AI Project, or Just a Fancy Spreadsheet? Knowing When to Build, Buy, or Wait
There is a quiet trend happening inside businesses right now: everything is being labelled an "AI project".
Some of these are genuinely strategic.
Some are useful experiments.
Some are... well, a spreadsheet with better marketing.
Not every workflow needs a custom model, a data science team, or a six-figure build. Sometimes the most powerful transformation is a smart prompt, a better template, or a plug-in you already have access to.
This article is about finding clarity before you overinvest.
The Problem: Not Every Idea Needs a Model
AI is exciting. Leaders are seeing potential everywhere.
But without a framework for decision-making, it is easy to fall into one of these traps:
Building something that already exists in a simpler form.
Overengineering a solution to a very normal problem.
Buying a tool you never fully adopt.
Creating technical debt that no one wants to maintain.
Investing too early, before your organisation is ready.
Competitive advantage comes from choosing the right solution, not the most complex one.
Build, Buy, or Wait: The Decision Framework
1. Build (custom solution) when...
You have a specific, high-value problem that no off-the-shelf tool can solve.
You control high-quality, well-structured, trusted data.
You have clear governance and security requirements that generic tools cannot meet.
The value of automation or prediction is significant and measurable.
You have the internal capability (or partner) to maintain it long-term.
Building is best when: AI becomes part of your core business operations or competitive advantage.
2. Buy (off-the-shelf tools) when...
Your needs match common business functions (summaries, drafting, analysis).
Tools already exist that can do 80 percent of what you need.
You want something fast, easy to train, and low-risk.
You prefer predictable cost and no long-term maintenance burden.
Your team needs capability now, not in six months.
Buying is best when: You want reliable outcomes without complexity.
3. Wait (or do nothing) when...
You don't yet have governance, clear goals, or a readiness plan.
Your data is scattered, inconsistent, or difficult to access.
The idea sounds appealing but you cannot articulate the problem it solves.
People are overwhelmed or unsure about AI in general.
A simpler workflow change could deliver the same benefit.
Waiting is best when: The environment is not ready and a premature project will create more chaos than value.
The Wry Truth: Sometimes It's Just a Fancy Spreadsheet
Here are clues that your "AI project" is really just a spreadsheet with extra steps:
It relies heavily on manual data input.
The question you are answering is simple arithmetic or filtering.
Outputs do not require prediction, judgment, or language processing.
You would be embarrassed explaining it to an actual data scientist.
Excel, Power BI, or a basic automation could achieve the same result.
If you can solve a problem with existing tools your team already understands, that is not a failure of innovation.
It is good leadership.
The Real Value: Making AI Calm, Responsible, and Useful
AI does not need to be complex to be transformative. Sometimes the simplest, safest, most sustainable option becomes the most valuable.
A smart prompt can save hours.
A standardised template can reduce legal risk.
A light automation can eliminate ten tiny tasks that drain your team's energy.
Those wins matter more than the flashiest project.
Binary Refinery's Take
Our role is not to sell custom builds or overengineer solutions. It is to help you choose the smartest path:
Build when it creates real advantage.
Buy when it delivers value immediately.
Wait when the foundations are not ready.
Improve processes before automating them.
Use AI where it strengthens people, not replaces them.
Great strategy is knowing when not to do something.
A Simple Rule of Thumb
If you can solve it with Excel, a prompt, or a safe off-the-shelf tool, do that first.
If you cannot, then consider an AI project.
This mindset saves money, builds capability, and keeps your organisation focused on real outcomes, not hype.
Next Steps
If you want clarity on whether an idea is worth building, buying, or shelving for now, we can help with:
AI opportunity assessments.
Workflow analysis.
Implementation guidance.
Governance and risk review.
Team training.
Good AI is practical AI.
And practical AI is how you get competitive advantage without the chaos.
About the Author
Kat Mac is the founder of Binary Refinery, where she translates complex AI and technology topics into practical, business-led guidance for organisations. Her focus is simple: clarity, integrity, and strategy that genuinely helps leaders move forward.
Disclaimer: This article is for general information only. It isn’t legal, financial, or technical advice. Every organisation is different – get tailored guidance before making decisions that affect your people, data, or systems.
