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    Discover how a global law firm uses intelligent automation to enhance client services. Learn how AI improves efficiency, document processing, and client satisfaction.
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      A startup in digital health trained a risk model to open up a robust, precise, and scalable processing pipeline so providers could move faster, and patients could move with confidence after spinal surgery. 
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            Mushrooms, Goats, and Machine Learning: What do they all have in common? You may never know unless you get started exploring the fundamentals of Machine Learning with Dr. Tim Oates, Synaptiq's Chief Data Scientist. You can read and visualize his new book in Python, tinker with inputs, and practice machine learning techniques for free. 
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                  Webinar

                  Why AI Projects Fail and How to Get Yours Right

                  Tuesday, February 24, 2026 | 1:00 PM EST

                  Register Now
                  Dr. Tim Oates
                  Co-founder and Chief Data Scientist, Synaptiq

                  Dr. Oates has 20+ years of experience in guiding organizations of all sizes in applying Al and ML to business problems and has guided 100+ clients to their Data & AI future states with Synaptiq. He is also a professor of machine learning at the University of Maryland, Baltimore County.

                  Why AI Projects Fail and How to Get Yours Right

                  A significant majority of AI projects fail to deliver measurable value, with recent reports from MIT and Gartner citing failure rates as high as 85% to 95% for pilots and overall projects, often stalling at the pilot stage. Many AI initiatives fail not because the technology doesn’t work, but because organizations underestimate what it takes to turn models into measurable business impact. From unclear goals and poor data readiness to lack of ownership and change management, the risks are often organizational, not technical.

                  In this webinar, Dr. Tim Oates breaks down the most common reasons AI projects stall or fail and shares a practical framework for avoiding them.

                  In this webinar, you will learn how to:

                  • Identify the most common failure points in AI projects before they derail progress
                  • Align AI initiatives with clear business objectives and success metrics
                  • Prepare data, teams, and workflows for real-world AI deployment
                  • Build the organizational ownership and governance needed to scale AI responsibly

                  Ready to Dive in?

                  Register Now