How Artificial Intelligence is Changing Software Development

AI is not a new concept anymore. Public understanding of its capabilities is broadening rapidly and changing how we view professional services. What AI has given us at this moment in time is not a threat to jobs, but rather an opportunity to get behind the remodelling of employment which is something that will become a threat to service providers who don’t.

Software development is one of the key industries that will face significant change from AI, but this is an excellent thing for companies who view it as an opportunity and pivot their strategy accordingly. Chat GPT can write code in seconds. Anyone who’s used it knows it can pretty much do anything you tell it to, which is why its rise has been so meteoric. 

Chat GPT from Open AI is just one example - 39 other companies are just as close in competition with Open AI, never mind all the startups and concepts that have yet to hit the market. With that in mind, it’s safe to say that, unlike other trends that eventually blow over, AI and Machine Learning are here to stay.

Software Development Using AI

The entire purpose of AI is to make processes/workflows better and enhance the results that can be delivered. For example; look at a company like Microsoft which not only invested $10 billion in Chat GPT  but has also taken its AI product development to the next level. What Microsoft did was create various platforms that assist in the programming process in ways that allow software developers to do their job more effectively.

Visual Studio IntelliCode and CodeSpace are both AI services built by Microsoft meant to correct errors made in code for developers. If you ask a software developer today who’s been coding for the last 20-30 years what a difference tools like Chat GPT, Visual Studio IntelliCode, and CodeSpace would’ve made in their past projects, they’d tell you it would’ve saved them countless hours of typing, debugging, and testing. 

That’s what this is all about— adapting to these new tools to enhance what we’ve been doing all this time but to do it better, faster, and more efficiently.

Shift in The Role of Software Developers

The tasks that Artificial Intelligence and Machine Learning handle are typically time-consuming, repetitive, and cover less comprehensive duties. Software development involves a significant amount of these tasks such as extending source code, going through iterations, and debugging. 

In the future, if AI is fully handling these responsibilities without the need for constant supervision - software developers would be able to take on more of a managerial role and handle tasks that are more complex or require hands-on creativity. Tasks that would classify as “complex” or “creative” in software would be as follows:

• Developing new algorithms or even programming languages

• UI/UX design

• Graphic design for software products

• Developing new software features that enhance the user experience

• Developing and implementing new software testing methodologies

• Solving complex software problems

• Creating complex software architectures

• Building new frameworks for development

• Conducting in-depth research and analysis related to software development and emerging technologies

• Collaborating with marketing and sales teams to effectively market and sell software products

• Collaborating with customer support teams to ensure that software products meet the needs of end-users

• Mentoring and coaching junior software developers

• Overall leading software development teams and projects

There is a myriad of things that developers will still be crucial for. As a matter of fact, there’s much more opportunity for a software development company who learns how to manage and integrate AI products than there’s ever been for any new technology in the industry before.

What AI Products Will/Do Clients Want?

The types of AI products that a software service provider would create are highly subjective based on the clients they serve. For example, it doesn’t take an expert to tell you that a hospital is going to require a much more sophisticated and elaborate architecture than an E-Commerce store. 

Nonetheless, there are common interests among companies in various industries when it comes to AI product development that includes the following:

Predictive Analytics: Machine learning and AI are great for analyzing large quantities of data which would allow them to make predictions that are more accurate toward outcomes. For example, a financial services provider could use predictive analytics to recognize patterns in customer behaviour and offer customized opportunities or products based on that data.

Virtual Assistants or Chatbots: Of course, with the popularity of Chat GPT - companies are going to want to integrate chatbots that improve the user experience. Some specific functions of these tools could include processing inquiries, and assisting site direction, or they could be complex like the ability to triage patient inquiries.

Deep Learning Applications: Using artificial neural networks, Deep Learning applications can replicate the function of a human brain. This is the technology behind driverless cars, voice recognition, structuring investment concepts, language processors, fraud detection, and much more. Now, Deep Learning is still a long way from its true potential which makes it a key area to watch for any service provider interested in artificial intelligence integration.

Video and Image Analysis: Video and image analysis is a game-changer for security companies as it can detect people, objects, emotions, and behaviours using AI technology. This not only assists with investigations but also has implications for industries such as advertising and entertainment, which are both finding new ways to leverage this technology to drive growth.

For example, advertisers can analyze consumer preferences as well as what’s trending - then design campaigns based on that information. It can enhance the visual design process by finding what design trends consumers are responding the best to and then incorporating them into marketing materials. 

The Next Steps For AI 

There’s a lot to unpack from everything we just covered, yet this is still just the tip of the iceberg. AI is becoming beyond comprehensible but, again— this is a time with immense opportunity for those who take the time to learn, adapt, and integrate.

Software development has long been a field that demands constant learning and adaptation which is why the best software providers will ensure their business responds remarkably to the demand. 

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.

The Benefits and Challenges of IT Project Outsourcing

Software development projects don’t always go to plan - the vast majority of them run into complications, but often for very preventable reasons. For example, many companies have in-house software development teams that handle their projects. Companies that can afford an in-house team tend to just be more comfortable with that which is understandable. 

However, in-house teams are not without risk; for example, let's say a company is taking on a big process automation project. The in-house team gets the brief and notices that some of the requests are not in their ballpark of expertise. This is a simple problem, it happens often. These are 6 common consequences of ignoring it:

• Going over budget

• Significant project delays

• Low-quality final product

• Dissatisfied stakeholders

• Damaged team morale

• Damaged company reputation

This is just one very simple instance of something that can go wrong in-house and there are endless potential challenges all with their own set of consequences when it comes to developing software. However, effective execution during software implementation has a major emphasis on RISK MITIGATION which is why businesses often choose to seek consultancy.

Alibaba Example

To give you a real-world example - we’ll examine Alibaba. Alibaba is a company doing well over $140 billion in revenue today - but it wasn’t always that way. In the early stages of being a startup, Jack Ma, the company's founder, was facing trouble accessing the resources he needed while living in China. 

What he needed was web development, and when he eventually found specialists in the United States, he outsourced development to them and started seeing results immediately. This led the company to set a record in funding at $25 billion by 2014.

What It Means To Outsource

Outsourcing software development doesn’t always mean you bring a project to a third-party right from the jump and get them to take care of it. It can sometimes, but outsourcing can also involve project takeover which is needed in cases where the project has been done poorly or the in-house team has too much on its plate and it’s not a priority at the moment. 

Whatever the case may be - bringing in a third-party development team can be a great move for businesses and avoid the trap of project failure before the project even has a chance to be completed. 

With that said, when the process is executed optimally, there are 3 key benefits seen from outsourcing that you wouldn’t see from in-house development:

1. Cost reduction: Software development is expensive, and when you add employee benefits and vacation time off to that, it adds up to paying for results you’re not getting. On the other hand, when you outsource, you're only paying for the results of the project you get from the team. You’re not paying the same salaries when work is slow or results aren’t optimal. With outsourcing, you quite literally get what you pay for and nothing less.

2. Talent pool: Software developers have a lot of flexibility in terms of their working conditions. Some may work for a company and freelance on the side, or even work at several companies each for short terms. The point is, top talent may not want to be confined to one company. So yes, while an in-house team will have developers with an employee mindset and dedication - their capabilities may be limited in certain aspects of performance that you could only get from a diverse portfolio. 

3. Further domain knowledge: Outsourcing companies will have experience working with various companies in your industry which means that their experience level is fueled by observing different approaches to serving other businesses. This is not disregarding the expertise of an in-house team, however, there is a tendency to follow the “do things the way we’ve always done it” mentality which is not sustainable in 2023.

When a company hires an outsourcing team, they now have access to new mindsets and opinions, not just from an outsider looking in - but from trained professionals who’re ready to pivot and take on whatever you give them.

Deciding Whether Or Not To Outsource

Anytime you’re faced with the question “Why should we outsource this project?” start by examining why you shouldn’t outsource it. When an investment is being made for the greater good of an organization, it makes the most sense to start by analyzing all the risks involved. 

From that point, you can see if the benefits outweigh the risks and make an informed decision. The reason we recommend the “negative first” approach is that oftentimes teams get caught up in fixating on all the potential rewards that they neglect to construct proper contingency planning. 

Challenges From Outsourcing

The most common industries that outsource IT services include Finance, IT, Payroll, and CRM services. The most common projects companies from these industries typically reach out for include cloud services, process automation (especially robotic process automation), API integration and customer service support.

While all this is going on, some obstacles will arise but the whole idea is that with an experienced team of experts, you won’t have to worry about these obstacles impacting your project. With that being said, here are some of the most common issues to look out for:

Project Delays

Delays often occur when there are communication barriers between the service provider and the client. Both parties can be at fault for not properly outlining the scope, timeline, deliverables, or even changes made to the project. To avoid this, both parties need to prioritize having regular check-ins that ensure that the project is on track.

Quality Issues

Outsourcing a software development project can also come with some quality risks. When working with a third-party provider, there is a chance that the quality of work may not meet your expectations. This can be due to a lack of understanding of the project requirements or inadequate skills on the provider's end. 

For this reason, It's non-negotiable to have a proper quality assurance process in place that will catch any issues early on. In addition to this, there needs to be sufficient communication before the project starts to ensure expectations are understood and that the outsourcing team can support your needs.

Security risks

The phrase alone emphasizes how important it is to team up with a reputable software development firm. Now, when you think you have the right team, remember that you will be sharing sensitive information. For this reason, companies looking for outsourcing services need to make sure they ask about the teams' security and confidentiality measures.

What’s Important to Understand

There is risk involved with any level of change in an organization, however, we’re living in an era where change is inevitable with technology, especially watching AI becoming more advanced by the day. Companies need to re-evaluate their business processes in 2023 and find ways to implement this technology to still be operating in 2033. 

Businesses need to asses their assets and find any that are expendable. In some cases, it may make sense to delegate work to a third-party software services provider. This is a recommended path if it means it’s going to help your business succeed.

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.

The 6 Best Programming Languages for Artificial Intelligence

Artificial Intelligence (AI) is seeing wicked adoption rates in 2023, so much so that the most successful companies in the world are finding unique ways to integrate it. Take Facebook for example, they’ve innovated and re-branded into Meta to show their intentions of branching beyond a social media platform. While services like Skype, once a mere video call platform, have now integrated chatbots that can be used just as you would Chat GPT.

On the back end, software engineers are using AI tools to enhance the coding process, but this raises the question; who’s building the platforms that do the building? Now, AI tools are in no way suggesting that software engineers will be out of jobs anytime soon; rather, it’s a step in the direction of enhanced results from developers since programming can now be done more efficiently. 

Who’s Interested in Using AI Extensions?

Integrating AI into your platform can be done on any size scale using tools like Chatbots, Machine Learning, process automation, etc. What’s important to know is that for a platform to measure the effectiveness of artificial intelligence solutions, the main place to pay attention to is customer satisfaction. Recent research is suggesting that AI improves customer retention by nearly 70% and that those who are most interested are aged between 18-24.

How to Make It Work for You

The future of AI technology is unfathomable right now because it’s still too new with so much left to discover. 

While the future of AI technology is still unfathomable due to its novelty, programming is the backbone of anything digital, making mastering AI a top priority for software engineers. With that being said, here are the 6 best programming languages that help software engineers create seamless AI platforms:

1) Python

There is no debate when it comes to AI development that Python is a necessity. It’s an interpreted language which means that programming can be executed on the fly without needing to be compiled. 

The important role that machine learning plays in AI performance should be noted. Machine learning trains the system to act a certain way and is a key subset of AI. During AI integration, this makes development happen fast and machine learning algorithms can be tested quickly. 

2) Java 

Java is a language that can be used in just about any platform to handle the server side (behind the scenes) of the operation. Software developers will resort to using Java when building AI solutions, specifically when they’re trying to build a multi-faceted application or incorporate machine learning algorithms. 

Outside of AI, there is a strong case for Java being a more efficient option for certain tasks than Python since it’s compiled. However, software development is contextual. For instance, if a FinTech was implementing software for data analysis, Python would be the better option. 

3) Julia

This language is fairly new in the programming world with a small community of software developers utilizing it already. Though it’s not as widely used as other languages, it has earned its position among the best options for AI programming languages.

The main draw to Julia for software developers is its performance and speed which is said to be greater than Python due to its compiled nature. Oftentimes, Julia is used for data analysis which is why it’s commonly compared to Python.

4) R

R is a language with one primary role in AI development; number crunching. Data is significant and difficult to handle during software development, which means that to arrange it efficiently, you’ll need some math skills. This means that R will come in handy during machine learning development.

With that being said, here are some of the best R packages for Machine Learning development:

• CARAT: Useful for training integration and predicting models. 

• Gmodels: Provides tools for fitting models.

• Nnet: Perfect for AI as its purpose is to replicate how the human mind functions. 

• Tidyr: As the name implies, this package is used to tidy up data. Specifically by observing rows, inserting variables in columns, and value in cells.

5) Lisp

Any seasoned software developer with experience in AI will tell you this is one of the oldest languages still used in software development.  The name is an abbreviation for “list processing” which makes the language a great option when working with complex data structures. 

A few features under the hood of Lisp include:

• Program modification (into data)

• Rapid prototyping

• Creating dynamic objects

• Collecting garbage

• Running programs based on data structures

The program remains highly sought after because it’s highly flexible and easily able to express the programmers’ intent.  

6) C++

This language is known, like many of the other languages we’ve listed, for its speed and interoperability with machine learning and neural networks. As anyone would guess, developing an AI application is highly complex and data-intensive which makes having languages that are quick, reliable, and efficient a necessity for software engineers. 

When it comes to AI, C++ is going to be able to handle a lot of low-level programming which translates directly to a programmer's ability to manage the production of AI models. 

Choosing a Programming Language

Tech stacks are chosen based on several factors that every project entails. First, what resources are available? What kind of budget is the team working with? What is the timeline, talent expertise, hardware and infrastructure involved, etc? Considering the languages we’ve looked into in this article, some strong tech stack choices would include:

• PyTorch: Supports Python, C++, and CUDA.

• OpenCV: Supports  C++, Python, MATLAB and Java.

• TensorFlow: Supports Python, C++, and Javascript

• Keras: Supports Python

This gives a sense of how software developers would structure the infrastructure for an AI platform and also demonstrates that languages complement each other to build something great. 

The Takeaway

Software development services are highly adaptable which is what makes them so sought after by businesses of all sizes. If you’re trying to find the best programming solutions for your business, you’ll need to evaluate the long-term goals driving that desire. 

AI is just another step in the direction of inevitable change that industries are undergoing as a response to the advancement in consumer interests and the technological resources available to accommodate them. If you think your business deserves these enhancements to respond to the changes in your industry, don’t hesitate— get started.

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.