QAD is positioning its latest manufacturing software push around a simple proposition: companies do not need to rip out their existing systems before they begin seeing value from artificial intelligence. In a blog published in August 2026, the company argued that manufacturers can start with AI agents on top of current ERP or legacy environments, use the results to build a business case, assess readiness for modernisation, or move directly to QAD Adaptive ERP if they are already prepa...
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The pitch centres on ChampionAI, which QAD describes as an agentic layer that works inside operational workflows rather than replacing the systems that manufacturers already run. According to the company, the platform supplies the infrastructure, large language model services, security, governance and integrations needed to support its AI tools, while leaving existing data, controls and operational structures in place.
QAD says the practical benefit comes from role-based agents, or “Champions”, built for specific functions such as procurement, sales and sourcing. These agents are designed to work alongside staff, surfacing information, flagging exceptions and reducing the administrative grind that often slows down manufacturing operations. In QAD’s framing, the idea is not to automate people out of the process, but to give teams faster access to the information they need to act.
The company claims the Procurement Champion can monitor supplier order books, flag issues for buyer review and take on repetitive follow-up work such as chasing confirmations, tracking certificates and matching invoices to purchase orders. QAD says that users can recover several hours a day and resolve supplier problems far more quickly. Its Sales Champion is intended to help prepare responses to customer RFQs by pulling together pricing, delivery terms and internal records into a draft quote pack, while the Sourcing Champion is meant to shorten the RFQ-to-selection cycle by assembling comparisons and supplier responses for human review.
QAD is also using customer examples to support the case. It points to Sonic Manufacturing, a Silicon Valley electronics manufacturing services provider, which it says uses a lean purchasing team of five to manage 61 suppliers and more than 57,000 purchase-order lines. After deploying the Procurement Champion, senior buyer Patti Humphreys said the agent gave her back about two hours a day and cut manual post-PO administrative work by as much as 80%.
“The Agent is telling me what’s going on,” Humphreys said. “It alerts me to my problems instead of me finding them later when I need the part.”
The broader argument is that manufacturers can begin with AI even if they are not ready for a full ERP overhaul. QAD says customers can start with an insight-led engagement to benchmark operations and model return on investment, move through technical-readiness planning, or proceed directly to a migration to QAD Adaptive ERP. The company describes the approach as a way to avoid disruptive, all-or-nothing transformation projects.
That message reflects a wider shift in enterprise software. Deloitte has argued that ERP systems are evolving from static systems of record towards more modular, agentic models in which AI agents act as an interface over a flexible core. QAD’s strategy appears to fit that trend, while keeping its emphasis squarely on manufacturing execution rather than broad enterprise automation.
The company has been expanding that theme for several months. In November 2025, it announced that QAD Adaptive had been updated with Champion AI, presenting the combination as an “action-first” ERP model built for manufacturing. In May 2026, QAD said the same agent-based approach could help food, beverage and consumer products manufacturers deal with volatile supply conditions and labour shortages by turning plans into execution more reliably.
For QAD, the commercial message is clear: manufacturers should not wait for a future systems project before tackling day-to-day inefficiencies. The company is betting that the fastest route to proving value is to embed AI into current workflows first, then decide whether a broader platform change is worth making.
Source: Noah Wire Services
