How Should Enterprises Deploy AI Packaging to Boost Market Competitiveness?

Answer Two Questions Before Deployment

Before deploying AI packaging, enterprises must first answer two questions. One is: what is the current most painful link? The other is: to what extent do you hope AI will solve it? If the answers are unclear, tool selection will go off track.

Common pain points include long prototyping cycles, frequent process rework, high material inventory, and slow quote response. Each pain point maps to different tools; introducing them blindly will become nothing more than a showpiece.

Evaluation Stage: Starting from Pain Points

The goal of the evaluation stage is to list the areas where AI can intervene. It is recommended to bring together four parties—design, process, procurement, and sales—to work through this together. Each department raises 3 to 5 specific questions.

Designers often raise questions about layout modification and element replacement. Process engineers care about prototype rework and process conflicts. Procurement cares about inquiry response speed and supplier price comparison. Sales cares about customer solution generation.

After sorting these out, prioritize them. For each question, evaluate whether it is frequent, repetitive, and governed by clear rules. Those that can be rule-based should be handed to AI first.

Pilot Stage: Start with a Small Closed Loop

The pilot stage does not recommend rolling out the full process. Choose a specific scenario for a small closed loop, see results within 3 months, and then expand. Common pilots include file pre-inspection, quotation generation, and process scheduling.

Take file pre-inspection as an example. After AI reads the design file, it marks the locations of process conflicts. The designer adjusts before sending the file to print, reducing prototype rework. This closed loop requires small investment and delivers visible returns.

During the pilot, evaluation metrics must be established. For example, the percentage reduction in prototype iterations, or hours saved in quote response time. Metrics must be quantifiable to avoid subjective judgment.

Expansion Stage: Progressing Scenario by Scenario

After the pilot proves effective, expand to adjacent scenarios. Once file pre-inspection is verified, it can be extended to material matching and cost estimation. Once quotation generation is verified, it can be extended to automatic generation of customer solution PPTs.

Expectations must be managed during expansion. AI models need historical data to feed on, and results may be unstable during the first 3 to 6 months. The team should reserve calibration time.

Data Security and Compliance

Data security is the issue most easily overlooked when enterprises deploy AI packaging. Design drafts may contain undisclosed product information, and customer solutions may contain business plans. When such data is uploaded to public cloud tools, the retention policy should be confirmed.

For projects involving customer confidentiality, on-premise deployment or private cloud solutions should be selected. A data desensitization mechanism should also be designed in advance to prevent unauthorized access by internal employees.

Team Capability Building

AI tools themselves do not create value; the people who use them do. After AI is introduced, the work content of designers and process engineers will change. They need to learn how to review AI outputs, how to adjust parameters, and how to judge the boundaries.

It is recommended to arrange 2 to 3 core personnel for in-depth training. Regular employees only need to use the standard functions. Power users are responsible for tool maintenance and scenario exploration.

Common Misconceptions and Pitfalls to Avoid

A common misconception is treating AI as a general-purpose tool. It excels at rule-based work; aesthetic judgment, brand storytelling, and the execution of complex processes still need people. Do not expect AI to replace core creativity.

Another misconception is buying everything at once. AI tools iterate quickly, so full-suite purchasing carries high risk. It is recommended to try by scenario first and expand licensing only after results are confirmed.

The third misconception is overlooking customer communication. AI-generated solutions should not be sent directly to customers. They should serve as internal drafts, polished by business personnel before going external.

The Rhythm of Continuous Optimization

AI models are not a one-and-done solution after launch. Enterprises should evaluate every quarter: which scenarios work, which are being bypassed, and which need to be redesigned. Applying AI is a process of continuous iteration.

After launching its internal AI tools, LeXiang documented feedback from each scenario. These documents in turn train the AI models, making the results increasingly aligned with actual business needs.