Automate Your Business Process — Quick, Easy, User-Friendly

 

No matter what industry your business or company is in, automation improves processes, eliminates a lot of paper, errors, and wasted time.

Don’t make the simple mistake of thinking that business process automation is only for large enterprises! Smaller businesses can definitely benefit from countless types of business automation. Whether you already run a business, want to start one, or work for a large corporation, automation is something everyone should be thinking about.

It could be something that revolutionizes your business! Here are 10 quick reasons why you should automate your business processes:

1. Stay Competitive

2. Reduce Error

3. Increase Speed

4. Increase Productivity

5. Build Volume

6. Eliminate Steps

7. Format Compliance

8. Reduce Waste

9. Save Money

10. Expand Capabilities

And to top it off, here are 6 different ways automation can help improve your business:

1. Improve Process Efficiency:

Every business is consistently looking for a way to effectively improve efficiency. If you truly want to become efficient, automation is something you will have to consider. There are so many different ways you can utilize automation to improve processes. It’s all about trying to make sure you don’t waste time on unnecessary process steps. These are the kinds of things that humans are worse at than machines.

Machines can do things over and over again and not make an error or tire. Humans tend to slow down and work at different speeds throughout the day. With that being said, some processes can be made much more efficient by using automation. For example, creating a workflow process using mobile forms or creating an automatic marketing campaign.

2. Reduce Costs:

Saving time and most importantly money is at the top of every business owner’s mind. Manual tasks are performed one at a time and are by nature slower than automated tasks and processes. Sure, there are some tasks that cannot be automated, but a surprising number of them can be. Automated tasks eliminate time-intensive steps like manually filling out customer orders and in turn mailing them, and they reduce paper and ink usage as well because they’re done electronically. Savings in terms of time and resources add up significantly over the course of a year. Think about how much money you spend on paper, ink, and envelopes, and an employee’s time spent on this particular activity in a given year.

3. Ensure Quality and Consistency:

Business process automation ensures processes are carried out with consistency and accuracy because users are stepped through it in a prescribed way every time. That means no steps are inadvertently omitted or forgotten, nobody can cut corners, and you can expect the results of a process to meet standards every single time. This can be valuable in heavily regulated industries where processes must be tracked and audit trails are required since business process automation software can automatically generate audit trails and record real-time analytics.

4. Speed up Processes:

It only makes sense that automated processes are faster than standard manual processes! Think about a process like business purchase orders. When done manually, the result is a pile of paperwork that could potentially be lost from passing around, and time spent writing down line items and information. With business process automation, however, purchase orders can be submitted electronically, and employees can create more in less time. You can even make the process mobile-friendly so travelers can submit purchase orders on the go.

5. Greater Accuracy/Reduce Errors:

In any business, it’s a good idea to ensure that things are accurate. The best way to deal with errors is to prevent the first. When accuracy becomes a problem, your products will become variable and inconsistent. That’s not what any business wants. There are all kinds of examples of machinery that can help you to automate processes and improve accuracy and consistency. Business process automation eliminates paper forms and can make it so blanks must be completed with the right type of information before a form is submitted. Fewer errors mean less time spent tracking down and correcting them. When you do this, it will be much easier to keep your customers satisfied because you can be confident that they are getting top products and services. That could help you to retain customers, as well as generate word-of-mouth sales. Don’t underestimate how important that could be to the long-term success of your company.

6. Easier to Track Data:

With business process automation, data can be collected every time a process happens. That means you are able to analyze your data in real-time. That data can be made into reports that track the metrics that are important to your business. How many employees completed safety training? What percentage of fleet vehicles was reserved on a given day? How many customer services calls come in on Mondays compared to Thursdays? The right business process automation software lets you track the metrics that matter for continual business improvement.

In Conclusion:

Business process automation is for organizations of all types and sizes. It doesn’t require custom programming or a big IT investment. In fact, you can create automated forms and workflows even if you know nothing about programming. Charter Global makes it easy, with an intuitive drag-and-drop form designer. Your electronic business forms can look exactly like your paper forms for a quick learning curve. And forms can easily be connected into workflows that are efficient, less error-prone, and easier to track.

reference :

https://www.charterglobal.com/3-ways-to-automate-your-business-to-increase-efficiency/

https://www.charterglobal.com/reaching-your-q4-goals-with-automation/

https://www.charterglobal.com/giving-the-green-light-with-qa-automation-testing/    

Machine Learning (ML) vs. Artificial Intelligence (AI) — Crucial Differences

Artificial intelligence and Machine Learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.

Although these are two related technologies and sometimes people use them as a synonym for each other, but still both are two different terms in various cases.

On a broad level, we can differentiate both AI and ML as:

Artificial Intelligence vs. Machine Learning: Required Skills

Because artificial intelligence is a catchall term for smart technologies, the necessary skill set is more theoretical than technical. Machine learning professionals, on the other hand, must have a high level of technical expertise.

Artificial Intelligence Skills

People pursuing a career in artificial intelligence must have a foundation in:

1. Algorithms, and techniques for analyzing them
2. Machine learning and how to apply techniques to draw inferences from data
3. The ethical concerns in developing responsible AI technologies
4. Data science
5. Robotics
6. Java programming
7. Programming design
8. Data mining
9. Problem-solving

Machine Learning Skills

People pursuing a career in machine learning must have a foundation in:

1. Applied mathematics
2. Neural network architectures
3. Physics
4. Data modeling and evaluation
5. Natural language processing
6. Programming languages
7. Probability and statistics
8. Algorithms

The Future is now with AI and ML 

So, by now, you’ve learned the basic differentiating factors between ML and AI. Machine learning uses past experiences to look for learned patterns, while Artificial Intelligence uses the experiences to acquire knowledge and skills, then applies that knowledge to new scenarios.

It’s clear that both AI and machine learning have valuable business applications, empowering companies to respond quickly and accurately to changes in customer behavior and solve critical business problems.

As the adoption of AI and ML become more commonplace, namely predictive analytics and data science will see a massive uptake in virtually all industries across the marketplace.

Reference Link:

https://www.charterglobal.com/machine-learning-vs-artificial-intelligence-whats-the-difference/

https://www.charterglobal.com/ai-machine-learning/

RPA: How Easy Is It To Get Started?

 In the previous blog, I have discussed the basic principles of Robotic Process Automation (RPA) and explored why the technology provides a tremendous opportunity for insurers. In this post, I’ll discuss how to get started implementing an RPA solution in your company.

The first thing to understand is that unlike traditional technology investments, the best way to get started in RPA is by jumping in with both feet. At Charter Global, we recommend starting with an affordable pilot program that can deliver real-world results in about four weeks.

The pilot begins by choosing a process to automate and identifying the underlying systems and platforms used to complete the task. The business owners then work with us to capture the entire process from start to finish, with each input, review, decision, and output identified and documented.

While this is taking place, we look at the hardware and software tools available and select the one that is the best fit for your organization. Because configuration does not require that the underlying systems and applications be modified, the configuration is relatively straightforward and efficient. The process documentation already captured above is entered into the software to replicate the previous manual workflow’s touchpoints and decisions.

Once the software is configured, new processes can be easily added and modified by business users, without the assistance of IT resources.

After the usual testing and output checks, the outcome of the four-week pilot is a working process automation system that can be explored and modified as you go, responding to identified goals and opportunities and providing real-world metrics and outcomes.

As this series of blog posts has outlined, RPA offers insurers a tremendous opportunity to reduce costs and improve service levels without a huge investment of either time or money. Maybe it’s time for your company to implement a pilot and see how it works for you.

Ready to get started with RPAs in your business? Set up a free consultation with the Charter Global automation team today!

How machine learning is different from conventional programming language?

The term ‘machine learning’ is not new and it has even become a buzzword for modern technology. On a daily basis, we’re all using machine learning from simple Google maps and Google assistants to complex self-driving cars and automatic language translation. This modern programming approach has revolutionized almost every sector including IT, finance, cybersecurity, and business.
 
Although both machine learning and conventional programming language are separate categories under the programming language category. Conventional programming language on the other hand has been around for quite some time.

Machine learning and conventional programming language are two different approaches to computer programming languages that yields different outcomes or expectations.

By definition, Machine Learning is a field of software engineering that enables PCs to learn without being unequivocally modified. AI shows PCs the capacity to take care of issues and perform complex errands all alone. Much of the time, issues unraveled utilizing AI depend on the PC's learning experience for which they wouldn't have been settled by ordinary programming dialects. Such issues can be face acknowledgment, driving, and ailments' conclusion. With regular programming language, then again, the conduct of the PC is coded by first making a reasonable calculation that keeps predesigned sets of rules.

In other words, machine learning depends on a rather different form of augmented analytics where input and output data are fed into algorithms. The algorithms then create the program. On the contrary, conventional programming languages involve manually creating programs by providing input data. The computer then generates an output based on programming logic. For instance, you can easily predict consumer behavior through trained machine learning algorithms.

Another significant contrast between machine learning and conventional programming language is the precision of expectations. Conventional programming language relies upon calculations inside an assortment of info boundaries. Machine Learning then again gathers information dependent on past occasions (verifiable information) which construct a model that is equipped for adjusting freely to new arrangements of information to create solid and repeatable outcomes. This sort of self-learning models can't be worked with customary programming dialects.

However, with machine learning, there are no restrictions on the number of data sets and models that can be generated since the built models are capable of learning independently. As long as you have enough processor power and memory, you can use as many input parameters and data sets as you see fit and you would generate reliable and repeatable outputs.

At Charter Global, we help organizations gain better control of their consumer data with machine learning so they can market their products smarter and scale faster than their competition.