With the rush to adopt AI accelerating, we find that clients are often undertaking projects before considering what problem or opportunity an AI system or solution is offering. The 7 patterns of AI provide a model to help executives understand the type of insights an AI solution offers. The concept is to identify the patterns to clarify the objectives, simplify the conversation, and better align projects with desired outcomes.
The Seven Patterns of AI are:
- Recognition
- Conversation and Human Interaction
- Predictive Analytics and Decisions
- Patterns and Anomalies
- Hyperpersonalization
- Goal Driven Systems
- Autonomous Systems
Recognition systems detect, identify or classify unstructured data converting it into meaningful categories. Inputs might include images, documents, handwriting, speech to text, and facial recognition.
Conversation and Human Interaction systems enable people to interact with technology using human communication practices allowing for back-and-forth exchange. Common uses are chatbots, voice assistants, document summarization, and language translation.
Predictive Analytics and Decision systems analyze historical and real-time data to predict what may happen next. Common uses include supporting human decision-making with tools like risk forecasting, demand forecasting, and scenario insights.
Patterns and Anomalies systems learn what usual activity or behavior is and flags deviations or emerging risks. Human involvement is required to review and act. Common uses include alerts for anomalies, risk detection, operational monitoring. You may see this type of alert when processing payroll or reviewing financial performance.
Hyperpersonalization systems develop and update individual profiles to deliver tailored recommendation, content, or experiences. Common uses include personalized financial insights and adaptive learning programs.
Goal Driven systems optimize toward a clearly stated goal through learning from outcomes, testing, and adjusting strategies. This model considers constraints and benefit tradeoffs over time. Common uses are simulation scenarios, pricing strategies, and route optimization.
Autonomous systems evaluate their operating environment, make decisions, and take prescribed actions with predefined goals, constraints, and guardrails. Human oversight is a crucial component. Common uses include autonomous vehicles, robots, and software agents.
Understanding these patterns promotes clarity of transformational objectives. As with all projects, begin with clearly identifying the opportunity or problem that you are focused on creating or impacting. Using this model facilitates clearer communication around AI based projects or even if an AI solution should be considered.
If you are moving ahead with an AI project, remember that human involvement and governance are a key component. While the recommended guidance changes based on the systems being implemented, responsible use is a key factor in this process to prevent bias, ensure appropriate human involvement, and identify outliers and unexplained outcomes.
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Sources:
Seven Patterns of AI, Quick Reference Guide, PMI,(Accessed 7/21/26)
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