Leadership overestimates the immediate productivity impact of generative AI
Generative AI is powerful. But assuming it will instantly make your engineering teams more productive is a mistake. Across nearly every industry right now, executives are being told that AI will boost efficiency, streamline processes, cut costs. While those things may be true over time, they don’t happen just because you add an AI tool. Especially not in development.
A survey of over 2,100 IT managers and developers exposed the disconnect. Engineering leaders placed AI at the top of the list for improving productivity and satisfaction. Developers reported something different, only about a third of them actually felt it helped. They aren’t rejecting the technology. They’re just not seeing immediate value because it’s solving the wrong problems.
Most teams today are using generative AI for code generation. That’s the task developers already enjoy the most and are usually pretty good at. Leaders miss the point when they evaluate productivity based on how fast developers can write code. The real time losses happen in other parts of the software development process: slow debugging, bad documentation, unclear ownership structures, none of which are solved just by speeding up code writing.
The productivity needle doesn’t move unless you focus on what developers actually find frustrating. It’s more useful to build systems that reduce friction in how they work across the entire development cycle. Productivity isn’t about pushing people to output faster, it’s about creating an environment where obstacles are removed. That’s where AI should focus.
C-suite leaders should step back from the hype and look for real ROI, measured by developer quality of life, team velocity, and system-wide improvements, not more lines of code.
Involving developers in AI adoption decisions builds trust and addresses genuine pain points
Top-down decisions around AI adoption miss too often. The people writing code every day, the ones who will use whatever tools you introduce, are being bypassed in the decision process. That’s not just inefficient leadership. It’s bad strategy.
You can avoid this by starting with a simple principle: ask your developers. When leadership imposes AI tools without consulting teams, it signals a lack of trust. That’s exactly where resistance begins. According to Andra Stefanescu, a neuro-mindfulness coach and trainer, “The brain is wired to resist solutions that feel imposed or misaligned with real pain points.” She’s right. Developers respond better when they’re part of the solution. Ask them what slows them down, then aim AI at those problems.
The engineering culture is built on logic, not hype. If AI creates new noise, more interfaces, more tool switching, it becomes another distraction. Too many leaders assume what developers need without checking. That leads to weak adoption, dropped tools, and eventually, wasted investment.
Psychological safety matters too. Developers need to feel they can be honest about what’s not working without fear of criticism or pushback. If your teams don’t feel safe giving real feedback, you’re building AI strategies on fiction, not fact.
This is basic management. Want AI to work? Create space for your developers to tell you where they actually need help. Then point AI tools in that direction. AI works best when the people using it believe in it. That belief doesn’t come from a product demo, it comes from being heard.
Generative AI should enhance the entire software development lifecycle (SDLC)
If your focus is limited to using AI to write code, you’re missing most of the opportunity.
AI is capable of far more than just speeding up syntax. Code generation is one step, one that developers often already enjoy and manage well. The bigger win lies in applying generative AI across the entire software development lifecycle. That includes testing, documentation, debugging, knowledge sharing, and planning. These are the friction points that slow teams down and erode productivity.
When AI is deployed only to automate what’s already working, the return is low. But when AI addresses bottlenecks, like resolving stack traces, reducing manual documentation, or scaling test coverage, it starts to clear space for engineers to build more effectively. These are the areas that tend to sap time and energy. AI is good at removing repetition and surfacing useful context. Let it do that.
The DORA report on AI adoption in software development supports this view. A 25% increase in AI involvement led to over a 2% increase in developer flow, productivity, and satisfaction. That’s real progress, and it doesn’t come from just writing more code, it comes from improving how code gets tested, reviewed, documented, and deployed.
The goal is system-wide intelligence, not isolated automation. Real productivity change happens when AI quietly improves the quality of everything around the code. That’s where the compounding effect kicks in.
Small-scale, experimental AI deployments outperform blanket, top-down implementations
Rolling out AI across the organization in one go doesn’t work. It introduces complexity, resistance, and noise. What works is starting small.
You don’t need to transform your entire engineering environment on day one. In fact, you shouldn’t. Let individual teams experiment. Give them space to test AI on the problems they prioritize. Let them measure results. Once a solution demonstrates value, scale it to other teams. It’s faster to build trust this way, and far cheaper to course-correct if something doesn’t work.
Top-down rollouts tend to overlook the nuances within teams. Every engineering team has different pain points and workflows. Giving them autonomy over how they evaluate AI leads to smarter adoption. It also removes friction between leadership and technical staff. You’re not pushing tools, you’re enabling progress.
Andrew Zigler, senior developer advocate at LinearB, calls this finding “atomic ways” to implement AI. He’s right. Bite-sized experiments allow for iteration, quick feedback, and meaningful metrics. You’re giving your engineering organization the opportunity to learn instead of react.
This approach also makes the impact of AI more visible and shareable. When one team gets better results, faster bug triage, leaner test coverage, smoother onboarding, others will follow. You get buy-in through results, not presentations.
If you’re a C-suite leader, you should already see the benefit of this: less resistance, faster learning cycles, and a solution grounded in real work, not assumptions. Strip away the hype until what’s left actually works. Then expand.
Developers view generative AI as a collaborative partner rather than a replacement
The anxiety around AI replacing developers is mostly a distraction. Developers aren’t afraid of working with technology, it’s what they do every day. What they push back against is being forced to accept tools that don’t help them work better.
The best way to position AI is as a partner. Not a surveillance system. Not a performance metric. A partner. This works when AI helps developers do more of what they’re good at, faster iteration, faster learning, better decision-making, and less of what slows them down.
This shift in framing matters. Most developers don’t want AI writing all their code. They want AI to help them focus, reduce mental load, and get them past blockers, whether it’s interpreting complex data, triaging bugs, or brainstorming implementation options. This is about multiplying skill, not replacing it.
Lizzie Matusov, CEO at Quotient, summed it up clearly, generative AI’s main value isn’t just in what it produces, it’s in how it improves how developers think, feel, and operate. That shift is what brings better ideas, better collaboration, and ultimately, better products.
If you’re leading an engineering organization, make it clear to your teams: AI is not there to replace you. It’s there to help you build more effectively. Focus on integration that respects their workflow, supports autonomy, and makes space for creativity. That’s how you get real adoption without resistance.
Seamless integration of AI tools within established workflows maximizes adoption and efficiency
The more natural it feels to use a tool, the more likely it is to be used.
AI has a better chance of helping when it doesn’t ask developers to change how they work. Tools that require less interaction, fewer shifts between systems, and minimal training see faster adoption and fewer dropouts. Developers want results that show up in their day-to-day flow, not features they need to remember to toggle on.
This is where embedded, context-aware AI performs best. Integrated code suggestions, smart notifications, automatic documentation prompts, these examples don’t force behavior changes. They reduce decisions. They remove repetitive steps. That’s how real productivity gains start to show.
Jamil Valliani, Head of Product for AI at Atlassian, explained it cleanly: AI should be integrated “almost without [developers] having to actively think about it.” That’s the threshold. If your teams are constantly debating how and when to use the tool, it’s not fully integrated. And if it’s not integrated, it won’t deliver consistent value.
Executives often underestimate the friction poor integration creates. A powerful tool that disrupts workflow is often worse than a simple tool that fits. Focus your AI investments on improving the core environment your teams already use. That’s where the ROI actually comes from.
Effective AI use centers on addressing developer pain points
Most developer time isn’t lost in writing code, it’s lost in navigating complexity. Debugging difficult errors. Understanding unfamiliar codebases. Figuring out why a query fails silently or where a legacy component was pulled from. These are the tasks developers most want help with.
Data from DX’s AI-assisted engineering guide confirms this: the most reported AI use case among developers is stack trace interpretation. It gives fast context where otherwise they’d be manually digging through logs or documentation. Refactoring came next, a task no developer loves but one that AI can now assist with meaningfully.
Learning and planning workflows also benefit from AI. When developers are working in new domains or handling unusually complex logic, AI can help surface insights and get them unstuck quickly. The same goes for writing complex queries or teasing out why a system is failing during integration.
Ironically, even though developers often cite poor documentation as a core productivity blocker, they’re not asking to spend more time writing it. They want a faster way to document without the overhead. Generative AI can assist here too, but it ranks only as the fifth most popular use case. This tells you where their priorities are: reducing friction in real-time, not polishing documentation after the fact.
The DORA report strengthens this with hard data, generative AI has been shown to improve documentation quality by 7.5%. It’s a meaningful boost, but documentation isn’t where developers seek the most support. If you’re leading engineering or product, aim your AI investments at the steps that drain energy and delay delivery: error handling, comprehension, code quality. That’s where the ROI will appear the fastest.
Management must bridge the gap between technical possibilities and executive expectations
Executives want clarity. They want performance gains, speed, efficiency, preferably all at once. The risk is assuming AI will deliver those results immediately if enough tools are added into engineering workflows. That’s not how this works in practice.
Engineering leaders are often stuck translating vague expectations, like “more AI, less cost”—into meaningful outcomes. That’s where friction starts. You can’t align teams by overpromising what AI will deliver. You align them by setting clear objectives, being transparent about the time investment needed to see results, and communicating what AI adoption actually involves.
Andrew Zigler, Senior Developer Advocate at LinearB, put it straight: most executives don’t speak the same language as engineering. A VP of sales can track pipeline in precise terms. Engineering work doesn’t offer the same visibility. As a result, AI expectations get inflated. Unless someone grounds those expectations in reality, things break down fast.
So if you’re in a decision-making seat, focus on honest metrics. What exactly will AI improve? Code quality? Time to resolution? Team bandwidth? Don’t generalize. Be specific. Treat AI as one component in a broader system, not a magic solution.
Set achievable goals, ensure your teams have the support they need to test and integrate tools properly, and measure adoption as much as outcome. Long-term impact only comes when the foundation is built right, and that begins with setting the right pace and keeping every stakeholder aligned.
Sustainable AI adoption relies on transparency and support
AI doesn’t succeed through mandates, it succeeds through trust, clear policy, and developer autonomy. Forcing adoption sends the wrong message. It tells developers the tool is more important than their judgment. That’s where resistance takes root.
If you want sustainable results, you need a policy environment that supports exploration, not expectation. Developers should know how AI is intended to be used, where it’s being tested, and what feedback loops exist. Transparency isn’t just good governance, it’s operational necessity. When people understand purpose, they engage more seriously.
DORA’s GenAI impact report outlines a clean path forward: communicate your intentions, share your framework, and let developers opt-in. Provide time for teams to learn, test, and think critically about integrations. Allow room for experimentation. Teams need freedom not just to adopt AI, but to reject tools that don’t create value.
Leadership must also invest in support structures. That includes internal documentation on AI usage, access to training, and policies for how performance will, and will not, be measured. If AI becomes a reporting metric before it becomes a productivity boost, you’ve lost alignment.
The bottom line is this: when developers feel they have room to assess AI tools for themselves, adoption becomes organic. You won’t need to force use, teams will seek out tools that actually help. That’s when you know your strategy is working.
AI adoption can introduce additional complexity
Rushed adoption creates downstream problems. Generative AI can be powerful, but without thoughtful systems in place, it creates more issues than it solves. Developers are already noticing it, more time spent debugging machine-generated code, more hours lost resolving security vulnerabilities. The promise of faster dev cycles falls apart when teams are stuck untangling errors introduced by AI suggestions they didn’t fully understand or control.
Zohar Einy, CEO of Port, warned that faster deployments often mean more complexity: more microservices, more decision points, more unstable interactions between systems. When AI generates code developers didn’t write themselves, it takes longer to debug, harder to test, and increases the risk of misconfiguration. It’s not just about speed, it’s about control.
According to Harness’s State of Software Delivery 2025 report, 67% of developers are now spending more time debugging AI-generated code. Another 68% report spending more time managing security issues introduced by AI-driven changes. These aren’t edge cases, they’re systemic impacts.
This doesn’t mean you abandon AI. It means you need structure. That includes boundaries for where AI-generated code enters your systems, checkpoints for validation, monitoring for regressions, and an internal culture that treats AI output as a draft, not a decision. Strategy beats speed here. Without a plan, AI amplifies complexity instead of reducing it.
Executives need to see the whole picture. AI can deliver velocity. But without precision, it creates drag. The systems you build to manage complexity, code review protocols, security policies, observability tooling, are what determine whether AI becomes an accelerant or a liability. Make those systems your focus if you want AI to scale without compromising safety or output quality.
Concluding thoughts
Generative AI isn’t a shortcut. It’s a capability shift, and it only delivers if you treat it that way. The biggest mistake leaders make is designing AI strategies around assumptions instead of actual developer friction. That’s why productivity gains remain limited, despite heavy investment.
Focus less on pushing tools and more on removing blockers. Give teams room to experiment, share results, and reject what doesn’t work. Respect workflow over features. If AI fits how developers think, they’ll adopt faster and drive better outcomes. If it gets in the way, they’ll bypass it, and you’ll see minimal return.
Make alignment your priority. Developers, engineering leads, and executives should be working from the same operational truth. That means consistent feedback loops, clear definitions of value, and policies that support trust rather than control. When adoption is voluntary and strategic, the lift is exponential.
The tools are here. The results depend on how well you listen, how clearly you lead, and whether you’re designing AI to solve real problems, not just the ones you assume exist.
