Are you confused between whether to choose AI automation or traditional? Or do you want to learn the differences between them? Or do you want to know when is the right to choose one? If these are your questions, then you are at the right place. Our detailed blog will help choose the best one for you. Let’s explore.
What is AI Automation?
AI automation refers to the use of artificial intelligence for automating business processes.
It uses software, coding, and configurations to automate manual processes and produce certain results.
The solution provided through AI automation addresses the above problems through the integration of artificial intelligence into business automation systems and knowledge databases.
Predictive AI algorithms are used along with generative algorithms to help organize and generate data to minimize human involvement in the most complicated processes.
Humans can also partner with AI to complete administrative work and relieve the cognitive burden of employees.
- Language understanding capability: they are capable of interpreting and producing natural human language, and not simply parsing data fields.
- Adaptive decision making during a series of steps that involve changing conditions.
- Interaction with third-party applications and systems to accomplish an objective from beginning to end.
- The potential for learning from new data and improving performance.
- Collaboration among themselves to perform compound actions.
What is Traditional Automation?
Traditional automation is any system that takes a defined sequence of actions and follows those rules to perform a defined task.
For example, you can create a series of tasks (or steps) in a program, and that program executes those tasks.
Robotic Process Automation (RPA) is one of the most well-known examples of traditional automation.
However, there are other types of systems that operate in this manner, including batch processing scripts, scheduled workflows and other automated systems.
They are used for moving data from one system to another based on a scheduler or timer.
Traditional automation works best when there is a high degree of consistency in the work being done.
- Logic based on rules: Each decision is made using an if-then-else logic
- Structured input dependence: It only works well if the inputs are structured
- Poor flexibility: If there is any alteration to the process, the whole thing has to be reprogrammed
- Consistency if confined to certain limits: It is very consistent as long as no deviation takes place
Key Differences Between AI and Traditional Automation
To get a better grasp of the differences between AI and traditional automation technologies, one should pay attention to their approaches to dealing with uncertainty.
Traditional automation is deterministic, which means that every time you feed it with the same input data, you get the exact same result.
While it makes traditional automation technologies consistent, it also prevents them from coping with scenarios that were not anticipated during programming.
As far as AI automation is concerned, they evaluate context, choose the best course of action, and respond accordingly. Their output can be different every time, but it is based on reasoning.
One should mention a third type of technology: intelligent automation, that falls somewhere in the middle..
It is based on RPA enhanced with some tools like OCR and NLP.
In turn, intelligent automation is less rigid than its predecessors.
Nevertheless, unlike AI agents, it operates within predefined limits, plans actions, maintains context across the entire conversation, and uses tools to accomplish goals.
Core Differences:
- AI automation is more flexible compared to traditional automation
- AI automation can seamlessly work messy and unstructured information whereas rule-based tools needs structured and clean inputs
- Traditional automation follow instructions whereas AI agents reason through the issues
- While traditional systems are limited to pre-written instructions, AI agents possess the reasoning skills to navigate complex problems.
- Instead of requiring constant manual updates like rule-based software, AI agents continuously learn and improve independently.
- Traditional automation is confined to rigid boundaries, whereas AI agents seamlessly adapt to fluid, evolving workflows.
Pros and Cons
Pros of AI Automation
- Handles unstructured data
- Learns and adapts
- Complex decision-making
- Hyper-personalized experiences
- Highly scalable workflows
Cons of AI Automation
- High implementation costs
- Biased, inaccurate outputs
- Needs massive data
- Unpredictable results
- High computing power
Pros of Traditional Automation
- Completely predictable results
- Highly cost-effective
- Lightning-fast execution
- Easy to audit
- Low computing needs
Cons of Traditional Automation
- Zero flexibility offered
- Cannot self-correct
- Structured data only
- High maintenance required
- No decision power
When to Use AI Automation
AI Agents get their place in the world by working with activities where conditions change regularly.
If you work with output that has no structure, where you don’t have a definable rule set to follow, or where your processes are changing too quickly for script development.
This makes it hard to keep up and then using rule-based automation to resolve your bottlenecks is going to continue creating them instead of eliminating them.
The frustration that was stemming from these bottlenecks has been the primary reason there has been such an explosion in the growth of AI for automating business operations.
The volume of time teams were wasting resolving exceptions, edge cases, and performing tasks that were too different to automate using traditional approaches to automation was extensive.
This has created a huge demand for AI agents to eliminate this frustration.
Examples of strong AI agent use cases include:
- Customer Service – Agents who understand what the customer is actually saying to them, are able to find the answer they are looking for and be able to resolve their question or issue without passing it along to a person all of the time.
- Document Analysis – Extracting key data from legal contracts, medical records and financial statements in any possible format.
- Sales Support – Sending outreach emails, qualifying sales leads and sending follow-up emails based on where each sales lead is within the purchase process.
- IT Helpdesk Support – Diagnosing technical issues based on a verbal description from a user and then providing step-by-step instructions on how to resolve the issue.
- Supply Chain – Identifying possible disruptions early and recommending or executing alternative shipping routes.
When to Use Traditional Automation
While traditional automation is not leaving the marketplace, it shouldn’t.
Simply put, traditional automation can be used for certain types of work quickly, reliably, and cost-effectively in a manner that current AI tools can’t replicate.
Rule-based automation is generally the best fit for work processes that are predictable, repetitive, and built on structured data.
For tasks such payroll processing and the generation of scheduled reports, you will probably not need an AI tool to complete these tasks.
A simple, deterministic system should be able to accomplish these tasks at lower cost and with less overhead than an AI-based system could.
Some ideal use cases for rule-based automation include:
- Payroll processing and financial reconciliation
- Automatic generation of reports on a scheduled basis
- Migrating data from one system to another when a consistent format exists
- Order confirmation and shipping notification processes
- Auditing compliance on standardized forms or documents
In regulated industries, where all decisions must be documented and be able to be recreated, traditional automation also has a governance advantage.
The logic built into traditional automation is completely transparent, the outputs are completely predictable, and there is no ambiguity about the reasons that made the automation system produce a specific output.
AI vs Traditional Automation: Which One is Better for You?
Most businesses will not pick one over the other; They will prefer both.
So the more appropriate question is “Which method suits which part of your operation?”
When actually going through whether or not to use AI agents or traditional automation, think through your workflows first.
Workflows that are stable, consistent, and high-volume in data are ripe for rule-based automation.
While workloads that include many unstructured inputs, frequent exceptions, or changing requirements may benefit from AI agents being used for business.
As time goes by, it has become clearer that the number of tasks available to autonomous AI agents is continually increasing.
In the past two years, tasks that were too difficult to automate are now able to be completed by the agents.
Businesses that begin to build up their familiarity with agent-driven technology now will be in the best position as that number continues to increase.
When trying to decide between the two approaches, consider these questions:
- Is the incoming data structured or unstructured?
- Is the workflow going to be stable or unstable over time?
- Will the task require judgement or just action?
- How much value will every decision made require an audit trail to be kept for each decision made?
- Are there any budgets or timeframes available?
Conclusion
In the end, we know that it’s not AI vs Traditional automation, but AI and traditional automation. To get the best out of your business, you need to combine both types of automation and go for the hybrid approach. It will ensure reliability and scalability. And for seamless operational flow, you need to hire AI developers who understand both and have experience in dealing with them.