When people talk about AI in software development, the conversation usually goes straight to coding or testing.
But software planning is another area where AI delivers real value.
In the software planning phase – when requirements are defined and priorities set – AI can save time, reduce costs and lower risks.
This is why AI has become an invaluable tool not just for developers, but for executives and teams mapping out what software should do in the first place.
In this article, I’ll share five concrete ways companies can use AI to plan software development projects.
These insights are based on how my team at Big Fish uses AI in our planning work with clients, combined with industry practices that are becoming the new norm.
1. Requirements Creation & Quality Review
One of the most time-consuming parts of planning software is translating scattered ideas, stakeholder input, and business needs into clear, actionable requirements.
AI can help here in a few ways.
Meeting-integrated AI assistants can listen in on Zoom or Teams calls, capturing and summarizing stakeholder discussions automatically.
Instead of someone frantically typing notes, AI produces a structured summary of what matters most.
From there, AI can draft requirements documents, highlighting recurring themes, clustering related needs, and even pointing out contradictions. This saves a tremendous amount of time when planning custom software.
For executives, the takeaway is simple: AI ensures your team leaves early meetings with actionable requirements, not scattered notes and post-meeting homework.
AI ensures your team leaves early meetings with actionable requirements, not scattered notes and post-meeting homework.
2. Writing User Stories & Acceptance Criteria
Once requirements are drafted, they need to be translated into backlog items – user stories with acceptance criteria. This step is critical, but it’s also repetitive and time-consuming.
AI can generate user stories directly from requirements, complete with acceptance criteria that describe how success will be measured.
Product owners and business analysts still need to review, refine and prioritize the stories. But AI dramatically accelerates the first draft.
For executives, this means your team gets to a structured backlog faster. You move from “what we think we need” to “what we need to do to get there” in less time, and with greater consistency across the project.
3. Dependency & Impact Mapping
In software planning, it’s not just about what features you build, but the order in which you build them. Overlook a dependency and you risk a stalled project.
AI can analyze requirements and automatically detect logical dependencies. For example, it may flag that a reporting dashboard can’t be built until data collection features are in place. It can also show you how changes in one area will impact another.
AI can analyze requirements and automatically detect logical dependencies.
This kind of automated impact analysis gives executives greater confidence that the roadmap they approve is realistic and risk-aware.
4. Diagramming Flows & Processes
A list of requirements is valuable and necessary, but visuals often tell the story better.
AI can take plain-text descriptions and generate draft user flows and process diagrams. These aren’t final design artifacts, but they give stakeholders a quick way to visualize the requirements early on.
5. Resource & Risk Forecasting
Budgeting and scheduling are some of the hardest, and riskiest, parts of planning a custom software project. Too often, estimates are based on gut feel or incomplete data.
AI can analyze historical project data, requirement complexity, and even industry benchmarks to forecast likely costs, timelines, and staffing needs.
It can also flag potential risks early, from compliance challenges to integration issues.
AI can flag compliance risks and potential integration issues.
For executives, this means making go/no-go decisions based on data-backed forecasts.
Limitations to Keep in Mind
AI is a powerful software planning partner, but it isn’t perfect. A few realities are important to remember:
- Data quality matters. AI’s recommendations are only as strong as the information it’s fed. If the inputs are incomplete or inaccurate, the outputs will be too.
- Human oversight is essential. Someone still needs to review AI outputs, refine them, and ensure they align with business goals and strategy. AI accelerates planning, but people still provide the input, and judgment of the output.
- AI requires management. Humans are also still needed to select the right AI tools, set them up, and guide them with the right prompts and direction.
The bottom line: AI accelerates planning; it doesn’t replace the people making the strategic calls.
Key Takeaways for Executives
By taking advantage of AI to help plan custom software (e.g. requirements creation, user stories, dependency mapping, diagramming, and forecasting), companies can move faster and with greater confidence.
And your team will spend less time bogged down in documentation and repetitive tasks that AI can do in minutes.
This is the strategic way to combine human expertise with AI’s ability to process, structure, and analyze information at speed.
How Big Fish Uses AI in Planning Custom Software
At Big Fish, we’ve built our Custom App Blueprint process around the reality that AI now belongs at the heart of software planning, helping leaders reach clarity faster.
Our team combines years of experience planning software with AI tools that make the work faster, more comprehensive, and less prone to blind spots.
Executives value this because they get to clarity sooner – clear requirements, a realistic roadmap, and a stronger foundation for development.
If you’re considering a custom software project, AI belongs in your planning process. And with the right partner, you can put it to work in a way that gives you both speed and confidence.
