Industrial automation companies are facing a change worthy of vigilance: It’s not that customers no longer come to you, but they ask AI first.
In the past, the customer acquisition path for industrial automation companies was relatively stable.
When customers have production line renovation needs, they usually look for suppliers through exhibitions, recommendations from industry friends, search platforms, introductions from old customers, bidding platforms, industry association resources and other channels.
What companies have to do is also very clear: participate in exhibitions, build official websites, maintain old customers, place search ads, and accumulate sales leads.
But now, a new entrance is quietly emerging.
More and more people in charge of procurement, production, and information technology will ask AI first before actually contacting suppliers.
They will not directly search for the name of an automation company, but will ask “What are the industrial automation solution companies?” “How to do factory production line automation transformation?” “How to choose non-standard automation equipment manufacturers” “What is the relationship between MES systems and industrial automation transformation?” “Which intelligent manufacturing service providers are recommended?”
These questions look like knowledge consultation, but are actually close to pre-purchase research.
In other words, the place where customers first learn about the industry, screen suppliers, and determine the direction of solutions may no longer be just search engines, but various mainstream AI platforms.
For industrial automation companies, this is not a simple change in traffic channels, but a forward shift in customer acquisition logic.
In the past, companies competed over whether customers could see you after searching for you; now, companies compete over whether AI will put you on the candidate list before customers search for you.
Many industrial automation companies are very powerful, but are rarely mentioned by AI. This is a very typical industry contradiction.
The enterprise has rich project experience, strong equipment capabilities, and a solid engineering team, but its presence in AI is very weak.
The reason is not that these companies are unprofessional, but that their professional capabilities have not been converted into knowledge assets that AI can understand.
The public content of many industrial automation companies still remains in the era of traditional official websites.
The home page of the official website says “Focus on industrial automation solutions”, the product page says “Provide non-standard automation equipment, robot integration, and automated production line transformation”, the case page says “Successfully completed an automation project for a certain customer”, and the news page says “The company participated in a certain exhibition”.
This content may be understandable to human customers because customers can further understand the company’s capabilities through offline communications, sales introductions, and project plans.
But for AI, these contents are far from enough.
What AI really needs to know is: what types of automation scenarios does the company specialize in, what production problems it has solved, what industries it has served, how its solution is different from that of ordinary equipment suppliers, how the project results are verified, and whether it is suitable to be recommended to customers who are upgrading their production lines.
If these questions are not answered openly, clearly, and structuredly, it will be difficult for AI to establish a reason for recommendations.
In other words, it is not that industrial automation companies lack capabilities, but that their capabilities are not “understood” by AI.
When AI recommends industrial automation companies, it is essentially looking for interpretable professionalism.
Many bosses will think that AI recommendation companies mainly depend on brand size.
But in the industrial automation industry, size is only one factor.
AI is more concerned about which customers this company is suitable for, what scenarios it is good at, what problems it has solved, whether there are any cases to prove it, and what the differences are compared with ordinary equipment vendors, software vendors, and integrators.
They are also industrial automation companies, and different expressions have completely different values for AI.
If a company only says “We are a professional industrial automation solution provider with rich experience”, this is more like a promotional slogan.
On the contrary, if the company can clearly state that it “serves auto parts, electronic manufacturing and equipment manufacturing companies for a long time, and provides automated production line transformation and intelligent manufacturing integration solutions around production line rhythm optimization, equipment networking, robot loading and unloading, visual inspection, MES docking and other scenarios”, it will be easier for AI to understand the company’s capability boundaries, and it will be easier to quote and recommend it in related issues.
In order to enter the AI recommendation system, industrial automation companies need to complete four transformations: First, product capabilities must be transformed into scenario expressions. Don’t just say “we do robot integration”, but clearly explain which companies are helped to solve loading and unloading, handling, testing, packaging, palletizing and other scenario problems; second, project experience must be transformed into case assets. Don’t just say “project successfully delivered”, but explain what problems customers encounter, how the company solves them, and what ultimately happens.
Third, industry experience must be transformed into a knowledge system. Don’t just write about “intelligent manufacturing trends”, but build knowledge content such as production line transformation methods, equipment networking knowledge, MES docking guides, and non-standard automation pitfall avoidance. Fourth, brand positioning must be transformed into AI labels. Do not frequently change positioning expressions, but form clear and stable labels, such as auto parts automation transformation service providers, electronic manufacturing visual inspection solution providers, etc. The clearer the label, the easier it is for AI to recommend.
In the future, the competition among industrial automation companies will be answered by AI in advance.
In the past, competition occurred during the project review stage, when customers issued demands and several suppliers compared plans, prices, and delivery capabilities.
But in the future, competition will become increasingly advanced. Customers may already ask AI for relevant recommendations before they send out their demands. If your company is not included in AI’s answer, you may not even get the first opportunity to communicate.
Industrial automation companies can now start with five things: first, test how mainstream AI introduces itself to see if AI can accurately describe the brand; second, test whether AI recommends itself, and do tests around common industry procurement issues; third, organize real project cases and transform them into public, understandable, and searchable case assets; fourth, establish industry scenario content and produce content around real customer problems; fifth, continue to monitor changes in recommendation rates and gradually improve the AI cognitive system.
