In the high-stakes world of industrial manufacturing, the digital landscape is undergoing a profound shift from traditional, siloed systems toward integrated, cloud-native ecosystems. Dean Forbes, a prominent leader in the enterprise software space, has been at the forefront of this evolution, guiding organizations through the complexities of cloud modernization and pragmatic AI adoption. As the industry moves toward 2025 and beyond, his insights provide a roadmap for mid-market manufacturers seeking to balance the need for cutting-edge innovation with the non-negotiable requirement for operational stability and predictable growth.
The following discussion explores the strategic drivers behind the current surge in cloud adoption and how data-driven intelligence is reshaping the shop floor.
Many industrial software providers are shifting toward recurring revenue models, which now account for nearly three-quarters of total earnings. How does this financial structure redefine your daily engagement with customers? What specific steps do you take to ensure ongoing value rather than focusing on one-off sales?
Moving to a model where 74% of our revenue is recurring fundamentally transforms our relationship from a vendor to a long-term partner. It forces us to move away from the “sell and forget” mentality and instead focuses our energy on supporting adoption and staying accountable for real-world outcomes every single month. This structure creates a “virtuous cycle” where our success is tied directly to the customer’s ability to thrive using our tools. We have to listen more closely to their challenges because if they don’t see value, they won’t stay. Ultimately, this stability allows us to reinvest back into the product, ensuring that the innovation we deliver today remains relevant through changing market dynamics for years to come.
Mid-market manufacturers often hesitate to move to the cloud due to fears of downtime and operational disruption. How can providers manage the “heavy lifting” of migration to maintain business continuity? What metrics should companies track to justify the transition toward more predictable upgrade paths?
The hesitation is understandable because, in manufacturing, a few hours of downtime can equate to massive financial losses. We address this by having our internal teams take on the “heavy lifting” of the migration process, meticulously planning the transition to ensure minimum disruption to daily operations. To justify the move, companies should look closely at metrics like system uptime, the speed of deployment for new security patches, and the total cost of ownership compared to aging on-premise hardware. When they see the stability and the much smoother, more predictable upgrade path that the cloud offers, the fear of “breaking the system” is replaced by the confidence to grow. We find that once the initial migration is complete, the visibility gained across the organization becomes a primary driver for further investment.
AI is frequently dismissed as hype, yet it offers potential for reducing friction in industrial planning and automation. Where are you seeing the most immediate impact on the shop floor? How should manufacturers prioritize these tools without overwhelming their existing specialist workflows?
Our customers are practical people who don’t have the luxury of time to develop their own AI strategies, so they look to us for “pragmatic AI” that solves actual bottlenecks. The most immediate impact is found where AI reduces friction in planning and improves the accuracy of automation, essentially identifying issues before they become expensive problems on the shop floor. To avoid overwhelming specialists, we integrate these capabilities directly into the workflows they already know, rather than adding a separate, complex layer of technology. It’s about empowering the person doing the hard work with better data, not replacing their expertise with a headline-grabbing tool. By focusing on quality and security, we ensure that AI acts as a support system that enhances existing precision.
Net revenue retention figures reaching 114% suggest that existing users are deepening their investment in ERP ecosystems. What drives this level of customer expansion during periods of market caution? Could you share a scenario where adding new modules or analytics solved a specific operational bottleneck?
A 114% net revenue retention rate is a powerful signal of trust, showing that even in cautious markets, customers are leaning in rather than pulling back. This expansion is driven by the fact that as businesses grow, they realize they need more than just a basic system; they need specialized modules for analytics, cloud services, and deeper functional support. For example, a manufacturer might start with core production features but later add advanced analytics to gain better visibility into their supply chain or finance. By integrating these new capabilities, they can move from simply recording data to using it to make faster, more confident decisions. This level of expansion proves that the ERP system is becoming more central to their daily survival and competitiveness every year.
Integrating new capabilities through acquisitions can often lead to internal distractions or customer confusion. How do you validate that a new company fits your technical strategy without disrupting current service? What processes ensure that innovation from these deals reaches the end user quickly?
We treat acquisitions as a way to fuel our customers’ access to innovation, not as a distraction for our engineering teams. To ensure a perfect fit, we utilize a specialist team—often comprised of people with deep product and customer experience—to validate that any new business aligns with our forward-looking technical strategy. This process ensures that we are only bringing in companies that enhance our vertical expertise and add immediate value to our existing users. Because these teams are dedicated to the integration lifecycle, our core developers stay focused on their current roadmaps. The result is that the benefits of the acquisition, such as a new AI tool or specialized analytics, reach the end user faster without any dip in service quality.
Looking toward the next few years, building strong data foundations and agentic capabilities is becoming a major priority. How can industrial firms move away from siloed information to create actionable insights? What foundational steps must they take today to prepare for more autonomous system behaviors?
The journey toward autonomous or “agentic” systems must begin with a rock-solid, secured data foundation. Industrial firms need to break down silos by ensuring that data flows seamlessly through every part of the operation—from the planning stage to inventory, and all the way to finance. The first step today is moving to a modern cloud architecture that can handle the volume and complexity of this information without creating extra risk. Once the data is unified and clean, you can begin to introduce technology that doesn’t just show you an insight but actually acts on it, like automating routine tasks or adjusting production schedules in real-time. Without that quality data foundation, even the most advanced AI will struggle to deliver meaningful results.
What is your forecast for the industrial ERP landscape?
The landscape is moving rapidly toward a future where resilience and competitiveness are defined by how quickly a company can turn data into action. Over the next few years, I expect to see a massive surge in agentic capabilities, where the ERP system acts as an intelligent partner that supports teams by handling routine complexities autonomously. Cloud modernization will no longer be an “option” but the standard baseline for any manufacturer that wants to remain adaptable in an unpredictable market. Success will belong to the firms that choose a partner capable of cutting through the noise to deliver high-quality, actionable insights that strengthen their ability to run and grow. The companies that build their data foundations now will be the ones that move faster and compete harder as these autonomous technologies mature.
