March 14, 2025 What Manufacturers Should Ask Before Implementing AI

Manufacturers are no longer questioning if they should implement artificial intelligence (AI), but how.

By Michael Simms

Manufacturers are no longer questioning if they should implement artificial intelligence (AI), but how. It’s no wonder — the returns on AI investments can be immense, with 70% of manufacturers reporting “strong ROI” already. Digital transformation projects, in particular, are seeing impressive boosts from AI. According to a recent Dynata survey commissioned by Columbus Global, 82% of manufacturing leaders are prioritizing AI for digitization projects.

However, leaders must approach AI adoption cautiously. They must step back and evaluate whether their operations are truly mature enough to handle AI. Additionally, they must seriously interrogate whether AI is the best solution to any particular problem. After all,  AI has the potential to drive efficiency, optimize production and enhance decision-making — but only if implemented with a clear strategy and the right data foundation.

AI is not a plug-and-play solution; it requires robust data governance, seamless integration with existing systems and alignment with business goals. Companies that rush into AI projects without first addressing critical questions about their enterprise readiness may run into inefficiencies, wasted resources and otherwise underwhelming results. Thus, to set themselves and their organizations up for success, manufacturers should consider the following key questions before implementing AI.

What Is the Business Case for AI?

AI is a buzzword right now, but beyond that, it’s become a boardroom imperative. For reference, many executives feel the pressure to adopt generative AI workflows, yet only 30% feel prepared to do so. These uncertainties can trickle down and impact the effectiveness of an AI program, significantly curtailing its ability to drive ROI.

The best way to avoid this problem is to create a thorough AI roadmap from the outset and ensure you’re choosing the right solution for your business. Specifically, manufacturers should ask themselves the following questions before investing in AI:

• How does AI align with our broader operational and strategic objectives? Creating a cohesive workflow is paramount to success. So, take a step back and assess your company’s mission and strategic priorities, and interrogate whether AI actually aligns with those objectives and key results (OKRs).
• What problem are we trying to solve with AI? This should be the easiest question to answer. Tie your proposed AI program back to a critical problem your organization is facing, whether that’s lackluster annual recurring revenue (ARR), security vulnerabilities or compliance issues.
• Is AI the most efficient way to address the problem? AI isn’t always a silver bullet. In fact, certain applications of AI may actually hinder your organization’s ability to hit goals. For example, moderately complex but repetitive tasks are great candidates for automation, whereas highly nuanced tasks will likely benefit from a human administrator.

The bottom line is that AI investments should be tied to measurable outcomes. Without a well-defined business case, organizations risk implementing AI for the sake of innovation rather than impact.

Is Our Data AI-Ready?

Many manufacturing organizations struggle with siloed, inconsistent or low-quality data, which can undermine the effectiveness of AI solutions long before they’re deployed. Worse, adopting AI before cleansing data can actually exacerbate organizational issues, creating additional confusion and jeopardizing your investment. Therefore, it’s critical to determine your organization’s data maturity well before AI deployment.

Key questions to assess data readiness include:

• Do we have a strong data foundation with clean, well-governed and integrated datasets? You’ll likely already know the answer to this question. If your department faces frequent silos or miscommunication, it’s a sign that your existing systems are in disagreement.
• Do we have the right tools and processes in place for data collection and management? AI systems are data-intensive, so it’s imperative to adopt systems that can seamlessly handle a high degree of data routing and complexity.

If the answer to these questions is unclear (or “no”), manufacturers should prioritize data governance initiatives before implementing AI. Standardizing data formats, eliminating redundancies and ensuring real-time data access will significantly enhance AI performance.

Do We Have the Right Talent and Expertise?

AI adoption requires more than just an excellent tech stack. You’ll need stakeholders (either externally or internally) who can understand AI implementation forward and backward. The exact skills you’ll need for this will depend on your use case and industry, so make sure to be thorough when answering the following questions:

• Do we have data scientists, AI engineers or IT staff equipped to manage AI deployment? Adopting AI requires a very specific skillset, so it’s best to hire a team or stakeholder who deeply understands the technology.
• Is leadership aligned with AI strategy and committed to its success? You can answer this question by prioritizing transparency in communication throughout the planning process. Ensure all executive leaders involved in the project can agree on key performance indicators (KPIs) early on. Otherwise, your progress may become marred by vanity metrics.
• Should we partner with external consultants or technology vendors for expertise? If you lack the internal expertise to implement AI, it’s prudent to consider partnering with an external AI consultant. These partnerships are becoming increasingly common. In fact, 55% of IT leaders are open to engaging third-party partners to bridge internal AI skill gaps.

Identifying the right talent and skills before implementing AI will ensure your program runs smoothly.

Prioritizing A Strategic Approach to AI

The excitement around AI in manufacturing is well-founded, but successful implementation requires careful planning and execution. By asking these key questions upfront, manufacturers can avoid common pitfalls and ensure that AI delivers meaningful business value. A strategic approach — grounded in a solid business case, strong data governance, skilled talent and cultural readiness — will enable manufacturers to harness AI’s full potential and drive lasting transformation.

About the Author
Michael Simms is a seasoned technical manager who has been developing Data and Artificial Intelligence solutions for nearly three decades. In addition, he has been at the leading edge of Microsoft ERP, database management, and emerging technologies. He plays a principal role in architecting and implementing projects from creation through go live. Michael also excels at creating and supporting offerings in the analytics/digital transformation space, specifically for Gen AI, Machine Learning, and Data Science. His extensive areas of expertise include data architecture, data migration, data engineering, and AI.

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