Ad hoc decision-making about AI, ML, and deep learning produces inconsistent results because it depends on whoever is loudest in the room. A framework replaces opinion with a repeatable process. What follows is a five-stage model we call CLEAR: Classify, Locate, Evaluate, Apply, and Re-assess. It is reusable across any project and deliberately simple enough to run in a meeting.
The point of a framework is not to be clever; it is to make good decisions boring and repeatable. Each stage below answers one question, and the stages run in order because earlier answers constrain later ones.
Stage 1: Classify the Goal
The first stage answers a deceptively basic question: what kind of intelligence does this problem require?
The three buckets
- Fixed-logic intelligence: the rules are knowable and stable. This is rules-based AI.
- Pattern-learning intelligence: the system must infer patterns from examples. This is machine learning.
- Representation-learning intelligence: the system must learn features from raw, unstructured data. This is deep learning, a subset of ML.
Classification matters because it sets the cost and data expectations for everything downstream. Skipping it is how teams scope a rules problem as a neural network. The common mistakes article shows what happens when this stage is skipped.
Stage 2: Locate on the Stack
Once you know the type, place the problem precisely on the nested hierarchy: AI contains ML, which contains deep learning.
Why location, not just classification
Locating forces you to acknowledge the nesting. A deep learning problem is also an ML problem and an AI problem, which means it inherits every constraint of the layers above plus its own. This stage prevents the error of treating the three terms as parallel options rather than nested sets. When you locate a problem at the deep learning layer, you are signing up for the data and compute burdens of that layer specifically.
Stage 3: Evaluate the Constraints
This is where the framework earns its keep. Score the problem against the axes that actually decide feasibility.
The evaluation axes
- Data volume: how many labeled examples exist? Small pushes up the stack toward simpler methods.
- Data shape: structured or unstructured? Unstructured pushes down toward deep learning.
- Interpretability: must you explain decisions? High need pushes toward simpler models.
- Latency and compute: what can the production path afford?
- Maintenance capacity: does the team have the expertise to keep this alive?
Each axis can independently veto a technique. A deep learning solution that is perfect on data shape but fails on interpretability is still the wrong choice in a regulated context. The trade-offs guide explores how these axes pull against each other.
Stage 4: Apply the Simplest Sufficient Technique
With constraints scored, choose the least complex technique that satisfies them all.
The application rule
Start at the top of the stack and descend only when forced. Try rules-based AI first; if the logic is not fully knowable, move to classical ML; escalate to deep learning only when data shape and volume demand it and no constraint vetoes it.
This "simplest sufficient" rule is the opposite of the common instinct to start with the most powerful tool. It produces faster builds, lower costs, and more maintainable systems. The best practices article treats this escalation discipline as a core habit.
Prove it with a baseline
Before full commitment, build a baseline at your chosen level. If a simpler level already hits the target, descend no further. The baseline is cheap and decisive.
Stage 5: Re-assess as Conditions Change
The framework is not run once. Conditions that constrained your choice will change.
Re-assessment triggers
- Data volume grows by roughly an order of magnitude.
- Accuracy plateaus below the business target.
- Interpretability or latency requirements shift.
- New data types (images, text) enter a previously tabular problem.
When any trigger fires, re-run stages 3 and 4. A classical model chosen under data scarcity may deserve a deep learning upgrade once data is abundant, and a deep model may deserve simplification if interpretability becomes mandatory.
Putting CLEAR to Work
Run Classify, Locate, and Evaluate during intake; that pass alone resolves most "AI versus ML versus deep learning" debates in minutes. Run Apply in your first sprint with a baseline gate. Schedule Re-assess against the triggers above. The framework's value is consistency: two different people running CLEAR on the same problem should reach the same answer, which is exactly what ad hoc decision-making cannot promise.
CLEAR in Action: A Quick Example
Walk a real request through the five stages: a client wants to automatically tag product photos with categories.
- Classify: the system must learn features from raw images, so this is representation-learning intelligence, deep learning.
- Locate: placed at the deep learning layer, it inherits the data and compute burdens of that layer plus the general ML requirement for labeled examples.
- Evaluate: data shape is unstructured (images), which favors deep learning. Data volume is the open question, you need thousands of labeled photos. Interpretability is low-stakes here. Latency is tolerant since tagging is not real-time.
- Apply: rather than training from scratch, fine-tune a pretrained vision model, the simplest sufficient technique that meets the need. Validate with a small batch before scaling labeling.
- Re-assess: if categories expand or accuracy plateaus, revisit. If the photo library grows enormously, a more specialized model may become worthwhile.
The framework took an ambiguous "tag our photos with AI" request and turned it into a specific, defensible plan in a single pass.
Why Frameworks Beat Instinct Here
Instinct fails on this topic in a particular way: it is biased toward whatever technique is most culturally prominent, which is almost always the flashiest one. CLEAR counteracts that bias structurally.
The structural safeguards
- Forcing an explicit Classify step stops "AI" from silently defaulting to "deep learning."
- The Evaluate stage gives every constraint a chance to veto, so no single appealing axis dominates the decision.
- The "simplest sufficient" rule in Apply biases toward the cheaper, more maintainable choice that instinct tends to skip.
A framework does not make you smarter; it makes your good judgment repeatable and protects it from the pull of hype. That reliability is worth more over many projects than any single brilliant call.
Frequently Asked Questions
Why use a framework instead of deciding case by case?
Case-by-case decisions depend on who is in the room and what they read recently, producing inconsistent and often hype-driven choices. A framework like CLEAR makes the reasoning explicit and repeatable, so the same problem yields the same answer regardless of who runs it.
What does the "simplest sufficient technique" rule mean?
It means starting at the top of the stack (rules-based AI) and descending to classical ML or deep learning only when a constraint forces you. This produces faster, cheaper, more maintainable systems than defaulting to the most powerful tool available.
How is "Locate" different from "Classify"?
Classify identifies the type of intelligence needed. Locate places it on the nested hierarchy and forces you to acknowledge that a deep learning problem inherits all the constraints of the ML and AI layers above it, not just its own.
When should I re-assess a decision?
Re-assess when data volume grows by an order of magnitude, accuracy plateaus, interpretability or latency needs change, or new unstructured data enters the problem. Any of these can invalidate a choice that was correct at launch.
Can CLEAR be run quickly?
Yes. The Classify, Locate, and Evaluate stages take well under an hour during intake and resolve most technique debates immediately. The Apply stage adds a short baseline in the first sprint, and Re-assess runs only when a trigger fires.
Key Takeaways
- CLEAR is a five-stage model: Classify, Locate, Evaluate, Apply, Re-assess.
- Classify the type of intelligence, then locate it on the nested AI/ML/deep learning hierarchy.
- Evaluate against data volume, data shape, interpretability, latency, and maintenance; any axis can veto a technique.
- Apply the simplest sufficient technique, proven with a cheap baseline, rather than defaulting to the most powerful.
- Re-assess when data, accuracy, or requirements shift, since a correct launch choice can later become wrong.