WEBINAR | DataRiseLab
We invite you to a webinar organized by a member company – DataRiseLab – Start with Data Quality: How to prepare for AI without burning budgets?
The most expensive mistake you can make today is trying to fix a business with AI without first organizing its foundations. According to Gartner, organizations will abandon as much as 60% of AI projects that are not supported by “AI-ready” data. If your data is fragmented, inconsistent, or incomplete, technology won’t solve your problems – it will only accelerate the generation of flawed decisions.
During the webinar:
Data Quality Assessment and AI implementation. How to start with AI without burning budgets. We will show you how to verify your organization’s foundations and why precise data transformation – rather than just the choice of model – determines whether your AI implementation will deliver a real profit.
When?
08.04.2026 | 11:00-12:00 | online (free – registration required)
Why is it worth your time?
This session is a practical lesson on how to avoid “taking shortcuts with technology.” We bring together analytical and business perspectives to show you exactly where profitable AI truly begins.
We invite leaders and decision-makers from Controlling, Accounting, Operations, Sales, Delivery, and Marketing. The webinar is specifically designed for leaders in high-growth, fast-paced industries (e.g., QSR – Quick Service Restaurants), where measurable profits and optimizing decisions under time pressure are critical.
On the program:
- The Myth of the All-Knowing AI: Why is trying to outsource technology to “fix all business optimization” bound to fail?
- The Economics of Failure (Data & ROI): Analyzing hard data (RAND, MIT, S&P Global, Gartner) – why 95% of GenAI pilots are unprofitable and how to get into the remaining 5%?
- The Fuel Analogy: How data errors (overfitting, edge cases) damage the AI “engine” and why data quality is a business discipline, not an IT problem.
- Operational Debt: How AI highlights broken processes instead of fixing them (case study).
- 6 Dimensions of Data Quality: A practical guide to measuring the accuracy, consistency, and timeliness of data for AI models.
- The “Smart Start” Strategy: How to avoid the trap of trying to clean everything up at once and focus on the 2-3 highest-value use cases.
- Readiness Assessment in practice: How to verify the readiness of the organization, processes and data step by step before any implementation to avoid burning through the budget.
Speakers:
· Bartłomiej Ordyk | Senior Business and Systems Analyst at DataRiseLab
· Bartosz Rutkowski | Head of Growth at DataRiseLab
