A year after the launch of ChatGPT, the matter of generative AI adoption still can cause heated discussions. However, despite different opinions on the technology, it’s hard to deny that embracing AI has become not an option but a necessity. And the software development field is no exception.

Artificial intelligence opens the door to unprecedented advancements in development practices. Even though the GenAI tools are relatively new, 2023 research by McKinsey showed that with generative AI-based tools, software developers could write new code nearly twice as fast and optimize existing code in nearly two-thirds of the time. As a software development company, Beetroot has also witnessed how AI empowers developers to complete projects faster, improve code quality, and tackle complex problem-solving scenarios.

Naturally, transformative technologies often come with certain challenges, and it’s crucial to acknowledge them. We prefer to refer to them as challenges, not disadvantages, because, like any worthwhile journey, the path to AI integration is not without obstacles. In this article, we explore how navigating these challenges can lead to a balanced and informed approach to leveraging AI’s potential in software development.

Benefits of AI in Software Development

According to Github’s survey, 92% of US-based developers already use AI coding tools in and outside of work. On top of that, 70% of the respondents said that AI coding tools provide them with a workplace advantage, citing better code quality, faster completion time, and effective incident resolution, among other benefits. Let’s figure out how these advantages of AI in software development work in practice.

Improved Accuracy and Bug Detection

Precision in software development directly impacts the reliability and functionality of the final product. Inaccuracies in code can lead to bugs, security vulnerabilities, system failures, and whatnot, jeopardizing the user experience and compromising data integrity. 

One of the core benefits of AI in software development and tools for coding specifically lies in their ability to enhance accuracy by automating specific tasks and performing code analyses. For instance, static code analysis tools can meticulously examine code without execution and identify potential bugs and vulnerabilities before the software goes live. For this, developers often use tools like SonarQube and ESLint that offer real-time insights into code quality and alert about the issues that might have otherwise gone unnoticed.

Personalization and Advanced User Experience

The positive impact of AI tools in software development goes beyond coding. On top of making the product more reliable, AI has the power to significantly enhance user experience by enabling personalized and adaptive interfaces. Machine learning (ML) algorithms analyze user behavior, allowing developers to create adaptive interfaces that cater to individual preferences, providing smoother and more personalized interactions. As a result, this approach fosters increased user satisfaction and engagement.

Predictive Analysis for Better Decision-making

By leveraging predictive analysis, developers can foresee and address potential challenges before they escalate. In this case, AI tools can be especially helpful, contributing to software products’ overall reliability and performance.

To be more specific, artificial intelligence allows software engineers to anticipate potential challenges and optimize strategies accordingly. For instance, AI-powered predictive analysis tools can evaluate historical data like past performance metrics, user feedback, and system logs to forecast potential issues that might arise with a new release. This enables developers to proactively address vulnerabilities and bugs before deployment, ensuring a smoother transition for end-users and reducing the likelihood of post-release disruptions. 

Moreover, AI-driven tools that offer predictive analytics functionality can help make software apps more secure. For example, Darktrace helps software engineers identify and neutralize potential threats by recognizing abnormal patterns in network traffic, thereby enhancing the overall security of a system.

To sum up, the use of AI affects many aspects of the development process, including decision-making, user experience, and project planning. As these tools advance, they will bring even more significant improvements in software development efficiency.

Challenges of AI in Software Development

Nearly every transformative technology comes with nuanced challenges, and it’s essential to recognize and address them. That’s exactly what this section is about.

Complexity and Learning Curve

Incorporating AI tools can introduce a significant learning curve for software development teams. After all, beyond coding, mastering machine learning concepts and frameworks demands time and effort.

Let’s take a look at deep learning as an example. Adapting to deep learning techniques for natural language processing or computer vision requires developers to comprehend the fundamentals of neural networks. This learning curve can slow down the adoption of AI, necessitating extensive training programs and support to enable engineers to leverage these tools effectively.

Despite being a challenge, this learning curve is also a chance for growth. It emphasizes the importance of training and support for developers, acting as a bridge between coding and AI. As teams learn, they boost their skills, paving the way for smoother AI integration. Understanding these layers is critical to unlocking AI’s transformative potential in software development.

artificial intelligence pros and cons

Data Dependency and Privacy Concerns

AI’s effectiveness relies on the availability and quality of data. However, obtaining diverse and relevant datasets comes with certain practical challenges that can impact the performance of AI algorithms. Securing access to substantial, diverse, and high-quality datasets is crucial for development teams. To deal with the challenge, developers need to employ rigorous data preprocessing techniques, which involve cleaning, filtering, and enhancing datasets to meet the standards of effective AI training. Moreover, the process may require collaborations and partnerships to access more extensive and varied datasets.

Among other risks of artificial intelligence implementation are privacy concerns. This issue becomes especially critical in software handling sensitive data, such as fintech and health-tech platforms. Finding a balance between data accessibility and privacy requires robust encryption methods, anonymization practices, and transparent data governance frameworks.

Managing data-related challenges should be a continuous effort backed by technical expertise and a commitment to ethical data practices.

Integration with Existing Systems

Sometimes, legacy systems lack inherent compatibility with AI technologies, leading to potential obstacles during integration. 

Consider an educational institution with a legacy Student Information System (SIS) to manage student records, schedules, administrative tasks, etc. Integrating AI applications for personalized learning experiences is a great opportunity for the institution. However, it can also pose challenges due to the limitations of the existing system. In this case, the developers will have to navigate compatibility issues between the traditional SIS and AI technologies, ensuring a smooth fusion of the two systems. 

While complex, tackling these challenges is possible. To address compatibility issues, data migration challenges, and potential downtimes in similar cases, the tech teams need to apply a strategic and phased approach to integration to ensure the transition is effective and non-disruptive.

Ethical and Employment Concerns

Ethical concerns with artificial intelligence are one of the reasons the adoption of these tools faces resistance. The most significant of these issues are connected to algorithm bias and the potential impact of AI automation on employment. 

Consider the use of AI in recruitment platforms that leverage algorithms for candidate screening. In this case, reducing bias is crucial to ensure all candidates have fair opportunities. For this, developers must design AI systems that follow ethical principles and focus on transparency and fairness during hiring.

Regardless of the project type, AI system developers need to address inherent biases and refine algorithms to promote inclusivity and create platforms that align with ethical standards. Additionally, it’s important to address the socio-economic implications of automation with proactive measures. These include reskilling programs and fostering a collaborative environment where humans and AI create a synergy rather than substitute one another.

Overcoming Challenges and Maximizing the Pros of AI Technology

In 2024 and beyond, being a software developer extends beyond writing code. Companies value tech experts who adapt easily and constantly look for ways to make their workflow more efficient with various environments and technologies, including AI-powered tools. 

New tech trends come with certain challenges — that’s just how it goes. But typically, if software engineers are open to exploring new tech opportunities, they are also willing to tackle the new challenges. At least, that’s what we noticed with our own tech teams. 

Here are the key strategies we at Beetroot have found helpful when working with AI.

Comprehensive Training Programs

For one of Beetroot’s webinars on AI adoption, we invited Philip Stelter, Clearly Partners co-founder, who has over 20 years of experience consulting on building roadmaps to AI implementation. Philip shared that though many businesses are willing to adopt AI, they admit that AI specialists are their biggest skills shortage. Therefore, the expert suggests that companies need to build cross-functional task forces of internal experts who will focus on experimentation and strategy development.

Dmytro Voloshyn, CTO and co-founder at an EdTech company called Preply, also shared an interesting story from their experience during the webinar. When Preply decided to incorporate AI within their internal team, they consulted engineers for suggestions to encourage tool adoption. While options like paid licenses and tool lists were considered, training emerged as the most popular answer.

Because of skills shortage, it’s essential for tech companies that adopt AI to address the learning curve and invest in comprehensive training programs for development teams. Educational courses, regular knowledge-sharing events, workshops, and access to resources help ensure that teams are well-prepared to leverage the full potential of AI while mitigating challenges. Beetroot also follows this strategy. In 2023, we’ve run 20+ AI projects in both R&D and Beetroot Academy training programs. 

Beetroot AI/Ml dedicated teams

Fostering Responsible AI Use

While bias can be considered one of the most significant negative effects of AI, this challenge can be tackled. AI impacts not just the tech aspect of your business but also areas connected to legal operations, marketing, customer service, human resources, finances, and more. Therefore, establishing comprehensive, responsible AI guidelines is essential. When working with artificial intelligence, developers and organizations should prioritize transparency and accountability in their AI systems, mitigate biases, and promote inclusivity.

It’s also worth noting that responsible AI use extends beyond compliance with regulations. It encompasses a commitment to ethical practices that prioritize human well-being and community values. Though it sounds complicated, this commitment is worth the effort in the long run. Responsible AI use fosters trust among users, stakeholders, and the broader community, ultimately contributing to the development of AI systems that benefit us all.

Balancing AI and Human Expertise

The same research by McKinsey we referenced earlier proves that while AI can do a lot, there are software development aspects where human oversight is irreplaceable (at least for now). These include examining code for bugs and errors, contributing to organizational context, and navigating tricky coding requirements.

Such observations prove that the most effective AI adoption strategy involves recognizing the symbiotic relationship between technology and human expertise. Finding a healthy balance here ensures that the strengths of AI, with its precision and speed, and human intelligence, with its creativity and mindful decision-making, enhance one another.

How does it work in practice? In software development, AI can be a valuable assistant, handling repetitive tasks, processing data, and providing insights. Meanwhile, humans can focus on high-level decision-making, creative problem-solving, and empathetic interactions. Building such synergy can help organizations maximize the AI benefits for business and ensure that the human touch remains integral to innovation.

You can learn more about this approach in Beetroot’s article on merging tech and human sensibilities.

Embracing AI with a Balanced Strategy in Mind

So, how will AI affect software engineering? We believe it depends on the approach tech teams take. 

The adoption of AI presents both unmatched opportunities and complex challenges. Tech experts who choose to work with artificial intelligence need to do it with a strategic approach in mind. Maximizing the benefits of the technology while minimizing its challenges requires a well-informed perspective, careful planning, continuous learning, and a commitment to ethical practices. Moreover, at the core of AI adoption should be an understanding that it is most effective when used in synergy with humans. In Beetroot’s experience, it’s also essential to continuously seek guidance from other experts, especially when exploring new technologies. We’ve found sharing insights and knowledge with like-minded teams navigating similar experiences an integral part of growth. Having successfully executed over 20 AI projects in the last year, we are happy to extend our expertise to those looking to adopt AI in their software development journey. So, if you seek guidance in software engineering and AI in particular, our team is here to help.

Find out how we can help extend your tech team for sustainable growth.

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