Try the Research Edition First, Then the Standard Edition: Skip a Detour

Many enterprises tend to go "all in" when applying AI, hoping to deploy a deeply customized system from day one. The result is often high investment, long cycles, and unsolved problems. A more pragmatic path is to start with a "research edition" — a lightweight, small-scale trial — evaluate the outcome, and then decide whether to move to the "standard edition". This way, you can avoid unnecessary detours.

Four Levels of Mindset, One Sentence Each

Our acceptance of new technology usually goes through four stages, each of which can be summarized in a single sentence:

• Curious: What can AI do for me?
• Exploring: Try it in a non-critical scenario, without expecting immediate returns.
• Applying: Embed AI into real business processes to solve concrete problems.
• Integrating: AI becomes the underlying capability of the organization, deeply aligned with workflows and data.

Enterprises need to first recognize which level they are at, then plan the next move — rather than rushing to the final stage.

Evaluate Before Commercializing, Let Facts Speak

The purpose of a "research edition" is to provide a low-cost environment for evaluation. Run a pilot within a limited scope, collect user feedback, and measure results. Only when these signals prove that the solution is viable and valuable should you enter the commercialization phase. This not only prevents wasteful spending but also gives the team enough time to learn and adapt.

Avoid Heavy Customization from the Start

Customization often means high costs and long coordination time. If even a standard solution has not been fully validated, rushing into customization is like building a house on soft ground. A more reliable approach is to first use the standard edition to run the core process, note the gaps, and then customize accordingly. By then, your requirements are based on evidence.

Skip the Detour

"Try the research edition first" is not a compromise; it's a smarter strategy. It lets you gain experience in a controlled setting and supports decision-making with real data. Once your judgment is clear, moving to the standard edition or building custom features comes naturally. In the AI era, skipping one unnecessary detour is already a significant advantage.