In today’s enterprise landscape, few technological shifts have created as much excitement and confusion as the rise of generative AI. With tools like ChatGPT, GitHub Copilot, and other large language models (LLMs), it is now possible to generate functional code using natural language prompts. TikTok and other social media are filled with folks flaunting their app they build just by prompting in a couple of hours or a day.
AI companies, their leaders and anyone in the AI Consulting space is now boldly saying
English is the New Programming language
You see news of doom and gloom for coding and why should anyone learn to code anymore. With Layoffs within the tech sector, that does not help promote the cause of why tech is a cool career anymore.
While this phrase captures the remarkable progress made in AI assisted development, it also presents a dangerous oversimplification. It implies that prompting alone is a sufficient replacement for coding skills, an idea that is now influencing organizational up-skilling efforts, hiring strategies, and digital transformation roadmaps.
Also if you read between the lines, enterprises which have been maturing their AI game are still building the scafolding for their AI Journey, laying the pipes, build their compliance, regulatory, governance frameworks and their data structures. They have seen pockets of productivity and coding being generated by prompt, but no one is saying that out loud to the world. So in these enterprises, Programming is still the Programming Language.
AI Prompting Is Accelerating but Not Replacing Software Development
Generative AI tools undeniably reduce the effort required to produce code. Business users can now generate automation scripts. Product teams can build prototypes. Marketing departments can embed generated HTML or analytics snippets. These developments are significant. However, what’s often overlooked is that prompting while powerful does not remove the need for logical thinking, debugging skills, or system-level understanding. In fact, in many enterprise environments, the overuse of prompting without engineering oversight has resulted in:
- Fragile or unmaintainable systems
- Critical logic errors missed during AI-assisted development
- Development cycles that move fast initially but require substantial rework later
Put simply: speed without knowledge introduces long term risk.
The Hidden Cost of Prompt Reliance
Over reliance on AI generated outputs is not only a technical risk—it also presents a cognitive one.
Emerging research suggests that teams relying heavily on AI prompting, without foundational knowledge of how code works, show a reduction in analytical depth. A 2025 Microsoft Research study found that professionals using AI tools extensively without underlying technical fluency scored lower on structured problem solving and root-cause analysis over time.
This pattern is not hypothetical. Enterprises are seeing early evidence in:
- Teams that cannot debug or explain the outputs they ship
- Increased dependency on AI models for basic logic or architectural task
- Lower confidence in code ownership, particularly in cross-functional teams
Why Foundational Skills Still Matter
Generative AI should be viewed as a force multiplier not a replacement for engineering fundamentals. Even in AI-augmented environments, organizations still require teams who can:
- Understand and review AI generated code
- Identify flaws or inefficiencies in logic
- Integrate AI outputs with broader system architecture
- Implement testing, monitoring, and compliance guardrails
For these reasons, coding, logic, and software design principles remain critical skills not just for developers, but for product managers, analysts, operations leaders, and even business functions increasingly involved in automation and tooling.
Strategic Takeaway for IT Leaders
The promise of AI-assisted development is real but so are the risks. Senior technology leaders must recognize that:
- Prompting is a valuable skill, but insufficient in isolation
- Teams that cannot validate or understand generated code create operational and reputational risk
- Investments in AI must be paired with technical up-skilling, across both IT and business functions
Questions Every Technology and Business Leader Should Be Asking:
- Do we have the technical depth to evaluate what AI tools are building on our behalf ?
- Can our non developer teams interpret and validate the outputs of generative tools or are they copying and pasting blindly?
- How are we ensuring that AI-generated solutions remain secure, compliant, and maintainable?
- Are we tracking whether AI is improving team capability or quietly eroding it?
- Have we clearly defined which roles require foundational coding fluency and have we re-skilled accordingly?
- What governance exists around prompt generated code entering production environments?
- Do we have a plan for growing “code fluency” across business teams not just in IT?
These are not theoretical questions. They’re the next-generation equivalents of the “build vs. buy” decisions of the past decade. And the answers will determine whether AI is a sustainable asset—or a strategic liability.
Your Organization in Five Years ?
You can automate execution. You can’t automate understanding. The next 5 years will be shaped not by how well we use AI but by how well your organization trains people to understand what AI is doing, and why.
Remember that entry level roles are not a cost center. They’re a knowledge preservation system.
Ignore them, and in five years, your organization may forget how it works.
