This article is part of a series around AI and finance, exploring the findings in our report, The Modern CFO’s AI Roadmap, in greater detail. Download the full report to learn more!
AI is maturing rapidly. For manufacturers, the finance team can help lead the transformation.
This past year, artificial intelligence (AI) has been an inescapable topic across all industries, and manufacturing is no exception. Whereas last year, companies were buying their employees ChatGPT subscriptions and calling it a day, there’s greater pressure to build solutions that drive efficiency and profitability. At the same time, enterprise AI capabilities have matured. Agentic AI systems can deliver faster insights, improved productivity, and new analytical capabilities using plain-language queries simple enough for anyone in your organization. Manufacturers who get it right will have an important tool to overcome chain volatility and margin pressure. Those that don’t will be outcompeted.
To get it right, manufacturers should let finance lead AI transformation. Finance teams are already positioned at the intersection of strategy, data, and value creation, and they own critical datasets such as financial transactions, forecasts, pricing, and performance metrics. From this position, they can translate technical potential into executive-level priorities, ensuring AI has a quantifiable positive impact on the company’s bottom line.
Prioritize your AI approach based on value and ROI in a manufacturing context.
The most critical role for finance will be to move beyond AI pilot programs and connect implementations to the ways your business actually makes money, and to the biggest challenges it faces. There’s an important balance here: You want use cases that are big enough to prove value, but small enough to prove value quickly. For manufacturers, we recommend picking use cases tied directly to sales generation and operating efficiency, in workstreams like planning and forecasting, dynamic pricing, and operational execution. These are areas where you have enough data to get started already, and where benefits created will show a dollar value quickly.
Potential use cases to give manufacturers an immediate advantage.
Manufacturers seeking value from AI should consider the following use cases. These use cases align with data sources most manufacturers already have and provide immediate evidence of their benefits.
- Predictive maintenance. By combining real-time sensor data, advanced predictive modeling, and intuitive visualizations, manufacturers can predict equipment failures before they occur. By allowing manufacturers to conduct proactive maintenance, this solution helps to avoid failures that hurt productivity. Spaulding Ridge has seen predictive maintenance solutions reduce unplanned downtime by 60% and maintenance costs by 25%.
- Demand intelligence and inventory optimization. AI models analyze market trends, supplier performance, and production patterns to optimize pricing, inventory levels, and supply chain decisions. A reduction in unsold inventory and better pricing can significantly reduce carrying costs and stockouts, leading to greater ROI.
- Integrated business planning. Disconnects between customer demand, vendor and production supply, and financial targets make it difficult for manufacturers to operate with the same numbers, the same set of assumptions, and therefore the same objectives. Automated workflows, coupled with agentic insights, can be the connective tissue needed to bridge functional silos.
- OMS-Style Control Tower. Organizations don’t have to wait for the three-year roadmap to consolidate disparate legacy ERPs and the rest of the SCM stack. Leverage modern data platforms and integration pipelines to quickly harmonize siloed ERP, TMS, WMS, CRM, and supplier systems to light up your supply chain and be able to quickly answer the customer’s “where’s my order” question without the spreadsheets or phone calls.
Evolve your data strategy to build a lasting advantage.
AI in manufacturing only performs as well as the data behind it—and most manufacturers still struggle with fragmented ERP instances, plant-level MES systems, supplier portals, OT data, and spreadsheets. A practical data strategy must connect these worlds and create repeatable, scalable value. Focus on three essentials:
1. Harmonize and connect operational data.
Inconsistent definitions across plants—SKUs, BOMs, routings, scrap codes—create friction and degrade AI accuracy. For a more practical approach:
- Standardize core master data (products, vendors, customers, work centers).
- Integrate ERP, MES, CRM, and supply chain systems into a unified data layer.
- Establish a common data model so planning, maintenance, and finance all operate from the same source of truth.
Everything you do to harmonize your data, even small tweaks like aligning production calendars or part hierarchies, can significantly improve model reliability.
2. Make plant‑floor (OT) data AI-ready.
Machine-level data is powerful but messy. Downtime codes, sensor streams, historian logs, and PLC outputs often lack context or consistency. Fortunately, a few easy steps can make it much more useful:
- Capturing machine data at the right granularity, including cycle times, downtime events, and sensor readings.
- Cleansing and governing OT data to remove gaps and misclassified events.
- Mapping OT data to business context, such as SKU, operator, or production order.
- Using event-driven pipelines so AI models can trigger real-time insights like maintenance alerts or schedule adjustments.
Bridging OT and IT data is often the single highest‑value step in enabling operational AI use cases.
3. Build semantic and financial context into your data.
AI agents need more than data—they need meaning. To answer questions such as “What’s driving margin erosion?” or “Where will we miss demand?”, your data must reflect how the business works.
That requires:
- Clear semantic definitions for concepts like yield, throughput, contribution margin, lead time, and profitability.
- Connecting operational events to P&L impacts, enabling AI to quantify financial outcomes.
- Reusable data models that support pricing, forecasting, maintenance, and supply chain workloads without rework.
With semantic and financial alignment, AI becomes a decision-making partner, not just an analytical tool.
How to start: small, focused, value-driven steps.
You don’t need a multi-year data overhaul. Instead:
- Improve quality on the data objects that matter most to working capital or production efficiency.
- Integrate only the systems required for your immediate AI use case.
- Layer in governance and semantics as you scale additional workloads.
This approach builds momentum quickly while laying the foundation for sustainable, enterprise-wide AI adoption.
Prepare for change.
Manufacturers know automation, but AI introduces a different kind of shift. Instead of replacing labor on the line, AI changes how decisions are made across production, planning, quality, and finance. That shift can cause anxiety, especially in environments where employees have long tenures, deep tribal knowledge, and established routines.
To prepare your workforce and organization, focus on three practical realities of AI-driven manufacturing change:
1. Address workforce concerns with clarity and role-specific value.
Plant teams often worry that AI will eliminate jobs or make long-held skills obsolete. This is especially true for operators, planners, schedulers, and maintenance techs who take pride in hands-on expertise.
Reframe the conversation around:
- Reducing low-value manual tasks like data entry, schedule adjustments, or spreadsheet reconciliation.
- Enhancing—not replacing—operator insight, using AI to flag anomalies, predict issues, or recommend optimal settings.
- Evolving roles toward problem‑solving, troubleshooting, and managing exceptions rather than performing routine administrative work.
When employees see AI as a tool that removes drudgery and elevates their expertise, adoption accelerates.
2. Reskill around cross-process understanding, not coding.
Most manufacturing AI doesn’t require workers to learn Python. It requires them to understand processes end-to-end so they can interpret model outputs, question assumptions, and validate recommendations.
Key reskilling priorities include:
- Basic data literacy, including what inputs matter, how models work, and why predictions change.
- Process flow understanding across production, quality, logistics, and finance.
- Comfort with AI-enabled tools such as predictive maintenance dashboards, AI-driven schedulers, or agent-powered reporting.
Employees who know why a recommendation matters can quickly trust, evaluate, or challenge it.
3. Use AI to simplify your technology footprint.
Manufacturers often run an oversized tech stack: multiple ERPs, legacy scheduling tools, niche planning systems, and custom spreadsheets to glue everything together. AI allows a more disciplined approach:
- Buy the core systems that require governance and reliability.
- Build the edge capabilities that differentiate—like predictive maintenance, pricing intelligence, or custom planning logic.
- Retire redundant tools that AI agents or unified data layers make unnecessary.
This reduces tech debt, simplifies training, and accelerates innovation.
The result: a workforce that treats AI as leverage, not a threat.
With transparent communication, targeted reskilling, and a simplified system landscape, manufacturers can help employees shift from fearing AI to relying on it. The goal is to give every role, from the factory floor to the CFO’s office, a smarter foundation for decisions. Once your team understands that, they’ll be AI’s biggest internal champions.
Manufacturers have an opportunity.
The pace of change around AI has been disorienting, and yet, few companies have scratched the surface of what they can do. If your company is looking to go beyond pilots and build AI solutions that drive real outcomes, Spaulding Ridge can help. As dedicated technology partners to manufacturers, we’ve helped finance integrate legacy systems to deliver better insights. Now, we can help you navigate the complexities of AI and unlock its full potential. Whether you’re looking for an overall plan, a data approach to support your AI goals, or support with specific capability areas, reach out to Spaulding Ridge.
