From Problem to Measurable Outcome: 3 Mistakes That Break AI Initiatives - and How to Avoid Them
From Problem to Measurable Outcome: 3 Mistakes That Break AI Initiatives - and How to Avoid Them
A practical briefing on how to take an AI/GenAI initiative from a clearly defined business problem to measurable results through controlled execution steps.
You will learn about three common failure patterns, key insights from MIT research, and receive a practical framework for launching, validating, and scaling AI initiatives.
A practical briefing on how to take an AI/GenAI initiative from a clearly defined business problem to measurable results through controlled execution steps.
You will learn about three common failure patterns, key insights from MIT research, and receive a practical framework for launching, validating, and scaling AI initiatives.
Free Webinar
What You Will Learn
- How to properly define and scope AI initiatives
- Key control points in AI projects and the risks of the “wow-effect” trap
- An ROI evaluation template to use before launch and scaling
March 31
19:00 GMT+2
60 minutes
English
Webinar Agenda
Webinar Agenda
Who Should Attend
Who Should Attend
CEO / COO / CFO / CTO
Owners
Head of Engineering
Head of Department
This Webinar Is for You If
This Webinar Is for You If
There is a request to launch an AI initiative, but no clear problem statement or defined success criteria
You are concerned that testing an AI initiative may not generate ROI or may fail to scale
You need a structured, manageable process: rapid test → measurement → decision → scaling – without chaos or internal politics
What We Will Cover
What We Will Cover
01
MIT insights: what actually differentiates successful AI pilots
02
Key success conditions
03
3 common mistakes that break AI initiatives
04
Quality and reliability criteria
05
Human-in-the-loop design principles
06
How to define and evaluate impact criteria for go/no-go decisions
3 of the 7 Use Cases We Will Cover:
3 of the 7 Use Cases We Will Cover:
Poorly Defined Problem Statements
Examples of incorrectly framed AI tasks, what a properly defined problem should look like, and how to fix flawed scoping.
Loss prevention / shrink reduction
What to do when a solution performs well in a demo but produces unreliable or inaccurate results in real operations.
Queue & staffing optimization
What happens when AI generates recommendations, but the business lacks the operational capability to act on them?
Structure of Each Case Review:
Structure of Each Case Review:
1
Сhallenge
2
Solution
3
Outcome
4
Pros and Cons
Experience and Expertise
Experience and Expertise
Who Should Attend
Who Should Attend
Healthcare
Manufacturing
Technology
Financial Services
Energy & Utilities
Retail & E-commerce
Speaker
Mike Neuenschwander
Mike Neuenschwander
Executive Director, North America
Executive Director, North America
- 25+ years of Experience in Tech Industry
- Former Sr Director at Oracle
- Advisor on AI adoption and digital transformation
What’s in it for you? Register to find out more
What’s in it for you? Register to find out more
Webinar attendees will additionally receive:
Webinar attendees will additionally receive:
Webinar recording
CV ROI Calculator
AI Readiness Evaluation Framework