Mind the GeekDeep Dive180min
Cheaper or Better? The Strategic Choice Behind AI Adoption
This session examines two AI adoption strategies: efficiency gains or customer value creation. It explores how incentives, metrics, and workflows can lead teams to automate broken processes or improve outcomes. Participants will learn to distinguish cost-cutting AI from value-driven AI and ask better strategic questions.
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Miriam SasseS&N Invent
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Tuesday, October 6, 09:30-12:30
TBA 4
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Most organizations adopt AI with an efficiency promise: faster delivery, leaner processes, fewer costs, more automation. That is useful — but it is only one possible game.
There is a second path: using AI to increase customer value. Better products, smarter services, more relevant experiences, faster learning from real usage, and stronger relationships with users. The problem is that these two paths often require different rules:
Efficiency asks: How can we deliver the same thing faster and cheaper?
Customer value asks: What can we now make possible that was impossible before?
In this session, we will explore the strategic choice behind AI adoption. Are we using AI to optimize existing processes, or to redesign the value we create? We will look at common traps: automating broken workflows, measuring productivity instead of impact, treating developers as cost factors, and accelerating delivery without improving user outcomes.
Participants will learn how to distinguish efficiency-driven AI from value-driven AI, how hidden metrics shape AI adoption, and how teams can ask better questions before introducing new tools, agents, or automation.
This is not a talk about specific AI tools. It is a conversation about the game we design around AI: the incentives, feedback loops, roles, metrics, and customer value assumptions that determine whether AI makes software merely cheaper — or genuinely better.
Target audience: anyone involved in AI adoption, software delivery, or product strategy.
There is a second path: using AI to increase customer value. Better products, smarter services, more relevant experiences, faster learning from real usage, and stronger relationships with users. The problem is that these two paths often require different rules:
Efficiency asks: How can we deliver the same thing faster and cheaper?
Customer value asks: What can we now make possible that was impossible before?
In this session, we will explore the strategic choice behind AI adoption. Are we using AI to optimize existing processes, or to redesign the value we create? We will look at common traps: automating broken workflows, measuring productivity instead of impact, treating developers as cost factors, and accelerating delivery without improving user outcomes.
Participants will learn how to distinguish efficiency-driven AI from value-driven AI, how hidden metrics shape AI adoption, and how teams can ask better questions before introducing new tools, agents, or automation.
This is not a talk about specific AI tools. It is a conversation about the game we design around AI: the incentives, feedback loops, roles, metrics, and customer value assumptions that determine whether AI makes software merely cheaper — or genuinely better.
Target audience: anyone involved in AI adoption, software delivery, or product strategy.
Miriam Sasse
Dr. Miriam Sasse is Lead Agile Transformation & Organizational Design at S&N Group, where she helps organizations navigate digital transformation and Artificial Intelligence. Her work bridges software engineering, systems thinking, organizational design, and game design. She holds a PhD in Machine Learning for manufacturing systems and is the author of LEVEL UP!, a book exploring what software teams, leaders, and organizations can learn from professional game designers. Miriam is a frequent international speaker known for interactive talks that challenge conventional thinking and inspire new perspectives on leadership, collaboration, and AI.