AI in Software Development: Conventional vs AI solutions

On November 30, 2022, ChatGPT was introduced, and concepts like prompt engineering, large language models, GPT, machine learning, etc. started being a wide part of our everyday work. ChatGPT became the fastest-growing consumer software application in history, gaining over 100 million users in January 2023. Other large language models have been developed and introduced and have reached the point of something we use to help us solve our everyday problems. This period has shown that this type of artificial intelligence can be a great solution to many problems we used to have that were solved by traditional programming.

At the same time, some conventional software development solutions have proven to be better than their AI alternative. In this article, I'm going to use my experience as a software developer to shed light on the distinctions between conventional software solutions and AI-based software solutions, and the optimal scenarios for utilising each. Also, we're going to look at some AI-based tools that help developers in their everyday work, not only for AI solution creation but also traditional programming.

With that out of the way, let's dive deeper into conventional vs AI solutions and see what are their pros and cons in various scenarios.

Conventional vs AI solutions - the difference:

Traditional approaches to problem-solving rely on predefined algorithms, where a set sequence of instructions directs the computer's actions. Analogous to providing a set of instructions to assemble a wardrobe, the computer will only be able to complete the task if provided with explicit guidance on how to proceed. Essentially, conventional solutions hinge upon predetermined instructions, executing tasks solely based on these instructions.

In contrast, artificial intelligence (AI) solutions leverage machine learning techniques, aiming to emulate human-like intelligence. Rather than following explicit programming instructions, AI systems analyse real-world data to inform decision-making processes. Taking the example of wardrobe assembly, instead of providing step-by-step instructions, AI systems can be trained using a dataset containing various assembly scenarios. When tasked with assembling a wardrobe, the AI would generate a sequence of steps deemed most suitable based on the patterns learned from the input dataset. This approach allows for adaptability and the ability to make decisions based on contextual information rather than relying solely on predefined instructions.

Next, I want to point out how this main difference in the approaches influence the end result:

Learning and Adaptation:

• In traditional computing, operations are bound by fixed instructions. Programs execute predetermined commands without the capacity to adapt or learn from novel information.
• AI, particularly machine learning, excels in learning from data. Through iterative processes, algorithms enhance their performance by discerning patterns within extensive datasets.

Human behaviour capabilities:

• Traditional computing systems lack human-like reasoning, learning, or comprehension capabilities, serving as potent tools devoid of cognitive emulation.
• AI endeavors to emulate and enhance human cognitive functions. From natural language processing to image recognition and even creative endeavors like generative AI applications such as MidJourney, DALL-E, and Adobe Firefly, AI spans a spectrum of human-like capabilities.

Flexibility and Problem Solving:

• Traditional computing systems proficiently solve specific tasks for which they are programmed, offering utility across various domains but within the confines of predefined functions.
• AI operates adeptly in dynamic settings, accommodating unforeseen challenges. By generalising knowledge, AI systems navigate diverse problem domains, often surpassing traditional computing in intricate, ambiguous scenarios.

Decision-Making:

• Decisions in traditional computing are deterministic, adhering strictly to predefined rules and lacking nuanced understanding or contextual awareness.
• AI decision-making involves probabilistic reasoning. Machine learning models assess probabilities based on data patterns, facilitating a nuanced decision-making process akin to human cognition.

AI solutions: Pros and cons

We talked a lot about how AI and its ability to imitate human behaviour are revolutionising the world, especially software development. But this also comes at a cost. Let’s take a look at the pros and cons of AI solutions related to their impact on software development:

Pros:

• Automated code generation: AI tools can automate parts of the software development process, including code generation. Through techniques like natural language processing (NLP) and machine learning, these tools can understand requirements and generate code snippets, reducing development time and effort.
• Bug detection and resolution: AI-powered tools can help detect and fix bugs in software code. By analysing code patterns and historical data, these tools can identify potential issues early in the development cycle, improving software quality and reliability.
• Performance optimisation: AI algorithms can optimise software performance by analysing usage patterns and system resources. Through techniques like predictive analytics and automated tuning, AI can fine-tune software parameters to improve efficiency and responsiveness.

Cons:

• Learning curve: Adopting AI solutions for software development requires developers to acquire new skills and knowledge. The learning curve associated with understanding AI algorithms and integrating them into existing workflows can be steep, requiring time and resources.
• Lack of transparency: Some AI-powered tools lack transparency in their decision-making processes, making it difficult for developers to understand how recommendations or suggestions are generated. This opacity can lead to distrust and uncertainty among development teams.
• Data dependence: AI algorithms rely on large datasets for training and validation. In software development, obtaining high-quality and diverse datasets that accurately represent the problem domain can be challenging. Additionally, biased or incomplete data can lead to suboptimal AI model performance and unreliable results. This occurs to be a solid problem, when working with AI, because it has limited data sources to train with, and opening it to all the internet knowledge can have irreversible consequences.
• Hallucination: AI hallucination, the phenomenon where AI systems produce outputs based on erroneous data interpretations, poses a significant problem across industries.

This issue can result in incorrect decision-making, the spread of misinformation, and potentially harmful outcomes in areas such as healthcare, finance, and security.

Conventional solutions and when to use them:

Let's circle back to the heart of our discussion: the age-old question of when to rely on traditional methods versus embracing the allure of AI wizardry. To kick things off, let's take a closer look at traditional approaches—those dependable techniques that have been guiding software development for years and are still as relevant as ever.

• Simple tasks: Sometimes, tasks aren't all that complicated, and you don't need fancy AI to get them done. In those cases, sticking to basic programming methods works just fine. Things like sorting data or making simple decisions based on rules are examples where you can keep it simple without needing AI.
• Limited resources: AI stuff can be pretty demanding—it needs a lot of computer power and smart people to work on it. But if you're working on a small project or your computer isn't super powerful, traditional methods might be easier and cheaper to handle.
• Easy to understand: Traditional ways of doing things are often easier to explain. AI can be pretty confusing, but with traditional methods, it's easier for everyone to understand how things work and why certain decisions are made.
• Time crunch: AI projects can take a long time to develop and fine-tune. But if you're in a hurry and need to get things done quickly, sticking to traditional methods can save time and get the job done faster.
• Not so data-hungry: AI loves and depends on data—it needs lots of high quality data to learn from. But if you don't have much data or it's not very good, traditional methods might be a better choice since they don't rely as much on having tons of data.

So, while AI is pretty cool, sometimes it's just easier and more practical to stick with the tried-and-true traditional methods. Understanding when to use each approach can help you make the best choice for your project.

AI solutions and when to use them:

At this point, you probably notice how complex AI ‘works’. This is a disruptive innovation and its capabilities are immense. In this section, we’ll see which situations warrant making it your first choice. You will probably think about situations opposite to the ones in the conventional section - and you’re probably right. But the fact is, conventional solutions are much more limited than AI ones - which is why AI will open a whole new world of possibilities to use.

• Complex Tasks: If the problem is complex and involves analyzing large amounts of data or making predictions based on patterns, an AI-based solution might be more appropriate. For example, if you're trying to predict customer behavior based on past interactions, AI can help uncover patterns that might not be obvious to humans.
• Big volumes of initial data: If you have a large amount of data that would be overwhelming for a human to analyze, AI can help by automatically identifying patterns and insights within the data. For example, if you're analyzing millions of customer transactions to detect fraud, AI can quickly flag suspicious activity.
• Efficiency for uncertain conditions: For tasks that are more ambiguous or require complex decision-making based on uncertain data, AI can outperform traditional methods in terms of speed and efficiency. For example, if you're trying to optimize a supply chain with many variables and constraints, AI algorithms can find solutions much faster than manual methods.
• Adaptability: While conventional solutions need strict and predefined rules, AI-based solutions, can adapt and learn from new data and experiences. They excel in scenarios where the problem is dynamic or uncertain, and the solution needs to evolve over time. For instance, in recommendation systems like those used by streaming services, AI learns from your viewing habits to suggest new content you might like.

As you see, AI has immense potential to produce adaptive self-learning systems that could solve many of our problems. Now, let’s look at specific tools that can help everyone in their everyday work.

AI tools examples:

In today's fast-paced digital landscape, the demand for increased productivity is ever-present. Thankfully, a myriad of AI-powered tools has emerged to streamline workflows, enhance efficiency, and revolutionize how we tackle tasks. From project management platforms leveraging AI algorithms to optimize resource allocation, to virtual assistants automating mundane administrative duties, these tools are reshaping the way we work. Here are some examples of powerful solutions almost all of us use without necessarily realizing they’re AI-based: 

• Grammarly: It is a digital writing assistant tool designed to help users improve the quality and correctness of their writing. It offers features such as grammar checking, spell checking, punctuation correction, style suggestions, and vocabulary enhancement. Grammarly can be used as a web-based application, a browser extension, or integrated into various writing platforms such as Microsoft Word, Google Docs, and more. My personal favorite when writing a long text (like this article)
• Zapier: An online automation tool that connects various apps and services, allowing them to work together seamlessly without the need for coding or manual intervention. It enables users to create automated workflows, known as "Zaps," that trigger actions in one app based on events that occur in another app.
• Clockwise: Scheduling and calendar optimization tool designed to help teams manage their time more efficiently. It integrates with existing calendars such as Google Calendar and Microsoft Outlook to streamline scheduling and minimize conflicts. Clockwise uses algorithms to analyze team members' calendars and automatically schedule meetings at the most convenient times for everyone, minimizing interruptions and optimizing productivity and also helps users protect their focus time by automatically scheduling blocks of uninterrupted work time based on their preferences and availability.
• MailButler: Productivity plugin designed to enhance email functionality for users of popular email clients such as Apple Mail, Gmail, and Outlook. It offers a range of features to help individuals and teams manage their email communication more effectively. Some key features of Mailbutler include Email Tracking, Scheduling, Templates, Tasks and Notes, Follow-up Reminders, and Integration.

Summary

In summary, both conventional and AI solutions play essential roles in software development, each offering unique strengths and limitations. While conventional methods provide stability and transparency, AI solutions offer automation, scalability, and predictive capabilities. The optimal approach often involves a combination of both, with AI influencing and augmenting conventional practices. By leveraging the strengths of both conventional and AI-driven methodologies, developers can address diverse challenges and drive innovation in software development.

PS:

Read part 2 of this article, where we are going to dive deeper into AI solutions that improve software engineers' productivity and become a crucial part of their everyday work.