DataPhilly Webinar Series: Why Good Models Fail in Business Decisions

We’re kicking off a new DataPhilly webinar series focused on the real-world challenges of applying analytics, ML, and AI in business—and our first session tackles a problem many teams know too well: great models that never get used. This is session 1 out of 3.
Most analytics teams assume adoption fails because models aren’t explainable or sophisticated enough. In reality, resistance often comes from how models shift control, accountability, and decision authority inside organizations. This session challenges analytics and ML professionals to rethink what it means to build “good” models—not just technically correct ones, but models designed for trust, acceptance, and real decision-making.

What you’ll learn:

• Why validated, accurate models are often ignored in pricing, forecasting, and commercial decisions
• How model outputs, confidence intervals, and “optimal” recommendations can build—or erode—trust
• What repeated requests for tweaks really signal about decision risk
• Why designing for adoption may be as important as designing for accuracy

If you’ve ever asked yourself, “If a model is accurate but never acted on, is it really a good model?”—this session is for you.

Abstract: Why Good Models Fail in Business Decisions
Most analytics teams believe their biggest challenge is building better models. In practice, the harder problem is getting good models used. Pricing, forecasting, and commercial decisions are full of technically sound analyses that were validated, approved, and then quietly ignored.
This session challenges analytics and ML professionals to rethink why adoption fails. Drawing on real-world experience, it argues that resistance to machine learning is rarely about lack of explainability or technical sophistication. Instead, it reflects how models redistribute control, accountability, and decision authority inside organizations.
Rather than asking how to simplify models for business users, the talk asks a more uncomfortable question: what responsibilities do analytics teams have in designing for acceptance, not just correctness? The session explores how model outputs, confidence intervals, and “optimal” recommendations can either build trust or undermine it, and why repeated requests for tweaks are often signals of unresolved decision risk.
Provocation: If a model is accurate but never acted on, is it really a good model?

Speakers:
Venu Gorti is the Founder and CEO of Quant Matrix AI Solutions. He has spent over 18 years working closely with C-suite decision makers across global consumer-focused companies, helping them apply analytics to pricing, promotions, media, and growth decisions in real business contexts.
His work spans FMCG, retail, and consumer businesses, with experience partnering with leadership teams at large global organizations on high-stakes commercial decisions. He has published in marketing and statistics journals, and his work on pricing with PepsiCo received the Best Paper award at the Advertising Research Foundation (ARF) conference in New York.
Venu’s current focus is on bridging the gap between advanced analytics and real-world decision-making, with particular interest in trust, adoption, and the human side of analytics. He is based in Mumbai, lives with his wife and two daughters, and enjoys conversations with fellow practitioners that challenge assumptions and spark new ways of thinking.

Rahul Maan is the Founder and Principal Solutions Advisor at RCS Analytics, helping banks and insurers modernize risk and compliance capabilities on cloud-native data and analytics platforms. With 20+ years of experience, he partners with business and technology leaders to translate regulatory requirements into scalable, audit-ready solutions that drive measurable outcomes.
His work spans Stress Testing, Credit Loss Forecasting (CECL), IFRS 9, IFRS 17, LDTI, and Model Risk Management (MRM), with end-to-end delivery for 20+ customers. Rahul leads programs from target architecture through implementation and adoption, including CECL implementations on Databricks and lakehouse-based risk pipelines with strong governance and reporting.
Rahul’s current focus is enabling large banks to implement and scale enterprise risk management, strengthening controls, model oversight, and operating processes while accelerating delivery. He works across risk, finance, and technology teams to ensure solutions are defensible to regulators and practical to run. Based in North Carolina, Rahul values execution discipline and transparent risk transformation.

The webinar link will be shared before the event.