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September 04.2025
2 Minutes Read

How to Navigate the Build vs. Buy Conversation for AI in Manufacturing

Panel on Build vs. Buy AI Solutions in Manufacturing with industry leaders.

Understanding the Build vs. Buy Dilemma in Service and Manufacturing

In the challenging landscape of service and manufacturing, executives are often met with a critical decision: to build proprietary solutions in-house or to buy ready-made technologies. This conversation has gained prominence as industries face increasing demands for operational efficiency and reliability, particularly in high-stakes sectors like healthcare and manufacturing.

Why Equipment Reliability Is Crucial

Recent statistics underscore the importance of reliability within these sectors. For instance, the NHS reported over 3,900 equipment malfunctions from 2022 to 2025, resulting in devastating consequences, including 87 fatalities. Additionally, a survey by ABB revealed that over two-thirds of industrial businesses confront unplanned outages at least once a month. This aligns with findings from a 2025 article in Harvard Business Review, demonstrating the potential operational and financial benefits of proactive strategies, such as adopting AI-driven preventive maintenance.

The Role of AI in Streamlining Operations

Emerj recently conducted a podcast series featuring industry leaders from companies like Aquant, Generac, and Electrolux, focusing on the implementation of AI in manufacturing and service sectors. During these conversations, executives emphasized the necessity of a hybrid approach that combines in-house capabilities with vendor-provided solutions. This model not only allows firms to balance speed and expertise but also enhances return on investment (ROI) and ensures smoother integration. As Tim Burge from Aquant noted, blending both strategies can significantly shorten the time to productivity and reduce costs.

Prioritizing High-Impact AI Use Cases

Another significant point raised in the discussions was the value of prioritizing AI initiatives based on their potential impact. Leaders advised using an impact–ease matrix to identify high-value opportunities that require minimal effort to implement. By doing so, businesses can focus their resources on projects that are likely to yield the best results, thus maximizing their investments in AI technology.

Partnering for Success: Leveraging AI Vendors

Choosing the right vendor is crucial for successfully implementing AI initiatives. Industry experts stressed the importance of partnering with AI vendors that deliver reliable, high-confidence predictions. This partnership can be pivotal in enhancing customer experience and driving cost savings. Eric Rivas from Electrolux highlighted the importance of ensuring that vendors have a history of maintaining robust development practices, which is critical for long-term success.

Building Trust Through Transparency

To further mitigate risks associated with AI implementation, it is beneficial to engage impartial external partners when structuring data and developing AI solutions. This approach helps avoid internal biases and instills confidence in the AI processes. Engaging these partners can result in cleaner, repeatable processes that enhance the delivery of value.

Conclusion: The Path Forward

Navigating the build vs. buy conversation is not merely a technical choice but a strategic one that can shape the future of service and manufacturing industries. As more companies explore AI integration, finding the right balance between in-house innovation and external expertise will be paramount in driving operational success and addressing challenges faced in fast-paced environments.

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