The Importance of ESG in Software Development

ESG is a massive movement in the global market that’s forcing businesses to adjust their approach to sustainability and corporate practices. ESG (environmental, social, and governance) practices are measured in scores that are beginning to dictate whether people invest in the company or not. 

The scores generally go from 0-100; anything below 50 is bad and will impact the company's reputation, bottom line, and long-term potential. Anything above 70 is great and consumers will look at the company as though supporting them is supporting the good of people and the planet.  

This recent commercial from Apple will give you a good idea of how that looks:

When it comes to software and technology, companies are under a ton of pressure to minimize their carbon footprint, reduce energy consumption, and implement eco-friendly practices throughout the software development lifecycle. 

The “S” or the social component is measured based on the work environment (diversity and inclusivity). This is going to be crucial for attracting and retaining talent. 

And lastly, when it comes to the governance of technology - the focus is on using data ethically and being transparent when making decisions. Data privacy is a focal point, especially now with six million data records being exposed in the first quarter of 2023.

Finding Your Solution

Sustainable IT isn’t an easy or replicable task but it’s absolutely necessary for companies to prioritize as we go forward with heightened environmental and ethical awareness. Custom software is a massive influence in this sense as it’s the focus of sustainable technology and can completely change the image of a company looking to improve its ESG.

These are some ways custom software enhances this process:

ESG practices for custom software development are like an umbrella that encompasses the process of tracking, managing, improving, and repeat. Microsoft is one of the best examples of this, and they check each box adequately:

Environmental sustainability: Microsoft wants to become carbon-negative by 2030 and to remove all the carbon it has emitted since it was founded by 2050. The company also invested in renewable energy and has implemented sustainable practices in its offices and data centers.

Social responsibility: Microsoft has implemented a handful of social responsibility initiatives, like giving $3.2 billion in donated and discounted technology to over 300,000 nonprofits serving over 1.2 billion people globally.

Governance practices: In the ESG report from Microsoft, they talk about prioritizing transparency, accountability, and ethical decision-making (As you would hope). In addition, the company has also set goals to improve its cybersecurity and data privacy practices. 

Managing and tracking this entire process in addition to the logistics aspect of technical adjustments are made possible through custom software. KPI monitoring, automating workflows, IoT tracking of energy usage, scalable metrics, and accessible data storage systems, all fall under the IT umbrella. 

Not Investing in Technology 

One way or another, the market is headed in this direction - companies who don’t pivot can expect the following: 

Missed Opportunities: Stakeholders are looking at ESG as a benchmark of a company's longevity. Without it, companies are going to struggle to attract environmentally and socially conscious customers as well as investors.

Increased Risk: Without adequate technology investments in data security and privacy, companies run the risk of data breaches and cyberattacks. These incidents can not only damage the company's reputation but also lead to financial losses and legal liabilities.

Competition: Imagine your competitor not investing in ESG practices, but you do. Would you say you have a leg up when it comes to marketability? If your answer is yes - why are you still in the reversed role?

ESG Going Forward

The futures for both ESG and technology are promising, and their relevance in the marketplace is only expected to grow. How will the two merge? Will AI embody quality assurance managers for companies' ESG practices? How will your company leverage each component?

Most companies don’t know where to begin, which is why we created this free tool to get you started on the right foot. While it won’t create an ESG plan for you, it can help you make informed decisions and navigate the dynamic landscape of sustainability and technology.

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 to Leverage AI in Manufacturing

The manufacturing industry is currently sitting on a wealth of opportunity. With never-ending demand, everyone around the world is always looking to receive more things. From cars to shampoo, toys, and gadgets, manufacturers are working hard to make Christmas happen year-round.

With this, however, comes a tough demand; whoever makes things better and faster wins the market. The future of manufacturing goes beyond automation - it lies in the integration of artificial intelligence.

This new technological development promises many things– it can write for you, do math for you, and even code for you. It doesn’t end there. For businesses in manufacturing, AI can handle every step of the manufacturing process with minimal mistakes and in a fraction of the time it would take a team of people. You can see how this could take a company that’s struggling against the competition and give it the leverage it needs to become a force in the industry. AI consulting could do exactly that.

AI and machine learning can offer manufacturers the ability to analyze huge amounts of data in real time, look out for patterns, and make intelligent decisions. This means AI can handle predictive maintenance, quality control, demand forecasting, and personalized production, among other benefits.

By efficiently managing resources, reducing downtime, and optimizing workflows, AI can transform the manufacturing landscape for those forward-thinking enough to implement it.

However, shifting toward AI-powered manufacturing comes with complications that should be addressed. It’s important to use AI mindfully and to consider the impacts it could have on the current workforce.

One solution is upskilling teams to move their priorities to higher-level responsibilities. Balancing human expertise with AI capabilities will be the deciding factor for success.

The evolution of automation

Automation has been a key driver of efficiency and productivity in the manufacturing industry for decades.

It started with the use of machines to replace manual labour, enabling mass production and faster turnaround times. Over time, automation evolved to include the use of robots, which are more flexible and versatile in performing a wide range of tasks.

The benefits of automation in manufacturing are undeniable. It allows for increased production output, improved product quality, and reduced labour costs.

By removing repetitive and mundane tasks from the hands of humans, automation frees up workers to focus on more complex and strategic activities. This not only enhances job satisfaction but also leads to higher levels of innovation within the industry.

However, automation also comes with its own set of challenges. The initial investment in automation technology can be significant, and smaller manufacturers may struggle to afford the necessary equipment and infrastructure.

Additionally, there is a concern about job displacement, as automation eliminates the need for certain roles that were previously performed by humans. To address these challenges, it is crucial for manufacturers to carefully plan and implement automation strategies that align with their business goals and workforce needs.

While automation has revolutionized the manufacturing industry, the next phase of innovation is – without a doubt– artificial intelligence. AI-powered manufacturing goes beyond the capabilities of traditional automation.

Instead of simply executing pre-programmed tasks, AI systems can learn from data and continuously improve their performance. They can adapt to changing conditions and make intelligent decisions at any given moment. This kind of ability allows for higher levels of efficiency and optimization in the manufacturing processes.

The benefits of AI and automation in Manufacturing

Predictive maintenance

The integration of AI in manufacturing has the potential to transform the industry in multiple ways. One area where AI is making a significant impact is predictive maintenance. By analyzing data from sensors and equipment, AI systems can identify potential equipment failures before they occur. This allows manufacturers to schedule maintenance proactively, reducing downtime and minimizing the risk of unplanned breakdowns.

Quality control

Another area where AI is revolutionizing manufacturing is quality control. AI systems can analyze visual data to detect defects and anomalies in products, ensuring that only high-quality items are produced. This not only makes it easier to keep customers satisfied, but it also reduces waste and expensive rework.

Demand forecasting

AI-powered demand forecasting is another game-changer in the manufacturing industry. Through analyzing historical data, market trends, and customer behaviour, AI systems can accurately predict future demand. This allows manufacturers to optimize production levels, minimize inventory costs, and reduce the risk of overproduction or stockouts, which can tank a business faster than you’d think.

Product personalization

Personalized production is also becoming a reality with AI in manufacturing. By leveraging data on individual customer preferences and requirements, manufacturers can create customized products with greater efficiency and accuracy. This opens up new opportunities for customization and personalization in the manufacturing industry.

How AI is transforming the manufacturing industry

Implementing AI in manufacturing takes careful planning and consideration. Manufacturers need to assess their current processes and identify areas where AI can bring the most value. It is crucial to involve employees in the decision-making process and provide them with the necessary training and support to adapt to the changes brought about by AI.

Data is the fuel that powers AI systems. That means manufacturers need to make sure that they have access to high-quality, reliable data. This may involve investing in data collection and storage infrastructure, as well as ensuring data security and privacy.

Collaboration and partnerships are also important when implementing AI in manufacturing. Manufacturers can benefit from working with technology providers to leverage the latest advancements in AI and share best practices. Ready to make the jump? Contact our AI specialist.

The impact of AI on job roles in manufacturing

Automation has long been a staple in the manufacturing industry. Robotic arms, conveyor belts, and assembly line machines have transformed the way products are made. These automated systems have not only increased production speed but have also improved accuracy and reduced human error. However, traditional automation systems lack the ability to adapt and learn from their environment.

This is where artificial intelligence comes into play. AI-powered robotics and automation systems have the capability to analyze real-time data and make smart decisions independently. By integrating AI algorithms, robots can now learn from their surroundings, identify patterns, and adjust their actions depending on the situation. This means that robots can now perform complex tasks that were once reserved for human workers. From precision machining to intricate assembly, AI-powered robots are powerful tools.

But AI-powered robotics is not just about replacing human workers. These intelligent machines can work alongside humans, augmenting their capabilities and improving overall productivity. By taking over repetitive and mundane tasks, AI-powered robots free up human workers to focus on more complex and creative aspects of manufacturing. This collaboration between humans and machines is the future of manufacturing.

Implementing AI in manufacturing: best practices and considerations

While the potential benefits of AI in manufacturing are endless, implementing AI-powered systems is best done with a few things in mind. Here are some best practices to consider:

1. Data collection and analysis: AI relies heavily on data. To successfully implement AI in manufacturing, it’s non-negotiable to collect and analyze relevant data. This data can include machine performance metrics, customer feedback, and supply chain data. By gathering and analyzing this data, manufacturers can gain valuable insights into their processes and make informed decisions.

2. Integration with existing systems: AI-powered systems should seamlessly integrate with existing manufacturing systems. This requires careful planning and coordination between different departments and stakeholders. Manufacturers should ensure that their AI systems can communicate and share data with other systems such as ERP (Enterprise Resource Planning) and MES (Manufacturing Execution System) to maximize efficiency and productivity. This can also be done with customized software that is built with every part of the process in mind.

3. Continuous learning and improvement: AI is not a one-time implementation. It requires continuous learning and improvement to stay relevant and effective. Manufacturers should invest in training their AI systems to adapt to changing circumstances and optimize performance. This can involve updating algorithms, fine-tuning parameters, and incorporating new data sources. 

4. Ethical considerations: As AI becomes more prevalent in manufacturing, ethical considerations come to the forefront. Manufacturers must ensure that AI systems are transparent, fair, and accountable. This includes addressing issues such as bias in algorithms, data privacy, and the impact on human workers. Ethical AI practices will not only build trust with customers and stakeholders but also ensure the long-term usability of AI-powered manufacturing.

Embracing the future of manufacturing with AI

The future of manufacturing lies in the integration of automation and artificial intelligence. This combination is a weapon every manufacturing company should consider having and leveraging. From transforming the way products are made to creating new opportunities for growth, AI is what the internet was for the 90s. The future is here, and it's time for manufacturers to embrace it.

Looking to implement AI in your manufacturing business? We have a team of specialists ready to start you on the right path. Contact us today.

Meet GitHub Copilot - The AI That Codes For You

2 years ago, GitHub announced they were teaming up with OpenAI - where they showed a technical preview of what they call “GitHub Copilot”. GitHub Copilot exceeded 1 million users in just 6 months and it’s popularity is no accident. 

GitHub Copilot is an extension for Visual Studio that takes the code you’re writing and gives you suggestions to complete lines or even entire functions that change while you write. The tool expedites finding alternative problem-solving approaches, writing tests, and delving into new APIs, all without having to search the internet or Stack Overflow. It will also adapt to your coding style, which is going to make your workflows perform much more efficiently.

Since it was created with OpenAI, GitHub Copilot relies on OpenAI Codex. Codex has extensive knowledge of coding practices and is great for code generation, primarily because of its training on a dataset rich in public source code. While Copilot is compatible with an array of frameworks and programming languages, it really shines when working with JavaScript, Python, Ruby, TypeScript, and Go.

Let’s look at an example:

As you can see in the video, writing code looks a lot faster with Copilot and not once did he have to look on Stack Overflow or GitHub to find solutions or code snippets. Some are even saying that the tool speeds up the coding process by 55% and handles 40% of writing the actual code.

So we have to ask then; Is this a revolution that will condense the scope of work for developers? Or at this moment, is it too good to be true?

What’s The Word Among Developers?

Even with how advanced GitHub Copilot is, developers seem to have mixed opinions on its implications and usefulness. Some of the main concerns about Copilot stem from security and copyright. Specifically, whether or not the system has access to the codes on GitHub, API keys, passwords, etc. This has actually landed Microsoft and OpenAI in some hot water - in the form of a lawsuit from developers who claimed that the system violated copyright laws.

Another key concern is that the quality of code the tool suggests for users is inaccurate, or not relevant to what the programmer is trying to accomplish. 

These are the two main pain points, but from a technical perspective, they don’t seem like anything that can’t be addressed and improved over time.

With that said, the main draws for developers obviously stem from the speed and precision with which Copilot can complete code. What’s more interesting though is that Copilot can keep the project's code consistent - for developers, this is huge because it means an easier debugging and maintenance process.

The other big attraction is how it aids in working with new frameworks and libraries that developers may not be completely familiar with. Often when developers are working in an unfamiliar environment, they’ll run into issues with the architecture, the codebase, what the business needs, and sometimes legacy code, all of which is very tedious work for developers. For this reason, Copilot is a great resource since it can synthesize code to fit the needs of the project.

Who Are GitHub Copilot’s Top Competitors?

Considering that the tool is still new and that it has strides to make before it reaches its maximum potential, it’s quite remarkable what it’s been able to accomplish. This “Copilot” movement with AI suggests a new future for software development, which we’ve always known was going to change dramatically as soon as mainstream AI tools caught momentum. The great thing about having tools is having options, and when we’re writing code in the 21st century we have tons of options.

Here are some code completion tools that are similar to GitHub Copilot: 

1. Tabnine: This is a top competitor for Copilot, and it also uses machine learning to complete code and offer suggestions. It too integrates with Visual Studio and other various IDEs and code editors like IntelliJ IDEA or PyCharm.

2. Codeium: Codeium supports multiple programming languages and can be used with editors like Visual Studio, Atom, and Sublime Text.

3. CodeGeex AI: CodeGeex AI is an AI-powered code completion tool that uses machine learning to suggest code completions. It too supports various programming languages and can be used with Visual Studio, Atom, and Sublime Text.

4. Code Whisperer: Code Whisperer is the same as the last two, it suggests code completions and also works with Visual Studio, Atom, and Sublime Text.

5. ChatGPT: ChatGPT is what we’re all likely most familiar with. It’s an AI chatbot that can help developers write code. It will suggest code completions and can also be used with Visual Studio, Atom, and Sublime Text.

6. Ask Codi: Ask Codi is another chatbot that can help developers write code. It performs the same functions as the rest and integrates with Visual Studio, Atom, and Sublime Text.

7. Google Bard: Google Bard is an AI chatbot that… You guessed it! Writes code for developers. It will also work with Visual Studio, Atom, and Sublime Text.

This list could go on seemingly forever and they’d all pretty much have the same descriptions. The point is that in a growing pool of so much competition, what is it that will give GitHub Copilot the upper hand?

Immediately what sticks out is it’s partners; GitHub, Microsoft, and OpenAI - the biggest names in artificial intelligence and software development. This inevitably raises the bar right away for GitHub Copilot. To meet the expectations of the market, Copilot is going to have to leverage being trained on the code from GitHub so it can generate original code more easily. It should also finalize, and effectively implement the experimental features such as Copilot Labs, Copilot Chat, and Copilot Voice. 

The Takeaway

The average computer science degree is around $20,000/year. Chat GPT is free along with most other AI tools. The highest you’ll get with code completion tools is about $20 per month, you see the pattern here?

More people can learn to code and do it a lot easier, perhaps even at scale, than ever before. Tools like GitHub copilot aren’t just “Shortcuts” - every developer should absolutely familiarize themselves with these tools and leverage them as much as they can before they are replaced by someone who’s already beat them to it. 

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.