t-suite

AI in engineering:
The transformation that can’t wait

insight
October 18, 2025
9 min read

Authors

Srinivas Bhaskara-pngSrinivasa Bhaskara

A CTO and Global Lead for AI-Native Digital Engineering at Nagarro, he is transforming how modern engineering teams build and deliver. With over three decades of experience, he partners with CxOs and engineering leaders worldwide to align engineering with strategy, embed GenAI-powered practices across the software lifecycle, and cultivate empowered, AI-native teams that innovate with purpose and velocity.

 

Dushyant-pngDushyant Sahni

A Global Practice Leader for Private Equity, Horizontal Tech, and Management Consulting at Nagarro. A seasoned technology consultant, he specializes in accelerating software delivery through GenAI, resilient engineering, and cloud efficiency— helping enterprises build fluid, high-performing organizations that deliver lasting value.

The path to successful, lasting digital and AI transformation begins with transforming the engineering function — a journey defined by exponential advantage rather than incremental gains. 

Advanced AI tools are ushering in this transformation, changing how teams think while accelerating the pace at which they deliver work. They are enabling engineering teams to approach problems differently, explore new possibilities, and design solutions that were once out of reach. For instance, AI can now auto-remediate security vulnerabilities as code is written, generate multiple design pathways in minutes, or predict failures before they occur. These shifts go well beyond efficiency, revealing how rapidly AI is starting to change the way we engineer solutions.   

While engineers around the world now use tools like GitHub Copilot, Claude Code, Cursor, and more in their daily work, not everyone is seeing the returns that match the promise. Instead of acceleration, many are encountering skepticism, delays, or silent resistance. Instead of speeding up work, teams spend valuable time correcting AI-generated code, fixing inaccuracies, or managing debugging overhead. Initiatives quickly lose their momentum as inconsistent adoption and increasing correction cycles take a toll on their confidence.

 


Engineering transformation is not measured by the tools you use, but by how people evolve, changing how they work, learn, collaborate, and harness the real power of AI. 


The great divide: why most AI engineering initiatives are plateauing 

Engineering teams today operate in two starkly different realities, a widening divide that directly affects the productivity and competitiveness of businesses. 


On one side are the AI-accelerators, teams where embedded intelligence and streamlined tooling deliver more than just speed. These teams reimagine roles and processes by combining automation with architectural discipline, resulting in higher-quality deliverables, shorter release cycles, and energized engineers who spend less time firefighting and more time innovating. In these organizations, AI is not treated as a supporting tool; it is an enabler of smarter, more resilient engineering.  

On the other side are the AI-laggards, teams trapped in pilot purgatory. Here, AI is still viewed as an experiment or merely as a coding assistant rather than an enabling system. Developers cycle through trial tools without trust; pipelines remain fragmented, and integration stalls at the proof-of-concept stage. The cost of this stagnation is not only a wasted investment, but it is also a harbinger of deep skepticism.  

The concerns are real. Stack Overflow’s 2025 survey reveals that: 

 

46% 
of developers distrust AI-generated code. 
66%
say it is “almost right, but not quite.
45%
are frustrated with debugging AI-generated code.
75.3%
  still prefer human insight over AI answers.

These numbers reflect a gap, but the business impact is even more sobering. According to the 2025 MIT State of AI in Business Report, 95% of GenAI pilots are failing to deliver measurable impact, undone by flawed integration, misaligned incentives, tool-first thinking, and a lack of trust-first governance. 

As intelligent engineering becomes the norm for a few, the question is no longer ‘Will AI change how we engineer software?’ It is, ‘Are we structurally ready to harness AI, capture its value, or risk being left behind?

AI is not failing; engineering approaches are. Teams either thrive with AI-native ways of working or struggle in pilots where distrust, misalignment, and poor integration keep success out of reach.

 

Why engineering transformation is different and difficult  


Engineering teams resist change differently from business functions. They operate in high-context, high-trust environments optimized for autonomy, technical depth, and precision. Progress here does not emerge from mandates alone; it grows from approaches that practitioners respect, test, and champion. Bottom-up momentum is essential for credibility, while top-down facilitation provides psychological safety along with a clear path to scale successfully.

Traditional levers such as training, dashboards, or usage targets rarely create lasting impact. At best, they deliver short bursts of acceptance; at worst, they introduce friction that erodes trust. Simply introducing new tools is not a transformation, which is why any early gains often plateau.

True transformation requires more than new tools. It demands a rethinking of roles, capabilities, teaming, governance, and outcomes. This involves a complete overhaul of how software is developed, measured, and maintained, with product owners, designers, and engineers engaged as co-creators of change rather than passive recipients.

Most attempts to implement AI in engineering fail for the same reasons. The challenge lies not in AI’s capability but in the structural gaps within how engineering organizations approach change. Without rethinking systems, capabilities, and governance, even the most advanced AI tools cannot deliver sustainable impact.

 

 

Organizations rush to purchase AI licenses or run training sessions, but adoption stalls when engineers can’t apply AI meaningfully in their daily work. Without integration into IDEs, CI/CD pipelines, or architecture-level decision-making, tools sit unused, shelfware disguised as progress.

The lost potential is significant. GitHub’s data confirms this, showing that junior developers gain 27–39% in productivity from copilots, yet these gains vanish when teams lack role-specific enablement. Senior engineers often see little benefit because AI is not aligned with their responsibilities, leading to frustration rather than empowerment.

AI enablement in engineering

 

Engineering isn’t a race; it’s a living system. When success is tracked solely by delivery speed, deeper signals, what often gets overlooked is design quality, maintainability, resilience, and user experience. The result is AI-accelerated outputs, at the expense of real-world outcomes.  

Faster delivery hides declining code quality and fragile architectures. For executives, this creates a false sense of momentum: dashboards glow green, but technical debt accumulates silently. Over time, these hidden liabilities hinder scalability, slow onboarding, increase regulatory risks, and erode customer trust. 

Leaders must resist the temptation to celebrate velocity and efficiency metrics alone. The only sustainable measure of AI in engineering is compound advantage: does every release make the system smarter, stronger, and more resilient? Does every release deliver impactful business outcomes? 

AI in engineering for speed

 

Most AI initiatives stall because they never escape the pilot stage. Tools tested in isolation don’t scale, because scaling requires systemic integration: 

  • Embedding AI across the lifecycle (Design → Develop → Test → Deploy → Monitor).
  • Building cross-functional fluency between product, design, engineering, operations, compliance, and quality assurance.
  • Establishing trust-first governance so engineering and stakeholders believe in outputs.

The reality is, only 34% of firms actively address AI risk (ISACA, 2025), and just 27% review all AI outputs before use (McKinsey, 2024). Without safeguards and clear accountability, credibility erodes; pilots collapse, and skeptics gain momentum. 

Scaling AI in engineering is a people and platform strategy. Without integration into the lifecycle, without governance, and without acceptance by experts, AI remains a side experiment. With them, it becomes a multiplier that reshapes value creation in engineering. 
 
AI pilots in engineering

Signals you’re falling behind in engineering transformation

  •  AI pilots that don’t scale beyond a couple of teams.
  • Usage dashboards show tool activity, but no outcome improvements.
  • Developer trust is low or declining.
  • Onboarding new engineers still takes weeks.
  • Architecture reviews rely on hero engineers, not system insights.
  • Business can’t trace how. engineering improvements impact revenue, cost, or risk.

Three shifts that enable AI-native engineering 

AI tools- engineering

From tools to outcomes





The real question is not “How do we use AI?” It is “What outcomes can only be unlocked if we rethink engineering with AI at its core?”


This shift elevates the conversation from training to advantage in AI-native organizations. In these environments, engineers aren’t just faster, they’re more engaged, focused, and fulfilled. A 2023 GitHub/Wakefield study found that developers using AI were twice as likely to report higher job satisfaction, citing not only better code quality but also fewer repetitive tasks and greater creative freedom. For leadership, this shows that AI is not just a lever for productivity; it is also a catalyst for retention, innovation, and a stronger engineering culture.


Implication: When leaders shift from tools to outcomes, AI becomes a lever for measurable business impact, driving innovation, resilience, and developer engagement rather than producing dashboards full of usage metrics with little to show for it.

AI structural integration

From workarounds to structural integration



AI adoption does not scale on top of legacy processes. It scales only when it is embedded into the system itself, from architecture to pipelines to governance. That means:

1: AI woven throughout the software lifecycle (Design → Develop → Test → Deploy → Monitor).

2: Reskilling by role.

3: Creating new hybrid roles, such as AI-native architects or prompt-first builders.

4: Redefining KPIs to measure resilience, maintainability, and impact, not just efficiency, productivity, and velocity.


Implication: By shifting from outputs to outcomes, teams can move beyond pilots to achieve sustainable productivity, trust, and measurable impact, rather than remaining stuck in pilot purgatory with limited gains and declining credibility.

AI development

From deployment to discovery





The most advanced teams don’t simply deploy AI tools; they continuously discover, adapt, and improve. That means:

1: Systems that optimize in real time

2: Predictive diagnostics that surface architectural risks before they scale

3: Intelligent onboarding and retrieval that shortens ramp-up time

4: AI-assisted planning that anticipates shifts in demand or capacity.


Implication: With continuous integration and discovery, AI evolves in tandem with the business, providing adaptability, resilience, and lasting benefits - rather than becoming a static overlay that quickly becomes obsolete while competitors pull ahead.

From unused licenses to fragile systems and isolated pilots, engineering change often remains purgatory, unable to scale. Without integrated, structural change, AI turns from an asset into a liability instead of a competitive edge.

Transformation in action: AI-native use cases

The shift to AI-native engineering is not theoretical; it is already reshaping organizations at scale. Leading organizations are embedding AI directly into engineering systems and reporting outcomes that extend beyond productivity to fundamentally redefine business performance.

Accelerated compliance and delivery:
HSBC

By embedding AI into API workflows, HSBC reduced security vulnerabilities by 40% and accelerated compliance. A clear demonstration of how AI-native engineering strengthens resilience while enabling faster time-to-market.

Boosting reliability and talent satisfaction:
European telecoms

AI-enabled testing reduced regression cycles by 70%, freeing engineers to focus on stronger architectures and demonstrating how AI-native practices enhance reliability and talent engagement. 

AI-Driven Resilience:
Cloud hyperscalers

AWS and Azure use AI in engineering for anomaly detection, achieving near-zero downtime and turning uptime into a distinct competitive advantage.

The strategic imperative

By 2027, AI agents are projected to handle the majority of baseline coding tasks, fundamentally altering the role of developers. To remain competitive, organizations need to begin restructuring their engineering processes now, transitioning from merely adopting AI tools to comprehensively reimagining their capabilities. This proactive approach will enhance efficiency and position them as leaders in the evolving technology landscape.

According to McKinsey, top-performing organizations already attribute over 20% of EBIT growth to AI and are three times more likely to reskill engineering teams at scale. Yet MIT’s 2025 report warns that 95% of GenAI projects fail to deliver lasting business impact, not because of the technology itself, but because engineering systems aren’t ready for it. The evidence is overwhelming: the gap between AI-accelerators and AI-laggards is not closing, it is increasing exponentially.

In this widening gap, engineering becomes the true differentiator. The firms that succeed won’t just use AI; they’ll transform how they build with it. It is about achieving an exponential advantage that compounds with every architectural decision, sprint, and release.

This marks the most profound shift in software development since the advent of the Internet. The organizations that master AI-native engineering won’t just build better software; they’ll shape the systems that power science, economic growth, and human progress for decades to come.

The real choices ahead

The next two years are not about experimenting with AI. They are about defining what kind of organization you want to become.
The choices are stark, and they are yours to make:

Integration
Build capability that compounds
Or get stuck in commodity adoption. 
Costs
Measure the impact that matters
Or hide behind dashboards of activity. 
Model drift
Reimagine engineering at its core
Or make small process tweaks at the edges. 

Own the future of engineering

 

The real question is no longer whether you’ll use AI. It is this: Will you design the future or be left to operate within someone else’s design? Discover how Nagarro’s Vanguards can unlock AI-native engineering in your team. 

 

Explore Vanguard: The framework powering AI-native teams. 

future of engineering
AI in engineering: The transformation that can’t wait

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