{"id":265711,"date":"2026-07-16T14:22:58","date_gmt":"2026-07-16T11:22:58","guid":{"rendered":"https:\/\/1kitap1.com\/en\/look-before-you-leap-meap-2-christophe-de-greift\/"},"modified":"2026-07-16T14:22:58","modified_gmt":"2026-07-16T11:22:58","slug":"look-before-you-leap-meap-2-christophe-de-greift","status":"publish","type":"post","link":"https:\/\/1kitap1.com\/en\/look-before-you-leap-meap-2-christophe-de-greift\/","title":{"rendered":"Look Before You Leap MEAP 2 – Christophe De Greift"},"content":{"rendered":"
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Before taking a deeper look at their needs, however, we must ensure that we have a sufficiently comprehensive list of people to investigate. This step is critical because a lack of stakeholder support is frequently cited as a reason for failure in AI projects, yet identifying the right stakeholders is not always straightforward. AI does not operate in isolation; it affects workflows, processes, costs, technology architecture, and more.<\/p>\n

In some cases, the number of users is so large that the investigation must be limited to a carefully selected sample. 4.2.2 Understanding context When improving a workflow with AI, understanding the broader, end-to-end process is essential because these technologies only optimize what they are asked to optimize. Without context, organizations may improve local efficiency while unintentionally degrading overall performance.<\/p>\n

Workflows are embedded in complex networks of upstream and downstream activities, controls, and decision points. Introducing AI into one part of the process can shift bottlenecks, remove implicit safeguards, or propagate errors at scale. Only by understanding how the workflow fits into the larger system can organizations ensure that AI improves end-to-end outcomes rather than creating faster, more efficient failures.<\/p>\n

Tip AI should be treated as a system component, not a standalone solution. 4.2.3 Uncovering hidden needs Never assume that the change being requested is the change people actually need. Investigating user and stakeholder needs is not about validating preconceived solutions; it is about intentionally uncovering both known unknowns (questions and gaps we are aware of) and unknown unknowns (unspoken frustrations, latent needs, hidden constraints, and emergent behaviors that neither users nor project teams initially recognize).<\/p>\n

Unknown unknowns are particularly valuable; effective investigation methods are designed to create the conditions in which these deeper insights can surface. By addressing both categories, teams move beyond incremental optimization of predefined solutions and open the door to more meaningful \u2014and often more impactful\u2014transformation. Figure 4.2 How to treat known unknown and unknown unknown during user and stakeholder investigations Capturing both hard information (e.g., measurable tasks and actions) and soft information (e.g., preferences, frustrations, and concerns) is an important part of identifying needs that truly matter.<\/p>\n

In one of our transport scheduling projects, we discovered that operators were primarily concerned with managing last-minute scheduling changes after working hours, rather than transport efficiency, which had been our original focus.<\/p>\n

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Thank you for purchasing the MEAP for Look Before You Leap. If you have ever faced the frustration of stalled AI projects\u2014as a project manager, developer, business user, or sponsor\u2014and are convinced that there is a better way, then this book is for you. Look Before You Leap presents a repeatable, pre-development methodology that stress-tests AI opportunities before any proof of concept (PoC) is launched.<\/p>\n

Technology-agnostic across generative AI, classical machine learning, and optimization, it helps leaders make evidence-based investment decisions before developing or acquiring any model. The method unifies user-centered design (desirability), hypothesis-based problem solving (economic viability), and data\/engineering (feasibility) into a step-by-step \u201croad test\u201d that teams can run in days, not months. Readers gain practical tools\u2014including one-page canvases, problem- solving templates, readiness and maturity scorecards, stakeholder interview guides, prioritization frameworks, and editable prompts\u2014along with explicit go\/kill\/pivot criteria. The result is a de-risked, continuously refreshed pipeline of AI initiatives that users want, that create measurable business value, and that can be delivered with the available data and tooling \u2014ensuring organizations avoid waste and deliver results.<\/p>\n

Although this book is accessible to both business and technical audiences, you will benefit most if you have a general understanding of Design Thinking (e.g., empathy mapping, prototyping), Agile principles and core practices (e.g., iterative design, \u201cDay in the Life\u201d exercises), hands-on experience in data analytics and AI (e.g., familiarity with the main machine learning, generative AI, or optimization approaches), and solid collaboration and presentation skills. This book is packed with practical insight on an important and timely topic, and I hope you find it as useful to read as I did to write it.<\/p>\n

Please be sure to post any questions, comments, or suggestions in the liveBook discussion forum. Your feedback is essential to developing the best book possible. \u2014Christophe De Greift In this book welcome 1 Why we need a new way to test AI opportunities 2 What should be the focus of AI opportunity testing?<\/p>\n

3 How to frame the right AI opportunity every time 4 Understand what users and stakeholders really want 5 Solve from a business perspective first 6 Architect the AI technology mix 7 Test user attractiveness OceanofPDF.com 1 Why we need a new way to test AI opportunities This chapter covers The main reasons AI projects fail to deliver impact The process we propose for testing AI ideas and avoiding failure Why AI ideas should be tested before any development begins The economics of testing AI opportunities early Artificial Intelligence (AI) offers extraordinary opportunities across every sector and business function.<\/p>\n

The use of classical machine learning, generative AI, optimization, rules-based systems, simulation, reinforcement learning, or any combination thereof\u2014which we simply refer to as AI in this book\u2014can produce predictions, recommendations, decisions, or content that influence environments and generate significant benefits. Yet, while organizations are racing to invest in AI, too many are failing to translate its promise into tangible value.<\/p>\n<\/blockquote>\n

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.<\/em><\/p>\n

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