EP 21 - Too much Hype, too little Impact: How to Avoid the AI Failures of OthersAbout the Episode

AI adoption is skyrocketing, but most AI projects don’t deliver on their promises. In this episode, Dr. Evan Shellshear, Managing Director and Group CEO of Ubidy, breaks down why 80% of AI projects fail and what organisations can do to improve their chances of success.

Drawing insights from his book Why Data Science Projects Fail, Evan explores the biggest blockers to AI success, the importance of strategic alignment, and how companies can avoid wasting millions on AI initiatives that don’t deliver business impact.

Whether you are an AI practitioner, business leader, or innovation manager, this episode will help you separate AI hype from reality and make smarter technology investment decisions.

Evan's Books

Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype:

https://www.amazon.com.au/Why-Data-Science-Projects-Fail/dp/103266133X?ref_=ast_author_dp 

Innovation Tools: The most successful techniques to innovate cheaply and effectively:

https://www.amazon.com.au/Innovation-Tools-Successful-Techniques-Effectively/dp/0646956469?ref_=ast_author_dp 

Topics and Insights[02:53] – Defining Innovation

Evan shares his perspective on what defines innovation, emphasising that it is not just about new ideas but the impact they create. He draws a clear distinction between invention and innovation, explaining why an idea without impact remains an invention rather than a true innovation.

[04:27] – Measuring Innovation Effectively

Innovation measurement starts with two key questions: Is it new? and What is its impact? Evan highlights the difficulty of measuring “newness” and discusses why impact should be measured based on purpose-driven innovation. He argues that metrics should align with a company’s innovation goals, whether it is patent creation, revenue growth, or industry transformation.

[06:11] – The Biggest Blockers to Innovation in Large Organizations

Evan contrasts the flexibility of small, nimble companies with the structural challenges of large enterprises. He explains how legacy systems, bureaucratic processes, and internal competition create significant barriers to innovation, making it difficult for new ideas to gain traction and survive within large corporations.

[07:31] – Why AI Projects Fail: The 80% Failure Rate

Evan breaks down the staggering failure rate of AI projects, estimating that up to 80% fail, with the number exceeding 90% for analytically immature organizations. He highlights the three biggest reasons AI initiatives go wrong:

1. Lack of strategic alignment – Organizations chase AI trends without clear business objectives.
2. Poor data quality and availability – Without the right data, even the best AI models fail.
3. Lack of experienced resources – Many AI teams lack deep expertise, leading to unrealistic expectations and weak execution.

[12:50] – The Outback AI Story: When AI Doesn’t Make Business Sense

Evan shares a fictitious story from Australia’s Outback, where a farmer was pitched an AI-driven solution for managing crops. While the technology sounded promising but overall, it was a poor investment. This example underscores why businesses should assess AI projects through a commercial viability lens, not just a technical one.

[24:01] – Prioritizing AI Investments: The 2x2 Framework

Evan introduces a simple framework to help organizations prioritize AI projects:

• Impact vs. Feasibility – Instead of chasing AI for AI’s sake, businesses should rank projects based on their potential impact and their actual ability to implement them.
• This portfolio approach helps companies avoid expensive AI failures by focusing on initiatives that align with strategy and available resources.

[42:00] – The Education Gap: Why Universities Aren’t Preparing AI Leaders

Evan and Elijah discuss a major issue in AI education—universities focus almost exclusively on technical skills, while the real reasons AI projects fail are business-related. He argues that universities must teach:

• Stakeholder management and project leadership
• Business strategy and problem framing
• How to align AI projects with real-world business needs

[55:44] – Final Thoughts: Avoiding AI Hype and Building Sustainable Success

The conversation wraps up with a reminder that AI projects should be treated like marathon training—organizations need to build capabilities gradually rather than jumping straight into large-scale, high-risk initiatives.

About the Guest

Dr Evan Shellshear is the Managing Director and Group Chief Executive Officer of Ubidy, an innovative global recruitment marketplace leveraging AI to connect employers to specialist agencies. He has a Bachelor of Arts and a Bachelor of Science (single and double majors in mathematics) from The University of Queensland, a Diplom (equivalent of both a BSc and MSc) from the University of Bielefeld (Germany) and a PhD in Mathematical Economics (Game Theory) from the Institute of Mathematical Economics at the University of Bielefeld. Before his appointment at Ubidy, Evan was Chief Analytics Officer and Co-Chief Executive Officer at Biarri – one of Australia’s leading specialist analytics consultancies. Evan has consulted to leading businesses worldwide across industries from manufacturing to retail, from healthcare to supply chain, and from oil and gas to energy. He has published or co-published four books on topics from innovation (Innovation Tools) to artificial intelligence, business analytics, and data science (Why Data Science Projects Fail) and has published almost 100 articles in leading blogs, magazines and news outlets. He has served on multiple advisory boards of state, national and international institutes, has multiple accreditations across numerous digital platforms and is a thought leader in the fields of AI, innovation and strategy. He holds Adjunct academic appointments at the Queensland University of Technology and at The University of Queensland.

Contact Evan

https://www.linkedin.com/in/eshellshear/