P2P

Fall25-2

Peer to Peer: ILTA's Quarterly Magazine

Issue link: https://epubs.iltanet.org/i/1540097

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P E E R T O P E E R M A G A Z I N E ยท F A L L 2 0 2 5 67 implementation to its particular business needs, infrastructure, resource constraints, risk tolerance, and strategic priorities. This article aims to re-envision GenAI implementation with humans at the center. MIND OVER MATTER: THE GENAI PARADIGM SHIFT There has been much ado about the failure of GenAI projects since the publication of the MIT NANDA report. This data may indicate that we have an opportune moment to revisit how GenAI differs from other technologies, so we have a better understanding of how to implement GenAI and how we measure success. Traditional artificial intelligence (AI) uses data analysis to identify patterns, make predictions, and execute specific tasks (e.g., fraud detection, product recommendations, route planning and navigation, spam filtering) based on preset rules. Unlike other technologies, including traditional AI, generative AI (GenAI) uses data analysis and human input (prompts) to create new content (text, images, music, code) based on patterns learned from vast data sets. The traditional fixed model of technology implementation typically follows a step-by-step approach: 1. Define business needs 2. Test and evaluate products based on business needs 3. Select the product best suited to business needs 4. Train users on specific product features 5. Integrate the product into a fixed workflow. GenAI requires a more dynamic human-centric model of implementation to realize its transformative impact. THE TRIPLE T APPROACH TO GENAI TRANSFORMATION The Triple T approach flips the traditional fixed implementation model on its head. Talent development Embracing a people-first mindset, the Triple T approach shifts from a narrower skill-based specialist model to a more adaptable T-shaped competency model. Emerging T-shaped technologists will possess foundational AI fluency and data literacy, coupled with deep subject matter expertise GenAI requires a more dynamic human-centric model of implementation to realize its transformative impact.

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