14 Ways Machine Learning Consulting Can Drive Business Growth

As technology continues to evolve, staying ahead demands more than just conventional strategies. Businesses across industries continue to integrate advanced technology, and the potential for growth has become boundless. Machine learning, a subset of artificial intelligence (AI), is proving to be a game-changer for businesses aiming to harness the power of data-driven insights. Let’s explore 14 ways machine learning consulting can propel your business to new heights.

What is Machine Learning Consulting?

Hiring a machine learning consultant will help your business develop the right strategies to implement AI and ML into its processes. An expert will analyze the workflows and research the right tools and technologies and how to best use them. While companies can definitely embark on this journey internally, it’s a wiser decision to outsource for a few reasons:

  • Get a fresh pair of eyes on your processes

  • Gain a new set of skills from someone who specializes in the field

  • Increase your chances of successful implementation

  • Avoid unnecessary complications that arise from hiring in-house

With the AI era swiftly taking hold, it’s important to consider if machine learning consulting should be the next step in your company’s growth. Let’s explore the 14 ways machine learning consulting can help your business thrive in the ever-changing digital market.

1. Data-Driven Decision-Making:

Data-driven decision-making is the process of utilizing advanced computer programs to examine large datasets. These programs act as smart analyzers, extracting valuable insights from the data to simplify more informed decision-making. Essentially, it's like having a highly trained advisor who meticulously considers all available information, aiding businesses in selecting the most optimal choices. This method empowers people to make decisions backed by solid information, ultimately resulting in more favourable results.

Making decisions without having all the information is a risk businesses should never take. Instead of trial and error, having a system that can analyze and calculate risks associated with certain choices can prevent critical errors. This is why machine learning consulting could be the turning point your company needs to continue to grow.

2. Enhanced Customer Experiences:

Personalized recommendations and predictive analysis based on customer behaviour lead to superior user experiences. When companies use advanced computer programs to analyze how customers behave, they can create personalized suggestions and make smart predictions. This leads to much better experiences for users. It's like having a knowledgeable guide who really understands your customers. These programs help businesses choose the best ways to make users happy, so they end up having really great experiences with the products or services.

Similar to the first point, the secret is in the data. But if you have all this data and no way to use it quickly, it’s all going to waste. Every customer interaction is gold to a company that is already harnessing machine learning– having AI that is constantly learning and coming up with ways to use that information for company growth is a gamechanger. Not sure if your business is prepared to implement machine learning? Talk to a consultant today and have your questions answered by the top AI and ML experts in the industry.

3. Efficient Operations:

Optimized processes, predictive maintenance, and resource allocation streamline operations, reducing costs and increasing efficiency. Making things run smoothly in a business involves using smart computer programs to improve how everything works. These programs help by making processes better, predicting when things need maintenance, and figuring out how to use resources wisely. This makes operations work really well, which saves money and makes everything run more efficiently.

If your business is in manufacturing of any sort, you know how tough it is to stay on top of maintenance and supply demand. If you had all the information you needed to accurately predict when machines needed work done, or how much product to create, this would minimize
any downtime or setbacks from fixing those issues. Instead of keeping track of a million moving parts, let machine learning do the job. All that’s left to do is keep an eye on the AI, and it takes care of the rest.

4. Fraud Detection:

Picture a sophisticated computer program that functions like a skilled investigator. This program has the ability to detect uncommon patterns and irregularities within data, similar to uncovering hidden clues. In a business context, these programs are vigilant for anything that appears unusual or out of place. Anomalies like this could signify a breach. By fulfilling this role, machine learning serves as a form of defence, helping businesses shield themselves from malicious actors.

This is particularly useful for any business that stores sensitive information. These industries could include:

  • Finance

  • Insurance

  • Cybersecurity

  • Communications

  • Healthcare

  • Government

These days, any business that isn’t entirely brick-and-mortar should be wary of online threats. Malicious actors are on the rise, with the cost of cybercrime predicted to go up to $10.5 trillion by 2025. With machine learning at the forefront, businesses can rest assured there is a 24/7, highly intelligent guardian on the lookout for anything suspicious. If you value your company’s security, it may be worth asking about how AI and ML could be implemented to help protect your business’s valuable assets.

5. Market Segmentation:

Imagine a diverse pool of customers, each having unique tastes and requirements. Machine learning algorithms can effectively categorize this diverse customer base into smaller segments defined by shared characteristics or interests. This practice is known as market segmentation. Through this approach, businesses gain precise insights into each distinct group. This data can help craft marketing campaigns tailored to resonate with each segment and offer products that align with their specific preferences, boosting the chances of sales. To put it simply, market segmentation empowers companies to strategize better when catering to different customer types, leading to finely-tuned marketing approaches. 

This is actually a win-win for everybody. As a customer, it’s important to get what you want when you want it. With this approach, businesses can use their data to offer exactly that; if you’re the first company that pops up offering to fulfill a customer’s need, that person can receive what they wanted and your business gets the sale. This is useful not just for marketing agencies, but for anyone that has a service or a product to sell.

6. Supply Chain Optimization:

Supply Chain Optimization is all about using math and data analysis to make supply chains work better. This means using predictions to figure out how much inventory to have ready and the best ways to move things around smoothly. The goal is to avoid any hiccups by foreseeing what's needed and planning how things should flow. Machine learning consulting can help ensure that products move along the supply chain without any interruptions or troubles.

From cars, medicine, and fashion to food and transportation, all industries that deal with creating and delivering goods will benefit from supply chain optimization. Letting machine learning take on the work could cut costs, reduce waste, and create a highly efficient supply chain operation. Each phase along the supply chain sees some form of avoidable expense; whether it’s the fuel needed to transport products, the amount of materials needed to create products, or the scheduling for each part of the process. Machine learning consulting can help identify where every resource leak is and patch it up. With the right data and machine learning solution, your business could 10x its profits by running a tighter ship through AI. Ask us how here.

7. Risk Management:

Machine learning models operate like experienced risk experts. They look at loads of data to find patterns that might show upcoming risks. Using past and current info, these programs can guess how likely different outcomes are and how bad they could be. With these smart guesses, businesses can make better choices by thinking about the possible risks and rewards. While they can't remove all the risks, machine learning helps companies handle them better, like a guide helping you navigate through uncertain situations.

Flying blind is a risk no one should be taking. Especially in the volatile global economy, utilizing every resource to make the right choices is a must. Would you agree that having a map of the future would allow you to grow your business through any situation? We definitely believe this. And finally, we have the technology to give this to you. Of course, ML and AI are not perfect, but it can give you a fighting chance. Why take the risk? It’s better to invest early in the technology that could eventually steer your business to avoid rocky outcomes.

8. Real-time Insights: 

Imagine having information at your fingertips that's so up-to-date it's like looking at things happening at any given moment. Using special tools, you can quickly study data and understand what's going on in the market. This knowledge gives you the power to react fast when things change. It's like having a radar that helps you adjust your plans in a flash, making sure you stay on top of the game even when the situation shifts.

Much like the previous point, this gives you a sort of map, only this one helps your business stay agile in a quickly changing market. This is useful for businesses that rely on the day-to-day actions of the business and of the world at large to keep the company afloat. This means industries like transportation, retail, e-commerce, healthcare, advertising, and more. In truth, most businesses could benefit from using real-time insights. But if you want to be the best and make an impact on your customers and clients, then it’s a tool you can’t afford to leave unused.

9. Competitive Edge:

By using machine learning consulting, customers and competitors know that you're not just following the crowd, but rather leading with innovative ideas, making your business unique and adaptable. This signals to others that you're always ready to embrace new ways of doing things and boosts your brand reputation, too.

This is more of a positive side-effect of utilizing ML. Regardless of what reason you decide
to implement it, it shows that there’s a spirit of forward-thinking and innovation within your organization. With technology moving headfirst into the future, the perception of your business will matter a great deal. We’ve seen it happen before; businesses that didn’t adapt eventually died. This was true of the industrial revolution, of globalization, and then of the development of the Internet. Make a stance now and show your customers that you’re here to stay.

10. Product Innovation:

With machine learning consulting, you can dig into data to discover new ideas and inspiration. These insights from machine learning can become the building blocks for crafting groundbreaking offerings that capture people's attention and meet their needs in ways they never imagined.

In a similar vein as #9, this proves not only the spirit of innovation but puts it into action. For businesses that are product-centred, using AI along with the creativity of a team can bring new and better creations into the world. Product research and market demand can be covered by machine learning, and with the right use, can launch a company to new heights.

11. Revenue Growth:

Another side-effect of machine learning consulting is the end result of increasing efficiency: higher profits. Whether it’s cutting unnecessary costs, inventing better products, or analyzing current and future insights, utilizing ML will help your revenue grow.

Here is a more specific example of how ML can contribute to revenue growth. If you’re in e-commerce, pricing strategies can grow your business faster. With personalized pricing strategies, you set prices in a way that makes customers want to spend more. Plus, there's cross-selling, where you suggest other things they might like along with what they're buying. These techniques work together to bring in more cash. =

12. Customer Retention:

Predictive analysis plays a crucial role in holding onto your customers. With this powerful tool, you can foresee what your customers might need in the future. By examining patterns and data, you gain insights that allow you to anticipate their preferences. This means you can make the right moves to keep them happy and prevent them from leaving. This isn't just about numbers – it's about building strong relationships and trust. When your customers see that you understand their needs, they're more likely to stick around and remain loyal to your business.


If your customers feel cared for on a personal level, that will ensure they stay loyal to your products or services. Machine learning consulting can help you identify ways to do exactly that. There may be opportunities to build those relationships being left on the table that machine learning consultants can help you discover. Once you’ve identified these opportunities, you can then implement ML to take action and begin doing the work.

13. Compliance and Regulation:

Through meticulous data analysis, machine learning ensures that your operations align with the necessary regulations. By continuously monitoring processes and data, it acts as a dependable partner that helps maintain your business within the boundaries of compliance, saving you time and providing peace of mind.

For some industries, there is an endless list of rules and regulations that have to be followed. Businesses have tanked thanks to mistakes that slipped through the cracks. It can be as small as something that warrants a warning from compliance enforcers, to a massive failure that leads to the recall of thousands of products. Using ML to ensure every person in the business is working according to regulation standards is paramount. Wondering how this could work for your business? Ask us.


14. Scalability:

Embracing machine learning equips your business with the capability to efficiently manage larger quantities of data and tasks without sacrificing the quality of outcomes. This technology empowers your operations to expand seamlessly while maintaining the same level of excellence in performance.

Maintaining quality is what really sets businesses apart. Once word begins to spread of a company’s goods or services deteriorating, it’s a poison that’s hard to clean out. Bad reviews can clog up your Google service page and create an empty sales funnel. Sometimes it’s a quick transition, but the real danger is when the quality of your business goes down slowly. To be able to grow quickly, you have to be able to maintain or improve the end results.

Machine learning can also help with this. If you’re in technology, machine learning can help your site stay up and running when there’s a traffic spike. If your business blows up overnight, and half of everyone interested in checking you out can’t even get onto your page, that ruins your reputation fast.

The Takeaway

From informed decision-making to personalized customer experiences, the opportunities that come with machine learning consulting are limitless. By embracing machine learning's capabilities, businesses can not only navigate the complexities of today's market but also position themselves as leaders in adaptability, efficiency, and growth. All it takes is the right team of experts to guide you in the right direction.

Check out ISU Corp for the best in machine learning consulting.

Written By Natalie Mansilla

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.

 
 

Top 7 Best Java Build Tools

Cutting down workflows is the name of the game in the world of custom software development. Build tools are great for B2B companies in this case since they handle the process of creating, compiling, and managing code. Yet, it’s no easy task to find a match between the features of tools and the particular demands of a project.

Nonetheless, streamlining workflows during development heavily relies on Java build tools - being aware of what they can do and how they can be integrated will set you up for success. By automating the Java application creation process through source code, managing dependencies, and handling bytecode, developers can reduce the likelihood of errors in addition to labour. 

This mechanized coding process also lets developers create unit and integration tests, which expands code coverage analysis. Additionally, it aids in packaging and distributing Java applications, thereby streamlining the software deployment process as a whole. 

Java Build Tools Explained

A build tool is a utility that operates through command-line instructions while automating the process of building applications systematically. Within the Java context, these tools turn code files into bytecode and then package them into executable files or resource libraries.

Having build tools simplifies the process of managing dependencies for enterprises undergoing Java development. By automating tasks, these tools reduce the chances of errors. This, in turn, creates space for the implementation of testing protocols before the deployment of the actual product. The reduction in potential complications augments the likelihood of the final product meeting the criteria set by the client or end-user, thereby fostering confidence in the development team.

What to Look For When Choosing a Tool

When you’re looking for a Java build tool, there are several factors to consider. Personal preference is of course important, but it's best to focus on the complexity of the project, community support and adoption, integration with other tools, and even consider the learning curve for Java developers.

For smaller-volume projects with simple requirements, lightweight tools like Apache Ant can be helpful. However, multiple-module projects that you want to scale up or down include more dependencies and customization needs that require tools with more advanced features. So to put it simply; look for a build tool that offers extensibility and customization with additional plugin features that allow for further customization to meet the specific needs of a project.

Always do your research beforehand, especially when it comes to the scope of the project and how libraries will contribute to performance, structure, or scalability. General libraries, like Boost, STL, or Qt, take more time to research, especially if you need a highly specific function. You’re more likely to find these types of features in libraries meant for particular purposes, such as logging, HTTP, MXL, and connection pools.

A mark of a good tool is best seen in its integration with other tools and the control system version. Through the continual integration of modern Java build tools, there are standard practices that can enhance the development, testing, and deployment of an application, ensuring that any code changes are rapidly delivered and integrated smoothly.

The learning curve for Java can be steep, so always look at starting with tools that are beginner-friendly, like Apache Ant. It will help you, as a developer, gain a practical understanding of the working schematics, while a tool like Maven requires more experience. 

After mastering the basics of Java, make a list of steps for completing just one simple project. Create a map or list that will help you visualize the process. Each step will demand a varying amount of research, so think of a way to keep track of resources and tips. Find online forums with members who have experience and expertise with Java.

Top 7 Java Build Tools

  1. Gradle: Gradle stands as a widely embraced build tool that uses a Groovy-based domain-specific language (DSL) for scripting. Its flexibility is a big draw, and it makes it useful during the construction of diverse software projects.

  2. Apache Maven: Maven, another popular build tool, places a strong emphasis on convention rather than configuration. With an XML-based configuration file, it manages project dependencies and construction processes.

  3. Apache Ant: Despite its age, Apache Ant is still widely utilized as a build tool. It also uses an XML-based configuration file and is super customizable which are both key features that persist in its popularity.

  4. Bazel: Bazel is meant for expansive, multi-language ventures, and is as a purpose-built build tool that’s highly scalable. 

  5. SBT: Engineered explicitly for Scala projects, SBT is a dedicated build tool. The utility relies on a Scala-based DSL for scripting, and its configurability sets it apart.

  6. Dragonboat: Dragonboat is a high-performance multi-group Raft library in Go. With its feature-complete implementation of the Raft consensus algorithm, it has a strong foundation for constructing distributed systems that demand consistency.

  7. Go-Zero: Go-Zero offers a web and RPC framework designed for crafting high-performance and resilient services. It’s a comprehensive solution that encompasses a productivity-enhancing code generation tool named goctl, which elevates its efficiency.

Tool Comparison

The general features and use cases of these tools are something developers should absolutely be aware of. We’ll use Gradle as a point of reference, this is a brief look at how they compare and contrast:

Gradle vs. Apache Maven:

  • Gradle uses a Groovy-based DSL for scripting, while Maven relies on XML configuration.

  • Gradle's flexibility is good for diverse projects, whereas Maven's emphasis on convention specifically simplifies setup.

  • Gradle's learning curve might be steeper because of its scripting language, while Maven's XML configuration is a bit more approachable.

Gradle vs. Apache Ant:

  • Both Gradle and Ant use XML-based configuration, but Gradle's DSL provides more expressiveness.

  • Gradle's adaptability suits varied projects, whereas Ant's customization options are key to its popularity.

  • Gradle often requires less manual XML coding than Ant.

Gradle vs. Bazel:

  • Gradle is versatile and Groovy-based, while Bazel excels in scalability for large, multi-language projects.

  • Gradle's syntax might be more user-friendly for anyone accustomed to scripting languages.

  • Bazel's strengths lie in distributed caching and parallelized builds, making it better suited for more complex projects.

Gradle vs. SBT:

  • Gradle's Groovy-based DSL contrasts with SBT's Scala-based scripting.

  • SBT is optimized for Scala projects, while Gradle's flexibility accommodates a broader range of languages.

  • Gradle's learning curve might be gentler because of its scripting familiarity for many developers.

Gradle vs. Dragonboat:

  • Gradle is a build tool, while Dragonboat is a high-performance Raft library.

  • Gradle focuses on automating build processes, whereas Dragonboat is used for distributed system consistency.

  • Comparing the two is like comparing construction tools to foundational components for distributed systems.

Gradle vs. Go-Zero:

  • Gradle is a general build tool, while Go-Zero is a web/RPC framework.

  • Gradle handles software construction, whereas Go-Zero specializes in high-performance, resilient services.

The Comparison to Gradle is simply because it’s super well-known and widely used within the software development community. 

The Takeaway

It can’t be stressed enough that choosing the best build automation tool hinges largely on how well it aligns with the prerequisites of your Java project. Preliminary research is fundamental here. 

Having a project management tool like Trello, Jira, Asana, or Basecamp can also help out when it comes to tracking your project and keeping it organized. If your project is something you think could benefit from expert help, we’re ready to answer any questions you may have!

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 Consultancy Works in Manufacturing

Manufacturing is a very broad term when you look at how the industry is defined; “any industry that makes products from raw materials by the use of manual labour or machinery and that is usually carried out systematically with a division of labour”. Nonetheless, this is a space that benefits from automation arguably more so than any other industry. That’s another subject to get into but essentially, time, money, and efficiency is where the appeal to automation stems from.

A company on its own can’t just look for the metaphorical “band-aid” to put over its current processes and magically scale performance. For this reason, they outsource to consultants; a team that can see not just the current shortcomings but ones the company can expect down the line. 

What we as vendors know about manufacturing companies in North America in 2023, is that procurement and sourcing, as well as supply chain disruptions, are among the critical challenges faced. Artificial intelligence is a great resource to overcome these challenges, but the specifics that an AI system needs to address in the context of manufacturing are intricate and multifaceted.

Solving Problems Across Industry Models

As we’ve already covered, the industry is broad and every business will have different outcomes they’re looking for. With that said, here are some practical application examples that can be leveraged across multiple business models:

AI-Powered Procurement Platforms: By analyzing information from supplier databases, market trends, historical data, and other relevant data sources, AI procurement platforms can match specific procurement requirements with supplier capabilities and qualifications. Resources for this include Globality, Zycus, and JAGGAER.

Spend Analysis: AI can analyze spending patterns and identify opportunities to save money by scrutinizing large volumes of procurement data. It also offers insights into spending behaviour, supplier performance, and contract compliance. Some platforms for this could be SpendHQ or SAP Ariba

Supplier Risk Management: These AI tools can assess supplier risk by analyzing various data sources, which might include financial data, news articles, and social media. They’ll help identify potential risks and enable proactive risk mitigation strategies. Platforms for this might include Resilinc and Zycus iContract.

Contract Management: Platforms like Icertis or Evisort can automate contract management processes by extracting key information from contracts, identifying risks and opportunities, and ensuring compliance with contractual terms and conditions.

Demand Forecasting: This is simply for optimizing inventory levels, reducing stockouts, and enhancing the efficiency of your supply chain overall. For this, you’ll want to look at BlueYonder.

Where AI Consulting Comes In

Consulting at a base level is about creativity, bringing an outsider perspective into your organization with expertise in problem-solving to create solutions that will optimize your business long term, great. With AI in the picture, this process looks the same, but even more promising - specifically when it comes to data management. Whether you’re a small-scale manufacturer or a large industrial player, data is at the heart of your operations and it can be extremely tedious and time-consuming. 

Most companies with in-house development teams may either be trying to navigate multiple priorities or simply might not have the expertise to construct a system that can manage data using machine learning and AI. For a company this is a problem, for a consultancy this is a massive opportunity. When you say the words “trouble managing data”, a consultancy correlates wasted time and resources, which would push them to create a roadmap looking something like this: 

Know the Plan: The consultants start by understanding what needs to be done. They figure out what problems and good things we want to achieve with your information.

Make a Smart Plan: Next, they think about how to do it in a smart way that matches your needs. They decide what kinds of information we need and how to keep it safe.

Choose the Right Rules: They make rules like a game. They say where the information comes from, what it looks like, and how to take care of it. This helps everyone use the information the right way.

Think about AI's Job: They talk about how AI can help. They make sure the AI does what you want and is safe.

Stay Safe: They check to make sure the AI is working well and doesn't do anything bad. They also make rules to stop the AI from doing bad things.

Keep Everything Nice: They make sure the information stays good and not messy. They also make sure it's always correct and up-to-date.

Make It Even Better: They look at how things are going and think about how to make them even better. They use the plan to keep improving how you take care of your information.

The System

At the core of AI's prowess lies its ability to learn. Think of it as a virtual apprentice that gets better with experience. Initially, AI algorithms are fed historical data - production records, machine performance, and even external factors like market trends. Through sophisticated techniques like machine learning, AI identifies hidden relationships between variables and develops predictive models.

An ERP system infused with AI is the ultimate vision for manufacturing companies. For anyone who’s never imagined that, it’d look something like this: As raw materials flow into the factory, embedded sensors send real-time data to the AI-powered ERP system. The AI, like an orchestra conductor, processes this influx of information. It then predicts maintenance requirements for the machines before they break down, optimizes production schedules based on market demand, and even fine-tunes the supply chain to minimize delays.

Too good to be true? We think not. 

The Takeaway

AI consultancy isn’t so much about thinking up and designing systems as it is about giving companies the best shot at success for the least amount of risk. It’s not an easy process, but that’s what experts are for. We’d love to make your problems our solutions, so be sure to bring us all of them. 

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.