Episode

Engineered Intelligence and The Data Science Problem in AI

Jordan Morrow, SVP of Data & AI Transformation at AgileOne and the "Godfather of Data Literacy," joins High Signal to discuss the shift from being "data-driven" to becoming "AI-enabled." Jordan warns that many organizations are walking into the same traps that derailed the data science era a decade ago: prioritizing expensive tooling and hype over the cultural change and literacy required to actually move the needle. The pattern is already visible: enterprise AI projects are failing at around 90%, while individuals doing shadow AI are quietly thriving. The catch is that shadow AI brings its own risks, with people feeding sensitive data into public models without governance. He argues that because AI is probabilistic rather than deterministic, the bottleneck for success has shifted from technical coding ability to a user’s ability to apply "Engineered Intelligence," a blend of machine capability and human emotional intelligence.

April 2, 2026

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Guest

Jordan Morrow

AgileOne

Jordan Morrow is known as the "Godfather of Data Literacy", having helped pioneer the field by building one of the world's first data literacy programs and driving thought leadership. Jordan is a global trailblazer in the world of data literacy, having built one of the world's first data literacy programs. He served as the Chair of the Advisory Board for The Data Literacy Project, has spoken at numerous conferences around the world and is an active voice in the data and analytics community. He has also helped companies and organizations around the world, including the United Nations, build and understand data literacy. When not found within his work of Data, Jordan is happily married with 5 kids. Jordan is also an avid trail runner and loves fitness, entering and racing in multiple ultra-marathons and having fun adventures in the mountains. Jordan is an avid reader, often reading (or using Audible) to go through multiple books at a time.

Guest

HOST

Hugo Bowne-Anderson

Delphina

Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He is the host of the industry podcast Vanishing Gradients, a podcast exploring developments in data science and AI. Previously, Hugo served as Head of Developer Relations at Outerbounds and held roles at Coiled and DataCamp, where his work in data science education reached over 3 million learners. He has taught at Yale University, Cold Spring Harbor Laboratory, and conferences like SciPy and PyCon, and is a passionate advocate for democratizing data skills and open-source tools.

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Key Quotes

Key Takeaways

Questioning replaces coding as literacy. AI has shifted the fundamental definition of data literacy from technical execution (like writing Pandas code) to "contextual application." The core skill for the modern worker is the ability to ask the right questions and interpret probabilistic results rather than mastering specific software syntax.

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Human data misuse dwarfs AI hallucinations. Leaders frequently over-index on the risks of machine error while ignoring the fact that humans have "hallucinated" with data for decades. The primary threat isn't a probabilistic model being wrong; it’s the lack of human critical thinking and emotional intelligence to verify and apply the model’s output correctly.

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Organizations are repeating 2012’s mistakes. Just as companies failed to extract value from "the sexiest job of the 21st century" by hiring data scientists without building a data-literate culture, they are currently buying AI tools while neglecting the fundamental change management and mindset shifts required for the 99% of non-technical staff to use them.

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IQ and EQ anchor Engineered Intelligence. Jordan Morrow defines the formula for successful decision-making as "Engineered Intelligence": two parts data and AI combined with two parts human IQ and EQ. This framework ensures that AI augments rather than replaces the human intuition, gut feel, and empathy necessary for high-stakes business outcomes.

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Shadow AI proves individual value. While 90% of enterprise-level generative AI projects are reportedly failing, "Shadow AI"—where individuals use tools outside of official channels—is thriving. This disconnect suggests that the value is real and immediate at the task level, but enterprise governance and product wrappers are lagging behind individual curiosity.

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AI identifies human blind spots. Rather than just making old processes more efficient, leaders should use AI as a "PhD partner" to identify what humans are missing. By feeding deep research into models and asking for the "hidden pieces," teams can overcome the structural biases of their own mental models to prepare for Black Swan events.

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AI ROI demands a two-year horizon. True return on investment from AI is typically 12 to 24 months out, but many leaders are too "dopamine-driven" to wait. Success requires a willingness to accept short-term losses and dedicated "experimentation time" for employees to ensure long-term competitive gains.

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Data stacks fail without networking. The most successful Chief AI Officers don't win by building the perfect technical architecture in isolation. They succeed by "leaving their desks" and acting as change management specialists who bridge the gap between technical capability and on-the-ground business problems through constant internal networking.

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Predictive modeling is the overlooked quick win. While leaders chase generative AI, mature ML-based predictive modeling has been delivering reliable business value for decades. Jordan steers clients toward predictive use cases first: take the data you already have, build predictions that "see around the corner," and capture ROI before investing in more complex generative or agentic systems.

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Six of the top ten fastest-growing skills are human. The World Economic Forum projects that by 2030, adaptability, creativity, analytical thinking, and similar human capabilities will dominate the fastest-growing skills list. As AI automates more tasks, the competitive edge shifts to people who can problem-solve, communicate, and think critically, not those who know the most tools.

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You can read the full transcript here.

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00:00 Is AI Repeating the Same Mistakes as Data Science?

03:50 Defining Literacy in AI Era

06:10 Democratization and Risk

08:36 Prompting and Learning Fast

11:19 Augmentation and Culture Shift

12:20 Engineered Intelligence Framework

14:42 Why Data Science Fell Short

18:51 Tooling Mistakes and Shadow AI

20:08 From Chatbots to Use Cases