Source: Be Datable. Plot your use cases to identify where to invest.
Frequency-Complexity Matrix
Stop asking which AI model to use. Start asking how often you'll use it and how complex it needs to be.
Why This Matters for Your Business
Most people ask the wrong question when starting with AI. They fixate on model selection: Should I use ChatGPT, Claude, Gemini, or Grok? This misses the point entirely.
Models share similar capabilities. As they improve, differences blur. The real question isn't which AI you choose. It's what you must accomplish and how easily you can do it.
This framework maps AI applications across two axes: frequency (how often you'll use the solution) and complexity (how sophisticated the implementation must be). Use it to prioritize investments and avoid wasting resources on misaligned initiatives.
The Four Quadrants
High Frequency + Low Complexity
The Sweet Spot: Start here
Daily tasks that work well with out-of-box solutions. Highest ROI for lowest effort.
Examples: Email responses, content creation, calendar scheduling, ad copy generation
High Frequency + High Complexity
Worth the investment
Daily complex tasks requiring custom integrations. High investment but high payoff.
Examples: Real-time data processing, predictive maintenance, sales call analysis
Low Frequency + Low Complexity
Use current AI interfaces directly
One-off simple tasks. No custom development needed.
Examples: Campaign ideation, research summaries, one-time document drafts
Low Frequency + High Complexity
Consider external experts
Rare but sophisticated needs. Often better to outsource than build.
Examples: Personalized marketing systems, custom forecasting models, supply chain optimization
Common Mistakes to Avoid
Building complex solutions for infrequent tasks
If you only need it quarterly, don't invest weeks building it. Use the AI directly.
Avoiding moderate complexity for frequent tasks
Setting up saved projects and custom instructions pays off massively for daily use.
Prioritizing "interesting" over "impactful"
The highest ROI often comes from unglamorous applications that happen constantly.
Ignoring data quality requirements
Many AI implementations fail due to poor data, not poor positioning on the matrix.
How to Use This Framework
Step 1: List Your Potential AI Use Cases
- Write down every task where AI could help. Include both obvious ones (content creation) and less obvious (meeting summarization, data entry, customer support).
Step 2: Plot Each on the Matrix
- For each use case, honestly assess: How often will this run? (daily, weekly, monthly, one-time) How complex is the implementation? (out-of-box, automation tools, custom integration)
Step 3: Prioritize by Quadrant
- Start with high-frequency, low-complexity (immediate wins)
- Evaluate high-frequency, high-complexity (worth investment)
- Handle low-frequency, low-complexity ad hoc (no setup needed)
- Outsource or delay low-frequency, high-complexity
Step 4: Match Tools to Position
- Out-of-box (Low Complexity): ChatGPT, Claude, Gemini directly
- Automation Tools (Medium): Zapier, Make.com, n8n
- Custom Integration (High): API development, specialists
- External Experts: Consultants, agencies
Self-Assessment
Answer these questions to evaluate your position.
Which quadrant contains most of your current AI initiatives?
Are you balancing frequency and complexity appropriately?
What high-frequency tasks remain unautomated that could benefit from AI?
Are you building custom solutions for things that could use out-of-box tools?
“Stop asking which model to use. Start asking how often you'll use it and how complex the implementation needs to be.”
Companies prioritizing high-frequency, moderate-complexity use cases achieve 1.5x higher revenue growth from AI initiatives.
Sources & Further Reading
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