Digital Transformation Starts on the Plant Floor

We've all experienced strategy being sound at the top but falling apart when it's put into action. That's the current reality for many manufacturing companies amid their digital transformation journeys, but it doesn't have to be that way.

What does digital transformation look like on the plant floor?

For most manufacturers, it looks like a shift from testing to running. Sensors, machines, and quality checkpoints on the floor generate enormous amounts of data every shift. Most of it was built to answer a specific question for a specific person, update a maintenance log, adjust a production count, or verify a quality checklist. Each one typically lives in its own system, disconnected from the systems around it. That may be useful for supervisors on the shift but doesn't lend itself to automation or digital transformation at scale.

And the technology built to solve this problem is still in it's infancy. Digital thread, the connected data trail that follows a product from design through production, is in active use for only 10% of manufacturers today. Digital twins show a similar pattern.19% are operating at scale today, with 34% expected by 2028.

That same survey found that that digital integration with customers and suppliers lags even further behind, with only 1% of manufactures reporting extensive integration today against 35% expected by 2028. The floor doesn't just need to connect internally, eventually it needs to connect outward too, and most operations aren't close to accomplishing that yet.

Closing the gap takes works at across three stages:

• IT Modernization & Talent Sourcing: Legacy systems and infrastructure need to be ready to build on before anything else can work. Trying to layer AI or automation onto a system that can't reliably support them is the fastest way to stall a transformation effort before it starts. This foundation includes talent as well. Your skills bench needs to match your goals, not current state.  

• Data Reconfiguration & Process Improvement. Connecting systems, structuring data, and making information usable across the organization is critical to turning junk data into machine-ready data. And it's easier done early than later. It's technical work, but it's also process work, so you can make sure that you're capturing the right information the first time.  

• AI & Automation Implementation. Once the foundation is solid and the data is connected, AI enablement, governance, and automation can deliver on what the strategy promises. This is where building that pipeline of specialized talent starts to flow. If you don't have the talent in place at this stage, then you're going to stall out.

How is the skills gap impacting digital transformation?

Knowing the data problem exists is one thing. Having the people to fix it is another, and that's where most manufacturers are getting hung up right now. Data engineering, AI Governance, and system integration are not roles most manufacturing organizations have historically staffed for. A plant can have a strong maintenance team and a well-run production floor, and still have no one on staff who can build a data pipeline or evaluate whether an AI model is actually production-ready.

According to Rootstock's 2026 State of Manufacturing Technology Survey, 33% of respondents cite a lack of the right specialized talent as a primary barrier to their digital transformation efforts, and 31% point to insufficient cross-departmental collaboration. Both numbers point to two underlying issues: a shortage of specific technical skills like data engineering and AI governance, and the lack of an internal pipeline to make up the difference.

Hiring new talent is the most common approach to building the digital skills, used by 68% of manufacturers, but between 65 and 70% are also currently outsourcing at least some roles across technology, data, and cybersecurity because internal hiring alone isn't keeping pace. 40% are supplementing with contract or contingent labor in the meantime.

A few things make recruiting for these roles different from filling the traditional plant position:

• The candidate pool often sits outside of manufacturing. Data engineers and AI governance specialists typically come from software, analytics, or system integration backgrounds.

• Speed to offer counts more here. Candidates in these fields often are fielding multiple offers at once, frequently from outside manufacturing entirely, and a standard plant-hire timeline loses them before the second interview.

• Contract-to-hire lowers risk on both sides. By letting a plant confirm fit before committing to a full-time hire while giving the candidate an actual look at the environment ensures a stronger skills and culture fit for long-term success.

• The workforce is more open to upskilling than current usage suggests. MAU’s 2026 Mindset of the Market Survey found that 67% of manufacturing employees feel neutral or positive about AI tools, yet only 31% have actually used one in their role. That gap points to limited exposure rather than resistance, making internal upskilling into data and AI adjacent work a realistic complement to external hiring.

What's the ROI on digital transformation?

Deloitte's 2025 Smart Manufacturing and Operations Survey of 600 senior executives at large manufacturing companies found that 78% have allocated more than 20% of their overall improvement budget to smart manufacturing initiatives, and 88% expect that investment to hold steady or increase in the next fiscal year.

Spending at that level should produce clear returns.  Broader research on digital transformation suggests that it hasn't, at least not yet. Boston Consulting Group's analysis of more than 850 companies worldwide found that only 35% achieve their stated digital transformation objectives.  

Deloitte's survey found that respondents rates human capital as the lowest-maturity category of everything surveyed, behind quality management, operation, continuous improvement, and technology. Between 69 and 72% of respondents reported moderate to significant challenges hiring skilled workers specifically in IT, OT, data science, and engineering, application development, and cybersecurity, the exact technical domains and transformation strategy depend on.  

Throwing money won't fix a broken system, or a shallow talent pool.

Digital transformation in manufacturing now hinges on execution. It comes down to three things: whether the processes in place are scalable, whether the data being collected is actually usable, and whether the right people are in place to solve for both.

Building the right data architecture and securing the specialized talent behind it both take deliberate, sustained work, which is exactly why the manufacturers that start now are the ones most likely to be pulling ahead when the next new technology wave strikes.

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