet), a deliberate build or co-build will reap the most benefits long-term.
How are you currently managing your asset data?
Many AI programs stall on data plumbing and governance. Analysts routinely report that data prep dominates efforts, which is why accessing, cleaning, and contextualizing remain a priority in any AI project. If your team is new to MLOps and OT data engineering, buy or co-build to learn safely.
What sort of mandates or restrictions do you have?
When explainability, auditability, and on-prem control are non-negotiable—grid operations, process safety interlocks— plan for a build or tightly regulated co-build with clear data residency and model transparency obligation bias toward
When to build?
Build where your IP compounds: physics-infused models, reliability scoring unique to your assets, or optimization that shifts your business model (e.g., performance-as-a-service). When done right, it’s an edge; when done casually, it’s a tax.
Beware of common traps, including:
Underestimating integration with historians/SCADA
Wave-after-wave of model drift
Continuous pipeline management
When to buy?
Buy when speed, safety, and interoperability rule. Contract smart to de-risk and keep your options open as you scale: insist on standard export for data and metadata, clarity on IP for models trained with your data, and robust APIs to prevent walled gardens. Data portability, API access, and IP terms on day one keeps you agile on day 1,000.
When to co-build?
Consensus is emerging across boardrooms: buy the proven foundation, build the differentiators, and partner where it accelerates learning and lowers risk. This pragmatic playbook helps accelerate innovation without mortgaging tomorrow’s flexibility.
Land quick operational wins (e.g., condition monitoring on standard assets) while incubating high-priority builds. Close partnerships with training data and AI experts balances credibility with compounding IP.
Getting started
Build vs. buy is a strategic portfolio call. Start by mapping use cases into three buckets—buy (utilities), co-build (emerging differentiators), build (crown-jewel IP)—and fund the data layer first. Strong data governance (lineage, access controls, quality checks, and audit trails) is the multiplier that turns pilots into production. With clean, contextualized OT/IT data, both buy and build strategies lift off; without it, both stall.
From there, stage delivery for time-to-value. Put change-management on the critical path—train operators and engineers, define new workflows, and make model transparency routine rather than exceptional.
Ready to pressure-test your path?
Keyfive partners with industrial and energy leaders to make this decision with eyes wide open. Our platform delivers plug-and-play interoperability and unified monitoring across front-of-meter and behind-the-meter assets. As technology partners, we provide the digital-twin scaffolding and predictive analytics so your team can co-design the features that matter while we handle real-time data capture, model lifecycle, and secure integration.
Contact us now for more info on developing, deploying and upholding your AI strategy.