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Key Lessons from January & March

Lesson 1: Human-AI Pairing Outperforms Solo Work

Participants found that workflows where human experts collaborated with GPT agents led to faster iterations and fewer blind spots in problem solving.

Lesson 2: Strategic Prompting Unlocks Real Value

Success came from prompts framed around business goals—not tasks—enabling AI outputs that advanced strategic intent.

Lesson 3: Frameworks Beat Freeform Exploration

Teams using structured frameworks for GPT inputs (e.g., SWOT, journey maps) reported more coherent and reusable outcomes.

Lesson 4: Model Confidence Requires Verification Loops

Cross-checking GPT output against second models or human judgment was seen as essential to reduce hallucination risk in decision-critical tasks.

Lesson 5: Domain Adaptation Is the Missing Link

Generic models performed significantly worse in sector-specific problems—fine-tuning or curation was flagged as key to performance.

Lesson 6: AI Literacy Drives Innovation Speed

Teams with foundational knowledge of how GPT works accelerated integration and discovered novel applications more readily than others.

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