The ABCs of AI Technology
I was inspired to write this blog to help the industry adopt a shared baseline education on how AI works in the context of promotional review, specifically speeding up the MLR review process without sacrificing accuracy.
At the Momentum Promotional Review Summit, marketing veteran and promotional review maven, Lynn Carroll, shared this insight: “Clear, basic education on the different types of AI can help remove uncertainty and build trust and confidence in technology to increase MLR efficiency. Most MLR teams already see how AI can improve efficiency, but without understanding the building blocks of AI, the accuracy part of the value equation is missing. Teams want to know why AI output can be trusted, not just that it was generated quickly.”
I couldn’t agree more. There is basic confusion, including the tendency to equate “AI” with “GenAI,” reinforcing a narrow—and sometimes risky—view. GenAI is just one branch of a much larger AI ecosystem. AI isn’t one thing. It’s a coordinated set of analytical, rules-based, generative, and retrieval-driven methods that—when integrated—create a controlled, dependable, and transparent way to automate manual tasks at speed without sacrificing accuracy.
To fill this educational gap, I took a pass at capturing the ABCs of AI below.
Analytical AI – Finds and Organizes Information
Identifies patterns, facts, and relationships in text or data
Excels at sorting, labeling, and structuring information so it can be evaluated later
Ideal for building reliable claims libraries
Rules-Based AI – Checks What’s Allowed
Ensures content follows defined requirements, guardrails, and approved language
Verifies whether a statement, reference, or data point aligns with what has been authorized
Ideal for confirming whether the correct reference supports the correct claim
Natural Language Processing (NLP) – Understands Language
Allows machines to read, interpret, and classify human language
Enables tasks like claim detection, summarization, and content classification
Ideal for strengthening modern GenAI by grounding it in structured, well-understood inputs
Generative AI (GenAI) – Summarizes, Reasons, Rephrases (and Generates Content)
Creates or restates content, summarize documents, and explain complex ideas
Is powerful but inherently probabilistic, so it can introduce errors or “hallucinations”
Ideal when guided by accurate, curated, and well-structured data
Retrieval-Augmented Generation (RAG) – Keeps GenAI Grounded in Facts
Connects GenAI to real, approved, and up-to-date information
Enables outputs that are based on trusted sources rather than outdated training data.
Ideal to prevent generative models to “overwrite” retrieved facts
Human Oversight – The Final Check
Ensures humans validate accuracy, context, and compliance while providing accountability and traceability
Enables human judgement to direct the system
Delivers outputs meet regulatory and medical standards
Full Hybrid AI Model – Combines 1–6 for Superior Accuracy, Verifiability, and Auditability
Integrates all the approaches above to deliver the most dependable AI system integrate
Allows each layer to compensate for the others’ limitations.
Creates results that are accurate, explainable, consistent, and defensible in regulated life sciences environments
Conclusion
No single AI method is flawless — but when these technologies are deliberately combined, the full hybrid AI model becomes the gold standard for safe, efficient, and verifiable automation.
Click here or more information about SecureCHEK AI’s hybrid architecture.