The AI Shift in Software Development: What's Next for Our Jobs?

As a Manager in engineering and Solution architect, I follow market and technology trends to understand changes. The current change is AI. Generative AI is everywhere. This touches my job security and furthers the essence of work. And I want to share my personal view on what is happening.

The integration of generative AI and code generation tools into the software development service industry will likely produce significant changes across various roles. Some of them may become irrelevant, and all of them will need to accept big changes.

Let’s take a look on short-term and long-term perspectives for such popular roles as Project Managers, Business Analysts, User interface developers aka Front-End and Mobile, Back-End Developers (e.g. Node.js, Java, .NET, Python, etc.), Automation QA Engineers, Manual QA Engineers, Solution Architects.

I will break my predictions into the following categories:

????Tools & Routine - how the job tools will be changed and the approach to work.

????Job Security - the degree to which classical job responsibilities will remain protected and unchanged

???Demand Perspective - what will be the demand for this role in a market.

So let’s see what does my crystal ball says about the near future? ??

What can be expected in 1 year

Project Managers:

• Tools & Routine: Begin adopting AI tools for project forecasting, risk analysis, and resource allocation.
• Job Security: Greater reliance on AI to optimize project execution but maintains a pivotal role in communication and leadership.
• Demand Perspective: Classic project management skills are complemented by AI tools for risk analysis and resource management. However, AI cannot replace human leadership and decision-making, keeping the demand for human-led project management strong.

Business Analysts:

• Tools & Routine: Increased use of AI to assist with data analysis and requirements gathering. Tools like ChatGPT may help in drafting initial project specifications and conducting stakeholder interviews.
• Job Security: Job security remains stable as roles evolve to include AI tool management and data interpretation rather than being replaced by AI.
• Demand Perspective: Classic specialists remain in demand for their nuanced understanding of business needs, although there's increasing competition from those proficient with AI tools for data analysis. AI-only solutions handle routine data processing but lack strategic depth.

Front-End and Mobile Developers:

• Tools & Routine: Initial integration of AI tools to auto-generate some code snippets and assist with debugging. Increased use of AI-driven design tools that suggest UI improvements.
• Job Security: High demand continues as developers adapt to integrate AI suggestions into creative and functional design.
• Demand Perspective: Demand for classic developers remains strong, particularly for those who can creatively integrate AI suggestions. AI-only solutions assist with basic coding tasks, but full project creativity and execution require human input. Increased demand for multi-skilled and multi-platform specialists. Expectations from junior to senior roles will grow as everyone understands they utilize AI code helpers. A +20-50% productivity is expected from those roles.

Back-End Developers:

• Tools & Routine: Adoption of AI-powered code assistants that suggest optimizations and security enhancements. Begin to integrate automated testing tools for back-end components. A portion of unit and integration tests and documentation are generated.
• Job Security: As back-end complexities grow, developers will need to manage increasingly sophisticated AI tools, to secure their role.
• Demand Perspective: While AI aids in code optimization and routine tasks, there's a critical need for developers who can integrate these AI tools into complex system architectures, ensuring demand for AI-skilled back-end developers. A demand for full-stack devs grow as well as expectations from different seniority levels. At the middle+ level, engineers should incorporate skills in different technologies to stay in demand.

Automation QA Engineers:

• Tools & Routine: Introduction of AI to generate test cases and scripts. AI begins to assist in identifying high-risk areas for focused testing.
• Job Security: Shifts towards more strategic roles, overseeing AI tools and refining test processes.
• Demand Perspective: Classic QA roles are being augmented by AI, especially in generating and executing test cases. AI-enhanced roles are becoming more strategic, focusing on tool management and process improvement over routine testing. Specialists in demand are those who combine several technologies and soft skills.

Manual QA Engineers:

• Tools & Routine: Start using AI for routine test generation and anomaly detection. AI helps prioritize test scenarios.
• Job Security: Initial threat to job security as some tasks begin to automate, but critical thinking and exploratory testing remain valued.
• Demand Perspective: While AI begins to handle routine tests, demand for classic manual QA lowers. Specialists adapting AI into their testing strategies are securing their roles more effectively. Those who can see and provide value within a business context and have good interpersonal skillsets will win the game.

Solution Architects:

• Tools & Routine: Use AI to validate architecture decisions against best practices and simulate outcomes.
• Job Security: Enhanced role in integrating AI into solutions, maintaining job security through high-value decision-making.
• Demand Perspective: There is a slight shift toward AI-enhanced roles as AI tools assist in simulating and validating architecture decisions. Classic architects remain vital for their deep expertise in integrating complex systems that AI cannot fully comprehend.

3 years forecast

Project Managers:

• Tools & Routine: Enhanced use of AI for real-time project tracking and advanced decision-making tools. AI helps in dynamic resource reallocation and adapting project scopes based on predictive models. PMs become key negotiators on all levels as the complexity of the project grows and involves the interaction of even more stakeholders.
• Job Security: The role evolves to focus more on strategic oversight, integration of AI tools, and maintaining human-centric leadership and motivation.
• Demand Perspective: AI integration in project management is becoming more prevalent, but the demand for managers who can interpret AI data and maintain team dynamics remains critical. The PM will require more knowledge of business acumen to strengthen their positioning.

Business Analysts:

• Tools & Routine: AI becomes more deeply integrated into predictive analytics, customer behaviour analysis, and complex system modelling. BA is expected to generate prototypes and test them.
• Job Security: Continues to evolve; AI tools handle more data processing, and analysts focus on interpretation, strategy, and decision support.
• Demand Perspective: AI-enhanced analysts dominate as they leverage AI for complex modelling and predictive analytics, greatly enhancing productivity and strategic value. Purely classic roles diminish as data handling becomes largely automated. Communication, client service, and product management skills become must-haves for all seniority levels.

Front-End and Mobile Developers:

• Tools & Routine: AI tools are more deeply integrated, providing advanced code optimization suggestions and more sophisticated interface design prototypes. User interfaces have no limits in technologies. It is expected that specialists understand product demand, prepare relevant interfaces for all platforms and evolve them.
• Job Security: Emphasis on creative input and user experience expertise keeps this role secure and essential.
• Demand Perspective: As AI tools advance, developers integrating AI effectively remain in high demand for their ability to innovate and customize user experiences beyond what AI can generate alone. A good understanding of product development, and communication skills will give ability to progress in this role. Expected there is no distinguish between platforms, a specialist should handle all user-related applications.

Back-End Developers:

• Tools & Routine: More sophisticated AI integration for building and maintaining complex, scalable back-end systems. AI helps in refining performance optimization and automated scalability solutions. Modules, tests, integration solutions, and infrastructure are expected to be covered by such specialists. Each should generate results as x2, x3 specialists if combined with pre-AI times.
• Job Security: Focus shifts towards strategic system architecture and advanced problem solving, securing their position as integral to creating robust software solutions.
• Demand Perspective: Demand grows for developers who not only understand back-end technologies but also can leverage AI to enhance system performance and scalability. As more complex systems are integrated there is a more holistic view expected. This prioritises engineers’ skills in communication with stakeholders.

Automation QA Engineers:

• Tools & Routine: AI is extensively used for complete test suite generation, execution, and real-time results analysis. AQA is expected to cover all layers of quality: test plans, integration, end-to-end, performance, data quality and security.
• Job Security: Shift towards oversight and AI-driven strategy formulation, enhancing the strategic importance of QA engineers who can deliver quality on several layers. Single-focus and single technology specialists become less and less relevant.
• Demand Perspective: The shift toward AI-driven QA continues, reducing the number of classic QA roles needed. Those who manage and innovate with AI tools are better positioned in the job market.

Manual QA Engineers:

• Tools & Routine: AI integration deepens; manual testing focuses on areas AI struggles with, like user experience and complex scenario testing.
• Job Security: The job requires a wider skillset to remain on the market. Manual QA work remains only in the field where the human touch is crucial.
• Demand Perspective: Increased specialization in testing areas where AI underperforms, keeps demand. Such engineers should provide x2-x5 productivity compared to pre-AI times. Specialists with high soft skills and business understanding will stay in demand.

Solution Architects:

• Tools & Routine: Advanced AI models simulate detailed system behaviours and predict future needs, aiding in scalable and efficient design.
• Job Security & Essence: Remains highly secure as architects are needed to oversee AI-driven designs and ensure alignment with business goals.
• Demand Perspective: Increased reliance on AI for routine simulations and data analysis, but architects skilled in AI use who can also handle the strategic application of technology are in higher demand. As there are more people who want to join this group of individuals with high soft skills are in higher demand.

And in 5 years…

All Roles:

• Tools & Routine: AI tools are fully integrated and highly customized to each aspect of the development lifecycle, providing significant automation and predictive capabilities. Small teams are replaced with single AI-augmented professionals.
• Job Security & Essence: The landscape will focus heavily on roles that manage, interpret, and strategically guide AI tools. Job security will depend on the ability to adapt and integrate these tools into higher-level decision-making and creative processes. Essential roles will pivot towards AI collaboration and performance. Small cross-functional teams to handle big aspects of a business.

Project Managers:

AI will greatly assist with project management tasks such as scheduling, resource allocation, and risk assessment. However, Project Managers who excel at integrating AI tools while maintaining strong leadership and communication skills will be crucial. They will need to manage multidisciplinary teams (which are expected to deliver x3-x5 results as combined to the pre-AI era) and ensure that AI tools are used effectively, aligning technological solutions with business objectives.

Business Analysts:

AI's ability to handle vast amounts of data and perform complex predictive analytics will see a decline in traditional roles focused solely on manual data analysis. Business Analysts who use AI to synthesize insights and drive strategic business decisions will become indispensable. They'll act as the bridge between raw AI outputs and actionable business strategies, ensuring that insights are implemented effectively. The role mostly mixed with product managers. Expected that most of the minor product changes and small projects will be implemented end-to-end by a single BA (without the involvement of development teams).

Front-End and Mobile Developers:

As AI tools advance in generating more complex code and even entire application sections, the need for classic coding may decrease or become irrelevant. However, developers who combine AI tools with a deep understanding of user experience and innovative design will remain in high demand. Higher overall expectations in soft skills and design techniques during job interviews. These professionals will be needed to oversee AI outputs, ensuring that applications are not only functional but also provide exceptional user experiences.

Back-End Developers:

The landscape for Back-End Developers will involve extensive use of AI for routine coding tasks, performance optimization, and security checks. There is no language of framework specialisation. Engineers are expected to deliver complex modules and platforms. These developers will need to possess a deep understanding of both traditional back-end development, solution design and AI capabilities to ensure seamless functionality and scalability of systems. Those who are flexible and have good soft skills will be desired additions to teams.

Automation QA Engineers:

AI will likely dominate the routine testing landscape, performing tasks ranging from test case generation to real-time problem identification and solving. Automation QA Engineers who evolve into roles that focus on setting up, monitoring, and interpreting AI systems will find their positions secure. Their focus will shift towards ensuring that AI systems are effectively integrated into the testing pipeline at all levels and that they align with overall project goals.

Manual QA Engineers:

As AI takes over more routine testing tasks, manual QA Engineers will mostly disappear. Those who specialize in areas that require nuanced human judgment, such as usability, accessibility, and security, will continue to be essential but will need to combine multi-domain knowledge. These roles will increasingly require a hybrid skill set of traditional QA expertise combined with the ability to integrate and leverage AI insights for more complex scenario testing, great soft skills and business drivers understanding.

Solution Architects:

Solution Architects will rely heavily on AI for routine simulation and validation tasks, but their expertise will be crucial in designing complex, scalable systems that integrate multiple AI technologies and legacy systems. Those who can effectively combine AI with innovative architecture solutions will be highly sought after to create systems that are both robust and flexible. In this complex environment, there is a demand for highly skilled professionals combined with those who have developed soft skills and business value understanding.

Overall, across all roles, the trend is toward a synergy between AI capabilities and human expertise. Professionals who adapt to incorporating AI into their skill set, focusing on business strategy, and interpersonal soft skills, will need to embrace continuous learning. The landscape of AI is constantly evolving, and new skills will be required to stay relevant. This might involve learning to work with new AI tools, understanding how to interpret AI outputs, and developing critical thinking skills to assess the limitations of AI. Professionals who are willing to adapt and learn will be best prepared to navigate the future of work.

This article was written in April 2024, so we’ll check how it goes ??

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