Artificial intelligence has moved rapidly from experimentation to mainstream adoption across the manufacturing sector. According to recent research, 94% of manufacturers are now using some form of AI, reflecting a significant shift in how organizations approach data, planning, and operational decision-making. Yet widespread adoption does not necessarily translate into meaningful operational impact. For many manufacturers, the challenge now lies in integrating AI capabilities into core business systems and workflows — particularly within ERP environments where critical operational decisions are made.
At the same time, manufacturers are navigating a complex landscape shaped by supply chain volatility, tariff pressures, and persistent talent shortages. These pressures are accelerating investments in enterprise software and increasing the importance of unified ERP platforms that can support real-time data visibility and AI-driven insights across planning, production, procurement, and finance.
In this Q&A with Ohad Idan, Vice President of Product at Rootstock Software, we explore how manufacturers are progressing along the AI maturity curve, why ERP consolidation is becoming a strategic priority, and where AI is already delivering measurable value in areas such as process optimization, supply chain planning, and operational forecasting. Idan also shares perspectives on governance, workforce augmentation, and the architectural foundations required to support the next phase of AI-enabled manufacturing operations.
Q: Your survey shows that 94% of manufacturers are now using some form of AI. What does “AI maturity” look like in practice, and where do you still see gaps between adoption and real operational impact?
A: The fact that 94% of manufacturers are now using AI tells us that AI has moved into the mainstream. Manufacturers are no longer wondering, “Should we implement AI?” They’re asking, “How can we leverage AI to achieve the most value?”
AI maturity doesn’t mean having the most AI tools. It means understanding which decisions can be improved with AI and what business outcomes you’re trying to drive. Organizations that are further along are embedding AI into real operational workflows. They aren’t just using it to answer questions, but allowing it to influence planning, purchasing, inventory management, and other day-to-day decisions.
The biggest gap we still see is in integration across the enterprise. Many companies are using AI in isolated ways, but it’s not yet fully connected to ERP systems or core processes. Without that integration, AI remains informational rather than operational. Another gap is governance. Organizations need to clearly define where AI can assist, where it can recommend, and where humans must remain in control.
Q: Predictive AI, supply chain planning, and process optimization stood out as high-growth use cases. Which of these areas are delivering the fastest time-to-value for manufacturers today?
A: Process optimization is where rapid time-to-value can be achieved because it directly impacts daily operations. The most immediate gains tend to come from addressing repetitive, manual, or reactive work. When AI is embedded into ERP-driven workflows, such as analyzing purchasing delays, checking inventory availability, or evaluating supplier impact, manufacturers can reduce manual steps and respond faster. Those improvements are measurable and show up quickly in operational metrics.
That said, the real acceleration happens when predictive AI, supply chain planning, and process optimization operate together on a unified cloud platform like Salesforce. When demand signals, production data, supplier performance, and financial information are all connected, AI can work with full context rather than isolated data points. Predictive insights can influence planning immediately. In addition, planning changes can flow straight into production, and execution data feeds back into forecasting.
In a fragmented architecture, these use cases operate in silos. On a unified platform, they execute across the enterprise, and that’s where you start to see significant impact.
Q: The survey highlights ERP platform consolidation as a leading priority. Why is a unified ERP foundation becoming essential as manufacturers expand AI initiatives?
A: AI is only as effective as the data foundation underneath it. If your ERP, CRM, and supply chain systems are loosely connected with middleware or batch integrations, AI is forced to operate with fragmented information and that limits impact.
But beyond performance, there’s also architectural risk. ERP is a long-term investment. As manufacturers expand AI initiatives, they need confidence that their ERP platform can scale, adapt, and support emerging capabilities without having to rebuild integrations every time something changes. A unified data model reduces complexity, minimizes integration fragility, and creates a stable foundation for innovation.
Within this architectural context, consolidation isn’t just about efficiency. It’s about futureproofing the business for what comes next.
Q: Despite economic uncertainty and trade pressures, 61% of manufacturers plan to increase enterprise software spending. What’s driving this continued investment confidence?
A: While manufacturers waited for volatility to subside, they reduced investment in new systems. As more organizations have realized that volatility is the new norm and is not going away anytime soon, they are re-evaluating their investment strategies. When demand signals are mixed and costs are volatile, manufacturers need greater visibility and tighter coordination across sales, operations, supply chain, and finance. The increase from 51% in 2024 to 61% in 2026 suggests pent-up demand, but it also signals that manufacturers will be being selective about where they invest. They’re prioritizing the initiatives that can improve performance and decision-making, allowing them to respond to changing conditions faster. Enterprise software plays a central role because it strengthens the core systems manufacturers rely on every day.
Enterprise software underpins the operational processes that determine performance. In uncertain conditions, that foundation becomes even more important.
Q: Talent shortages and cross-department collaboration remain key barriers to digital transformation. How should manufacturers rethink their operating models to overcome these internal challenges?
A: That’s right, many of today’s barriers to digital transformation are internal to an organization, with 33% of manufacturers saying they lack the right talent and 31% citing challenges in cross-department collaboration. These statistics show that execution is often the real constraint.
Due to talent shortages, one thing we see companies doing is trying to offload decision-making to implementation partners. Partners bring expertise, but manufacturers must remain actively involved and own the changes in their business. No external team understands the nuances of an operation better than the people running it.
Closely related to this is underestimating the internal commitment required. Too often, employees working on transformation initiatives are expected to maintain their full day-to-day responsibilities. That leads to rushed decisions, insufficient testing, and lower engagement. Organizations that dedicate time, stay involved, and focus on business outcomes tend to see smoother implementations and faster time to value.
Q: With tariffs and cost volatility impacting planning, how are modern ERP and planning tools helping manufacturers respond more dynamically to external shocks?
A: Our survey shows that 39% of manufacturers expect higher raw material costs due to tariffs, and 29% anticipate greater complexity in cost forecasting. This shows that volatility is directly affecting planning. When cost structures and supplier conditions shift quickly, traditional planning approaches struggle to keep up.
Manufacturers need modern ERP and planning solutions that can provide real-time visibility into cost changes and supplier impact. Instead of reacting after costs rise, they need to identify risks earlier and adjust sourcing, pricing, and purchasing decisions accordingly. Insights into inventory levels, supplier performance, and margin impact also allow teams to respond with increased speed and precision.
Q: As AI moves deeper into execution-focused applications, how should manufacturers balance automation, decision support, and human oversight?
A: AI works best when it’s applied deliberately and layered. Some tasks are repetitive and low-risk, and those can be automated. For higher-impact decisions, AI can provide recommendations, which humans still need to review and approve. Strategic decisions that may involve trade-offs and broader business context should remain human-led. The objective isn’t to remove oversight; it’s to reduce cognitive load and allow teams to focus on higher-value work.
As AI becomes more agentic and capable of executing defined tasks, the importance of governance increases. Manufacturers must establish clear guardrails to establish where AI can act independently, when it should escalate decisions for approval, and at what thresholds it should trigger human review. The most mature organizations define these boundaries intentionally. When the balance is right, AI becomes a practical partner in execution rather than a replacement for human judgment.
Q: From a product and roadmap perspective, how is Rootstock evolving its ERP capabilities to support increasingly complex, AI-driven manufacturing environments?
A: From a roadmap perspective, Rootstock is embedding AI directly into its ERP workflows to help manufacturers operate more intelligently in increasingly complex environments. We’re not building AI for the sake of saying we have AI. We’re identifying specific, high-impact use cases inside sales, purchasing, inventory, and production where agents can reduce manual effort, surface insights quickly, and support day-to-day operational decisions. Our approach is to start with capabilities that provide clear value in both in terms of information and assistance. Then we want to evolve toward more agentic functionality where the system can suggest actions and, within defined guardrails, execute tasks responsibly.
A big part of that evolution involves collaboration. Through Rootstock’s AI Advisory Council and early pilot programs, we’re working directly with customers who use our ERP every day to shape which workflows we prioritize and how those agents behave. We’ve invested heavily in foundational architecture so we can iterate quickly. In this way, we can add capabilities, refine behavior, and improve usability based on real feedback rather than assumptions. At the same time, we’re keeping governance and trust at the center. Core ERP logic remains deterministic, permissions are respected, and agents operate within clearly defined boundaries.
Ultimately, supporting increasingly complex manufacturing environments means making AI practical, embedded, and reliable. The goal isn’t to replace ERP logic or human oversight. It’s to make ERP more intelligent and responsive as operations become more dynamic.
Q: While 73% of manufacturers believe they are “on par” or “ahead” of peers in AI adoption, what truly differentiates AI leaders from the rest?
A: Yes, the data shows that 73% of manufacturers believe they’re at least keeping up with peers in terms of AI adoption, but only 5% consider themselves far ahead of the curve. That gap is important. It tells us AI use is widespread, but reaching the highest level of maturity is still a goal for many.
What differentiates those at the top is how deeply AI is embedded into their operations. They’re not just deploying tools they’re integrating AI into ERP, supply chain, and production workflows so it can influence real day-to-day decisions. They’re also prioritizing data quality and accessibility because they understand AI is only as strong as the context it operates within.
Most companies are expanding AI usage. Leaders go further as they redesign processes around AI-enabled decision cycles, measure impact, iterate, and align AI with clear business outcomes. This type of approach is what moves a company from basic adoption to true AI leadership.
Q: Looking ahead, what do you believe will most fundamentally reshape manufacturing technology strategies over the next 2–3 years?
A: Over the next two to three years, we’ll see AI become more operational. That shift means more agentic capabilities operating within predefined guardrails, automating specific tasks, supporting workflows, and reporting findings back for review. As those capabilities mature, AI will move closer to the core of how daily work gets done, rather than sitting on the periphery.
At the same time, platform consolidation will accelerate. As AI becomes more embedded in execution, organizations will find it increasingly difficult to operate across fragmented systems. AI depends on unified, contextual data. This is something fragmented architectures make difficult to maintain. Companies operating on unified platforms will be able to scale faster and adapt more easily as new capabilities emerge.
Workforce augmentation will also become more strategic. Talent shortages are real, and AI will increasingly be used to close skill gaps, preserve institutional knowledge, and guide less-experienced employees through complex workflows. The companies that succeed will be the ones that integrate AI into the fabric of their operations.
ERP News Editorial Team
The ERPNews Editorial Team covers global developments in ERP (Enterprise Resource Planning), enterprise software, cloud platforms, AI, automation, and digital transformation, providing independent news and editorial analysis for senior business and technology leaders. Our reporting focuses on market signals, strategic shifts, and enterprise impact across the ERP and enterprise technology ecosystem.
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