Apacer is a global leader in digital storage solutions, with end-to-end capabilities across R&D, design, manufacturing and marketing, delivering competitive customized products and services to our customers. B2B industrial control products have long been our core business and our source of competitive strength. As AI applications become more widespread, one question continues to drive us: how do we respond faster and more precisely to the order requirements of customers around the world?
What needs to change first is the process, not the tool. The right sequence for enterprise AI is: operational goals → process review → AI tools. Skipping straight to tool selection tends to automate an existing inefficiency rather than remove it.
Take our customer order handling as an example. What looks like a single order is in fact an entire operational chain — receiving, interpreting, validating, handling exceptions, and finally passing through back-end systems and human judgment. Because order formats vary widely from customer to customer, this stage has historically been both time-consuming and labor-intensive.
As AI capabilities have matured and AI agents have drawn growing attention, the question for enterprises is no longer simply “Can AI do this?” It is a set of harder questions: How do we improve the operational outcome? Does the process itself need to be redesigned? Which tasks can be handed to AI? Which judgments must remain with people? And are the boundaries of authority, risk, and accountability clearly defined?
Working from those questions, we applied OCR (Optical Character Recognition) to extract data from the many forms an order can take — images, scanned documents, email text, web pages — and convert them into machine-readable digital text. To ensure accuracy, staff then perform a verification review; once the data is confirmed, RPA (Robotic Process Automation) transfers it into the ERP system to trigger the downstream workflow, reducing both processing time and the opportunity for error.
To date, this redesigned process has handled more than 14,000 order records, with parsing accuracy above 90%, order coverage of 75%, and a 30% reduction in manual processing time. But the greater significance of these figures is what they allow us to observe in live operations: once AI is genuinely in use, what actually changes in the process, in people’s roles, and in how the work is managed?
The numbers confirm that automated order recognition has moved beyond proof of concept and into real operations. For management, however, the question worth asking is not “How accurate is the AI?” but rather: Have operational outcomes actually improved? Which waiting periods create no value? Which steps consume the most labor? Which errors occur most often? And has people’s time been redirected toward higher-value work?
These are the questions that determine whether the sequence — operational goals, then process review, then AI tools — is actually being followed.
People’s time should move toward exception judgment, customer understanding, problem solving, cross-functional collaboration, commercial judgment and final accountability — the work that requires context and carries responsibility. As AI takes on more of the repetitive reading, recognition, organizing and initial validation, the role of our people shifts away from administrative data handling and toward these higher-value tasks.
That shift only happens if the process changes with it. If AI simply replaces “a person reading an order” with “AI reading an order” while everything upstream and downstream stays the same, the value created is usually limited. Meaningful AI adoption means redesigning the entire process so that high-confidence, low-risk work flows through automatically, leaving people’s time for exceptions and judgment. At that point AI stops being a point solution and begins to change how the enterprise works — from isolated tools, to connected processes, to enterprise-wide change.
What genuinely needs to be redefined, therefore, is not only the value of people, but the content of the work, the boundaries of each role, and the new division of labor between people and AI. Behind every AI transformation lies a parallel transformation in employees’ work, roles and capabilities.
Mature enterprise AI is not an accumulation of isolated projects. It is the process of turning individual cases into a reusable, governable, measurable and scalable enterprise capability — an ongoing cycle that runs as follows:
The vision we are working toward is one in which AI absorbs more of the repetitive work, while people are placed where understanding, judgment, communication and responsibility matter most. When an enterprise commits to continually redesigning the relationship between people and machines, AI moves beyond being a tool and becomes part of how the business is run and how the organization evolves — creating collaborative value and competitiveness together. That is the goal we are working toward, and AI transformation begins with every function and every colleague.
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