Business

The KPIs Your Business Needs When Integrating Machine Learning

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:

  1. How they benefit the service delivery/experience to the end-user 

  2. 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

  1. 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.

  2. 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.

  3. 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:

  1. F1 Score: Balances precision and recall, providing a single metric that represents the model's overall performance.

  2. Mean Absolute Error (MAE): Measures the average difference between predicted and actual values, which will indicate the model's average prediction error rate.

  3. Mean Squared Error (MSE): Computes the average squared difference between predicted and actual values, which will emphasize larger errors than MAE would. 

  4. Root Mean Squared Error (RMSE): Takes the square root of MSE, then provides a metric on the original scale of the targeted variable.

  5. 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:

  1. 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.

  2. 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

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.

 
 

Artificial Intelligence: The 5 Simple Components of AI Software Systems

How does artificial intelligence actually work? How does chatGPT work? Let’s be honest: everyone is talking about AI changing their industry, but not many really know the specifics of how that will happen. We all know that machines can perform at a faster rate and higher consistency than people. We can look at this and say “Okay, it’s likely this can be automated… and that can be augmented… but is my boss going to be the one controlling the system?”

At the moment, based on what’s publicly available, AI tools need to be managed by people who know how to input, what to take from an AI-generated output, how to train algorithms, and then monitor KPIs and adjust methods accordingly. Again, we’re heading down a path of skepticism and unanswered questions, so let’s simplify this.

Many products and services use artificial intelligence, but if we were to look at them from an outsider's perspective, how they work can be very confusing. “How does this chatbot put these ideas together?” “How can this create an elaborate picture based on a few words I provided?” “How am I getting product recommendations that are so relevant to me?” These are all common and valid questions. Well, we can answer those questions for you.

AI operates and depends on 5 simple components: learning, perception, problem-solving, reasoning, and language understanding. Software engineers use these pillars as a framework when they create solutions that lead to products that can perform a seemingly endless amount of tasks.

Of course, there are programming languages and software frameworks going on behind the scenes but that is useless without these factors at play.

Breaking Down The 5 Components

Ok, so we know that Artificial Intelligence is able to perform the way it does because of learning, perception, problem-solving, reasoning, and language understanding. But these are all the same fundamentals of how people function— what is it about the machine mind that makes them special?

Learning

We’re taught from a young age that in order to thrive, you have to enter a learning environment. The same principle applies to artificial intelligence and machine learning - trial and error, getting comfortable with certain skills, introducing new skills, and adapting to different contexts are all part of how AI systems develop. While people learn through experiences, machines learn through algorithms and data which undergo continuous innovation.

An example of the learning phase for AI could be training a neural network by teaching it a big dataset meant to classify images the system is fed. The machine would need to learn by continuously being exposed to various images and their respective labels. Over time, the system will adjust its parameters so the accuracy can improve. 

Perception

We know perception as how we view the world and various topics or situations. In the case of AI systems, perception is the ability to understand information and interpret that information. This involves not only being able to recognize different datasets— whether it be image, text, or sound— but knowing what to do with that data. 

When AI systems perceive data, they need to provide valuable insights from that data. For instance, a natural language processor (NLP) allows AI to understand human language. So if you think about when you’re using Chat GPT, the NLP lets the system know what you’re saying which it is then able to provide you with insights. 

Problem-solving

The simplest explanation is that AI uses and manipulates data to find solutions. Many will argue that the most appealing aspect of AI is its ability to provide solutions and do so rapidly, but what makes that happen?

Many key factors and mechanisms at play dictate the problem-solving ability of artificial intelligence to provide solutions rapidly. The one to pay attention to is the immense computational power of AI systems. Take for instance the utilization of specialized hardware, such as graphics processing units (GPUs) or tensor processing units (TPUs), which are designed to handle large-scale computations and accelerate AI algorithms. These hardware advancements enable AI systems to process massive amounts of data all at once, significantly speeding up the problem-solving process.

Reasoning

AI relies on software programs that can draw conclusions and make inferences entirely on their own. These inferences can be categorized as either inductive or deductive reasoning.

With inductive reasoning, conclusions are made based on specific observations and patterns, often referred to as a “bottom-up approach”. On the other hand, deductive reasoning is a funnel, taking broad information and cutting it down to find specific conclusions.

Inductive reasoning has been particularly useful in creating AI products and systems that are consistent with delivering results for specific problems. Tools like Grammarly are a great example, a cloud-based grammar and writing assistant that uses inductive reasoning to analyze patterns in the text and provide the appropriate suggestions.

Language Understanding

We touched upon it briefly but let's dive deeper into it. AI systems need to be able to interpret language to effectively interact with users. But language understanding involves more than just recognizing words, it’s understanding the context, intent, and meaning behind that language. 

This brings us back to NLP which AI systems use to understand the structure and meaning of sentences, extract relevant information, and derive insights which then produce the output to the user.

What’s Most Important For You To Know

Every aspect of these 5 pillars contributes to the materialized AI application. Like any project, while these components can build the system, the premise needs to be focused on a specific use case. Yes, businesses need to have an AI strategy to survive the rapid change of the business landscape but to rely solely on its implementation is irresponsible. 

An AI strategy needs to align with the business, for instance, IKEA built a visual experience for consumers curious about their products. This use case addresses a specific customer need and enhances the overall shopping experience, something that will allow them to remain ahead in the future. How can your company do the same?

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.

 
 

How AI Architects Can Save Your Business

Artificial Intelligence is here for one thing: to move the world forward using technology. Like the introduction of the internet and computers, it will take over jobs, but it will also create opportunities for new jobs and redefine existing ones. The idea that work needs to be labour intensive, demand immense creativity, and require various levels of collaboration is challenged at the sight of AI.

Again, as everyone knows by now, this is a tool— a series of tools— that will change the way people approach their work in ways we cannot fully predict. What can be predicted is that machines will execute tasks based on what they’ve been told to do and will continue to learn and adapt based on the data and feedback they receive. When it comes to running a business, it’s critical to find ways to incorporate this now as it truly is a matter of staying up or going way under.

Data Management

The foundation and overall value of AI tools come down to their ability to manipulate and manage data. With businesses re-evaluating their strategies and looking for opportunities to incorporate AI solutions - the most important aspect to consider is how these tools can handle their organization's information. First, let’s gain some perspective on the level of data that needs to be managed. 

The average enterprise manages about 10 Petabytes of data - for reference, 1 Petabyte is equal to 20 million large filing cabinets or even 500 billion pages of standard print text. Now, something this data-intensive won’t be managed by Chat GPT 3000 words at a time. What will happen is a robust system will be built that is not only efficient but can handle data at scale, at any level of complexity, and that can collect, process, store, analyze, and retrieve data within seconds. 

The Data Life Cycle

Data management involves several stages, including data acquisition, data integration, data quality, data governance, and data lifecycle management. What’s important to know as a business shaking in its boots trying to strategize using AI and Machine Learning is that implementing these solutions demands a coordinated approach. 

What that means is that stakeholders need to work together to establish clear goals, clear objectives, and be open to new ways of working with AI and Machine Learning solutions (some businesses still resist change even when seeking advice).

Key stakeholders vary but generally, when it comes to enterprises, the ones who need to collaborate cross-functionally include IT, data science, operations managers, and legal.

This is all great to know but it doesn't do much in terms of specificity, so with that said, here are a few additional considerations for businesses looking to implement AI and Machine Learning:

Skill Set: Implementing AI and Machine Learning solutions requires skill sets that may not be available in-house. Businesses in this case will need to invest in training or hiring data scientists, AI engineers, or various other professionals. Outsourcing is a great option in this case as it provides access to the required expertise that can not only build the solution but identify gaps to fill with it and then maintain the solution over time. 

Data Preparation: Before implementing AI and Machine Learning solutions, businesses need to prepare their data for analysis. This will involve cleaning and processing data, as well as transforming data into a format that can easily be read by AI tools. Labeling all data when training Machine Learning models is very useful in this case.

As you can see, it’s not an easy “one-and-done” task and it can actually be very time-consuming. Data preparation is one of the most complex but also most important parts of AI and ML model integration.

Iterative Process: Implementing AI and Machine Learning solutions, at this moment in time, is an iterative process, meaning it requires ongoing testing, validation, and refinement. Businesses must be prepared to adapt and adjust their strategies based on the insights generated by these AI tools (going back to the point about embracing change).

When AI and Machine Learning Meet Your Business

The AI tools that we are currently aware of and using such as Chat GPT, Jasper AI, MidJourney, and anything of the sort are mere droplets compared to the ocean of capabilities that Artificial Intelligence and Machine Learning technology will have to offer. 

No matter what industry you’re in, you’d be surprised at the amount of untapped potential in your business that a team of AI consultants can uncover to not only save time and money but also improve the overall efficiency of your business and the effectiveness of your workflows.

This is a lot to take in and, frankly, it’s a little overwhelming. But the truth is, AI and Machine Learning are not going anywhere. They are here to stay and will continue to shape the way we work, live, and interact with the world around us. With that said, here are 3 digestible takeaways from this for you to consider when re-evaluating how your business can strategize using AI and Machine Learning:

Data management is key: The effectiveness of AI and Machine Learning tools is only as good as the data they are given. By investing in efficient and effective data management systems, businesses will be able to leverage the full potential of these technologies.

Embrace change: It’s essential to adapt and adjust strategies based on the insights generated by AI and ML tools. Businesses must be open to change and be willing to work collaboratively across departments to make this happen.

Invest in expertise: Implementing AI and Machine Learning solutions requires expertise. Businesses need to be prepared to invest in training or hiring experts with expertise in AI integration or consider outsourcing to access it instantly.

The Takeaway

Avoiding the inevitable hammer coming down on companies who fail to integrate AI starts with assessing vulnerabilities. Of course, businesses never want to admit their flaws or focus on weaknesses because that would take away from the feeling that everything is going well. However, if that’s the mentality companies have throughout the next 5 years, their window for enjoyment will be very small. Now is the time to step into the future.

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.