Automation
AI + Automation
Bank
Insurance
Logistics
We have heard a lot about the concept of agent automation, but there are very few real landing scenes.
Almost all enterprises are thinking: counting, customer service, reimbursement... But agents that can really put into production and truly bring business value tend to focus only on these repetitive and well-defined tasks.
Agent automation landing: the gap between ideal and reality
Agent automation landing: the gap between ideal and reality
- The truth of intelligent workflow
This leads to a question: is the agent we are doing now an intelligent workflow?
The answer is: not much.
The real intelligent workflow is not only to answer questions or generate reports, but also to truly execute tasks, handle exceptions, write back results, and find optimization space during execution.
If an agent can only understand the process, but cannot operate it, it will always be a bystander, not an engine of enterprise productivity.
Automated execution is the core, which is the lifeline of intelligent automation and the key to whether an enterprise can obtain organizational perception.
RPA: the key bridge for advanced agent
RPA: the key bridge for advanced agent
- RPA: a bridge from bystander to executor
After understanding this, many enterprises will naturally think of RPA.
RPA is the most direct means for agents to perform tasks across systems. It allows agents to trigger operations, move data between systems, and complete approval and entry. This may seem simple, but it is this ability that upgrades agents from "bystanders" to real performers.
Because of this, RPA is often regarded as one of the landing tools of intelligent workflow. It is not the ultimate goal, but a bridge to realize automatic execution.
- Realistic challenges in landing
In the actual implementation process, enterprises also face many practical challenges. Most managers are well aware of the potential value of agents and large models, and have imagined various application scenarios, but it is not easy to land.
System docking problem: how to deeply integrate the large model with the existing business system?
Customized development cost: behind it often means a lot of R&D investment
Lack of integration solutions: most domestic business system manufacturers have not yet provided out of the box large model integration solutions
Therefore, enterprises start from peripheral and low-risk scenarios and gradually explore intelligence. This is not only a realistic constraint, but also a pragmatic evolution path.
The way to break the situation: collaborative efforts of process optimization and intelligent automation
- Step by step practical wisdom
In this context, we find that if enterprises can gradually pilot and learn while doing in a controllable way, it will be easier:
Understand the real solvable pain points of agents
Identify key links in cross system automation
Gradually accumulate their own intelligent workflow capabilities
This is not a rash advance, but a step-by-step exploration idea: respect the existing resources and rhythm of the enterprise, and constantly verify, adjust and optimize in practice. This approach can not only enable enterprises to gradually establish the landing ability, but also help them find a practical leading path in a complex environment.
At the same time, there is a reality that needs attention: whether the process itself is reasonable and efficient determines the value of automation.
If the process itself has redundant, inefficient or even repetitive links, even if RPA can replace manual work, it can only improve the surface efficiency, and the perception and decision agility of the organization will not be enhanced synchronously. This is why many managers have reservations about traditional automation tools.
- Effective path: process optimization+Intelligent Automation
Therefore, the more effective path is: process optimization+Intelligent Automation
Comb the process first: find redundancy and reconstruct logic
Re Intelligent Execution: cooperating with RPA through agents
Realize real qualitative change: let organizational decision-making and executive ability evolve synchronously
The core value of an agent is not "how smart it looks", but whether it can actually execute, perceive problems, discover optimization space, and promote continuous process improvement.
In the practice of China and even the world, this is particularly obvious: no matter the size of the enterprise, complex processes and system islands are the norm.
In the next five years, the core competitiveness of enterprise digitalization may no longer be limited to the tool itself, but whether the organization can act like an agent - perception, judgment and action.
Only those enterprises that can organically combine automated execution, process optimization and intelligent decision-making can truly achieve a leap in efficiency, agility and strategic insight.
In the next five years, the core competitiveness of enterprise digitalization may no longer be limited to the tool itself, but whether the organization can act like an agent - perception, judgment and action.