Developers must become better technical managers to effectively harness AI as a coding assistant
AI is shaping how we build software, by making faster execution possible when paired with clear direction. Think of AI tools like GitHub Copilot, Cursor, or ChatGPT. They simplify tasks, but they don’t replace deep engineering skill. They’re not senior engineers. They’re more like highly capable interns that work fast, but only if you tell them what to do, specifically, and with detail.
Most developers aren’t trained to think like managers. They don’t write instructions for someone else. They’re used to writing code, not leading it.
So they type vague prompts, “fix the database,” or “make the UI blue”, and get poor results. The AI might hallucinate a library that doesn’t exist or break security protocols. Then users blame the model. But most of the time, the model’s not the problem. Miscommunication is. AI can’t read between the lines. It doesn’t guess. It parses what you write, nothing more. If the prompt is sloppy, the result will be, too.
Success with AI in coding doesn’t require better models. It requires better management of the models. That means structured hand-offs, clear expectations, and accountability, just like leading a team.
Executives should take this seriously because the core challenge isn’t technical. It’s organizational. When developers write like managers, clear vision, structure, goals, AI becomes fast, safe, and scalable. If they don’t, systems behave unpredictably, and oversight becomes reactive. That downtime is costly.
Integrating AI into your development workflow is a culture change, less about syntax, more about leadership.
Writing quality specifications is the key skill in AI-augmented development
The most underrated skill in AI-driven development right now isn’t machine learning, it’s writing a clear specification. AI doesn’t invent your vision. It runs with the one you give it. Bad specs create bad outcomes. Good specs turn AI into serious leverage.
Addy Osmani, Engineering Manager at Google, laid out a framework that actually works. He calls it a “smart spec”, a scalable, structured blueprint the AI can use across sessions. It’s becoming the go-to for teams serious about making code quality predictable. The critical components? Objectives, non-goals, technical constraints, security rules, integration points, “do not touch” areas, and acceptance criteria.
Most developers skip at least half of this. That’s a problem. Without explicit non-goals, the AI overreaches. Without architectural context, it guesses. Without constraints, it breaks things that should stay fixed. If you don’t say what finished looks like, the AI doesn’t know when to stop. You end up with scope creep, wasted cycles, and technical debt that could’ve been avoided with five extra minutes of preparation.
What’s changing now is that writing specifications isn’t optional anymore. It’s the unlock. You don’t just write specs because they’re nice to have. You write them because it’s the only way your AI tooling delivers results reliably.
If your teams don’t build the habit of writing detailed specs, they’re not working with AI, they’re working against it. That eats productivity.
For C-suite leadership, this is a signal. Tooling will continue to evolve. Models will get better. But if your teams are still dropping ambiguous prompts into black boxes, you’re leaking velocity. The path forward is operational discipline, start with structure, not code.
AI success depends on ‘context engineering’, structuring information for sustained, accurate execution
Most developers still treat AI prompting as a one-step action. You type out a task, hit enter, and wait for magic. But that’s not how the best results happen. AI performance drops sharply when overloaded or misdirected. It doesn’t scale well with chaotic input. That’s not a model flaw, it’s how these systems are wired. Attention is limited. If you try to include everything, the important details get lost.
What’s needed instead is structured input, what some engineers now call “context engineering.” This means setting up the instructions, tools, constraints, background, and outputs in an ordered way the model can follow. It’s not about giving the AI more, it’s about giving it only what matters.
Addy Osmani points out that piling on instructions doesn’t improve performance. It introduces confusion. Anthropic, a company that works on large language models, came to the same conclusion: too much instruction density leads to worse results. That’s why structuring context with precision is becoming a baseline requirement when using AI at scale.
From an executive view, this is less about prompt quality and more about workflow design. AI tools don’t make judgment calls. They operate within the boundaries you define. Without clear segmentation of responsibilities, what the model should do, what it should ignore, what it should return, you get unstable output and unpredictable value.
The implication here is critical. The developer’s role is shifting. It’s no longer just about knowing function calls or syntax. It’s about knowing what information the AI needs to execute a task correctly and how to present it cleanly. That requires domain knowledge, product awareness, and operational clarity.
In short, context engineering is not a feature. It’s a skillset that separates effective AI deployment from noise.
Effective AI use revives classic engineering discipline
AI is not a shortcut past the core disciplines of software development, it’s a multiplier of whatever practices are already in place. If your processes are chaotic and objectives are unclear, AI only spreads those problems faster. But when your systems are grounded in structured thinking and institutional clarity, AI becomes a force multiplier.
One of the clearest signals of this trend is the return of precise, testable specifications. Osmani’s “smart spec” approach isn’t a new invention. It’s a restatement of key engineering principles: define scope, boundaries, invariants, and evaluation criteria before execution begins. These practices are as old as solid engineering itself, but many teams drifted away from them as tools became more powerful.
Now, AI forces a reset. Success demands that developers tighten process control. For example, you can’t rely on the AI to infer regulatory constraints, interpret fragile dependencies, or protect critical legacy integrations, it needs to be told, explicitly. Acceptance criteria need to be tied to test results and edge cases. Failure to define them means failure to control output.
Executives need to evaluate whether their organizations are prepared for that level of clarity. Tools will keep improving, but if teams aren’t aligned on scope, version control, or QA checkpoints, the system is exposed. AI doesn’t let you skip steps. It penalizes you for skipping them faster.
What’s happening now isn’t a simplification of engineering, it’s a refinement. So while workflows are getting faster with tools like Copilot and Spec Kit, the fundamental expectations remain the same: structure, control, and readiness to manage complexity.
Leaders who expect performance from AI must first ensure their teams are fluent in engineering basics. That fluency is now the foundation for leveraging intelligent systems responsibly and at scale.
Overreliance on AI risks technical skill erosion and weakens code ownership
AI brings speed, but speed without depth comes at a cost. When developers lean too heavily on AI-generated output, without engaging deeply with the underlying architecture or implementation, they begin losing command of their own software systems. The ability to reason about performance, debug regression issues, or maintain production reliability starts to slip. And that weakness compounds over time.
Charity Majors, CTO of Honeycomb, makes a sharp distinction here. She points out that with AI, authorship is becoming cheap, almost free. The system will give you code on demand. But ownership, knowing how that code behaves, where it breaks, how it scales, is getting more expensive. You can’t maintain control over software you don’t fully understand.
This exposes a blind spot in the “developer as manager” model. Delegating work to AI agents assumes deep architectural knowledge so you can write strong specifications and later validate the results. But if developers continuously hand off execution to machines and stop engaging directly, they lose the technical grounding needed to manage those systems.
For executive teams, this is a governance concern. You’re not just deploying AI tools, you’re adjusting the skill curve of your technical employees. If the balance tips too far toward dependence, the long-term risk is talent hollowing. Teams may ship faster in the short term, but resilience drops.
What’s required is active skill preservation, especially on complex or high-impact projects. Even in AI-assisted environments, engineers need to stay involved in implementation when it matters. This builds confidence, reinforces code familiarity, and ensures that critical decisions are still made by humans who understand the implications.
Reliability doesn’t just depend on code accuracy. It depends on people taking responsibility for what the code does once it’s live.
A hybrid approach balances AI use with human expertise
Strong development teams already understand that not every task needs to be automated, and not every task should be. Discretion and judgment are now more valuable than ever. Developers working effectively with AI know when to move fast, when to slow down, and when to take control.
Sankalp Shubham describes this clearly. He proposes a practical framework, use AI for repetitive and well-scoped tasks, retain human oversight for original, complex, or high-risk work. That might mean writing out the intricate part of a solution manually and asking the AI to handle tests. Or using your own architecture decision before prompting the model to fill in the details.
This approach creates control at scale. It lets developers collaborate with AI without compromising accuracy, security, or long-term maintainability. It also counteracts the risk of skill erosion. The more technical ownership stays intact, the more confident your teams will be when supporting these systems later.
From an executive standpoint, this hybrid model delivers the clearest strategic value. You don’t slow down on innovation, but you also don’t trade away resilience. You retain the expertise and technical context your teams have built, while multiplying their output selectively with AI.
The balance is critical. Too much automation introduces risk. Too little automation leaves performance gains on the table. Executives building forward-looking teams should focus not only on deploying intelligent tools but on embedding intelligent thinking, discretion, context, and judgment, inside the workflows those tools support.
That’s the real differentiator now. Tools that scale your team’s capability are useful. Tools that replace thinking are dangerous.
Soft skills, planning, communication, and judgment, are increasingly critical for developers
AI tools are shifting the baseline. The value of memorizing syntax, navigating APIs, or writing boilerplate code is dropping. What’s rising in value are practical leadership skills, clarity in planning, precise communication, strategic oversight. These used to be optional in individual contributor roles. Now, they’re non-negotiable.
Developers working with AI must not only define a problem clearly, they also need to explain it in language that a model can parse unambiguously. That’s a higher bar than traditional coding. It requires developers to translate business goals into implementation-ready instructions and preempt where ambiguity can break the result. These are management-level responsibilities being performed under technical pressure.
This evolution changes talent expectations. You won’t get ahead by typing faster or knowing more libraries. You’ll get ahead by thinking clearly and communicating with precision. These aren’t soft skills, they’re core drivers of productivity in AI-led environments.
For the C-suite, this is a signal: workforce development must expand beyond technical upskilling. Competitive teams now need communication frameworks, decision-making training, and structured templates for defining work. These enable engineers to collaborate effectively with agents, other developers, and cross-functional stakeholders, driving alignment at scale.
Machines are handling the mechanical effort. The human advantage is strategic thinking. Organizations that invest in this shift will outperform those that treat AI as a plug-and-play automation layer. The future of engineering is no longer just technical. It’s also deeply human.
Organizational clarity must improve for AI to succeed on teams
AI tools can’t compensate for unclear communication inside a company. When product teams fail to articulate priorities, or when stakeholder inputs conflict, generative AI doesn’t solve that disconnect, it amplifies it. The result is faster execution toward misaligned goals.
Birgitta Böckeler from Thoughtworks highlights this tension. She points out that many AI-led demos assume developers will do the heavy lifting of requirements analysis before generating specs for code. But in real-world product workflows, that process is often unclear or split across roles. If your teams already struggle with alignment, AI broadens the gap, not narrows it.
This is key for leadership to understand. The effectiveness of AI is not a reflection of the model alone, it’s a reflection of how well structured your organization is. The clarity of your requirements, the consistency of your frameworks, and the reliability of your decision chains all impact how well AI-driven development performs.
Teams that skip the fundamentals, precise scoping, stakeholder sign-off, product alignment, won’t just waste time. They’ll push flawed assumptions into production, faster than before.
To build sustainable AI integration, organizations must tighten communication, define accountability, and ensure that planning processes are working at every level. That might mean updating your product management systems, introducing guardrails for autonomous code generation, or simply training teams to be more deliberate when expressing outcomes.
AI systems move fast. Businesses that thrive will be the ones that move in sync, with internal clarity, not chaos.
In conclusion
AI isn’t here to replace developers. It’s reshaping the job. The ones who thrive aren’t just good at writing code, they’re good at defining scope, communicating clearly, and managing complexity with precision. That requires structure. It requires leadership.
For organizations, the message is straightforward: don’t confuse automation with autonomy. AI speeds things up, but it doesn’t make decisions for you. It can generate, execute, and assist, but only within the parameters you set. When those parameters are vague, performance drops. When they’re sharp, output scales.
The teams that get value from AI aren’t just experimenting with tools, they’re building the systems around those tools. They’re aligning specs, owning context, and maintaining direct control of impact. And they’re doing it without trading away human expertise.
If you’re leading teams today, you don’t need a roadmap packed with AI features. You need clarity in roles, discipline in process, and judgment at the point of execution. Scale depends on it. Quality depends on it. And long-term competitiveness will depend on it even more.
