Rootstock Software’s move toward improving its ERP strategy through the acquisition of Praxis Solutions is part of a shift many enterprises and companies are taking. The emphasis on agentic AI, in particular, is a natural evolution, but it comes with challenges that are common in digital transformation and Industry 4.0 initiatives.
Manufacturers are eager to get on the AI bandwagon, but that does not mean they are ready, according to Ohad Idan, VP of product at Rootstock. “From a pragmatic standpoint, an AI-ready manufacturer is one that is open to experimenting with AI and understands this is an emerging technology,” he says. “Until you actually start using it, it’s difficult to understand how it can impact your business.”
It is also difficult, he says, because there are major misconceptions about AI and its role not just in manufacturing, but society as a whole. There is the belief AI is nothing more than hype or a danger to people’s jobs. Idan says neither assertion is true—AI works best as a partner that improves productivity and decision-making for humans, which has been a main focal point of digital transformation initiatives.
Learning From Implementation Failures
Like many manufacturing revolutions, companies embracing AI, as they did with digital transformation, fail because they believe the concept itself is all they needed, according to Idan.
“Implementing technology without considering this will always result in disappointment and missed value,” he says. “Digital transformation must start with business processes, desired outcomes, and only then consider how technology will support those goals; not the other way around.”
Another recurring issue is giving away the problem to implementation partners instead of tackling the problem head-on. Idan believes partners are valuable because of their specific expertise, but when it comes to the company’s inner workings, only staff truly understand the nuances of their business and operations, as they have been there all along.
Companies also fail to understand or realize the commitment they are taking on because they view the process as much simpler than it is. “Too often, employees working on transformation initiatives are expected to maintain their full day-to-day responsibilities, which leads to lower than expected participation, rushed decisions, and insufficient testing,” Idan says, arguing companies that find success focus on business outcomes.
Embracing Cloud, AI’s Role in Modern ERP Systems
ERP is a long-term investment for companies, and projects have generally been slow and plodding. AI’s ability to cut through the noise and provide more actionable information can help companies realize their ROI. To that end, Idan says companies should choose an ERP system that is built on a mature cloud platform.
Having the right ecosystem and infrastructure in place is also critical. “A strong platform provides built-in tools for automation, integration and extension, allowing organizations to adapt as their business needs evolve,” he adds.
Those needs will continue evolving as manufacturing becomes more sophisticated to meet changing demands from customers. A strong ERP system, backed by AI, can help them realize that value.
“AI itself is the major shift in ERP, but the conversation is moving beyond buzz to real value,” Idan explains. “Over the past few years, many vendors rushed to add AI simply to claim their products had these capabilities. Now customers are starting to evaluate which solutions actually deliver meaningful results.”
What This Means for ERP Insiders
Business-process primacy must precede technology selection in AI-enabled ERP transformations. Rootstock’s acquisition of Praxis Solutions and appointment of Ohad Idan as VP of product signals that successful AI integration requires outcome-first design rather than capability-driven implementation. Manufacturers often fail by viewing AI as a standalone concept instead of starting with business processes and desired outcomes before considering technological support.
Implementation partner expertise gaps create adoption risks. Organizations delegating transformation responsibilities to partners without involving employees who understand internal workflows produce insufficient testing, rushed decisions, and lower participation because workers maintain full-time responsibilities alongside transformation expectations, ultimately undermining AI value realization.
Cloud platform maturity determines long-term AI extensibility. Salesforce-native architecture provides built-in automation, integration, and extension tools allowing organizations to adapt as business needs evolve, while vendors previously rushed to claim AI capabilities without delivering meaningful results, which is forcing customers to now evaluate which solutions produce measurable outcomes rather than marketing promises.
