The question is no longer “Should we throw AI on the shop floor?” but “Is our existing software up to the task of supporting it in a real-world, no-holds-barred kind of way?” Shaky supply chains, trouble finding good workers, and thinner profit margins have all just sped up a shift that was already underway.
The main thing that kept progress from happening wasn’t the AI itself; it was the foundation that it sits on. Most of the ERP, MES, and shop floor software we’re still using was built to track transactions and report on progress, not to exchange data in real-time. This meant that many AI projects stalled before they ever reached production, as there was no way to get them to work without completely disrupting the core systems they relied on.
This report looks at how manufacturers made real progress on this problem by 2026. It’s all about the dirty work of updating old software, getting reliable real-time data piped in, putting the infrastructure in place to keep everything running 24/7, and actually tying the AI projects to something measurable, like actual business results.
The problem here is structural. Companies that invested in software up to the demands of AI were able to get faster, become more resilient, and turn all that fancy AI power into real production advantages. The ones that didn’t were stuck with the same old problems because they were trying to slap new tech onto systems that weren’t ever designed to handle it in the first place.
Decoding Software for AI Readiness
The market for AI has already reached a respectable 244 billion U.S. dollars in 2025, and experts predict it’ll just keep growing from there.
Artificial Intelligence (AI) agents and data are racing ahead on the Gartner Hype Cycle for AI, and by a pretty significant margin, according to Gartner. It’s sobering to learn that 57% of organizations believe their data isn’t yet ready for AI — a major obstacle to achieving business objectives with it.
49% of the organizations we looked at don’t have much confidence in their manufacturing strategy to deliver on business outcomes over the next three years either. Meanwhile, the Semiconductor industry racked up over $400 billion in sales in 2025, and AI was one of the key drivers behind that growth.
Chinese private AI investments totaled approximately $35 billion during the 2025 fiscal year. Despite the hype, 91% of mid-sized manufacturers now use Generative AI, yet most are struggling to move beyond the pilot phase.
A massive 62% of companies we spoke to said that poor cross-functional collaboration was holding them back from getting the most out of AI, while a further 63% said they need to rethink their workflows if they want to make AI work in their business.
By the way, McKinsey reports a striking paradox: nearly every company surveyed is investing in AI — yet only one in a hundred believes it is operating at peak performance.
Overhauling Infrastructure for AI
With the demand for AI on the rise, Gartner warns that energy demand will surge more than threefold by 2030, which means we’re in for a radical overhaul of the power and cooling infrastructure in our data centers.
To really make the most of this technology, enterprises need to get all their different teams working together to stitch together IaaS, PaaS, and SaaS systems to deliver AI-enabling solutions. At the same time, they should build or engage with industry-specific ecosystems to develop smarter AI agents that deliver results.
While many leading software companies have embraced cutting-edge technology, few are truly prepared to meet the radical demands of the Gen AI era. To build the kind of products that are needed — the ones that involve real-time processing and autonomous agents working at a huge scale (they will require an entirely new infrastructure foundation), one that supports not only cloud and analytics capabilities, but also high-performance and real-time demands.
This kind of overhaul affects two core areas: the company’s value proposition and its internal operations. So, that means totally rethinking the products, business models, and sales and marketing approach from the ground up, as well as fundamentally changing how the company operates, which involves scaling AI across internal workflows and building the right infrastructure and cultures to achieve significant productivity gains.
As AI moves from experimentation to active business use, infrastructure decisions become a critical factor in overall business success. Unlike traditional infrastructure refresh cycles, decisions about AI infrastructure will squeeze timelines and make trade-offs even harder.
CIOs need to incorporate model governance, stress testing, and disaster response protocols into their infrastructure plans. And one of the key things they need to do is treat AI resilience as a core infrastructure requirement, not an afterthought, if they want to be trusted and compliant.
Mastering Data
Without solid data to work with, organizations are unlikely to achieve their business goals, and they’ll also leave themselves open to significant risks. 57% of businesses say their data isn’t yet ready for AI.
53% of the time, organizations don’t know whether they’re managing data for AI right. But it all comes down to this: organizations just don’t have the right skills or the right info about their data to figure out if it’s up to the job for AI.
To effectively manage metadata, organizations must first organize their technical metadata and then expand to incorporate real business context. Identifying gaps, quality issues, and cost inefficiencies in data is critical to making meaningful progress in AI initiatives.
Assessing available data and evaluating its suitability for AI projects is an essential step.
Maximizing the value of data for AI requires datasets to be aligned with the AI tools in use, resulting in stronger outcomes and improved financial performance.
At present, the value of data is best demonstrated by assessing its fitness for a specific AI use case.
The presence of appropriate expertise, supported by suitable technology and data infrastructure, is crucial to achieving meaningful returns from AI initiatives.
Navigating AI Risks and Ethics
In general, 51% of respondents from organizations already using AI say they’ve seen at least one negative consequence, while nearly a third say they’re seeing issues with AI being just plain wrong. And the stats are pretty alarming: already, 80% of orgs say they’ve encountered dodgy behavior from AI agents, including exposing data where it shouldn’t be exposed and gaining access to systems they shouldn’t.
57% of orgs reckon their data isn’t even AI-ready yet, which is pretty concerning. If they can’t get their act together and deliver on their business objectives, they’re basically leaving themselves wide open to all sorts of unnecessary risks. In his book Superagency, Hoffman makes the point that as we develop new capabilities, new risks will inevitably follow, so we need to stay on top of them, but that doesn’t necessarily mean we need to eliminate them altogether.
Existing AI risk programs, including the whole ethics and cybersecurity aspects, really need to be updated if organizations are to make the most of AI without losing their shirts or damaging the people they impact.
Maintaining human oversight of AI is essential, especially now that we’re seeing agentic AI that can handle all sorts of complex tasks with minimal supervision. To get around this, orgs should augment IAM with guardrails to prevent agents from being misused or triggered into doing something bad by a tricky prompt or by misaligned objectives.
This is all part of the shift in productivity that’s also opening up a whole new set of risks. Insider threats can take the form of a rogue AI agent capable of causing significant damage, whether by hijacking goals, misusing tools, or escalating privileges so quickly that it’s hard for people to step in.
If orgs don’t get a move on, they’re facing serious legal, reputational, organizational, and financial risks. And then there’s the added complication of multi-agentic workflows, which just creates an even bigger risk of hallucinations. Not to mention that AI tools expand the threat surface and create new security vulnerabilities for orgs to address.
It’s interesting that despite these risks, 71% of employees trust their employer to do the right thing when it comes to developing AI.
Unleashing ROI in AI Manufacturing
It was a banner year for AI in supply chain management, too, with many leading manufacturers seeing returns exceeding 20% on their 2025 investments.
Manufacturing firms that adopted AI early recorded an average ROI of 22% for the full year 2025, driven by predictive maintenance and supply chain optimization.
AI-driven manufacturing automation provided the average manufacturer with an 18% efficiency boost across key metrics in 2025.
In terms of quality control, companies that invested in AI successfully reduced defects by 25% over the course of 2025. The global market for AI in manufacturing closed 2025 with $15 billion in revenue, delivering an average ROI of approximately 19%.In a pretty surprising trend, 91% of manufacturers using generative AI reported a measurable boost in their 2025 operations.
US firms that added AI to their production lines saw a 17% increase in ROI in 2025. And according to Bloomberg, the fact that the entire manufacturing industry was going digital in 2025 helped boost AI ROI in manufacturing to a pretty healthy 21%.
Mechanical engineers were also raving about a 16% ROI boost they got from using AI tools in their design processes in 2025.
Companies that have adopted supply chain AI were able to save a pretty big chunk of cash (23%) by streamlining their logistics in 2025.
The final data for 2025 shows that the ROI on AI in manufacturing workflows globally reached a peak of 24%.
Visioning Future AI Trends
Artificial Intelligence and AI-ready data are the 2 fastest-developing technologies, according to the Gartner Hype Cycle for Artificial Intelligence. And it’s no surprise, really: at the manufacturing end of things, generative AI can unlock a whole lot more productivity by spotting problems before they happen, dramatically reducing defects on the production line, and then using that data to write plain-English work instructions that are easy to follow.
As we enter 2026, firms are no longer merely dabbling with AI; the evolution toward Agentic AI has forced companies into a race to keep pace with autonomous automation. One of the major trends you can expect to see is the integration of AI with robotics, plus the rise of collaborative robots (cobots) and the emergence of Cloud robotics that can operate autonomously without direct human input. Roland Berger, a consultancy firm, reckons industrial automation equipment sales will see a pretty decent rise in growth over the next couple of years: 3-4% in 2026, then up to 6-7% for the remainder of the decade.
America’s industrial heartland is just about to get a major AI injection, which is a pretty big deal, and shows just how much the economy is being transformed, or even kept on its feet, by the huge investment being made in software, chip-making, and data centers.
Manufacturing and automotive supply chains are starting to shift towards AI-first operations, but to really scale, you need good, clean data, standardized processes, and some decent governance. One of the things we can expect to see in 2025 is even more integration of AI and machine learning into CAD/CAM systems.
Companies that optimize their supplier networks can achieve better product quality, lower costs, and quicker delivery times, and AI tools can help mitigate risks such as supplier disruptions or quality issues.
According to a McKinsey survey of Manufacturing COOs, AI hopes are high, and budgets are sizable, but some companies may be underinvesting in the resources needed for AI to deliver long-term value.
In a relatively short time, technology will no longer be the only thing holding back lights-out factory transformations, meaning the factory can operate fully autonomously without human presence.
Thanks to advances in language models, computer vision, sensors, and the fact that hardware is now much cheaper, AI is getting close to becoming a reality on the shop floor, in logistics, and in service jobs.
Blueprint for AI Integration in Manufacturing
Based on Devox Software’s hands-on experience delivering production-grade AI systems to small and medium businesses and fast-growing manufacturers, this blueprint is designed to show you a practical way to embed AI on your factory floor without destabilizing or over-engineering the system.
System and data baseline
First, you need to know what you’ve got: your current ERP, MES, SCADA, and shop floor systems. You need to understand what data you’ve got, where the gaps are, and what’s stopping you from getting the most out of your systems. So set some basic metrics.
Unpicking AI use cases
Next, you need to define what AI is going to do for you. What are the specific AI use cases you need? Predictive maintenance? Quality inspection? Demand forecasting? Prioritize them.
Data readiness layer
You can’t do AI without good data, so sort out the governance, cleansing, and labeling of your data. Store it safely and securely. And make sure you can get real-time and historical data from your shop floor into the system.
Choosing the right model
Now you need to pick the AI tools that work best with what you’ve got. Choose the ones that communicate easily with each other via APIs and modular services. Tools that have been tested in real-world environments are the best bet.
Phased deployment
Start with the simple stuff: introduce AI into your lower-risk processes first, and use iterative delivery and automated testing to ensure it all works as it should.
Continuous improvement
Keep an eye on how your models are doing, how stable the system is, and your business KPIs after you go live. Retrain your models as things change and scale your infrastructure up as needed using containerisation and automation.
