Software development is undergoing a seismic shift. AI is becoming an integral part of the developer’s workflow. 70% of developers have embraced AI according to a Stack Overflow 2023 Developer Survey. This shift is revolutionizing the way that developers build, innovate and create new products.
However, this growing adoption of AI has also led to an acute shortage of skilled talent in the software industry. Engineering managers who are urgently seeking to hire talented software resources for AI development and integration face a significant challenge:
Demand Supply Imbalance:
Demand for software engineering talent far outweighs the available supply. Consider these alarming statistics:
- LinkedIn reports over 20 million open software engineering roles worldwide, while only 10 million qualified candidates are available. The gap is expected to widen further as the demand for software continues to grow.
- Gartner predicts a shortage of 2.7 million software developers by 2023, with demand outpacing supply by two-fold.
- A survey conducted by Robert Half reveals that 91% of technology managers face challenges in finding skilled talent.
AI to the Rescue?
Fortunately, engineering managers can turn to AI as a powerful tool to address the talent shortage. Forward-thinking companies are already leveraging AI adoption throughout their SDLC as a competitive advantage in the race for top talent.
Let us first explore the various ways AI is rapidly transforming the SDLC:
- Code Analysis: AI-powered tools can analyze code for errors, security vulnerabilities, and performance bottlenecks. This helps developers write better code and avoid costly mistakes.
- Automated Testing: AI automation streamlines the testing of software applications, generating unit test cases and increasing test coverage. This frees up developers’ time to focus on more creative tasks.
- Recommendation Engines: AI algorithms recommend code snippets, libraries, and resources to developers, enhancing their productivity and efficiency.
- Documentation: AI assists developers in effectively documenting their code, making it easier for others to understand and maintain it. Developers hate having to spend too much time doing documentation.
- Personalized Feedback: AI provides personalized feedback to developers, helping them improve their coding skills and avoid common errors.
Metrics of AI Integration Look Impressive
Integrating AI into the SDLC brings impressive benefits that attract top talent and enhance development processes:
- Speed: AI automates manual tasks, such as code linting and testing, accelerating development cycles. Developers using AI-powered tools can code up to 55% faster, as revealed by a study conducted by GitHub.
- Quality: AI-driven code analysis identifies potential errors, security vulnerabilities, and performance bottlenecks, enabling developers to write high-quality code. AI-powered tools can find up to 90% of security vulnerabilities, according to a study by Snyk.
- Velocity: AI-generated code, feature suggestions, and data pattern recognition enable developers to iterate quickly and efficiently. Google’s study demonstrates that AI-powered tools can increase feature velocity by up to 30%.
Building The Competitive Moat
Consider these three strategies to help you to build a competitive moat and differentiate in a crowded market looking for AI talent:
1. Enriching the Developer Experience
Use AI Tools as your Lead Magnet for acquiring top tier talent in the marketplace. Making AI an integral part of your development process can help in building a competitive moat, turbocharge developer productivity and significantly increasing job satisfaction.
By embedding AI tools into the developer life cycle, engineering managers are sending a powerful message to the developers they are trying to hire and retain. That they value the engineer’s time, they want to eliminate the time-consuming manual aspects of the roles. Enabling the engineers to focus on genuine value addition like accelerating feature velocity, product security and architecture.
Developers love a balanced, stimulating work environment that liberates them from the boredom of daily routines. They are excited to work on critical areas requiring creative thinking, problem-solving abilities, and deep customer understanding.
2. Experimenting With New AI Tools.
Looking for that perfect AI enabled DevOps tool chain so that you can begin your AI journey? Such a toolchain may not exist today. Start experimenting with AI tools you may never have heard of before; tools like GitHub CoPilot, Codey from Google or specialized AI testing tools like DiffBlue Cover for Unit Testing & TabNine for Code Completion.
Look for some quick hit wins to gain visibility and senior leadership commitment. A good place to start is using AI assisted tools for software test automation. Use cases like automated test generation, intelligent test prioritization, performance testing and API testing.
One of the world’s largest financial institutions using an AI tool for unit testing was able to double unit test coverage by 100% in less than 24 hours. Creating the same number of unit tests manually would have taken more than eight days of developer time compared to three-quarters of a workday with the tool—a time saving of more than 90%.
Improved testing efficiency, increased code coverage, enhanced software quality, and faster time to market. Your developers are delighted, and your leadership team will plead with you to take more funds for accelerating AI adoption. A win-win for all stakeholders!
3. Implement an AI Use Policy to Guide Development
Consider implementing an AI use policy to guide the adoption of AI in your development team. Such a policy can provide a framework for responsible and ethical AI practices and ensure that AI technologies are used effectively and in alignment with your organization’s goals.
Developers are often concerned about the ethical implications of AI, such as bias, privacy, and transparency. An AI Use Policy addresses these concerns by outlining the organization’s commitment to ethical practices. It can also play a role in attracting talent by creating a strong ethical reputation and establishing trust and transparency.
In conclusion, while there is no one single silver bullet to attract AI talent in a high demand market environment, engineering managers who can act proactively and boldly can build a competitive moat to attract and retain the best AI talent.