The AI Era: Moving Beyond Tool-Centric Thinking to Win with AI Business Flows

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Last night, while chatting with a client over dinner, the conversation naturally gravitated towards the recently popular large models. Our discussion provided several insights.

Especially for small and medium-sized enterprises (SMEs), their initial approach might be a bit off.

AI: Stop Treating It Like Traditional Software

It’s crucial to understand that traditional software and AI are fundamentally different.

What’s the logic behind traditional software? You develop it, and as long as there are no bugs, everything’s fine, and you continue using it. But AI is something that needs to be constantly iterated and continuously evolved.

Therefore, the core of AI is the business flow, not a disposable tool. This is extremely critical, just as mentioned in the article AI Era: Stop Obsessing Over Technology, Understanding Business is Key to Dominating. Simply focusing on technology is no longer enough; it’s more important to understand how AI drives business. A McKinsey report on artificial intelligence points out that AI is reshaping business models across various industries.

For example, you develop a customer analysis tool, and it works well initially. But what happens a few months later when customer behavior changes and market trends shift? Does that tool need to be updated or iterated simultaneously?

Data Loop is King

More importantly, when you accumulate enough data, it can form a positive cycle, bringing more business opportunities. For instance, you can refer to the data security and application strategies mentioned in How SMEs Can Seize the AI Marketing Dividend: Pitfalls and Growth Strategies to build a secure and efficient data loop.

In this process, AI will become smarter and better at understanding users’ real needs.

In the Future, It’s About Whose AI is Smarter

I even have a bold hypothesis: when AI becomes ubiquitous, what we’ll be competing on might not be whose model is more advanced, but whose AI understands users better and is more adept at making money.

A smarter AI could lead to a tenfold or even a hundredfold increase in efficiency compared to others, directly widening the gap.

How to Build Your Exclusive AI Workflow?

So, the question is, how can you make your AI smarter?

Don’t worry; I’ve summarized several key points:

  1. Define Your Business Scenarios

Don’t try to achieve everything at once. Start with your most critical, pain-point business scenarios.

For example, if your sales team spends a lot of time organizing customer information daily, you could first create an AI tool to help them automatically extract key information and generate customer profiles.

  1. Continuous Data Feeding

AI isn’t built overnight; it requires constant “feeding” of data to learn and grow.

Therefore, you need to establish a comprehensive data collection system to ensure AI has access to the latest and most complete data. And continuous data feeding is essential.

  1. Embrace Change and Iterate Quickly

Market and customer demands are constantly changing, and your AI must keep pace.

Regularly evaluate the AI’s effectiveness and make adjustments and optimizations based on feedback. Don’t be afraid to start over; rapid iteration is key.

  1. Don’t Blindly Believe in General Large Models

General large models are powerful, but they solve common problems.

To truly implement AI, you need to build a customized AI workflow based on the uniqueness of your own business.

AI is Not the End, But the Beginning

Stop treating AI as just a tool; instead, view it as an continuously evolving work partner. Ultimately, AI should serve business growth. Only then can you gain the upper hand in the future AI competition.