Why Planning Matters: The Critical Step AI Development Forgot

AI has made it easier than ever to start building — and exponentially harder to build the right thing.

Here’s the paradox facing every development team in 2025: AI agents can generate thousands of lines of code in minutes, yet 95% of GenAI pilots never reach production. Developers spend 90% of their time working with AI tools, yet more developers distrust AI output (46%) than trust it (33%). The bottleneck isn’t generation speed. It’s direction.

Without planning, AI becomes a chaos multiplier. It generates code before understanding requirements. It creates solutions without defining success. Planning is how we transform speed into progress. And it’s measurable. The difference between projects that ship and those that stall still comes down to one thing: how clearly we define intent before execution begins.

The Evidence: Why Upfront Planning Delivers Better Downstream Results

The Compounding Cost of Change

For decades, software engineering research has shown that the later you discover a misunderstanding, the more expensive it becomes to fix. Synopsys’s 2024 State of Software Integrity Report and Tricentis’s 2025 Quality Transformation Report both reaffirm that the cost of rework and redesign can be 10–100× higher when issues are caught after build rather than before.

That same law of compounding change now plays out at AI speed.

When a developer gives a vague prompt such as  “improve login flow,”  the AI fills in the blanks with its own assumptions. Each unspoken requirement becomes a hidden branch of logic, and every missed constraint becomes a rewrite later. What used to unfold quietly through change requests and iteration cycles now happens instantly, in full view. AI hasn’t eliminated the cost of poor requirement capture;  it’s just made it visible in real time. 

And each missed requirement doesn’t just cause a bug; it quietly alters architecture. Once intent drifts, every design choice downstream becomes a retrofit.

The Prompt Engineering Multiplier

Prompt engineering is requirement gathering by another name.

Research from Deloitte (2024) shows that structured, standardized prompting can reduce AI implementation costs by up to 30%.  When you turn that structure into a plan — a persistent, evolving set of goals, success criteria, and dependencies — you move from single-turn prompting to continuous understanding.

A plan is simply a multi-layer prompt for the entire engineering process. Every step, every dependency, every acceptance test tells AI why it’s building, not just what.

ROI Impact: Planning’s Return on Investment

Teams that invest time in planning see 6–10× higher ROI across delivery cycles, as shown in Gemstone IT’s Ensuring ROI in Software Projects: A Decision-Maker’s Guide.

Clear objectives, measurable criteria, and continuous alignment prevent the most expensive outcome in any system — building the wrong thing well. AI accelerates both the good and the bad. Good plans executed quickly compound returns. Bad plans executed quickly compound waste.

From Principle to Practice. Why We Built It In

At TurinTech, we’ve tried to treat planning not as a preamble to building, but as a living part of it. The lesson across every failed GenAI pilot is clear: speed without clear understanding only multiplies rework. So our approach has been to look at the entire process end-to-end, weaving collaborative, dynamic planning into how AI and humans work together from the start.

By integrating planning directly into Artemis, we’ve found the conversation shifts. Developers spend less time correcting guesses and more time refining ideas. Each plan captures not just what to build, but why.  Making intent visible, execution traceable, and learning continuous. The result isn’t faster code for its own sake, but higher-quality outcomes that stay aligned with user intent as projects evolve.

A New Foundation for the AI Era

The future of software development isn’t about replacing planning with faster execution. It’s about making planning intelligent and collaborative enough to deliver the right outcomes. AI didn’t solve the old problem of misaligned requirements, it just compressed it into seconds. The misunderstandings that used to unfold quietly across months of design reviews now explode across chat windows and pull requests. The speed has changed; the lesson hasn’t.

The power of AI doesn’t come from how fast it generates code. It comes from how well it stays aligned with intent as it builds.

P.S. We’ve been hard at work turning Artemis into a true AI engineering partner. Soon, we’ll unveil the new agentic capabilities. Sign up to be among the first to try it out!