{"id":13354,"date":"2026-03-17T06:00:22","date_gmt":"2026-03-17T13:00:22","guid":{"rendered":"https:\/\/wp-admin-prod.qad.com\/blog\/?p=13354"},"modified":"2026-06-26T13:22:54","modified_gmt":"2026-06-26T20:22:54","slug":"how-agentic-ai-is-turning-systems-of-record-into-systems-of-action-for-manufacturers","status":"publish","type":"post","link":"https:\/\/www.qad.com\/blog\/2026\/03\/how-agentic-ai-is-turning-systems-of-record-into-systems-of-action-for-manufacturers","title":{"rendered":"How Agentic AI is Turning \u2018Systems of Record\u2019 into \u2018Systems of Action\u2019 for Manufacturers"},"content":{"rendered":"

[vc_row][vc_column][vc_single_image image=”13355″ img_size=”full”][vc_column_text]Manufacturing is in the middle of a once-in-a-generation reset. Tariffs, reshoring and legislation like the CHIPS and Science Act are restructuring supply chains and driving billions in new investment.\u00a0<\/span><\/p>\n

Across the U.S., new plants are being built. But the pressure is just as real as the opportunity. Volatile demand, persistent labor shortages and a global market that favors whoever moves fastest.<\/span><\/p>\n

In a <\/span>recent Gartner piece<\/span><\/a>, QAD | Redzone CEO Sanjay Brahmawar explored why this moment demands a new kind of ERP. Gartner\u2019s Greg Leiter and Denis Torii echoed similar sentiments in the research note, <\/span>Innovation Insight: AI Is on the Cusp of Reshaping ERP<\/span><\/a>. Combined, the articles are a powerful indicator that the systems manufacturers have long relied on are fundamentally unprepared for the complexity they now face.\u00a0<\/span><\/p>\n

The solution isn\u2019t a better version of the same tool. It\u2019s a new operating model entirely.<\/span><\/p>\n

The Problem with Traditional ERP for Modern Manufacturing<\/span><\/h2>\n

ERP was never designed to make decisions; it was designed to track them. A transaction happens, the system records it. An inventory level drops, someone notices and reacts. A production schedule changes, someone rebuilds it manually. Every step involves a person in the loop, which was fine when manufacturing moved at a certain speed. It\u2019s less fine now.<\/span><\/p>\n

\u201cModern, agentic ERPs don\u2019t just preserve history\u2014they proactively influence future outcomes by initiating and executing next-best actions.\u201d shared Sanjay.<\/span><\/p>\n

The problem isn\u2019t a lack of investment or effort. It\u2019s simply that legacy ERP systems, especially those still running on-premise, weren\u2019t built to sense changes in real time or reason through options. The result is slower decision-making, reliance on manual processes and a disconnect between the speed of the market and the speed of the business.<\/span><\/p>\n

What Makes Agentic AI Different?<\/span><\/h2>\n

Agentic AI consists of autonomous software agents that perceive context, learn from patterns, make real-time decisions, and execute tasks with human oversight\u2014escalating when guardrails require. <\/span><\/p>\n

Humans define goals, constraints, and guardrails; agents execute within these boundaries and escalate exceptions for review, ensuring accountability and control.<\/span><\/p>\n

And that\u2019s significant, explained Sanjay, because agentic AI is so much more than a layer of intelligence. It\u2019s an operating model for how ERP works. Embedded AI agents can detect anomalies, simulate outcomes, recommend actions and even execute next-best steps autonomously, escalating only when needed.<\/span><\/p>\n

The Value of Agentic AI on the Factory Floor<\/span><\/h2>\n

The power of agentic AI is visible in several critical aspects of manufacturing operations. Supply chain management is one of the most immediate. AI agents can identify unusual supplier transactions and manage the initial resolution steps independently, empowering procurement leaders to focus on other important tasks.<\/span><\/p>\n

Agentic AI is also a game changer on the labor side, which is a massive opportunity considering the industry is facing a <\/span>3.8 million skills gap by 2033<\/span><\/a>. It can help with simple tasks like automating job posts and candidate sourcing, but also address a much larger challenge: preparing the next generation of manufacturing workers. Younger workers demand technology that feels as intuitive as the apps and devices they interface with every day, making outdated systems a recruiting liability. Modern, conversational AI interfaces are essential.<\/span><\/p>\n

On the plant floor, agentic AI can model multiple production scenarios, create new schedules in response to shifting demand and surface actionable insights from data that would otherwise sit in a dashboard waiting for human review. In an environment where keeping up with delivery demands and shifting customer expectations are key, agentic AI provides the competitive edge that modern manufacturers need.\u00a0<\/span><\/p>\n

For new facilities, the technology is especially transformational. Manufacturers using QAD\u2019s Agentic AI capabilities have gone live in 90 days. Traditional ERP implementations at greenfield sites have routinely taken six to eighteen months.<\/span><\/p>\n

Cross-System Agent Scenarios<\/b><\/h3>\n

The real power of agentic AI emerges when workflows span systems and teams. Instead of operating in silos, agents coordinate across ERP, MES, SRM, planning, and connected workforce systems to respond to events in real time \u2014 at the seams where execution typically breaks down.<\/span><\/p>\n

Scenario 1: Demand change triggers coordinated response<\/b>
\n<\/b> A customer demand change exceeds forecast. An agent:<\/span><\/p>\n