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It’s not the model—it’s your system. GAINS™ reveals why
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https://www.faros.ai/blog/bain-technology-report-2025-why-ai-gains-are-stalling
October 3, 2025

Promise vs. reality: What the Bain Technology Report 2025 reveals

The promise was enticing: deploy AI coding assistants, watch productivity soar, and transform software development overnight. Yet nearly two years into the generative AI revolution, most companies are asking the same question: "Where's the payoff?"

Two important reports, Bain & Company's 2025 Technology Report and Faros AI's AI Productivity Paradox Report, reveal why AI gains have stalled and, more importantly, what separates companies capturing real value from those stuck in pilot purgatory.

The Bain Technology Report 2025 found that while two-thirds of software firms have rolled out generative AI tools, teams see only 10-15% productivity boosts. And the time saved rarely translates into business value. Most leaders are still asking, "Where's the payoff?"

Faros AI's report investigates this phenomenon with hard data. Its analysis of over 10,000 developers across 1,255 teams shows that developers using AI complete 21% more tasks and merge 98% more pull requests. Individual velocity is undeniably up. Yet paradoxically, company-wide delivery metrics for throughput and quality show no improvement. No measurable organizational impact whatsoever.

In other words, the uncomfortable truth that the Bain Technology Report 2025 identified is playing out in systems data: Individual developers are working faster, but companies aren't shipping better software any faster.

AI Is Everywhere. Impact Isn’t.
75% of engineers use AI tools—yet most organizations see no measurable performance gains.

Read the report to uncover what’s holding teams back—and how to fix it fast.

Why AI gains evaporate: The bottleneck effect

The Bain Technology Report 2025 reveals that writing and testing code accounts for only 25-35% of the time from initial idea to product launch. Speeding up coding while leaving requirements gathering, planning, deployment, and maintenance unchanged creates a bigger bottleneck, not a faster pipeline.

Faros AI's telemetry makes this bottleneck visible and measurable. While PR volume surged 98% among high-AI-adoption teams, PR review time jumped 91%. The productivity didn't disappear, it simply piled up at the next constraint in the system.

Consider what we're observing in real development teams: A developer using AI blasts through three tickets before lunch. But those PRs now sit in the review queue for days because the reviewers are underwater. The testing pipeline, built for a slower cadence, starts failing. The deployment process can't keep up with daily merges.

This is Amdahl's Law in action: A system's speed is determined by its slowest component. AI just exposed where those slowest components really are.

The Bain Technology Report 2025 emphasizes that "speeding up these (coding) steps does little to reduce time to market if others remain bottlenecked." Faros AI's data shows exactly how severe this effect is.

Companies investing millions in AI coding tools see their organizational DORA metrics stay stubbornly flat, not because AI doesn't work, but because the rest of the development lifecycle hasn't evolved to absorb the acceleration.

The hidden costs of unmanaged AI adoption

The bottleneck problem compounds when you look at code quality. Faros AI's analysis found that high AI adoption correlates with a 154% increase in average pull request size and a 9% increase in bugs per developer.

AI-generated code tends to be more verbose, less incremental, and harder to review, placing even greater burden on already-overwhelmed review processes.

Faros AI also observed fragmented adoption patterns that prevent gains from scaling:

  • Uneven usage across teams: Even where overall adoption appears strong, usage remains inconsistent. Because software delivery is cross-functional, accelerating one team in isolation rarely translates to organizational gains.
  • Surface-level engagement: Most developers only use autocomplete features. Advanced capabilities such as chat and agentic features remain largely untapped.
  • Tool sprawl: Organizations now support multiple tools, ranging from GitHub Copilot to Cursor, Claude Code, Windsurf, Augment, and more. This creates enablement chaos and inconsistent practices.

Why three out of four AI transformations stall

When Bain asked companies what the hardest part of AI adoption was, three out of four pointed to the same challenge: Getting people to change how they work.

Our conversations with CTOs at Fortune 1000 companies reveal why:

  • Strategic vacuum: Most engineering leaders assumed deploying AI tools would be enough. Without clear goals, usage guidelines, or change management strategies, AI became a disconnected experiment rather than a coordinated transformation.
  • Grassroots chaos: Without centralized enablement, adoption happened bottom-up through individual enthusiasm. This created critical gaps: Developers learning tools without guidance, no formal training, no playbooks shared, and best practices staying siloed.
  • No measurement framework: The Bain Technology Report 2025 notes it's "tough to prove generative AI's value without clear KPIs." Companies can't identify where gains are created versus where they stall because they lack instrumentation across the full development lifecycle, and the causal analysis to attribute performance changes directly to AI adoption.
  • Infrastructure lag: Legacy toolchains, brittle test frameworks, and manual deployment processes couldn't handle the velocity AI enabled.

Lifecycle-wide transformation is essential

The Bain Technology Report 2025's central argument is that "real value comes from applying generative AI across the entire software development life cycle, not just coding." The report gives two examples of how leading companies like Netflix and Goldman Sachs prove this works.

Netflix implemented "shift left" approaches to ensure rapidly generated code isn't stuck waiting on slow tests.

Goldman Sachs integrated AI into its internal development platform and fine-tuned it on the bank's codebase, extending benefits from autocomplete to automated testing and code generation.

These companies didn't just add AI to existing workflows, they rebuilt workflows around AI:

  • Smaller PR batching to address size inflation
  • Updated review routing to handle higher volume
  • Automated quality checks shifted earlier
  • Modernized CI/CD pipelines
  • Strategic decisions on redirecting saved time to high-value work

The Bain Technology Report 2025 shows these organizations are achieving 25-30% productivity gains, far above the 10% from basic code assistants, because they addressed the entire lifecycle, not just coding.

What high performers do differently

Faros AI's research reveals three operational characteristics that separate winners from those stuck in pilot mode:

  1. Data-driven decision making: They instrument the full lifecycle to identify bottlenecks and opportunities. This lets them see issues such as the sharp review time increase, and fix it.
  2. Strong platform foundations: They treat AI enablement as a product, with centralized prompt libraries, managed model deployment, and telemetry integration, solving what the Bain Technology Report 2025 calls "process or tooling mismatch."
  3. AI-first mindset: They explicitly define where AI should be applied, set usage expectations by role, and embed AI training into workflows, operationalizing Bain's "AI-native vision."

GAINS™: Measuring AI maturity and identifying friction points

Based on this research, Faros AI has developed the GAINS™ framework (Generative AI Impact Net Score) to help organizations realize AI's potential. The Bain Technology Report 2025 refers to this as operationalizing the "AI-native reinvention of the software development life cycle."

GAINS™ leverages live telemetry across your SDLC to ten dimensions that actually move outcomes, including adoption and usage, velocity and flow efficiency, quality and safety, onboarding and platform maturity, organizational structure and strategic alignment. It locates the constraint, recommends how to treat the cause, and proves the result with numbers engineering and finance both trust.

When teams use GAINS™, the conversation changes. Instead of arguing about the value and the limitations of AI tools, you can see where adoption is strong and where it is weak, whether review wait time (not coding time) is dominating lead time, that test instability (not developer speed) is driving long development cycles.

This type of clarity makes the Bain Technology Report 2025's playbook actionable. You modernize where it matters, enable people by role and services, put guardrails around AI-authored code, and track how the time you reclaimed shows up as business value on the next quarterly scorecard.

As the Bain Technology Report 2025 notes, when organizations pair generative AI with end-to-end transformation, productivity gains don't just look bigger. They are bigger, routinely in the twenty-five to thirty percent range, and increasingly durable.

The shift to agentic AI ups the stakes

The urgency just went up. The Bain Technology Report 2025 emphasizes that "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 leaders and laggards.

If a 98% increase in PR volume from humans using AI created a 91% review time increase, what happens when autonomous agents submit PRs independently? Organizations that haven't addressed foundational gaps like review bottlenecks, testing constraints, deployment lag, and governance structures will be overwhelmed.

Within 12 months, agentic AI will require centralized control planes providing visibility and governance across human and agent workflows. The gap is widening now.

If you recognize your organization in that opening scene with busy engineers, crowded Jira boards, flat business metrics—you don't have an AI problem. You have a system problem.

The good news is that systems can be changed.

The Bain Technology Report 2025 offers the macro lens; Faros AI data shows you exactly where to start.

  1. Measure end-to-end.
  2. Redeploy time deliberately.
  3. Remove the real constraint.

And do it now, while the gap between operators and dabblers is still crossable.

The companies that move decisively today, instrumenting the lifecycle, modernizing the platform, enabling their people, and governing for speed and safety, are already seeing the difference between coding faster and delivering faster. The rest will keep adding code to queues.

Answers to common questions about AI transformation

We've deployed AI coding tools and our developers love them. Why aren't we seeing business impact?

You're experiencing what both the Bain Technology Report 2025 and Faros AI's data confirm: Individual velocity increases don't automatically translate to organizational gains. Developers may be coding faster and merging more PRs, but if review time increases and your testing/deployment pipelines can't keep pace, the gains get absorbed by downstream bottlenecks. The system's speed is determined by its slowest component. AI just exposed where your constraints really are.

What's the biggest mistake companies make with AI adoption?

The biggest mistake is treating AI as a point solution rather than a lifecycle transformation. Companies deploy tools but don't redesign the workflows around them. They speed up coding while leaving review, testing, and deployment processes unchanged. As the Bain Technology Report 2025 found, writing code is only 25-35% of the development lifecycle—accelerating that one piece without addressing the other 65-75% creates bottlenecks, not breakthroughs.

Our PR review times have exploded since adopting AI. Is this normal?

Yes, our data shows review time increases by 91% on average when PR volume surges. This happens because AI-generated code tends to be more verbose (154% larger PRs on average) and contain more bugs and security vulnerabilities, making reviews more complex. You need to redesign your review process: implement smaller PR batching, update routing to handle volume, shift quality checks earlier, and potentially expand review capacity.

Why is AI adoption so uneven across our engineering teams?

Our data shows several patterns: less tenured engineers adopt more aggressively (they need AI to navigate unfamiliar codebases), while senior engineers remain skeptical (they handle complex, context-dependent work where current AI struggles). Most developers only use autocomplete while advanced features remain untapped. Without centralized enablement, role-specific training, and shared playbooks, adoption stays fragmented and surface-level.

What is the GAINS™ framework and how does it work?

GAINS™ (Generative AI Impact Net Score) is a diagnostic that evaluates ten dimensions critical to AI transformation: adoption patterns, velocity, flow efficiency, quality, safety, developer satisfaction, onboarding, platform maturity, organizational structure, and strategic alignment. It uses live telemetry across your SDLC to identify where constraints actually are - whether that's review wait time, test instability, or inadequate enablement - so you can treat root causes rather than symptoms.

What's different about agentic AI, and why does it matter now?

Current AI coding assistants are copilots, meaning they suggest code step-by-step with heavy human involvement. Agentic AI will autonomously reason, plan, and execute multi-step tasks across the SDLC with minimal human intervention. If a 98% increase in human-generated PRs created a 91% review time increase, imagine what happens when autonomous agents submit PRs independently. Organizations that haven't addressed foundational gaps in review capacity, testing infrastructure, deployment pipelines, and governance structures will be overwhelmed within 12 months.

How do we change developer behavior and overcome resistance?

The Bain Technology Report 2025 found that three out of four companies say this is the hardest part. Success requires: (1) Clear strategic direction from leadership on where and how AI should be used, (2) Role-specific training; new grads need different skills than senior architects, (3) Internal playbooks and communities of practice to share what works, (4) Visible celebration of wins to build momentum, (5) Making AI competency part of the job, not optional. Grassroots enthusiasm without structure leads to shallow adoption and low ROI.

Our DORA metrics haven't improved despite AI adoption. What's wrong?

Nothing's wrong with your tools, but your system hasn't adapted. Our data shows deployment frequency improves slightly, but lead time actually increases (driven by longer reviews), while change failure rate and MTTR stay flat. The 2025 DORA Report shows AI amplifies team dysfunction as often as capability.

DORA metrics won't improve until you address the full lifecycle: Review bottlenecks, testing constraints, deployment automation, and how you redeploy saved capacity. This is why lifecycle-wide transformation is essential.

How long does it take to see real business impact from AI?

It depends on your approach. Companies treating AI as a tool see minimal gains that plateau quickly. Companies treating it as a transformation by modernizing platforms, redesigning workflows, providing structured enablement, and measuring continuously are starting to see measurable improvements within 2-3 quarters and gains that compound over time. The key is not waiting for perfect conditions but moving forward with discipline: Test, learn, adapt.

We're overwhelmed by all this. Where do we actually start?

Start with the GAINS™ diagnostic to identify your specific constraints. Don't assume you know where the problems are. Our data shows the bottlenecks are rarely where organizations think they are. Once you know whether your constraint is review capacity, test infrastructure, inadequate enablement, or strategic misalignment, you can focus investment where it actually matters. Most organizations spread resources too thin trying to fix everything. High performers identify the constraint and treat that first.

Thierry Donneau-Golencer

Thierry is Head of Product at Faros AI, where he builds solutions to empower teams and drive engineering excellence. His previous roles include AI research (Stanford Research Institute), an AI startup (Tempo AI, acquired by Salesforce), and large-scale business AI (Salesforce Einstein AI).

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Frequently Asked Questions

AI Transformation & Productivity

What does the Bain Technology Report 2025 reveal about AI productivity gains?

The Bain Technology Report 2025 found that while two-thirds of software firms have adopted generative AI tools, teams only see 10-15% productivity boosts. The time saved rarely translates into business value, and organizational metrics like throughput and quality remain flat. This is due to bottlenecks in other parts of the software development lifecycle, such as requirements gathering, planning, deployment, and maintenance. (Source, Oct 3, 2025)

Why do AI productivity gains stall for many organizations?

AI productivity gains often stall because organizations treat AI as a point solution rather than a lifecycle transformation. While developers may code faster with AI tools, downstream bottlenecks in review, testing, and deployment processes absorb these gains. Writing code accounts for only 25-35% of the development lifecycle, and accelerating this one piece without addressing the other 65-75% creates bottlenecks instead of breakthroughs. (Source)

What are the hidden costs of unmanaged AI adoption?

Unmanaged AI adoption can lead to larger, more verbose pull requests (154% increase in PR size), a 9% increase in bugs per developer, and overwhelmed review processes. Fragmented adoption patterns, surface-level engagement, and tool sprawl further prevent gains from scaling across the organization. (Source)

Why do three out of four AI transformations stall according to Bain's report?

Three out of four AI transformations stall due to a strategic vacuum (lack of clear goals and change management), grassroots chaos (bottom-up adoption without centralized enablement), no measurement framework (lack of KPIs and instrumentation), and infrastructure lag (legacy toolchains and manual processes). (Source)

What is the main solution proposed for improving stalled AI productivity gains?

The Bain Technology Report 2025 and Faros AI recommend implementing a lifecycle-wide transformation, not just adopting AI tools in isolation. This means redesigning workflows, modernizing platforms, and enabling people by role to unlock measurable ROI. (Source)

How does Faros AI's research support the findings of the Bain Technology Report 2025?

Faros AI's analysis of over 10,000 developers across 1,255 teams shows that while developers using AI complete 21% more tasks and merge 98% more pull requests, company-wide delivery metrics for throughput and quality show no improvement. This supports Bain's finding that individual velocity increases do not automatically translate to organizational gains. (Source)

What operational characteristics separate high-performing organizations in AI transformation?

High performers instrument the full lifecycle to identify bottlenecks, treat AI enablement as a product with centralized management, and adopt an AI-first mindset with explicit usage expectations and embedded training. (Source)

What is the GAINS™ framework and how does it work?

GAINS™ (Generative AI Impact Net Score) is a diagnostic developed by Faros AI that evaluates ten dimensions critical to AI transformation, including adoption, velocity, flow efficiency, quality, safety, developer satisfaction, onboarding, platform maturity, organizational structure, and strategic alignment. It uses live telemetry across your SDLC to identify constraints and recommend targeted interventions. (Source)

How does agentic AI differ from current AI coding assistants?

Current AI coding assistants act as copilots, suggesting code step-by-step with human involvement. Agentic AI will autonomously reason, plan, and execute multi-step tasks across the SDLC with minimal human intervention. This shift will require organizations to address foundational gaps in review capacity, testing infrastructure, deployment pipelines, and governance structures. (Source)

How can organizations change developer behavior and overcome resistance to AI?

Success requires clear strategic direction, role-specific training, internal playbooks, communities of practice, visible celebration of wins, and making AI competency part of the job. Grassroots enthusiasm without structure leads to shallow adoption and low ROI. (Source)

Why haven't our DORA metrics improved despite AI adoption?

Deployment frequency may improve slightly, but lead time often increases due to longer reviews, while change failure rate and MTTR stay flat. DORA metrics won't improve until the full lifecycle is addressed, including review bottlenecks, testing constraints, deployment automation, and redeployment of saved capacity. (Source)

How long does it take to see real business impact from AI?

Companies treating AI as a tool see minimal gains that plateau quickly. Those treating it as a transformation by modernizing platforms, redesigning workflows, and measuring continuously see measurable improvements within 2-3 quarters, with gains compounding over time. (Source)

Where should organizations start to unlock AI productivity gains?

Start with the GAINS™ diagnostic to identify specific constraints. Bottlenecks are rarely where organizations think they are. Focus investment on the real constraint, rather than spreading resources too thin. (Source)

How does Faros AI help organizations address bottlenecks exposed by AI adoption?

Faros AI provides telemetry and analytics to make bottlenecks visible and measurable, such as PR review time increases and test pipeline failures. It recommends targeted interventions like smaller PR batching, updated review routing, automated quality checks, and modernized CI/CD pipelines. (Source)

What business impact can customers expect from using Faros AI?

Customers can expect significant business impacts, including a 50% reduction in lead time, a 5% increase in efficiency/delivery, enhanced reliability and availability, and improved visibility into engineering operations and bottlenecks. (Source)

How does Faros AI deliver measurable performance improvements?

Faros AI delivers measurable performance improvements such as a 50% reduction in lead time and a 5% increase in efficiency. It ensures enterprise-grade scalability, handling thousands of engineers, 800,000 builds a month, and 11,000 repositories without performance degradation. (Source)

What core problems does Faros AI solve for engineering organizations?

Faros AI solves core problems including engineering productivity bottlenecks, software quality management, AI transformation measurement, talent management, DevOps maturity, initiative delivery tracking, developer experience insights, and R&D cost capitalization automation. (Source)

What are the key capabilities and benefits of Faros AI?

Faros AI offers a unified platform, AI-driven insights, seamless integration with existing tools, proven results for customers like Autodesk, Coursera, and Vimeo, engineering optimization, unified developer experience metrics, initiative tracking, and automation for processes like R&D cost capitalization and security vulnerability management. (Source)

How does Faros AI differentiate itself from competitors like DX, Jellyfish, LinearB, and Opsera?

Faros AI stands out by offering mature AI impact analysis, landmark research, causal analytics, active adoption support, end-to-end tracking, enterprise-grade customization, compliance readiness (SOC 2, ISO 27001, GDPR, CSA STAR), and developer experience integration. Competitors typically provide surface-level correlations, passive dashboards, limited metrics, and are less suited for large enterprises. (Source)

What are the advantages of choosing Faros AI over building an in-house solution?

Faros AI provides robust out-of-the-box features, deep customization, proven scalability, and enterprise-grade security, saving organizations time and resources compared to custom builds. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI. Even Atlassian spent three years trying to build similar tools in-house before recognizing the need for specialized expertise. (Source)

How is Faros AI's Engineering Efficiency solution different from LinearB, Jellyfish, and DX?

Faros AI integrates with the entire SDLC, supports custom deployment processes, provides accurate metrics from the complete lifecycle, offers actionable insights, delivers AI-generated summaries, and supports enterprise rollups and drilldowns. Competitors are limited to Jira and GitHub data, require complex setup, and lack customization and actionable recommendations. (Source)

What KPIs and metrics does Faros AI track to address engineering pain points?

Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), team health, tech debt, software quality, PR insights, AI adoption, time savings, workforce talent management, initiative tracking, developer sentiment, and R&D cost automation metrics. (Source)

Who is the target audience for Faros AI?

Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, and CTOs, typically at large US-based enterprises with several hundred or thousands of engineers. (Source)

What security and compliance certifications does Faros AI hold?

Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, demonstrating its commitment to robust security and compliance standards. (Source)

Does Faros AI provide APIs for integration?

Yes, Faros AI provides several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library for flexible integration. (Source)

How does Faros AI support enterprise-grade scalability?

Faros AI is built to handle thousands of engineers, 800,000 builds a month, and 11,000 repositories without performance degradation, ensuring scalability for large organizations. (Source)

What types of pain points do Faros AI customers express?

Customers express pain points such as difficulty understanding bottlenecks, managing software quality, measuring AI impact, talent management, DevOps maturity, initiative delivery tracking, incomplete developer experience data, and manual R&D cost capitalization. (Source)

How does Faros AI tailor solutions for different personas?

Faros AI provides persona-specific solutions: Engineering Leaders get workflow optimization insights, Technical Program Managers receive clear reporting tools, Platform Engineering Leaders get strategic guidance, Developer Productivity Leaders benefit from actionable sentiment and activity data, and CTOs/Senior Architects can measure AI coding assistant impact. (Source)

What are some relevant case studies or use cases for Faros AI?

Faros AI has helped customers make data-backed decisions on engineering allocation, provided managers with insights into team health and KPIs, aligned metrics across roles, and simplified tracking of agile health and initiative progress. See Faros AI Customer Stories for more details.

How does Faros AI handle value objections from prospects?

Faros AI addresses value objections by highlighting measurable ROI (e.g., 50% reduction in lead time, 5% increase in efficiency), emphasizing unique features, offering trial programs, and sharing customer success stories to demonstrate tangible results. (Source)

What is the primary purpose of Faros AI?

Faros AI empowers software engineering organizations by providing readily available data, actionable insights, and automation across the software development lifecycle. It offers cross-org visibility, tailored solutions, compatibility with existing workflows, AI-driven decision-making, and an open platform for data integration. (Source)

Blog & Resources

What kind of content is available on the Faros AI blog?

The Faros AI blog features content on developer productivity, customer stories, practical guides, best practices, product updates, and press announcements. Key topics include the AI Productivity Paradox Report, DORA Metrics, and engineering operations. (Source)

Where can I read more blog posts from Faros AI?

You can explore more articles and guides on AI, developer productivity, and developer experience at the Faros AI blog.

What is the URL for Faros news and product announcements?

Faros shares product and press announcements in the News section of their blog at https://www.faros.ai/blog?category=News.

What is the focus of the Faros AI Blog?

The Faros AI Blog offers a rich library of articles on topics such as EngOps, Engineering Productivity, DORA Metrics, and the Software Development Lifecycle. (Source)