Across the steel and chemical industries, digital transformation is still uneven. Some mills and plants see measurable gains from new software, while others invest heavily and struggle to realize value. That variation isn’t about capability or commitment. It comes down to how technology is selected, deployed, and used in the realities of production.
The good news is that the pattern is changing. A growing number of mills and plants are proving that digital systems can deliver consistent, measurable results - when they’re implemented with clear goals, realistic scope, and direct engagement from the people who run the process. It’s not that software has suddenly improved overnight; it’s that teams are approaching digital investments differently, rebuilding confidence in how technology can perform when deployed the right way.
The Results Gap
Research from McKinsey, BCG, and Deloitte continues to show a wide performance gap between digital projects that deliver and those that stall.
McKinsey’s 2023 Global Industrial AI report found that while nearly every manufacturer has launched a digital initiative, fewer than one in three achieve their expected financial impact.
Most of the shortfall isn’t due to technology quality but to deployment challenges: weak integration, limited operator engagement, and unclear accountability for outcomes.
Steel and chemical operations amplify these challenges. The processes are complex, conditions change daily, and even small deviations can have major consequences for quality or throughput. That environment exposes any disconnect between software assumptions and plant reality.
Yet, within the same industries, other facilities are achieving rapid ROI from new AI-driven tools. The difference isn’t that they chose a perfect platform, it’s that they made technology fit their process — not the other way around. Each successful deployment helps rebuild trust that digital tools can make a measurable difference when integrated with how real operations actually work.
What's Changed in Digital Deployment
The mills and plants seeing consistent results share a few common practices. These are less about technical specifications and more about disciplined implementation.
- Focus on specific, measurable problems.
Rather than launching large, abstract transformation programs, successful teams start with one or two targeted use cases — alloy optimization, grade change optimization, or slag management. They define the metrics up front: improved margin, tighter quality variation, yield improvement, time efficiency. Small scope builds evidence and operational trust. - Make transparency non-negotiable.
Operators and engineers adopt tools they understand. Black-box recommendations that can’t be explained mean slow adoption, no matter how sophisticated the algorithm. Projects that succeed now require explainable AI — models that show why a recommendation was made and what variables drive performance. That transparency is central to sustaining trust between technology and the people who rely on it. - Integrate within existing operations.
New systems should connect to existing data sources and control infrastructure. Integration should happen without long delays or re-engineering. The goal is to improve the current workflow, not rebuild it. - We recently worked with a plant whose operator pulpit had no space for a new screen. We helped to find a way to make our solution fit into their current workflow without interrupting the process that works for them right now. This meant their process engineers could begin using Fero immediately and their operators could take action on Fero recommendations without delays or pushback. Examples like this help demonstrate that digital change can be seamless - an important part of restoring confidence in new tools.
- Adapt as processes evolve.
Mills and plants rarely run in a steady state. Feedstock variation, maintenance cycles, and recipe changes are constant. Models that update automatically and maintain performance through those shifts prevent the slow erosion of results that undermined earlier digital tools. - Each of these principles has been validated repeatedly in field studies and independent research. Together, they represent a shift from software procurement to operational problem-solving which is the foundation for rebuilding lasting trust in digital transformation.
An Example of What That Looks Like
Fero Labs was built around this new model. Our industrial AI platform applies explainable ML to help process engineers improve yield, quality, and energy use without disrupting existing operations.
The approach begins with a single high-impact use case, typically alloy optimization for steelmakers. Within the first few weeks, engineers see results quantified in their own metrics: less off-spec production, lower alloy usage, more consistent output. Every recommendation is transparent, showing which inputs drive the prediction and how adjustments affect outcomes.
That explainability matters. It allows experienced teams to validate insights against their own expertise instead of taking them on faith. It’s not Fero teaching metallurgy; it’s using the mill’s own historical data to make real-time decisions. It turns AI into a collaborative tool rather than an external authority - a form of technology that earns trust through visible, verifiable performance.
Because Fero integrates with existing data historians, the learning loop stays live. When raw materials or product specifications change, the model can adapt automatically - maintaining reliability without re-implementation and alerting engineers or operators that conditions are changing before they reach a critical stage.
This approach doesn’t rely on large-scale transformation. It builds trust one measurable improvement at a time, scaling only after results are proven. That’s how the most successful digital programs in heavy industry now operate.
Why This Approach Works
Across recent case studies in both steel and chemicals, this pragmatic model consistently achieves faster payback and higher adoption. Typical outcomes include:
- 2–5% yield improvement within the first 60 days of deployment
- 5–10% reduction in energy consumption through optimized setpoints
- Increased process stability, lowering off-spec output and rework
These aren’t exceptional results; they’re the kind that well-implemented AI systems can now deliver reliably and cumulatively. What sets them apart is not the software alone, but the structure around it - narrow focus, operator alignment, and explainability that captures and shares knowledge across every shift.
As digital technology matures, this repeatable formula is replacing the high-risk, all-or-nothing transformation model that once defined the field. Mills and plants are learning that success is not about betting on a platform; it’s about engineering measurable improvement into daily operations. That’s the foundation on which confidence in digital technology is being rebuilt.
Moving the Industry Forward
Digital transformation in steel and chemical operations is far from over. The process of transformation is constant. Poorly scoped or over-promised projects still occur, and some will continue to disappoint. But it no longer has to be that way. The industry now has enough evidence - from both research and real-world examples - to know what works.
When software is transparent, when deployment starts small, when the end users are engaged before procurement, and when engineers remain in control, digital tools deliver consistent value. That’s the model Fero Labs was built to support.
For mills and plants evaluating their next investment, the question isn’t whether digital can deliver. It’s how to ensure it does — by choosing an approach proven to align technology with process expertise and results with reality. Each successful implementation strengthens the industry’s trust that digital transformation, when executed with focus and transparency, truly works.
