ben brown

July 7, 2023

Business

Top Process Automation Tools For Businesses in 2023

ben brown

July 7, 2023

Business

Process automation is a big topic on everyone’s mind in the face of AI. What jobs will be overtaken by computers? How can companies cut costs by leveraging business process automation tools (BPA)? How can I run with technology instead of away from it?

There are a lot of questions and speculation, but the ultimate question is; are companies putting their money where their mouth is when it comes to integrating AI? Well, research is showing us that of about 2600 companies surveyed globally, more than 94% believe AI is critical to success, almost 80% have begun implementing a variety of AI solutions, and 82% found a boost in job satisfaction from AI tools.

If you’re curious about the direction of AI's future with software, watch this video.

Integrating AI is something companies must do, but how they execute this integration will vary. Of the use cases for process automation, the biggest one we’re seeing is end-to-end visibility, which essentially is what allows companies to track their entire workflow from start to finish. Why would they want to do that? It’s quite simple: by having end-to-end visibility, companies can identify bottlenecks and proactively address them.

Among many reasons for incorporating process automation, efficiency and cost-cutting are two of the most important factors. Let’s use the example of a plastics manufacturing company that uses inventory management, production scheduling, and quality control software as its main time-consuming, repetitive tasks. BPA in this case would automate all of these tasks, and now the staff’s role shifts its focus to oversight, creativity, and decision-making. For most companies today, that is the goal; streamline and optimize operations end-to-end.

In 2023, there are several tools that are most commonly used across industries to make this happen. Here are some to pay attention to:

1) UiPath

UiPath is a robotic process automation (RPA) platform that takes over repetitive tasks (as most of these tools do) at scale. It has a visual drag-and-drop interface for designing automation workflows and integrates with various applications and systems.

For instance, a human resources department can use UiPath to automate the employee onboarding process, where the software automatically generates employee contracts or updates employee records in HR systems, and notifies the relevant stakeholders, reducing manual effort. Now you have a system that can be scaled.

2) Pega

Pega is a platform that combines business process management (BPM) and intelligent automation. Pega is a comprehensive platform that offers a unified view of the entire business process and ideally leads companies to a solution for end-to-end automation. For example, a retail organization can use Pega to automate its order management process. The platform can allocate resources, track inventory levels, and then adjust production schedules based on current demand as well as forecasted demand.

3) Blue Prism

Blue Prism's RPA software can automate rule-based tasks (Data entry, processing invoices, QC, etc) across different departments. For instance, think of a healthcare organization, that’ll use Blue Prism to automate something like claims processing, where the software first validates claims, then checks for errors, and initiates payment processes.

Blue Prism is best utilized for repetitive tasks that ideally can be scaled. For instance, we used examples from healthcare, but email marketing is another common task for companies that would benefit from scalable RPA.

4) Appian

This is a low-code development platform that’s meant for companies to design and automate workflows. Appian connects with data sources and external applications, supporting standard protocols and APIs like REST, SOAP, and JDBC, which makes integration easy. This is what’s going to attract something like a big manufacturing company that would use this to speed up their approval process or integrate it with systems for inventory management.

5) Automation Anywhere

Automation Anywhere is one of the top RPA platforms that are great for enterprise automation. This is another platform with a drag-and-drop interface (easy to use) and end-to-end process automation. This is one that a FinTech (or other various large-scale entities) could leverage to ultimately reduce manual effort and scale the operation.

The Value In BPA

Think about a 30-year multi-billion dollar business with tens of thousands of employees. How can they leverage process automation across the board? Ultimately, it comes down to recognizing what can be optimized and what’s in the best interest of the product or service's long-term sustainability. An easy one is Amazon— if tomorrow they decided to get rid of warehouse workers and fully leverage RPAs like automated guided vehicles (AGVs), and intelligent warehouse management systems, their inventory management would streamline.

These are the kind of gaps that companies need to be looking for in the coming years. It’s less about what you do and all about how efficiently you do it.

The Takeaway

Finding gaps in your current processes can be difficult without a thorough analysis and understanding of your operations. This is where AI consulting comes in. By leveraging this level of expertise, companies will identify latent pains and receive the most suitable automation solutions for their specific needs. It’s not a cookie cutter; it’s a comprehensive approach tailored to the unique challenges and goals of each business.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

ben brown

June 12, 2023

Business

8 Things to Know When Building a Reactive Machine Learning System

ben brown

June 12, 2023

Business

Every day that a business isn’t working to differentiate itself from its competitors is a day it’s going backward in its industry. As sophisticated IT infrastructures become the minimum standard and with data-driven decision-making fueling innovation, businesses must be proactive about finding the technology that gives them a competitive edge.

One of the big topics right now when it comes to gaining this edge is integrating Reactive Machine Learning, which to say the least, can be a game-changer for the businesses who utilize it effectively.

What is a Reactive Machine Learning System?

Instead of telling you all the things a reactive system is, it’s better to tell you what it isn’t:

Reactive is the opposite of batch learning where a system takes one big dataset and then uses that to generate its insights and make predictions. Instead, it can process real-time data and respond immediately.

Reactive learning is not a deliberative agent which focuses on analysis and reasoning before taking action. A reactive system instead uses predetermined rules or patterns that are meant to make the system act quickly.
Reactive systems are not useful in complex decision-making processes such as long-term forecasting. Instead, something like fraud detection would benefit from a pre-determined system protocol.

This covers the basics. Machine Learning models are made from algorithms that analyze data, recognize patterns and outliers in that data, and then make predictions or decisions.

Where Does it Fit in a Business?

The evolution of the internet has exceeded comprehension looking back 20 years. With this, user standards have risen as well which has made integrating machine learning models essential for businesses to meet the standards of their industry.

3 Ways a Business Might Utilize Reactive ML:

Automating processes: Think about a chemical testing laboratory with a vast amount of highly sensitive data to be managed. Reactive ML can be used to prevent errors by automating the analysis aspect. As a result, the laboratory cuts down its processing time and increases the efficiency of instrumentation.
Energy consumption: Take a utility provider, for example. Reactive ML can optimize how much energy is consumed using real-time data to determine the appropriate adjustment. In addition to this, it can implement demand response programs by taking past data and identifying patterns to make recommendations on energy usage.
Personalizing recommended content: This is what streaming services like Netflix or Disney+ use in the “suggested” section, or social media platforms for the type of content someone is fed. In this case, ML algorithms will be used to analyze user data and recognize patterns that determine what they’re fed.

How to Build It

There’s a lot that goes into building a reactive ML system and the specifics will always vary just as with the construction of any complex IT platform. What businesses must do to carry it out effectively can be understood with these basics principles:

Gather data: Collect relevant data that you want to train and validate the reactive ML algorithms on. Make sure that the data is accurate and diverse, and that it fits in the problems domain. Then, clean and preprocess that data to remove noise and handle missing values.
Train the algorithms: Choose the ML algorithms that you think best fit the problem at hand. Train the algorithms using your gathered data, adjust hyperparameters and then evaluate performance. Consider using techniques like cross-validation to ensure the system is well-rounded and that you’ll avoid overfitting.
Integrate the system: Once you’ve developed the necessary software infrastructure to integrate the reactive ML system, connect components. This may involve building pipelines, creating ingestion and processing mechanisms, and implementing decision-making modules based on the trained data we mentioned previously.
Test and evaluate: This is an essential piece of this puzzle. Use the appropriate evaluation metrics to assess the accuracy and effectiveness of the system. Then fine-tune the system based on the results and make the necessary iterations as you go (which leads to the next point).
Monitor and maintain: Consistently monitor the performance of the reactive ML system in your production environment. In addition to this, update the model periodically as new data becomes available or when business requirements change. And lastly, regularly assess the system's impact on organizational outcomes and make adjustments as needed.

Again, these are very baseline as every project is going to have unique variables and every business is going to have unique goals. With that said, the most important part of digital transformation is what comes next, so with that in mind, consider this:

How scalable is the system? Whether you’re using a distributed computing framework, cloud services, or anything of the sort, the system needs to be designed while thinking about the volume of data and user requests it will need to handle.
What are your requirements for processing speed? If your reactive ML system needs to respond in real-time to user requests or traffic, processing speed becomes a major concern. To ensure it fits your ideal framework, you can optimize algorithms and hardware or use a distributed framework such as Apache Spark. Again, monitor the changes you make and keep looking for opportunities to refine.
How does it fit with current systems? When introducing reactive ML into your current systems, you have to consider how it will fit and interact with the infrastructure. APIs or connectors that enable data exchange are what you’re going to need if you want interoperability with existing systems.

The Takeaway

Finding components to build a framework that will support a business long-term is a never-ending quest. Doing what other companies are doing without an in-depth analysis of how the things you want to introduce will serve you long-term could set you back. It’s best to consult with an organization who’s overseen various projects

Written By Ben Brown

—

ben brown

May 15, 2023

Business

The KPIs Your Business Needs When Integrating Machine Learning

ben brown

May 15, 2023

Business

In the age of Artificial Intelligence (AI) and Machine Learning (ML), the race to integration is a real thing. As businesses continue to find ways they can leverage these technologies, the importance of quality performance cannot be overstated. For this reason, KPIs are essential…

The two most important assets of AI and ML are:

How they benefit the service delivery/experience to the end-user
How they enhance back-end functionality

These two components are what put these technologies on a pedestal to a point where, should they fail in the future when dependency peaks, it will be catastrophic.

Where KPIs Come In

So we know that KPIs were created for the very reason to measure and evaluate the performance and impact of digital systems, but we should also note that KPIs alone are not sufficient for ensuring your system's success. Again, we’re preaching the “drawing board” approach where businesses outline and focus on their unique goals because it’s not a one-size-fits-all situation.

Continuous improvement is the name of the game, and while KPIs cannot directly guarantee the success of your systems, they play a crucial role in monitoring and guiding your progress.

Machine Learning - User-Friendly Industry Disruptor

Of course, these systems are user-friendly, but when it comes to leveraging these tools from a business standpoint, certain things cannot be ignored when competing in an AI-hungry industry:

1. Machine Learning Takes on Your Objectives:

The integration of Machine Learning initiatives is meant to simplify your current processes and enhance results. When you consult with a team of AI architects, they’re not going to show you an entirely new way to do your job; rather, they’re pinpointing the more efficient methodology for your current process.

For instance, let’s look at sales: a salesman typically has to make around 50 contacts before they get a single person who’s interested. Let’s say he does 50 cold emails per day and 50 cold calls, landing about 10 interested prospects per work week.

If the company then implements AI and ML algorithms that can analyze data and recognize the behaviours and characteristics of successful clients, the system can generate lead scores or rankings that indicate the likelihood of a lead converting into a sale. The sales team can then automate lead scoring and prioritize their efforts based on the highest-ranked leads, instead of manually sifting through and wasting touchpoints with unqualified prospects.

2. Predictive Analytics and Pattern Recognition Are Your Bread & Butter

The healthcare sector is a great example of a well-rounded execution through AI and ML strategizing. Hospitals are extremely data-intensive in nature, and that data is highly valuable when it comes to things like patient care, operational efficiency, and medical research as a whole.

Something really interesting that predictive analytics and pattern recognition have done to revolutionize this landscape already is early diagnosis. Basically, by identifying patterns in a patient's data, Machine Learning can outline potential risks with that patient at an early stage.

Now the healthcare sector is certainly something that deserves a much more thorough analysis, but this gives a sense of the standards that this aspect of your system needs to be performing at.

3. Agile Implementation

This has been a staple in software development for decades as a means to implement solutions as quickly and efficiently as possible. For a company just beginning its journey with AI and ML, this is a great method to familiarize yourself with these technologies and to determine how cohesive they are with your current processes.

Implementing Machine Learning systems and Artificial Intelligence is highly transformative in an organization and is a lot more comprehensive of a stage than planning. Agile is a great approach for this reason as it allows adaptation to happen gradually, and the system can be refined consistently as the needs of its users evolve.

So, we’ve highlighted some key factors that businesses need to keep in mind when preparing for AI and ML integration, but now we have to identify what specifically can ensure your systems are going to consistently perform up to standard.

10 KPIs for Machine Learning:

Evaluation Metrics

Accuracy: How accurate are the predictions made by the Machine Learning model compared to actual outcomes?
- Accuracy is a crucial measure of the system's performance and its ability to make correct predictions. It provides an overall assessment of the model's effectiveness.
Recall: How well does the recall of the Machine Learning model evaluate the proportion of true positive predictions compared to all actual positive instances in the dataset?
- Recall measures the ML model's ability to capture all relevant positive instances, avoiding false negatives which leads us to the next point.
Precision: How does the precision of the Machine Learning model assess the proportion of true positive predictions?
- Precision is significant in cases where the cost of false positives is high. It evaluates the system's ability to make accurate positive predictions, minimizing false positives.

Performance Metrics:

F1 Score: Balances precision and recall, providing a single metric that represents the model's overall performance.
Mean Absolute Error (MAE): Measures the average difference between predicted and actual values, which will indicate the model's average prediction error rate.
Mean Squared Error (MSE): Computes the average squared difference between predicted and actual values, which will emphasize larger errors than MAE would.
Root Mean Squared Error (RMSE): Takes the square root of MSE, then provides a metric on the original scale of the targeted variable.
R-squared (R2) Score: Indicates the proportion of the variance in the target variable that can be explained by the model's predictions.

Evaluation Techniques:

Precision-Recall Curve: This plots the trade-off between precision and recall at different prediction thresholds, and helps to set an optimal threshold for the ML model's performance.
Receiver Operating Characteristic (ROC) Curve: Illustrates the trade-off between the true positive rate and the false positive rate at various classification thresholds, which ultimately aids in the selection of an appropriate threshold.

Moving Forward

The next step with your system is to experiment and evaluate. Planning is crucial but it can’t provide you with results. Focus on your unique goals. Performance evaluations might involve running simulations or real market tests and then analyzing the data you collect to dictate which metrics best suit your system.

Whatever it looks like for you, consider the nature of your system, the industry you're operating in, and the outcomes you want to achieve.

Written By Ben Brown

—