ben brown

September 20, 2023

Business

AI’s Revolutionary Strides in Custom Software Development

ben brown

September 20, 2023

Business

As custom software developers, design thinking is standard practice when it comes to any project. We put the user first and build the solution around their needs… This is nothing new. What’s new is that design thinking has changed and evolved into “platform thinking.”

Platform thinking is the understanding that modern consumers have evolved from passive observers in the product lifecycle to active contributors in the value creation process. For example:

Uber uses platform thinking to connect drivers with people who need rides. The more drivers and riders who use Uber, the more valuable it becomes, because there are more rides available and more people to connect with.
Instagram connects people who want to share pictures and videos. The more people use Instagram, the more valuable it becomes; there are more photos and videos to see and more people to connect with.

Simple enough? Hope so, because now we’re really going to change pace.

Artificial Intelligence Tools in Software Development

AI can assist people in creating and enhancing things, no matter how skilled they are. This approach to platform thinking will become something every business grasps. It’ll eventually get to the point where all employees are also materializing their ideas quickly.

Bear in mind that 41% of all code on GitHub is AI-generated, and as AI becomes an important part of making software, the teams and skills needed will change. AI is not a replacement, so much as it is an extension of work, which in software development always comes back to the team around platform engineering.

The Impact of AI on Software Development Roles

Businesses must anticipate AI's role in platform engineering as they look ahead. With the evolving approach to development, the following are some jobs that will change.

Interaction design roles will surpass UI design roles in demand. As visual AI progresses, the need for a manual UI layout and structuring of business processes will diminish. Interaction designers will guide AI in crafting user interfaces and user experiences through JavaScript design systems, visual guidelines, and consistent user testing.

Business analysts will be dramatically more important in shaping business strategies. AI will likely take on tasks like writing user stories, defining requirements, and even setting acceptance criteria. Instead of just documenting these criteria, BAs will evaluate the AI-generated concepts and align them with the platform-oriented mindset. AI will become the key driver of business strategies, with analysts guiding it in the right direction.

The role of test architecture will be a highly sought-after and well-paid position. With autonomously generated software, continuous testing will be crucial. As the development cycle shortens, the demand for testing will skyrocket. Simply automating user tests based on acceptance criteria won't work anymore.

Test architects will be responsible for designing, implementing, and maintaining intricate test architectures, conducting end-to-end testing of new features, consistently performing exploratory testing, and executing dynamic regression suites that evolve with time.

Software architects will arguably get the most out of AI under this umbrella. Even though we’re still technically in the early stages of AI integration in software development, we are seeing tons of growth in platform engineering. Businesses are shifting away from single-point SaaS solutions and consolidating their efforts on custom-built and SaaS-enabled platforms like ServiceNow, Salesforce, and Workday.

In addition, software architects are devising governance systems to shape code standards, development processes, and other aspects along those lines. Going forward, they will leverage AI to create, enforce, and evolve these systems autonomously.

Putting It All Together

Custom software development is in an interesting spot because even though the industry itself is changing, it too has the power to influence and change other industries. Every facet of a business's operations spiderwebs with AI integration.

In healthcare, maybe it’s automating diagnosis and treatment recommendations. In finance, there could be new approaches to financial planning. Maybe in manufacturing, it’s personless warehouses. These are the kinds of visions we need to dream up as software developers while the change happens in real-time. Clients are looking for partners in transformation which means as a software development company, AI needs to be a priority internally and externally.

The Takeaway

Integrating AI in your business processes starts by knowing when you’re ready and where it’s needed. With that in mind, we created a free tool to help you determine whether or not your business is ready for AI.

How is your industry changing right now? How do you think it will continue to change in the next 5 years? These are the questions you need to be asking yourself in today’s marketplace because there will be stark differences between the companies who do ask and the ones who don’t.

Written By Ben Brown

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

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

ben brown

July 17, 2023

Business

Best Chat GPT Plug-ins For Software Development

ben brown

July 17, 2023

Business

We know about the prompts Chat GPT has to offer software developers, which are great starting points— but truly, they are nowhere near the full scope of leveraging streamlined workflows from AI. Since the dawn of its existence, coding has been about 3 things; creativity, problem-solving, and innovation. With artificial intelligence, the horizon for these aspects of coding expands exponentially.

Plug-ins elevate this further, especially when a programmer knows exactly which to use and when. What plug-ins do is leverage AI algorithms to cut down the development process by automating tasks and getting data-driven insights. A typical scenario for development with plug-ins might look like this:

Scenario of Leveraging Plug-ins

A programmer is working on developing a web application for a client. The project involves developing systems for user authentication and authorization, implementing complex business logic, integrating with external APIs, and load balancing. The deadline is also tight.

To start, they use a plug-in that automates the process of setting up secure user authentication systems, including user registration, login, password encryption, and session management. Once that’s done, they go to an AI-driven tool that will give them 3 things; intelligent suggestions, code templates, and algorithms for handling not just super complex workflows, but also calculations. Still with me? We’re almost there.

As the project involves integration with external APIs, the programmer moves to an AI-based tool that simplifies the process of connecting to external services. The main purpose of this tool is 4 things: automatically generating code for authentication, making API requests, handling responses, and managing errors. By now, communication with external APIs is seamless and they move on to the last phase.

They need load balancing for performance and scalability. For this reason, they use a tool that does 3 things: monitors server loads, distributes incoming traffic across servers, and optimizes resource allocation. With this, the workload can be distributed which ultimately lets the system handle user traffic without compromising stability.

Throughout this entire process, the programmer is leveraging a tool that analyzes code and performance metrics. The Chat GPT plug-ins utilize these insights and data from the analysis to generate code snippets and insights. So if you thought plug-ins were limited to providing prompts, think again. With that said, here are some Chat GPT plug-ins that developers need to pay attention to:

Zapier

This is likely the most popular integration tool as it integrates over 5,000 apps with ChatGPT. You can connect apps like Google Sheets, Gmail, and Slack directly to ChatGPT. Users can leverage any of Zapier's 50,000 actions, such as search, update, and write to automate tasks.

Code Interpreter

Although currently in a closed alpha phase, this plug-in is very promising for the future of software development. It enables the execution of Python code directly within a chat session with ChatGPT. The plugin allows users to upload and download files, make code adjustments, and even receive suggestions and modifications from ChatGPT.

ChatWithGit

ChatWithGit is a plugin that enables users to search GitHub directly within the ChatGPT interface. It allows users to find relevant code snippets and provides a preview of the code along with a link to the corresponding repository. This feature saves developers time by leveraging existing code resources on GitHub without having to reinvent the wheel.

Visual ChatGPT Studio

Visual ChatGPT Studio is an extension from the Visual Studio Marketplace. It integrates with Visual Studio. Its features include method code autocompletion, adding unit tests, bug detection, method optimization, explanation writing, commenting, providing summaries for C# methods, and users can ask questions (code-related questions) and get answers within the editor.

Prompt Perfect

The Prompt Perfect plug-in helps users create effective prompts by optimizing and rewording them for better results. It’s also not exclusive to ChatGPT. All you do as a user is begin your prompt with “perfect” followed by your request.

Wolfram Alpha

The Wolfram Alpha plugin addresses ChatGPT's limitations in math. It gives ChatGPT access to computation, math functionalities, curated knowledge, and real-time data through Wolfram Alpha and the Wolfram language. Using this plugin, ChatGPT can give you improved responses and visualizations for math-related queries.

Link Reader

Link Reader enables ChatGPT to extract and analyze information from various types of links, like PDFs, PPTs, Word docs, and all the fun stuff alike. Users then have ChatGPT translate, summarize, interpret, and even analyze the contents of the given link, to get insights far more in-depth than a typical search engine.

Moving Forward

Emad Mostaque's statement that "There are no developers in 5 years" is something companies in the tech space need to really think about. The amount that one person can achieve increases exponentially with AI-powered tools. And trust me, you won’t lose your job to a machine, but the person who knows how to leverage machines will blindside you.

Written By Ben Brown

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ben brown

July 11, 2023

Business

How Your Business Benefits From Cloud Computing and AI/ML Synergy

ben brown

July 11, 2023

Business

Cloud computing has become central to scaling your business in 2023. What we’re uncovering is that AI and ML capabilities in the cloud make businesses more efficient, strategic, and insight-driven. Of course, while leveraging new technologies is highly subjective in terms of use cases, understanding when an opportunity arises to get the most out of your workflows from the least amount of effort will directly correlate to longevity.

Cloud computing, Artificial Intelligence, and Machine Learning are tools that were created for this very reason which makes the synergistic integration of the three quite lucrative for organizations.

Now when it comes to placing AI in a cloud environment, it’s there to enhance the operation, not replace it. Saas companies are incorporating AI and ML into their bigger software packages which is an effort focused on end-user functionality and the UX overall.

Two Industry Examples

1) Insurance:

Practices in the insurance industry are limitless when it comes to integrated extensions from AI and ML in the cloud. Traditionally as an insurance broker, you have to manually assess customer information, research policies, and make recommendations. With process automation enabled by AI and ML in the cloud, you expedite that entire process. Here are some examples:

Claims Processing and Fraud Detection:

Cloud-based AI and ML tools can analyze claims data and assess validity.
ML algorithms can identify patterns of fraudulent claims by analyzing past data, trends in customer behaviour, and other various external factors, which ultimately improves fraud detection and prevention.

Risk Assessment and Underwriting:

AI-powered algorithms in the cloud can analyze customer data, market trends, and historical claims to accurately assess risk.
ML models can generate underwriting recommendations, ensuring that policies align with customer needs and risk profiles.

2) Manufacturing:

In the manufacturing industry, the integration of cloud computing, AI, and ML changes the entire production process as well as overall quality control. Oversight and automation are enhanced through the following:

Production Process Optimization:

Cloud-based AI and ML platforms enable real-time monitoring of production lines, capturing data from sensors and IoT devices.
AI algorithms can analyze this data to identify bottlenecks and areas for process improvement.
ML models integrated with the cloud can predict equipment failures and in turn, optimize maintenance schedules and minimize downtime.

Quality Control and Detecting Defects:

Cloud computing allows for the storage and processing of vast amounts of quality control data, be it images, sensor readings, or even product specifications.
AI algorithms in the cloud can automatically analyze this data and recognize defects, deviations from standards, and of course anomalies.
ML models can continuously learn from historical data to improve defect detection accuracy and ultimately enable proactive quality control measures.

Supply Chain Management:

Cloud-based AI and ML solutions provide visibility into the supply chain, integrating data from various sources such as suppliers, logistics partners, and inventory systems.
AI algorithms can optimize inventory levels and demand forecasting, which aids in logistics planning to minimize stockouts, reduce costs, and improve delivery times.
ML models integrated with the cloud can identify patterns in demand, supplier performance, and market trends, which allows for more accurate procurement decisions.

Safety and Predictive Maintenance:

Cloud-based AI systems can analyze data from IoT sensors and machinery to monitor and assess safety conditions.
AI algorithms can identify potential safety hazards and issue alerts to prevent accidents.
ML models in the cloud can predict equipment failures based on historical data and sensor readings, which makes maintenance proactive and minimizes downtime in addition to optimizing asset performance.

Technical Logistics

From a technical standpoint, integrating AI and ML in cloud computing involves leveraging various specific tools, languages, and frameworks that can be quite complex. To give you a sense of what that’d look like, here is a rough breakdown of the technical aspects:

Cloud Computing Infrastructure:

For cloud computing, you’ve got your pick at service providers which include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). These platforms provide services such as virtual machines (EC2 instances), storage (S3, Azure Blob Storage), and of course, data processing (AWS Lambda, Azure Functions) which leads to the next point.

Data Storage and Processing:

To handle large volumes of data, IT teams can utilize cloud-based storage solutions like AWS S3, Azure Blob Storage, or Google Cloud Storage. For data processing, distributed processing frameworks such as Apache Hadoop and Apache Spark are a couple of options.

AI and ML Libraries/Frameworks:

Python is the most widely used language in AI and ML, because of its extensive libraries and frameworks. Some great libraries for AI and ML in this case include:

TensorFlow: An open-source framework developed by Google for building ML models, particularly neural networks. It provides APIs for high-level model development and deployment.
PyTorch: Another popular open-source ML library with dynamic computational graphs, making it well-suited for research purposes.
Scikit-learn: A versatile library that provides a range of algorithms and tools for data preprocessing, feature selection, and model evaluation.
Keras: A user-friendly deep learning library that runs on top of TensorFlow, simplifying the development in addition to training deep neural networks.

Developing and Deploying The AI Model

The team can use TensorFlow or PyTorch to build the actual AI model. The reason we isolated those two is that they offer APIs for creating, training, and most importantly evaluating the model. They can then be deployed using cloud-based services like SageMaker, Azure Machine Learning, or Google Cloud AI Platform, which all have managed environments for training and deploying ML models at scale.

Integrating The Model

To ensure effective integration and interoperability between cloud services and your AI/ML models, API frameworks like REST (Representational State Transfer) or GraphQL can be some good options. These frameworks are what allow for communication and data exchange between different components of the system.

Moving Forward

Once you’ve integrated everything, use tools like CloudWatch or Azure Monitor to gain some insight as to how your system is utilizing its resources and always be ready to make adjustments.

The Takeaway

Innovation on top of what many already consider cutting-edge technology is a recipe for success. Cloud computing, artificial intelligence, and machine learning are all powerful tools that, when integrated synergistically, can revolutionize businesses across various industries. The key takeaway from this discussion is that the combination of the three offers tremendous potential when it comes to driving strategic decision-making and operating as efficiently as possible.

Written By Ben Brown

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