Though AI holds immense potential in aiding programmers, who are already progressively utilizing tools like Chat GPT and GitHub Copilot to enhance their efficiency and productivity, their knowledge, experience, sharp minds, and human touch remain vital for crafting innovative and unique solutions. The U.S. Bureau of Labor Statistics (BLS) projects a 25% increase in employment in this sector over the next decade, significantly outpacing the average for other professions. Hence, how AI impacts their work directly translates to the growth of our businesses, affecting the pace of project delivery and potential cost savings.
What can artificial intelligence do today?
Currently, artificial intelligence is successfully used for automating and simplifying routine programming tasks. AI models can offer suggestions, provide code examples, optimize project management processes, and even identify potential errors and conduct various types of automated tests. Additionally, AI analyzes trends and user data, helps in making strategic decisions, and assesses projects for their complexity, delivering accurate time and resource estimates for development.
Where does it operate but still has shortcomings?
Let’s remember that although AI significantly streamlines the programmer’s work in many cases, it’s still not a perfect tool. An example includes its capabilities in natural language processing, which rely on learned patterns and statistical analysis. This may generate coherent responses, but without genuinely understanding them. Similarly, with machine learning that uses large data sets – AI handles this very well, but its intelligence is limited only to the trained tasks and specific data it’s been provided.
Where does it fall short?
Software development requires creative thinking and problem-solving skills that go beyond standard code writing. As of now, AI models won’t design complex systems, understand business requirements, or make strategic decisions for us – unlike an experienced, highly qualified programmer.
Moreover, generative artificial intelligence raises numerous privacy concerns. If training data contains sensitive information, such as medical records or financial data, there’s a risk of unintentional disclosure or misuse.
According to Gartner, by 2026, 80% of software companies will establish internal teams responsible for generating reusable services, components, and tools for building applications. As a result, AI developers will be able to utilize these ready-made resources, which will significantly speed up and facilitate their work, aligning with the trend for faster and more efficient software delivery.
Will we still need code?
All indications are that AI-based software development may soon overshadow the standard coding process. Developers are increasingly using platforms that require no code or very little code. In this new reality, their role might evolve from traditional coding to “teaching” machines how to effectively and intelligently solve problems, which opens the door for individuals without prior technical experience.
The kingdom of bots
Advanced AI algorithms are driving the trend of creating increasingly intelligent chatbots, which are becoming a popular tool in software development. A case in point is the renowned Chat GPT, which, while not yet perfect, is capable of communicating with humans at an increasingly advanced level, often giving the illusion of conversing with a real person. Thanks to the development of such services, chatbots can learn from their users in real-time, adapting and even anticipating their needs and preferences.
Another intriguing solution we’re introducing to our clients is AI-based voice bots, essentially advanced speech processing engines. They’re currently being used in the broader scope of customer service, effectively elevating its quality, automating repetitive tasks, and generating noticeable savings.
So, what will the bots of the future look like? They’ll certainly be more “human” and prone to adaptation, learning, and interacting with humans on a deeper level. They’ll not just be communication tools, but valuable partners in the tech world. However, with growing capabilities come challenges, especially concerning security, privacy, and ethics.
AI as a Service
AI as a Service (AIaaS) is essentially a cloud-based offering that allows developers to use AI tools and services without the need to create and maintain their own infrastructure. This means that in the future, instead of hiring a specialist to develop AI-based software, we can rely on AIaaS solutions, which will facilitate the integration of AI into the digital product creation process without investing in expensive infrastructure.
The Power of Augmented Reality
AR technology may soon play a pivotal role in the app development process. It can assist in visualizing and testing software concepts in real-time and environments, creating interactive prototypes that allow users to engage with the solution before its final deployment, and enabling developers to learn using three-dimensional models of code or system architecture.
XAI - Explainable Artificial Intelligence
One of the main barriers to adopting AI in sensitive areas such as healthcare, finance, and law is the lack of trust stemming from the frequent misunderstanding of decisions made by artificial intelligence. But what if AI could explain its motivations to us in a clear manner?
XAI (Explainable Artificial Intelligence) is an emerging field of AI research that, while creating AI models that are the source of predictions and decisions, also offers their comprehensible explanation. With more transparent decision-making processes, it’s easier to identify and eliminate potential biases in AI decisions, leading to more ethical outcomes.
In the future, the development of AI-based software might hasten the evolution of autonomous systems that execute technical tasks without human intervention. As a result, artificial intelligence will be better equipped to support self-driving cars, drones, and robotics, enhancing their efficiency and reducing costs.
In the face of escalating cybercrime risks, the continuous enhancement of security methods becomes paramount. AI-based software development may soon emerge on this stage like a superhero, paving the way for cyber-resilience. Its mission will be to design a holistic approach to cybersecurity, integrating observability, automated testing, machine learning, chaos engineering, auto-repair, site reliability engineering, and software supply chain security – all practices geared towards bolstering the resilience of products, services, and systems.
Man against the machine?
Researchers from Microsoft and MIT have estimated that developers using AI tools can perform their tasks 55.8% faster (Business Insider). Proficiency in effectively implementing artificial intelligence will soon become an industry standard, not only for business analysts and software architects. Organizations that invest in developing their own applications will discover that automating repetitive tasks with AI becomes a potential growth point. All of this boils down to improving the quality of end products, faster project turnaround times, and lower costs. The combination of the human mind and machine capabilities forms a powerful technological tandem, open to ever more interesting challenges and solutions.
At iteo, we test such solutions firsthand. We see that automating simple, repetitive actions frees up valuable time for our specialists – not only developers but also our administration and marketing departments. With no-code tools, we can automate actions such as creating content or graphics for social media, as well as streamline internal processes within our organization. And this is just the beginning!