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Finding the first application takes work. Filtering AI's vast array of business applications can be overwhelming. This 4-Step Guide was created to help you find the perfect project to start AI process optimization in your company. We've also created a Google Sheet to help you evaluate and list the processes in your company.
What Is Intelligent Automation (IA)?
Intelligent automation (IA), also known as robotic process automation, combines robotic process automation and artificial intelligence. IA is a form of automation that has been given superpowers by AI and other cognitive technologies. The goal is to make robots more intelligent to think for themselves and do things quickly.
Intelligent Automation Has Many Benefits
IA can make your life much easier. Here are some reasons why you should implement it:
- Savings In Employee Time And Effort: Automation can reduce a seven-hour manual job into five minutes. What if an automation tool could also collect data, analyze it, and make complex decisions? This can accelerate processes exponentially, allowing your employees to focus on higher-value tasks.
- Reduces Errors: Robots are far more accurate when programmed correctly (sorry, humans). The human brain is not as structured and logical as automated tools. AI allows IA to use huge amounts of data to make informed decisions.
- Identifies Opportunities: AI can quickly identify and surface opportunities for your business. Use AI tools such as ChatGPT and Notion AI for brainstorming innovative ideas. You can even integrate AI tools with your existing workflows using CISIN's AI Integrations.
- Increases Customer Satisfaction: Your customers will be happier if your delivery process is more intelligent and streamlined. Imagine chatbots that can understand your customers, surface relevant resources and answers, and know when to connect them with a human representative. It's a dream come true.
- Identifies And Patches Security Vulnerabilities: IA can be a fast process when given enough processing power. It can scan software to identify and correct potential security risks faster than humans.
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IA Components That Are Technological
IA combines RPA and technologies such as AI, optical character (OCR) recognition, and intelligent character (ICR). This is a quick overview of IA technologies.
- Robotic Process Automation: RPA Bots can automate repetitive and routine tasks such as data extraction, transfer, or transferring. This will save you time.
- Artificial Intelligence: AI software mimics the human mind and has improved in recent years. It can make decisions quickly, analyze data quickly to make accurate predictions, and learn from past choices.
- Computer Vision: OCR or ICR tools can "read" images and scanned documents. They can then convert them into text.
- Business Process Management (BPM) BPM streamlines processes to increase company efficiency. IA uses BPM strategies by using process mining. This involves diagnosing and improving inefficient or broken business processes.
Examples Of IA Applications
We have a lot of technical terms to throw at you. Let's make it up by speaking (without jargon, this time) How to apply it practically in a business environment.
- Automate The Writing Of Emails By Connecting To Tools Such As OpenAI: You could set up a Zap to have AI create a few emails to customers and save them as drafts. Spending 20 minutes trying to figure out how to phrase an email is over.
- Analyze Leads: You can also use AI to score and analyze leads. GPT can be taught to differentiate between high and low priority lead types by giving examples. Then, you can link GPT with your lead management software to have it perform the lead analysis.
- Adjust Production: The IA can reconfigure manufacturing equipment to produce more products or less, minimizing the chances of shortages or surpluses. The smarter robots are telling the automation robots when and what to do.
- A/B Test: In a matter of seconds, IA can compare two versions of assets product prototypes or CTAs and give insight as to which one is more effective. IA was designed to replace this type of time-consuming process.
What Is The Difference Between IA & RPA?
RPA can be described as a less intelligent IA. RPA automates repetitive and manual work with bots. IA uses cognitive technologies such as AI and computer vision to automate tasks that previously required human thought. RPA can be implemented as a bot that automatically categorizes subscribers to a newsletter based on their subscription method. IA might involve using subscriber interaction data (clicks, bounce rate, etc.) Add suggestions to the CRM of a company to inform future newsletter content.
How To Launch Your IA Strategy
It's crucial to implement intelligent automation strategically. Only pull the rug from under your staff with a plan.
- Get Top Management Buy-In: Don't just list the generic benefits of IA, but explain how you can use it in your organization to increase efficiency and achieve tangible ROI.
- Take Baby Steps: Once implementation becomes feasible, implement IA for everything at once. Automate the time-consuming tasks in your organization first before moving on to the "it would be nice" category.
- Adopt A Mindset That Puts Automation First: Ask yourself if IA can complete a task or project before assigning it to someone. You'll find that AI is improving every day. IA will make your job easier and your company more profitable in many ways.
- Use Learnings To Implement: IA implementations may sometimes go differently than planned. It's okay, but document your learnings to prevent repeating the same mistakes.
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Discover The Processes That AI Can Automate In Four Steps
Step 1: Know The Basics
You and your team must first understand what AI can offer you. You also need to be aware of its requirements. Take sure you know the following:
- Learn how AI algorithms work.
- What information is required?
- What are the problems that can be solved (e.g., an image classification system).
Artificial intelligence is a powerful tool that can assist you in many ways. However, it's not a panacea. It would be best to consider AI whenever your task meets these four criteria.
Does It Repeat Itself? AI should be used to automate tedious tasks, not to make complex decisions. A trained AI is capable of intelligently responding to a refund request. Sending a creative, unique email to a client is difficult.
Are The Rules Of A Task Fixed Or Not? You don't require Artificial Intelligence if you can describe a task using logical rules a machine understands. Instead, use an integration Platform as a Service or robotic process automation.
A machine could understand, for example, the following rule:
- If the email includes "job application" as a trigger (Task) send it to HR.
- When you see the words "job application," it is a good idea to email HR.
Only applications that contain these exact words will be accepted. Here, RPA stops, and intelligent automation starts. AI can identify patterns in job applications and then make a judgment based on that. RPA and iPaaS work only with triggers that are clearly defined.
Can A Human Expert Decide In Just A Few Moments? AI is excellent at automating decision-making with clear inputs and outputs. Don't automate a decision if a company employee needs to consider it carefully.
This is why there are better fits for intelligent automation for hiring decisions, which consider both qualitative and quantitative factors as well as gut feelings. Filtering through a database to find relevant CVs could take only a few seconds for a person per document, and a machine is likely capable of doing this.
Can You Easily Collect The Data Needed To Train A Machine? A machine-learning model isn't a super-brain capable of answering all your questions independently. Even if the task meets the criteria, training the model on a large amount of data is still necessary for it to function properly. The algorithm needs to be given examples of inputs (think of images of dogs) and the outputs it should predict.
The amount of data you need to label depends on the problem you're trying to solve and your desired accuracy level. A good rule of thumb is to have 100 images per category (e.g., You can predict dog breeds, but the more, the merrier). Hire AI developers from CISIN to get dedicated AI development services.
Step 2: List All Possible Uses Cases
Your team and you now know which applications to search for. You can then start to identify possible use cases. It would be best to get a good overview of the repetitive processes in your company before you can find the ideal starter use case. Stay calm about the criteria listed in step 1. We will cover them later. Brainstorm with your team members from various departments. Be creative. Think about AI-enabled "shortcuts," which are not yet available but could enable processes to be broken down into tasks.
You should be fine with the practicality of your solutions at this stage. Instead, imagine an ideal world. Imagine that you have the necessary resources and tools, and your employees can adapt easily to change. List any repetitive processes you can think of, regardless of their complexity. This can be done in the "Process Overview" sheet.
Start By Thinking About The Following:
- How do you share information within your organization?
- What is the best way to store information (e.g Email attachments)?
- What are email attachments?
Research:
- How do you generate new information?
- How does this information get filtered and analyzed for insights?
Customer Engagement & Service:
- What communication is repetitive and why?
- How can you learn from and handle customer feedback?
Supply & Logistics:
- How to source materials and choose suppliers.
- How can you avoid errors and track the flow of goods?
Quality Assurance For Your Platform/Product:
What are the ways to check for defects in products and services?
- How can you make sure that online content adheres to your guidelines?
- What could you do to improve your offer? Make it faster and more accurate.
Finance & Accounting:
- How do you create and update reports for invoices, expense reports, accounts payable, receivables, and other documents?
- How can you detect fraud and suspicious activity?
After Listing The Processes You Want To Improve:
- Think of ways to do so.
- Consider how AI can speed up your decisions by providing suggestions and automating entire processes.
- Think of creative ways to work around the problems that AI automation might solve.
It may be useful to break the original set of processes down into smaller parts. Focus on the connections between subprocesses, where small decisions are required. Make sure to identify each decision that requires manual labor. The same goes for updating a document, renaming files, or forwarding an email.
Read More: Is Artificial Intelligence Technology Solutions Business A Good Investment?
Step 3: Evaluate The Automation Potential
It's time to rank your list of use cases. This can be done in the "Potential Calculator" part of the Google sheet. You want to map out use cases based on their impact and feasibility so that you know where to begin. It would be best if you first determined the impact that the (sub-processes) you have chosen will have on your business. The overall impact for us combines financial impact (through savings in time) and criticality. (Alignment with company strategy, improvement to product/ service.)
What Is The Direct Impact On Financial Terms? What Is The Quantitative Impact?
To calculate the direct financial impact, we estimate how often an activity is performed daily. We then multiply this number by the time it takes to complete the task. Multiplying this value by the total number of people who perform the task will give us the daily cost. We compare all direct financial impacts and rank them from 1 to 5.
What Is Criticality? What Is The Qualitative Impact?
Your qualitative judgment will be crucial. What value does this process bring to your company? We use the following scale as a reference:
- 5 = Could become a competitive advantage.
- 4 = Will increase customer satisfaction.
Follows An Industry Trend:
The product/service will be slightly improved. Changes will not be noticeable. It would be best if you also determined whether cognitive automation is feasible. It's time to eliminate sub-processes:
- If you need to think deeply or use creativity, split up the task. If this doesn't work out, continue to do it manually. This task is a key part of your work.
- Rules can be clearly defined (If you have this requirement, then it is recommended that you implement RPA or iPaaS). It would be best if you were prepared to deal with cases that the RPA does not pick up.
Assess the practical feasibility of your idea.
- You can easily collect the data you need if you already have it.
- The automation could be adapted to current workflows, or the redesign could be completed quickly. Will integration be seamless, or will employees need extensive training?
- The automation could be integrated into current systems. Is the software and hardware you have now compatible with your automation vision?
- Do you know what it looks like Can you imagine the end solution? You know what tools you can use and how to connect them.
- Think about the time it would take to implement a solution (consider all stakeholders). Are they willing to change? What will it take for you to find a solution?
Step 4: Create A Plan For Action
It is now time to create an action plan. Creating a showcase solution that can be used as a springboard for future AI development solution is important. Start with projects that are easiest to implement (Feasibility > 3) in order to get quick wins.
When deciding which use case to begin with, consider synergies, start with existing automated processes in your company that are similar, or draw data from the same source. If you have to start over, choose the easiest solution to implement.
Once you have several candidates who are quick and easy to implement, then you should consider which one could be the best. When your first solution has been implemented, keep the other, more valuable use cases in the backlog. This will reduce future friction and resistance.
We hope you've gained a good understanding of your daily business processes and how they are interconnected if you follow our guide. We also hope you've developed an analytical eye for spotting opportunities to automate and optimize AI wherever they occur.
What Does Robotic Process Automation (RPA) Mean?
Intelligent automation based on RPA can be defined as integrating digital robots into an organization to perform tasks previously only considered possible by human employees. RPA, when combined with innovative technologies such as Artificial Intelligence (AI) and Machine Learning, can augment human behavior to perform highly advanced tasks. RPA is classified into two types: RPA automates routine office tasks, and RPA automates intelligent processes using artificial intelligence.
RPA has many benefits, including:
- Less Code: RPA doesn't require a developer. Drag-and-drop functionality in user interfaces makes it easier to integrate non-technical employees.
- Rapid Cost Saving: Because RPA reduces employee workload, resources can be allocated to other tasks requiring human help, resulting in progress and ROI.
- Increased Customer Satisfaction: Because robots, chatbots, and digital workers can work round the clock, this can lead to a higher rate of customer satisfaction.
- Improved Morale Of Employees: By removing repetitive and high-volume work from employees, RPA allows them to concentrate on thoughtful, strategic decision-making. This work shift has a positive impact on employee happiness and well-being.
- Improved Accuracy And Compliance: Since artificial intelligence algorithms can process RPA robots to mimic specific workflows, commands, and commands, so human error is significantly reduced. RPA is a great asset for businesses that require precision and compliance.
- Can RPA Be Implemented In Legacy Systems? Legacy services will only need to be reconstructed partially to implement RPA. RPA does not disrupt underlying architectures, as bots only operate at the presentation layer.
Applications Of Artificial Intelligence To Robotic Process Automation
We know that robotic process automation is a powerful tool. It is constantly growing and complements other innovative technologies, such as machine learning or artificial intelligence. These intelligent bots, when combined, can mimic any human interaction. They can be used by any industry.
Explore how these capabilities could transform how businesses do business and create new opportunities for collaboration, growth, and increased productivity. AI and Machine Learning (ML) strategies based on AI have been used in recent years to implement complex work environments, such as data analysis, sentiment analysis, and self-learning, without human interaction.
Machine learning trains robots to deal better with data by stimulating the rational learning concept. Artificial intelligence (or AI techniques) can be used to make bots that mimic human behavior and make precise decisions. (Most commonly seen in Chatbot Development Services). ML/AI services allow bots, advanced computers, and chatbots to understand problems and leverage solutions like application interconnectedness and predictive analysis.
Artificial intelligence robots can also be trusted to analyze and extract data to classify, associate with, optimize, group, or identify patterns. Given AI's potential, intelligent automation is constantly improving its digitalization capabilities. This makes it more useful and relevant across multiple industrial sectors.
AI In Robotic Process Automation (RPA), Benefits
Enhance Security
A robot based on AI could be used to execute an analysis-based process. This eliminates the need for manual data entry or data collection by humans, who could misuse the information. AI automated bots can be very helpful when handling sensitive data, such as police files, administrative matters, or financial services because private pins and other credentials do not need to be revealed to a person.
Cognitive AI And Predictive Analysis
RPA can intercept exceptions, as AI can learn how to plan, discover and make decisions using predictive analytics. RPA can then match these patterns and events with expected or unexpected opportunities and threats. Smart automation bots can now make decisions and react to changing customer behavior and market trends. Chatbot Artificial Intelligence development services use this self-responding technology. Chatbots can now understand human emotions with the help of artificial intelligence. Sentiment Analysis is the name of this process. Chatbots can respond accurately based on emotions. They can determine whether a user is happy or angry.
Big Data: Promoting Its Use
Artificially intelligent bots that are self-aware could be used for collecting and organizing inconsistent data across disparate systems, allowing it to be used in big data analytics. AI-based Robotic Process Automation, for example, could be used to automate the ordering and provisioning services via a cloud interface.
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The Conclusion Of The Article Is:
You can use your IA initiatives as you brainstorm to help facilitate the robot takeover (kidding mostly). Where do you begin? Use the ChatGPT plugin for thousands of apps to automate and connect from ChatGPT. ChatGPT allows you to manage your databases, send emails, and more.
