Technology Report

From Pilots to Payoff: Generative AI in Software Development

AI tools improve productivity, but process changes are necessary to generate real value.

By Purna Doddapaneni, Bill Radzevych, Steven Breeden, Bharat Bansal, and Tanvee Rao

First published on septiembre 23, 2025
min read


Executive Summary Software coding was one of the first areas to deploy generative AI, but the savings have been unremarkable. Real gains come from applying AI across the software life cycle—not just code but also product requirements, planning, test, and maintenance. Redesigning processes and applying time saved to other work are among the best ways to improve returns on AI investments. The next wave of autonomous agents raises the stakes, allowing companies to redesign entire workflows to gain a competitive edge. This article is part of Bain’s Technology Report 2025 Explore the report Generative AI arrived on the scene with sky-high expectations, and many companies rushed into pilot projects. Yet the results haven’t lived up to the hype. Two out of three software firms have rolled out generative AI tools, and among those, developer adoption is low. Teams using AI assistants see 10% to 15% productivity boosts, but often the time saved isn’t redirected toward higher-value work. So even those modest gains don’t translate into positive returns. Without a plan to turn interest into habit, initial gains quickly evaporate, leaving leaders asking, “Where’s the payoff?” Beyond code completion: Generative AI for the entire life cycle Early initiatives often fixate on code generation—that is, using generative AI to write code faster. But writing and testing code only accounts for about 25% to 35% of the time from initial idea to product launch (see Figure 1). Speeding up these steps does little to reduce time to market if others remain bottlenecked. Figure 1 AI coding assistants may be able to take on as much as 40% of the work that coders do Note: Industry experience is based on software-as-a-service developer team surveys, with developer teams ranging from around 2,000 to 20,000 full-time–equivalent employees Source: Bain & Company Real value comes from applying generative AI across the entire software development life cycle, not just coding. Nearly every phase can benefit, from the earlier discovery and requirements stages, through planning and design, to testing, deployment, and maintenance. Broad adoption, however, requires process changes. If AI speeds up coding, then code review, integration, and release must speed up as well to avoid bottlenecks. Leading companies such as Netflix recognized this and shifted testing and quality checks earlier (the “shifting left” approach) to ensure that rapidly generated code isn’t stuck waiting on slow tests. So far, generative AI has served as a smart assistant, a copilot with a human in control. Agentic AI will usher in a more autonomous wave—namely, agents that can manage multiple steps of development with little to no human intervention. For example, start-up Cognition introduced an AI “software engineer” (named Devin) in 2024 that can build and troubleshoot applications from natural language prompts. How leaders scale generative AI Leading adopters treat generative AI as a fundamental transformation of their software development life cycle rather than a one-off project. They take a future-back approach to rearchitect their end-to-end software development life cycle around generative AI, embedding it deeply into workflows and scaling it enterprise-wide. They weave it into development workflows and scale it across use cases. Goldman Sachs, for example, integrated generative AI into its internal development platform and fine-tuned it on the bank’s internal codebase and project documentation. This gives engineers context-aware, real-time coding solutions far beyond basic autocompletion—extending to automated code generation and even code testing—thereby significantly accelerating development cycles and boosting programmer productivity. These leaders also make sure that generative AI’s benefits translate into business value. They measure how much time AI saves and redirect that capacity to high-value work, ensuring that efficiency gains become business gains. They also modernize their environments—adopting cloud development environments, automated continuous integration and delivery pipelines, and modular architectures—to remove friction that could limit AI’s impact. They also recognize that there’s no one-size-fits-all approach and tailor targeted tools, playbooks, and trainings to each team’s unique needs, ensuring smooth, fast adoption across diverse scenarios. Common roadblocks to scaling generative AI Even with generative AI’s promise, many firms are stuck in pilot mode because of some common obstacles. Lack of executive direction: If senior leadership doesn’t clearly prioritize generative AI, pilot efforts tend to fizzle. Adoption resistance: Under pressure, developers often fall back on old habits. Some engineers distrust AI or worry that it will undermine their role. Three of four companies say that the hardest part is getting people to change how they work. Overcoming this resistance requires strong change management. Skills gaps: Generative AI requires new skills such as writing prompts and reviewing AI output. But many firms haven’t provided adequate training, so even powerful tools go underused. No ROI tracking: It’s tough to prove generative AI’s value without clear key performance indicators or plans for using the time saved. If you don’t measure results, even real productivity gains won’t show up in business terms. Process or tooling mismatch: Slow, manual processes in build, testing, or release will choke generative AI’s benefits. Legacy toolchains that can’t handle AI-generated code will also blunt any speed gains. These issues explain why so many AI efforts never get out of the sandbox. The good news is that none of these barriers are insurmountable; each can be overcome with the right approach. Often, the toughest obstacles are people related, so overcoming them requires significant investment in training, communication, and cultural change. Reimagine the software life cycle with AI at its core To break out of pilot mode and get real returns from generative AI, tech leaders must go beyond incremental tool adoption and frame their roadmap as an AI-native reinvention of the software development life cycle. Starting with a vision of a future in which AI is seamlessly integrated into every phase of development allows teams to then plan backward to make that vision a reality. Leaders follow a roadmap to move from experimentation to scaled impact. Set an AI-native vision anchored in business outcomes, not just tech metrics. Define a bold, future-back ambition for how software will be built with AI at the core. Tie that directly to concrete business outcomes such as faster release cycles, lower defect rates, or higher customer satisfaction. And show where AI is generating real value (see Figure 2). Figure 2 Key performance indicators and performance targets measure progress Note: Example is illustrative—actual metrics will vary across industries Source: Bain & Company Turn saved time into business results. Don’t let productivity gains sit idle. Decide early how to use freed-up capacity—for instance, whether to ship more features, or reduce spending, or accelerate innovation—and tie those outcomes to financial impact. Scale successful practices across teams to maximize ROI. Start with high-impact, easy wins aligned with the future vision. Apply generative AI where it can succeed quickly—for example, generating new feature code or automating tests—and help pave the way toward an AI-native end state. Avoid fragile legacy systems at first; instead, focus on domains that are ready for AI. Early wins build momentum for broader use. Cultivate AI-native talent and culture. Provide hands-on training (such as prompt engineering or AI orchestration workshops), and actively manage the culture shift. Make upskilling a continuous effort, reassure engineers that AI is an assistant (not a replacement), and celebrate early wins to build buy-in. Modernize processes and architecture for AI at scale. A true AI-native approach often demands overhauling your development environment end to end. Eliminate process bottlenecks that could reduce AI’s speed advantage. Adjust workflows so that faster coding leads to faster releases and isn’t stuck in slow pipelines. Update development tools to handle AI outputs smoothly. Prepare for autonomous workflows. Finally, as generative AI evolves from copilot to autonomous agent, start experimenting with AI handling end-to-end development tasks. Developers’ roles may shift to guiding these agents as “intent engineers” or “AI orchestrators.” Assign an agent to build a simple app in a sandbox, with humans stepping in only if needed. These trials will show how far AI can go, where oversight is essential, and what skills or workflows need to evolve—thus signaling your intent to lead in the next wave of development. Closing the gap: From experimentation to execution Generative AI’s promise is real, but capturing it requires moving beyond one-off pilots. It takes bold leadership to drive adoption, revamped processes to embed AI at every step, and a focus on measurable outcomes to analyze results and make adjustments. The winners won’t be those dabbling in flashy demos but rather those redesigning their workflows to fully integrate AI and deliver tangible improvements. Already, some companies report 25% to 30% productivity boosts by pairing generative AI with end-to-end process transformation—far above the 10% gains from basic code assistants. An even bigger leap is on the horizon as AI evolves from assistant to autonomous agent—a shift that could redefine software development and widen the gap between firms that treat AI as a novelty and those that embrace it as transformative. Generative AI’s capabilities are steadily broadening, and the gains seen today are expected to continue growing over the next 12 to 24 months as models improve their performance and reliability. Tech executives must excel at implementing generative AI today while also preparing their teams for a more AI-driven development model tomorrow. Experiments pay off only when backed by a well-defined approach that converts innovation into measurable results. Now is the time to act. Organizations that move decisively with a clear vision and bold execution will capture real returns and redefine how software is built; those that hesitate risk being left behind. Read the Next Section Building the Foundation for Agentic AI More from the report Tech's Most Valuable Globally Fragmented Tech Tech Investing's New Rules AI Disrupts SaaS AI's Appetite for Compute Power Humanoid Robots Quantum's Potential Agentic AI Transformation AI in Sales Gen AI in Software Development Agentic AI Architecture Read our Technology Report 2025 EXPLORE THE FULL REPORT DOWNLOAD THE PDF

Executive Summary

Software coding was one of the first areas to deploy generative AI, but the savings have been unremarkable.
Real gains come from applying AI across the software life cycle—not just code but also product requirements, planning, test, and maintenance.
Redesigning processes and applying time saved to other work are among the best ways to improve returns on AI investments.
The next wave of autonomous agents raises the stakes, allowing companies to redesign entire workflows to gain a competitive edge.

Generative AI arrived on the scene with sky-high expectations, and many companies rushed into pilot projects. Yet the results haven’t lived up to the hype. Two out of three software firms have rolled out generative AI tools, and among those, developer adoption is low. Teams using AI assistants see 10% to 15% productivity boosts, but often the time saved isn’t redirected toward higher-value work. So even those modest gains don’t translate into positive returns. Without a plan to turn interest into habit, initial gains quickly evaporate, leaving leaders asking, “Where’s the payoff?”

Beyond code completion: Generative AI for the entire life cycle

Early initiatives often fixate on code generation—that is, using generative AI to write code faster. But writing and testing code only accounts for about 25% to 35% of the time from initial idea to product launch (see Figure 1). Speeding up these steps does little to reduce time to market if others remain bottlenecked.

AI coding assistants may be able to take on as much as 40% of the work that coders do

Note: Industry experience is based on software-as-a-service developer team surveys, with developer teams ranging from around 2,000 to 20,000 full-time–equivalent employees

Source: Bain & Company

Real value comes from applying generative AI across the entire software development life cycle, not just coding. Nearly every phase can benefit, from the earlier discovery and requirements stages, through planning and design, to testing, deployment, and maintenance. Broad adoption, however, requires process changes. If AI speeds up coding, then code review, integration, and release must speed up as well to avoid bottlenecks. Leading companies such as Netflix recognized this and shifted testing and quality checks earlier (the “shifting left” approach) to ensure that rapidly generated code isn’t stuck waiting on slow tests.

So far, generative AI has served as a smart assistant, a copilot with a human in control. Agentic AI will usher in a more autonomous wave—namely, agents that can manage multiple steps of development with little to no human intervention. For example, start-up Cognition introduced an AI “software engineer” (named Devin) in 2024 that can build and troubleshoot applications from natural language prompts.

How leaders scale generative AI

Leading adopters treat generative AI as a fundamental transformation of their software development life cycle rather than a one-off project. They take a future-back approach to rearchitect their end-to-end software development life cycle around generative AI, embedding it deeply into workflows and scaling it enterprise-wide. They weave it into development workflows and scale it across use cases.

Goldman Sachs, for example, integrated generative AI into its internal development platform and fine-tuned it on the bank’s internal codebase and project documentation. This gives engineers context-aware, real-time coding solutions far beyond basic autocompletion—extending to automated code generation and even code testing—thereby significantly accelerating development cycles and boosting programmer productivity.

These leaders also make sure that generative AI’s benefits translate into business value. They measure how much time AI saves and redirect that capacity to high-value work, ensuring that efficiency gains become business gains. They also modernize their environments—adopting cloud development environments, automated continuous integration and delivery pipelines, and modular architectures—to remove friction that could limit AI’s impact. They also recognize that there’s no one-size-fits-all approach and tailor targeted tools, playbooks, and trainings to each team’s unique needs, ensuring smooth, fast adoption across diverse scenarios.

Common roadblocks to scaling generative AI

Even with generative AI’s promise, many firms are stuck in pilot mode because of some common obstacles.

Lack of executive direction: If senior leadership doesn’t clearly prioritize generative AI, pilot efforts tend to fizzle.
Adoption resistance: Under pressure, developers often fall back on old habits. Some engineers distrust AI or worry that it will undermine their role. Three of four companies say that the hardest part is getting people to change how they work. Overcoming this resistance requires strong change management.
Skills gaps: Generative AI requires new skills such as writing prompts and reviewing AI output. But many firms haven’t provided adequate training, so even powerful tools go underused.
No ROI tracking: It’s tough to prove generative AI’s value without clear key performance indicators or plans for using the time saved. If you don’t measure results, even real productivity gains won’t show up in business terms.
Process or tooling mismatch: Slow, manual processes in build, testing, or release will choke generative AI’s benefits. Legacy toolchains that can’t handle AI-generated code will also blunt any speed gains.

These issues explain why so many AI efforts never get out of the sandbox. The good news is that none of these barriers are insurmountable; each can be overcome with the right approach. Often, the toughest obstacles are people related, so overcoming them requires significant investment in training, communication, and cultural change.

Reimagine the software life cycle with AI at its core

To break out of pilot mode and get real returns from generative AI, tech leaders must go beyond incremental tool adoption and frame their roadmap as an AI-native reinvention of the software development life cycle. Starting with a vision of a future in which AI is seamlessly integrated into every phase of development allows teams to then plan backward to make that vision a reality. Leaders follow a roadmap to move from experimentation to scaled impact.

Set an AI-native vision anchored in business outcomes, not just tech metrics. Define a bold, future-back ambition for how software will be built with AI at the core. Tie that directly to concrete business outcomes such as faster release cycles, lower defect rates, or higher customer satisfaction. And show where AI is generating real value (see Figure 2).

visualization — Key performance indicators and performance targets measure progress

Turn saved time into business results. Don’t let productivity gains sit idle. Decide early how to use freed-up capacity—for instance, whether to ship more features, or reduce spending, or accelerate innovation—and tie those outcomes to financial impact. Scale successful practices across teams to maximize ROI.
Start with high-impact, easy wins aligned with the future vision. Apply generative AI where it can succeed quickly—for example, generating new feature code or automating tests—and help pave the way toward an AI-native end state. Avoid fragile legacy systems at first; instead, focus on domains that are ready for AI. Early wins build momentum for broader use.
Cultivate AI-native talent and culture. Provide hands-on training (such as prompt engineering or AI orchestration workshops), and actively manage the culture shift. Make upskilling a continuous effort, reassure engineers that AI is an assistant (not a replacement), and celebrate early wins to build buy-in.
Modernize processes and architecture for AI at scale. A true AI-native approach often demands overhauling your development environment end to end. Eliminate process bottlenecks that could reduce AI’s speed advantage. Adjust workflows so that faster coding leads to faster releases and isn’t stuck in slow pipelines. Update development tools to handle AI outputs smoothly.
Prepare for autonomous workflows. Finally, as generative AI evolves from copilot to autonomous agent, start experimenting with AI handling end-to-end development tasks. Developers’ roles may shift to guiding these agents as “intent engineers” or “AI orchestrators.” Assign an agent to build a simple app in a sandbox, with humans stepping in only if needed. These trials will show how far AI can go, where oversight is essential, and what skills or workflows need to evolve—thus signaling your intent to lead in the next wave of development.

Closing the gap: From experimentation to execution

Generative AI’s promise is real, but capturing it requires moving beyond one-off pilots. It takes bold leadership to drive adoption, revamped processes to embed AI at every step, and a focus on measurable outcomes to analyze results and make adjustments. The winners won’t be those dabbling in flashy demos but rather those redesigning their workflows to fully integrate AI and deliver tangible improvements. Already, some companies report 25% to 30% productivity boosts by pairing generative AI with end-to-end process transformation—far above the 10% gains from basic code assistants.

An even bigger leap is on the horizon as AI evolves from assistant to autonomous agent—a shift that could redefine software development and widen the gap between firms that treat AI as a novelty and those that embrace it as transformative. Generative AI’s capabilities are steadily broadening, and the gains seen today are expected to continue growing over the next 12 to 24 months as models improve their performance and reliability. Tech executives must excel at implementing generative AI today while also preparing their teams for a more AI-driven development model tomorrow.

Experiments pay off only when backed by a well-defined approach that converts innovation into measurable results. Now is the time to act. Organizations that move decisively with a clear vision and bold execution will capture real returns and redefine how software is built; those that hesitate risk being left behind.

Read the Next Section

Building the Foundation for Agentic AI