Agentic AI
Automation
Accounts Payable
Order Management
Transport & Logistics
Customs Clearance
Banking and financial services
Insurance
Transport & Logistics
AI office tools have suddenly increased this year, and there is no threshold for ordinary people to use them. It used to take two hours for a person to do a thing. Now it takes ten minutes; You can get something you couldn't do before. After the employees used it, their working ability really jumped up.
If you work from home, this time probably belongs to you. If you are in the company, the boss will not say "rest the rest" because you saved an hour with AI today. More often, the efficiency is improved, and the workload follows. There is nothing to complain about. That is how the enterprise operates.
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Personal AI adapts to small and medium-sized teams
Personal AI adapts to small and medium-sized teams
Value is more focused on single person level
Who is most willing to pay for AI?
A boss will be very willing to spend money on AI, a sales director will be willing, a programmer will be willing, and a lawyer and consultant will also be willing, because they are close to the money brought by efficiency. Small companies are particularly obvious. In a company with 20 people, the boss manages sales, twoorthree people in finance and several people in operation. When the sales person buys an AI, does customer research, writes a plan, and sorts out the data, he can immediately see whether it is useful. He can sign more orders a month, and the money will come back.
A boss will be very willing to spend money on AI, a sales director will be willing, a programmer will be willing, and a lawyer and consultant will also be willing, because they are close to the money brought by efficiency. Small companies are particularly obvious. In a company with 20 people, the boss manages sales, twoorthree people in finance and several people in operation. When the sales person buys an AI, does customer research, writes a plan, and sorts out the data, he can immediately see whether it is useful. He can sign more orders a month, and the money will come back.
They only see one thing: can this thing make me spend less money or earn more money.
So it is not accurate to say that employees will not pay for personal efficiency. The key is: who is paying for efficiency and who can get the benefits brought by efficiency. Small and medium-sized enterprises may be a better market for personal Ai - the company is small, the decision-making is short, and the boss himself is ROI. When one person becomes stronger, the whole company will become stronger.
Core pain points of model enterprises
People speed up, but the process doesn't work
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Once the company grows up, the situation will change.
A company with hundreds of thousands of people uses CRM for sales, ERP for finance, MES for production, WMS for warehouse, SRM for procurement, and OA, HR and tax systems outside. Some are standard products, some are self-made, and some are no one dares to move, but they still need to be used.
After an order comes in, it is confirmed by sales, approved by contract, approved by price, checked by credit, and then entered into ERP, followed by purchase, production, inventory, logistics, invoicing, and collection.
Everyone here may be very busy, but everyone is busy, which does not mean that this thing runs fast.
After using AI for sales, it is a good thing to write email from half an hour to five minutes. But if the contract still needs two days, the price still needs to be approved by someone, the data in ERP still needs to be recorded again, and the inventory needs to be checked by another person, and finally the customer can only get the goods after three days - the 25 minutes saved from sales is not that big for the whole enterprise.
When people are fast, things may not be fast.
The real trouble for large enterprises is organization. No matter how fast a person is, he has to wait for another department. No matter how fast a department is, he has to wait for another system. No matter how fast the system is, it may be stuck in an approval node.
Therefore, after the enterprise reaches a certain scale, the efficiency is not only the problem of "how fast people work", but also the number of people, systems and times it needs to be confirmed.
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From RPA to enterprise agent
Automated upgrade
RPA used to do very practical things: log in to the system, download files, copy data, record it, and regenerate it into a result. This thing seems to have little technical content, but there are a lot of such jobs in enterprises.
However, the real business is not action by action.
When an order comes in, the machine needs to know: who is the customer? Is the price right? Is there any credit problem? Do you have any inventory? When will it be delivered? Which approval should I take? What to do in case of special circumstances? Then we have to go to different systems to finish things.
In the past, these things were driven by people one by one. With agent, it can move forward by itself in theory. It first understands things, then checks the enterprise knowledge, determines the next step, and then calls API, RPA or other systems to execute. If it cannot judge, it will find someone.
At this time, automation is taking over a period of work, which is really worth spending money in large enterprises.
Future digital employees, who have identity and authority, know what they can and cannot do, what systems they can call, what they have done, and who can find out if something goes wrong. To put it bluntly, the enterprise dares to give part of its work to it. And it doesn't need to be designed completely according to people's posts.
People are organized according to their posts, but machines are not necessarily. For example, posting is a kind of work, reconciliation is also a kind of work, and invoice processing is also a kind of work. They may be scattered in several positions, but the machine does not have to say "I am an accounts receivable accountant, so I can only do accounts receivable".
A digital employee can be specially responsible for a certain type of work, and then these jobs are linked by workflow.
In the past, one person was responsible for a process, but in the future, one person may be responsible for several digital employees and deal with those things that can not be done by the machine by himself, which will really change the organizational mode of the enterprise.
Enterprises of different sizes
Two differentiated landing paths
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However, not all companies should develop a set of complex enterprise agents.
However, not all companies should develop a set of complex enterprise agents.
A company with more than a dozen people may be doing this for themselves. A company with 500 people is beginning to encounter the problem of cross departmental processes. For a group with 50000 people, the gap between systems itself may be a huge cost.
There is no unified answer to how much the same AI is worth in different enterprises.
So there will not be only one winner in the AI market. Some people do personal assistant, some do team cooperation, some do enterprise agent, and some do workflow automation. They all have broad markets, because different enterprises have different problems to solve.
Personal AI solves the ability problem of a person, while enterprise automation solves the organization problem of a group of people.
A sales department uses AI, which used to manage only 50 customers, but now can manage 150- this is the ability to enlarge.
If his customer information, CRM entry, internal coordination and quotation approval do not need to be promoted by himself bit by bit - this is organizational amplification.
The former makes people stronger, and the latter makes organizations lighter.
Small companies may follow the first path: the boss uses it first, and then the sales and financial departments use it. When a person gets stronger, the company gets stronger.
Large companies are just the opposite: start with an order process, or pick the most troublesome process from purchasing, finance, and customer service, and connect the agent, RPA, and API with the original system to see if it can really move forward. One runs through, and then slowly expands.
The last two roads may still meet. Because enterprises need both stronger people and lighter organizations.