Find out how we can help you identify the right technology to implement and apply for the benefits that fit your business and goals.
Published: June 19, 2024
Updated: June 19, 2024
Advanced technologies, including artificial intelligence (AI) and data science, are critical enablers for boosting efficiency and competitiveness among manufacturers—but the path to full adoption is often viewed with apprehension.
While the initial steps towards data and AI may appear complex, the perceived barriers may be overstated. This article aims to clarify common misconceptions about advanced technology implementation in manufacturing and illustrate how forward-looking manufacturers can navigate this transformative journey with confidence and agility.
Tackling the misconceptions of advanced technology implementation in manufacturing
Myth #1: Leveraging data and AI is too expensive
Uncertainty around perceived return-on-investment (ROI) or the upfront costs of implementing digital solutions may seem daunting, but financing options and cost-effective solutions exist to mitigate financial concerns.
Government incentives can help manufacturers accelerate and facilitate their adoption of digital solutions at a discount. Our integrated team can help you leverage incentives like grants, tax credits, and zero-interest loans to unlock new opportunities and gain a competitive edge in the marketplace.
Myth #2: My company doesn't need data analysis or AI to succeed
Legacy processes hamper effectiveness. In today's fast-paced, data-driven environment, outdated operational techniques create bottlenecks, lead to inefficiencies, and hold back manufacturers from achieving their full potential.
Regardless of size or sector, every manufacturer stands to gain from the improvements that data and AI provide.
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Read moreMyth #3: My organization isn’t capable of leveraging data analytics or AI
The barriers to entry for incorporating data analytics and AI into manufacturing processes have lowered significantly, thanks to advances in technology and the availability of user-friendly platforms and low- and no-code solutions, like Microsoft Azure Power Apps and Copilot.
Regardless of size, sector, or technical proficiency, manufacturers can start small, using basic analytics to gain insights from their existing data, and gradually scale up to more sophisticated AI applications. Moreover, the modular nature of many AI systems allows them to be tailored to specific needs, ensuring that manufacturers can begin reaping the benefits of these technologies without requiring massive upfront investments or complete process overhauls.
A common misconception around generative AI (GenAI), for example, is that it requires clean data to be effective.
Your GenAI output is only going to be as good as the quality of the data that you're inputting, so the more data you have, the better the GenAI can behave, but we're getting away from a world where you need to spend a lot of time preparing and cleaning your data in order to extract value from it.
Myth #4: Predictions are the greatest value of data analytics
There's a misconception that by starting to use technology and data, you will immediately benefit from predictions and sophisticated analyses—but there's a lot of value that can be extracted in the stages between starting to adopt data and doing predictions using machine learning and AI.
"When you start looking at your data as an aggregate and seeing insights in a holistic way, that’s often how you can spot your most impactful disconnects. By making informed decisions to improve those disconnects, that's where you're going see the biggest ROI."
How BDO can help
The first foray into data analytics and AI for manufacturing can feel like venturing into the unknown. Intricate algorithms, vast data sets, and the sheer novelty of the technology can create a sense of complexity.
Through a hands-on approach, our team can help you understand what’s possible for your business and where the biggest impacts can be achieved. We collaborate closely with you to identify unique challenges, objectives, and existing workflows, allowing us to map out a clear roadmap for integrating data analytics and AI solutions effectively. Integral to this process is our knowledge and experience with identifying government incentives and other funding avenues designed to support innovation.