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Webinar
Why AI Projects Fail, and What to Do About It
Trusted by Industry Leaders for Delivering Cutting-Edge, AI-Driven Solutions That Drive Success.
abstract
Many AI initiatives fail not because the technology falls short, but because adoption does. This session explores why live solutions gradually lose traction, the warning signs that usage is declining, and the practical steps organizations can take to restore value or determine when a fresh approach is needed.
The failure statistics get quoted in every boardroom. The reasons behind them almost never get examined. Most AI projects do not fail because the technology did not work. They fail after it works. The solution goes live, performs as promised, and then quietly stops being used. Expectations were set at a level the problem could not support. The organization drifted back to systems it already trusted. Nobody owned the output.
This webinar gets to the core reasons behind these failures and, more importantly, what can be done about them. Drawing on cross-industry examples of solutions that were built, deployed, and still did not hold, the session covers the patterns that precede a stall and the practical steps available once one has started. The patterns are recognizable and the remedies are concrete, including how to judge whether a stalled initiative can be recovered or needs rebuilding.
webinar Details
Date
Wednesday,
October 14, 2026
Time
10:00 AM EST (New York)
5:00 PM KSA (Riyadh)
6:00 PM GST (Dubai)
duration
1 Hour
SPEAKER
Shravan Adapa
Shravan Adapa is the Director of AI at Soothsayer Analytics, where he helps organizations translate AI and Generative AI into measurable business outcomes. With over a decade of experience across manufacturing, retail, logistics, and financial services, he has led end-to-end initiatives from executive alignment and solution design to production rollout across cloud and hybrid environments.
His work spans high-impact, operational AI applications including demand and supply forecasting, quality and reliability intelligence, and document-driven automation for procurement and finance. Shravan is known for building practical, scalable solutions that fit real business constraints, delivering faster decision-making, stronger operational control, and sustained ROI.
Key Takeaways
This session is designed to provide practical clarity, not just conceptual understanding. Participants will leave knowing how to:
Why Projects Stall
Understand the core reasons AI solutions stop delivering value even after the technology is working.
Spot the Warning Signs
Recognize the early indicators that a live AI solution is drifting out of use before the problem becomes harder to reverse.
Recover or Rebuild
Learn how to assess whether a stalled AI initiative can be recovered or whether starting again is the better path.
Set Realistic Expectations
Understand why setting expectations at the wrong level is one of the most common and avoidable causes of AI project failure.
Make Ownership Stick
See how clear ownership of AI output determines whether a solution becomes part of everyday operations or fades away within its first year.
Who Should Attend
Executive Sponsors
CEOs, COOs, and business unit leaders accountable for the outcomes of AI investment
Digital, Data, and Transformation Leaders
Leaders driving enterprise AI programs and answerable for the results they produce
Technology Leadership
CIOs, CTOs, and heads of engineering supporting AI solutions running in production
Operations and Functional Leaders
Leaders whose teams are expected to work with AI output day to day
If an AI solution has gone live but is no longer delivering the value you expected, this session is for you.
