Software Development May Reap Big Rewards from Generative AI
Study predicts huge productivity gains when AI assists programmers
As generative AI and machine learning technologies seep into business use, executives rightfully want to know what the economic and productivity gains will be.
In June, McKinsey published The Economic Potential of Generative AI: The Next Productivity Frontier addressing these issues — and found that the impact is huge. Based on its analysis of 63 business use cases, and estimates of its effect on the productivity of the global workforce, McKinsey’s study projected that
generative AI could add an additional $6.1 trillion to $7.9 trillion annually, boosting the overall impact of artificial intelligence on the global economy by an annual $17.1 trillion to $25.6 trillion.
The McKinsey study found that while generative AI use cases could have an impact on most enterprise business functions, four in particularly could account for approximately 75% of the total annual value from generative AI use cases. These are: customer operations, marketing and sales, research and development, and software engineering.
A Hard Focus on Software
Digital technologies, including advanced analytics and machine learning algorithms, have been applied to the first three functions for the past few decades, so it’s easier to understand how GenAI would build on previous work as well as give rise to innovative new advances. But that hasn’t quite been the case with software engineering. As it turns out, software development may prove to be the biggest benefactor of these new tools when used to assist, not replace humans.
In a recent article, “Quantifying the Productivity Gains of Generative AI for Developers,” IBM Fellow Jerry Cuomo @JerryCuomo drills down to the productivity aspect of AI implementation, not just the economic value. He nicely explained the role of generative AI in enhancing the productivity of software developers by examining the potential benefits for developers of varying experience levels, from novice to experts. Over the years, Cuomo has played a major role in a number of major software advances, from IBM’s WebSphere Application Server to open source Hyperledger blockchain technologies. He’s currently CTO in IBM’s Consulting business unit where he’s responsible for AI-based automation technologies and strategy.
“The productivity gains enabled by the introduction of generative AI allow for a shift to value and create opportunities for increased innovation, improved quality, enhanced customer experience, and faster time-to-market,” wrote Cuomo.
“Specifically, we estimate that generative AI can lead to a 15–20% increase in the number of new products or features developed, a 10–15% reduction in the number of bugs found in production, a 5–10% increase in customer retention and loyalty, and a 10–15% reduction in time-to-market for new products or features.”
“Developing software is a complex task that requires a diverse team of engineers with specialized skills and experience,” added Cuomo. “For our productivity model, we have chosen to focus on four skill levels in development: junior, mid-level, senior, and technical leads/architects.” Let me summarize how Cuomo defines each of these skill levels.
Junior developers: 0–2 years of experience; estimated salary: $55,000 — $80,000 per year; typically around 10–20% of the software development team.
Junior developers are generally responsible for developing basic features, fixing bugs, and assisting mid-level and senior developers with more complex tasks. “AI can provide guidance and suggestions, allowing junior developers to learn and grow in their roles. For example, AI can help junior developers identify syntax errors, suggest code snippets, and automate repetitive testing tasks.”
Mid-level developers: 2–5 years of experience; estimated salary: $80,000 — $120,000 per year; at around 50–60%, they typically make up the largest percentage of the software development team.
Mid-level developers are generally responsible for developing core functionality, integrating different systems and components, and working with senior developers and technical leaders to ensure the architecture and design meet project requirements. “AI can provide optimization suggestions and help mid-level developers save time on routine tasks. For example, AI can help mid-level developers optimize code for performance and scalability, automate build and deployment tasks, and identify potential bugs and errors before they become problems.”
Senior developers: 5–10+ years of experience; estimated salary: $120,000 — $175,000 per year; typically around 15–25% of the software development team.
Senior developers are generally responsible for designing and developing critical features, optimizing the system for performance and scalability, and mentoring mid-level and junior developers. “AI can help senior developers identify potential issues and suggest improvements to the code-base. For example,
AI can help senior developers identify security vulnerabilities, suggest refactoring or optimization opportunities, and automate routine testing and deployment tasks.”
Technical leads/architects: 10+ years of experience; estimated salary: $175,000 — $250,000 per year; at around 5–10%, they typically make up a small percentage of the software development team.
Technical leads/architects are generally responsible for defining the system architecture, guiding the development strategy, and collaborating with stakeholders to ensure the system meets business requirements. “AI can help technical leads/architects identify potential issues and suggest improvements to the software architecture. For example, AI can help technical leads/architects identify potential scalability or performance issues, suggest design patterns or best practices, and automate code review and analysis tasks.”
A Five-year Model
Cuomo and his colleagues developed a model to help quantify the potential productivity gains of generative AI in assisting developers at each skill level. The model is based on the thesis that productivity gains are best realized across a set of skill groups over a period of time, e.g., five years.
The article illustrates how the model works, using a fairly standard five-year plan for the transformation of an insurance company’s claims processing system. “The following table illustrates the anticipated productivity gains for each developer role during the five-year time-frame:”
The article further explained the changes driving the productivity gains in year one, three, and five.
Year 1: “In the initial year, all roles experience productivity gains. Junior developers stand out with an impressive 14% increase, almost twice the gains of other roles. This highlights the effectiveness of generative AI in supporting junior developers, enabling them to approach their tasks more efficiently and further develop their skills.”
Year 3: “All roles demonstrate significant productivity gains, with junior developers showing remarkable improvement in tasks such as creating code unit tests. Mid-level developers also experience substantial progress, benefiting from increased efficiency and expanding expertise. The contributions of AI are particularly evident for senior developers and architects, enabling them to excel in code and security reviews, evaluating new technology, guiding technical strategies, and ensuring high code quality.”
Year 5: “The integration of AI has become well-established across all roles by Year 5, with varying growth rates.
While junior developers maintain strong progress, mid-level developers face the challenge of balancing their skill level with the complexity of their work.
Nonetheless, AI assistance continues to be instrumental in enhancing productivity and supporting developers in their tasks, enabling sustained gains throughout the 5-year period.”
Rethinking Pricing
Interestingly, Cuomo also makes a case for repricing based on the use of generative AI and similar productivity tools. He said it must be reevaluated to reflect the actual value delivered to the client.
Instead of pricing based on labor rates and billable hours, pricing should consider both the cost savings and the potential value that can be generated.
He recommends “value-based pricing or performance-based pricing models that align incentives for both the client and the provider, which can incentivize the provider to deliver greater productivity gains and create better profits while improving productivity for developers.”
“In conclusion, the introduction of generative AI models like GPT and CODEX can have a significant impact on developer productivity and ultimately lead to greater value delivery and growth opportunities for businesses.
With an average of 60% productivity improvement across these roles [over five years], the overall benefits to the business would be substantial. This level of productivity gain translates into significant time and cost savings, as tasks are completed more efficiently and effectively.
… By adopting the right pricing models and leveraging AI assistance effectively, businesses can unlock even greater productivity gains and position themselves for success in a rapidly evolving digital landscape.”
This blog first appeared August 31, here.
