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.

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

[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...

In this episode, Elijah Eilert sits down with Étienne Garbugli. Étienne, the CEO of Sliced, veteran founder, and author of several books, shares his insights.

The conversation centers around Étienne’s most recent book, Find Your Market. The book explores how to effectively find Product/Market Fit when innovations are already built or at least partially developed without having factored in the market need properly. Most innovators understand the need for research and experimentation before committing significant resources to but in reality, many organisations, Research and Development departments, and especially universities all too often do not adhere to it.

• Étienne kicks off by reframing how we should think about innovation—not just products, but improvements in services, processes, and distribution. He makes a strong case for continuous iteration as the key to staying relevant in today’s hyper-competitive landscape.

• Étienne discusses different approaches to measuring innovation, from immediate goals (Horizon 1) to long-term, high-uncertainty efforts. He shares his perspective on why understanding progress in increments is key, especially when navigating high-risk innovation projects.

• Elijah and Étienne explore a major, yet often overlooked, barrier to innovation—career progression. In organisations, those who get promoted are often seen as people who make the “right” decisions. This clashes with the exploratory and uncertain nature of genuine innovation, which frequently involves taking risks and failing.

• A significant portion of the conversation focuses on Étienne’s book, Find Your Market: Discover and Win Your Product’s Best Market Opportunity. He walks listeners through his bottom-up approach to identifying markets—starting from precise, actionable customer segments and expanding outward—contrasted against traditional top-down TAM analysis. Elijah and Étienne jokingly refer to it as “Lean Startup, but backwards,” emphasising how companies with an existing product can work backward to find Product/Market-Fit but in a lean way.

• They discuss how to align innovation metrics with broader business strategy. Measuring learning outcomes and value creation through insights—rather than just focusing on short-term financial gains. Étienne also shares how even seasoned entrepreneurs often realise their understanding of markets evolves significantly over time.

• Étienne explains how his company, Sliced helps companies break down broader target audiences into smaller, more actionable segments. This process enables companies to better understand where their true market opportunities...

Elijah Eilert is talking to Peter LePiane about innovation within large organizations. Part 2 of the podcast explores various aspects of innovation related decision-making. Peter opens up about his background in science and computer science, his Startup journey and how he nearly founded Airbnb and Shopify. He introduces Estimatic, his latest venture, software designed to model uncertain projects and ideas.

The episode addresses the concept of innovation accounting, common misconceptions in business case analysis, and the value of information by connecting learning to financial outcomes. The episode challenges traditional practices in organizations and focuses on the importance of understanding human behaviour to achieve success for new products and organisational change management.

Throughout this episode, the duo explores a myriad of topics central to innovation-related decision-making and highlights the value of information, connecting it to financial outcomes. Unravelling the concept of innovation accounting, Peter debunks the common misconceptions prevalent in business case analysis. The episode challenges traditional practices and underscores the pivotal role of comprehending human behaviour in achieving success, whether for new products or the intricacies of change management within organizations.

[01:43] Understanding Human Behavior in Change Management

• Products succeed or fail based on human behavior, as do organizational changes. Understanding human behavior is therefore crucial.
• Peter recommends checking out Lean Change Management and gives a shoutout to Jason Little!

[05:23] Peter’s Background

• Peter’s background as a software engineer.
• How he transitioned from science to computer science at the University of Waterloo.
• His journey into the startup world.
• How Peter launched Airbnb and Shopify (kinda).
• Peter’s experience working on web development and e-commerce projects.

[16:15] Estimatic

• Peter explains his startup, Estimatic, a Monte Carlo tool for modelling uncertainties.
• The importance of accurately depicting uncertainty in business models.
• Critique of the misleading hockey stick growth projections in startup presentations.
• The need to provide a range with a probability of future outcomes instead of point predictions.

[25:51] Challenges in Corporate Innovation Decision-Making

• Addressing the challenges of making decisions in large corporations.
• Mentioning the lack of recognition for risk management in decision-making.
• Highlighting the absence of structures rewarding risk management.

[26:53] The Concept of Innovation Accounting

• Elijah and Peter delve into the concept of innovation accounting and its evolution.
• Explain how innovation accounting combines the uncertain nature of innovation with concrete numerical measurements.
• Emphasize the importance of connecting learning to financial outcomes.
• Discussing the use of Monte Carlo simulations in innovation accounting.

Quantifying Learning and Progress...