By Drew Naukam, CEO of Gorilla Logic
This article first appeared in Forbes Technology Council.
Software engineering is being reinvented in real time. What we considered standard practice a year ago is already outdated. Having guided hundreds of developers through this AI transformation during two decades of building engineering teams, I’ve observed something fundamental: AI isn’t just accelerating code development. It’s reshaping how engineers think, work and deliver value.
From “Doing” To “Thinking”
Engineering has always involved two distinct phases: the creative problem-solving portion where developers architect solutions and design approaches, and the execution phase, translating those concepts into working code. Most veterans will tell you this second phase feels like zoning out—necessary work, but your brain’s on autopilot.
AI-assisted coding tools are collapsing that execution time. McKinsey’s research demonstrates that developers can complete coding tasks up to twice as fast with generative AI tools, fundamentally shifting an engineer’s day toward problem-solving, iteration and refinement. The work becomes less about syntax and more about exploring solutions.
It’s the difference between four hours of smooth highway driving versus four hours stuck in rush hour traffic. Both get you from point A to point B, but the quality and nature of the experience are radically different.
This transformation extends beyond individual productivity metrics. Engineering work is evolving rapidly, and managers must recalibrate how they measure productivity, structure teams and support developers who now spend significantly more time thinking deeply and less time on repetitive tasks.
Beyond Individual Productivity: Workflow Transformation
The first wave of AI adoption delivers steep productivity gains at the individual level. That curve will eventually flatten as engineers universally adopt these tools. The transformative change emerges when applying AI at the workflow and process level.
Consider the core processes in product engineering: feature decomposition, legacy system modernization, QA, DevOps and observability. These represent team-wide workflows, not isolated individual tasks. Applying AI here doesn’t merely save time. It redefines how software is delivered.
QA cycles are being cut dramatically as AI continuously tests builds. Observability platforms now self-heal rather than just monitor. When developers used multiple generative AI tools within a given task, they realized an additional time improvement of up to 250%. This represents the next frontier of engineering transformation.
The Code Advantage
AI adoption across industries faces a significant barrier: poor data quality. Customer data, supply chain information, financial records—scattered, inconsistent and messy datasets requiring extensive cleanup. I’ve seen organizations invest heavily in data engineering to address these challenges.
Software teams possess a unique advantage. Code repositories are already structured, governed and versioned. Unlike other enterprise data sources, commercially available AI tools that operate on code are mature and improving weekly. This creates immediately actionable opportunities.
Engineering teams can apply AI directly to core workflows without multi-year data preparation projects. No complex integration nightmares. The infrastructure already exists.
Legacy Modernization: A Concrete Example
Take the challenge of legacy code modernization. Most organizations assume modernization efforts are too costly, time-intensive and risky. They leave systems in place, wrap them with APIs or refactor incrementally over years.
These assumptions may no longer hold. With AI, you can document complex legacy environments and modernize code into modern stacks with validation against predefined success criteria. McKinsey’s research shows code refactoring can be completed in nearly two-thirds the time using AI tools.
The implications are substantial. Modernization transforms from a multi-year, multi-million-dollar undertaking into something achievable in months at a fraction of the cost. Organizations can redirect resources from maintaining legacy systems toward building future capabilities.
The New Era Of Software Engineering
Software development is shifting from labor-intensive coding to intelligence-driven problem solving. With 92% of U.S. developers already using AI coding tools, this isn’t emerging technology—it’s current reality.
AI catalyzes reimagining entire workflows, addressing decades of technical debt and accelerating time to market. Leaders who understand this shift and act decisively will separate themselves from those clinging to outdated assumptions.
The transformation of software engineering is happening now. The critical question facing technology leaders is how quickly they’re willing to embrace it.