Bloom’s Taxonomy for AI Capability

Severin Sorensen
21 hours ago
4 min read

Your customer service team wants to “use AI.” But what does that actually mean?

Do they need a system that can recall product specifications? Interpret customer sentiment across thousands of interactions? Or, generate tailored resolution strategies in real time?

\Without a shared framework to describe levels of AI capability, organizations struggle to match business needs with the right technical solutions. The result is often misalignment: either investing in systems more sophisticated than the problem requires, or underbuilding capabilities that limit impact.

Bloom’s Taxonomy offers an effective way forward.

Originally developed to categorize levels of human learning, Bloom’s framework can be repurposed as a strategic lens for AI: helping leaders clarify what kind of capability a business challenge actually demands, what outcomes to expect, and how different AI initiatives relate to one another.

A Quick Refresher on Bloom’s Taxonomy

Bloom’s Taxonomy organizes cognitive activity into six ascending levels of complexity:

Remember
Understand
Apply
Analyze
Evaluate
Create

Educators use this model to design curricula and assessments, and leaders can engage it to design AI strategies.

Importantly, Bloom’s Taxonomy is not a maturity ladder you must climb end-to-end. Instead, it’s a way to identify the dominant level of capability required for a given objective and to design intentionally at that level.

The Six Levels of AI Capability

1. Remember: Data Acquisition & Retrieval

At this foundational level, AI systems store and retrieve factual information accurately and efficiently.

AI function: Data is ingested, indexed, and made accessible through search, embeddings, or knowledge retrieval systems. The system recalls information without interpretation or transformation.
Example AI actions: A chatbot retrieving the exact wording of a return policy or a specific clause from a contract.
Key question: What facts or data points can the system reliably retrieve?
Why it matters: This level supports fast access to institutional knowledge and serves as the foundation for more advanced capabilities. When the challenge is primarily about access to information, this level may be sufficient on its own.

2. Understand: Pattern Recognition & Meaning

Here, AI moves beyond retrieval to interpret relationships, context, and semantic meaning.

AI function: Machine learning models encode patterns in data, capturing similarities, differences, and contextual nuance.
Example AI actions: Sentiment analysis across customer reviews, a language model summarizing complex material, or an image classifier identifying objects across varied conditions.
Key question: Can the system interpret or explain what the data represents?
Why it matters: This level enables organizations to make sense of large volumes of unstructured data, revealing trends, themes, and signals that would be difficult to detect manually.

3. Apply: Execution in New Contexts

At the application level, AI uses learned patterns to perform defined tasks in real-world situations.

AI function: The system takes new inputs and generates useful outputs such as predictions, classifications, recommendations, or standardized content.
Example AI actions: Forecasting demand based on historical and market data, scoring credit or risk profiles, or generating routine customer communications.
Key question: Can the system reliably perform a defined task in novel situations?
Why it matters: This is where AI becomes operationally useful, embedded into workflows and delivering repeatable value at scale.

4. Analyze: Decomposition & Explanation

Analysis focuses on breaking decisions apart to understand why outcomes occur.

AI function: Explainability and interpretability techniques reveal which factors influenced results and how inputs relate to outputs.
Example AI actions: A fraud model highlighting the variables that triggered an alert, a diagnostic system indicating which signals most influenced a recommendation, or a hiring model showing which qualifications drove rankings.
Key question: Can the system explain relationships, drivers, or contributing factors?
Why it matters: This level becomes critical when transparency, trust, or regulatory oversight is required and when insights from AI decisions inform broader strategy.

5. Evaluate: Judgment, Validation & Feedback

Evaluation assesses performance against defined criteria and feeds learning back into the system.

AI function: Outputs are measured against benchmarks, rules, or human feedback to determine quality, safety, and effectiveness.
Example AI actions: Model performance tested against validation datasets, human reviewers rating AI-generated outputs, or continuous monitoring of accuracy, bias, or resolution rates.
Key question: Does the system meet agreed-upon standards for success?
Why it matters: Every AI system requires evaluation; the difference lies in how rigorous and continuous that evaluation must be. This level ensures AI remains aligned with organizational expectations over time.

6. Create: Generative Synthesis & Novel Output

At the highest level, AI produces new artifacts by synthesizing knowledge in original ways.

AI function: Generative models recombine patterns to produce outputs not explicitly contained in training data.
Example AI actions: Designing new molecules or materials, generating original music or visual concepts, or proposing novel solutions to complex problems.
Key question: Can the system produce new, valuable ideas or solutions?
Why it matters: This level enables exploration, innovation, and creative problem-solving, particularly in domains where existing solutions are insufficient or unknown.

How to Use This Framework

Rather than asking “How advanced should our AI be?”, Bloom’s Taxonomy encourages better questions:

What outcome are we trying to achieve?
What level of capability does that outcome actually require?
How do different AI initiatives complement one another across levels?

The Main Takeaway

Bloom’s Taxonomy doesn’t describe how AI thinks. It helps leaders clarify what kind of capability a business problem demands.

From foundational retrieval to generative synthesis, each level serves a distinct purpose. By understanding the terrain (rather than chasing the frontier), you can design AI systems that are intentional, aligned, and effective.

The goal isn’t to reach the highest level. The goal is to build the right capability for the problem at hand.