Manufacturers can use artificial intelligence to modernize decades-old operations, address critical labor shortages, and deliver measurable returns within 90 days-without first replacing their legacy ERP systems, according to QAD CEO Sanjay Brahmawar. In a July 30 interview, Brahmawar described the current environment as a "watershed moment" for the industry, where AI enables companies to leapfrog traditional digital transformation projects that have historically stalled due to cost and complexity.
Leapfrogging tech debt with AI agents
Brahmawar, who joined the nearly 40-year-old ERP provider in March after leading Software AG's cloud transition, said QAD's manufacturing-first platform is now embedding AI directly into workflows through its Champion AI orchestration layer. "AI allows humans to make decisions, and it operates within a controlled setting," he said. That controlled environment is critical for highly regulated sectors like automotive, medical technology, and food and beverage, where traceability and compliance are non-negotiable.
Rather than asking manufacturers to rip out existing systems, QAD has introduced eight AI agents that support procurement, production, and supply chain operations. Brahmawar emphasized that success will be measured by business value, not agent count. "It is not how many agents we create, but are we providing value to our clients," he said.
Budgeting for AI: no new money, fast ROI
During a recent executive roundtable, three themes emerged. First, manufacturers are funding AI by reallocating 5% to 7% of existing IT budgets. "There is no extra money to spend on AI," Brahmawar said. Second, companies are demanding rapid proof of value. "If the AI doesn't deliver ROI in 90 days, they are killing them," he said. This puts pressure on software partners to deliver embedded capabilities that require minimal implementation effort.
Third, many manufacturers recognize they cannot compete with tech giants for AI talent. Instead, they are relying on trusted vendors to provide AI that respects data isolation and governance. Brahmawar noted that customer data remains separate from the large language models powering the AI functions, ensuring proprietary production data never trains foundation models.
Addressing labor shortages without replacing workers
With roughly 500,000 unfilled U.S. manufacturing jobs and projections of over 2 million openings later this decade, AI is emerging as a productivity multiplier rather than a job killer. "The whole idea that AI is taking away jobs is not happening in manufacturing," Brahmawar said. He outlined three options for manufacturers: retrain an aging workforce, invest heavily in robotics, or use AI to make existing workers more effective. "The third area is the most viable today," he added.
For management teams, this means AI investments should augment experienced employees, capture institutional knowledge, and improve operational efficiency-not cut headcount.
Why this matters for management
Brahmawar's message is a clear signal to manufacturing leaders: AI can deliver near-term operational gains without requiring a multi-year digital transformation. The 90-day ROI expectation and the practice of funding AI from existing budgets mean that managers must be prepared to evaluate AI proposals with concrete metrics and kill projects that don't perform. The shift also demands rigorous governance frameworks to protect proprietary data and maintain compliance.
As companies look to AI for Operations and AI for Management, the ability to partner with vendors that embed AI directly into manufacturing workflows-rather than offering generic tools-will be a key differentiator. For manufacturing executives, the AI era is less about wholesale technology replacement and more about strategic, incremental modernization that delivers measurable results fast.
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