The Expected Value of Artificial Intelligence
Let’s face it—AI is everywhere. It’s powering chatbots, writing code, making art, and, increasingly, reshaping industries that rely on gears more than gigabytes. Manufacturing is one of those industries. And while it might not be the fastest adopter, it's quietly and steadily redefining what “smart factories” actually mean.
Manufacturers Are Jumping In (Just a Bit More Slowly)
According to the Dataiku & Databricks 2024 Survey Report, only 51% of manufacturing organizations are at the more advanced stages of AI adoption—either “expanding” or “embedding” AI into their operations. That’s behind the all-industry average of 57% (Dataiku & Databricks, 2024).
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Still, the enthusiasm is real: 45% of manufacturing firms believe GenAI has “many valuable applications” that are worth developing now—higher than the 43% average across all industries (Dataiku & Databricks, 2024).
What’s Fueling the Fire? It’s not cost reduction!
Manufacturing companies aren’t investing in AI because it’s trendy—they’re doing it because it’s starting to work. According to Lenovo’s 2024 Global CIO Report, the biggest push is around productivity, with 83% of CIOs reporting that AI is already delivering or is expected to deliver significant gains. This isn’t hypothetical. AI is being used to:
Streamline labor-intensive tasks (49%)
Improve the employee experience by automating repetitive interactions and decisions (38%)
Sharpen management reporting for faster, more accurate decision-making (34%)
Enhance performance via AI-enabled hardware and smart devices (26%)
That’s the kind of efficiency manufacturers live for. But AI’s influence doesn’t stop at internal optimization. It’s becoming a real competitive weapon, with 77% of CIOs saying it’s enhancing their ability to stand out in the market (Lenovo, 2024). They’re seeing this play out through:
More personalized and responsive customer experiences (35%)
The development of entirely new product lines informed by AI insights (35%)
Smarter, more effective marketing and sales tactics (29%)
Improved products and services driven by real-time customer feedback loops (28%)
And then there’s the GenAI factor. According to Dataiku and Databricks (2024), 45% of manufacturing leaders believe GenAI has immediate, valuable use cases worth investing in now—more than any other industry segment. That includes applications like:
Automatically generating quality and compliance reports
Assisting technicians with AI-powered Q&A systems
Speeding up design and R&D through generative iterations
Creating synthetic data for rare but critical failure scenarios
What makes this even more exciting is that AI is no longer the exclusive domain of IT. Manufacturing firms are integrating it directly into:
Operations (45%)
Strategic management (36%)
Procurement and supply chain planning (31%)
It’s moving from the edge of the org chart to the center of the business model. And while not every investment has paid off yet, momentum is building. Sixty-four percent of manufacturers are already seeing positive ROI from GenAI in production use cases (Dataiku & Databricks, 2024). On top of that:
32% expect a $2–$5 return for every $1 spent on AI
17% expect more than $5 for every $1 invested
So yeah, there’s still a long way to go—but AI in manufacturing is no longer about proof of concept. It’s about proving its worth—and increasingly, it is.
But It’s Not All Smooth Sailing
While the potential is enormous, the path to realizing that value is still riddled with obstacles, and many manufacturers are hitting familiar roadblocks. The most persistent challenge? Data. It’s the lifeblood of AI, but in many manufacturing environments, data is fragmented, inconsistent, or locked away in legacy systems that weren’t built for today’s analytics.
According to Dataiku and Databricks (2024), poor data quality and limited access to the right data is the number one barrier keeping manufacturing firms from unlocking more value from AI. That’s followed closely by a difficulty in operationalizing AI projects—taking them from pilot to production—and a surprising lack of compelling business cases that resonate with decision-makers outside the IT department.
Add to that the structural challenges inside many organizations. Manufacturing firms lag behind other industries when it comes to having strong data leadership and governance:
Only 45% have a C-level data leader (compared to 60% across all industries)
Just 43% have clear ownership over data quality
And only 37% say they have adequate visibility into AI initiatives across the company
That lack of centralized oversight and accountability creates silos—both technically and culturally—that make scaling AI a massive challenge. Even when there’s interest and budget, aligning departments, ensuring data readiness, and tracking ROI often become stumbling blocks.
And then there’s the budget tension. While 96% of CIOs say they’re planning to increase AI investments this year (Lenovo, 2024), only a small fraction expect their total IT budgets to rise significantly. That means something else has to give. In fact:
48% say digital transformation efforts are being de-prioritized due to the shift in focus to AI
38% say sustainability initiatives and employee compensation are taking a hit
So yes, the momentum is real. But so are the growing pains. The companies that succeed with AI in manufacturing won’t just be the ones with the coolest tools—they’ll be the ones that can navigate these organizational, cultural, and strategic hurdles better than the rest.
Moving Forward
It’s not in trimming a few bucks off the supply chain or automating your way out of a headcount dilemma. It’s in completely reframing how work gets done.
AI isn’t your CFO’s favorite coupon—it’s your COO’s secret weapon.
Think about it:
It boosts productivity by freeing up teams from repetitive grind so they can focus on high-value thinking.
It sharpened competitiveness by delivering new products, better customer experiences, and smarter decisions—all at the speed of machine learning.
It reinforces resilience by tightening up security, streamlining operations, and catching problems before they hit the fan.
That’s the kind of ROI you can’t squeeze out of cost-cutting alone.
In manufacturing, this means predictive maintenance, adaptive production lines, and supply chains that can actually think for themselves. But the playbook isn’t just industrial—it applies to every organization brave enough to rethink the rules.
AI’s not here to save pennies—it’s here to create possibilities.
So yeah… maybe stop measuring it like it’s an expense report.
References:
Dataiku & Databricks - 2024 AI, Today in Manufacturing & Energy: https://content.dataiku.com/ai-today-in-manufacturing-2024
Lenovo - Global CIO Report 2024 - Inside the Tornado: How AI is Reshaping Corporate IT Today: https://p4-ofp.static.pub/ShareResource/ww/docs/lenovo-global-cio-report-2024.pdf
