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The Value Creators Podcast Episode #69. AI, Trust, and the Return to Human-Centered Marketing: A New Marketing Framework with Bryan Phelps

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Many businesses and business functions are grappling with the question of the role of AI in future value creation, none more so than marketing agencies and the marketing function in business. Bryan Phelps is CEO of the marketing agency Big Leap, actively navigating this challenge every day. Bryan shares how his team developed a clear AI policy to guide innovation—and why humans must remain at the center of creativity, decision-making, and brand expression.

This episode is a practical and forward-thinking look at how businesses can build trust, scale responsibly, and stay human in the age of algorithms.

Key insights include:

  • Why your AI strategy should start with a policy—and how to align teams with clear principles.
  • How to preserve brand essence and emotional connection while integrating AI into marketing.
  • Why trust and empathy are the new growth engines—and how to lead with values, not just data.

If you’re building a brand in the age of AI, this conversation will help you navigate the future with clarity, integrity, and confidence.

Resources:

➡️ Learn What They Didn’t Teach You In Business School: The Value Creators Online Business Course

Learn more about Big Leap

Connect with Bryan Phelps on LinkedIn

Bryan’s Newsletter

Connect with Hunter Hastings on LinkedIn

Subscribe to The Value Creators on Substack

Knowledge Capsule

1. Don’t let AI replace or eclipse Human Creativity

  • Big Leap’s first principle is keeping humans at the center of marketing strategy.
  • AI tools support ideation, but don’t originate brand essence.
  • The best outcomes emerge from human-AI collaboration.

2. The best way to navigate the AI challenge is to start with Policy, not with Technology

  • Big Leap created a 9-point policy before deploying AI tools.
  • A clear framework builds internal confidence and external trust.
  • Policy drives alignment across teams and clients.

3. Responsible Design Demands Human Oversight

  • AI can generate inaccurate or misleading – or just not very good – outputs.
  • Users must verify quotes, facts, and context, and provide critique.
  • Responsibility is shared between tool and operator.

4. Brand Essence Must Be Protected

  • AI should enhance—not dilute—core brand identity. Does it truly understand?
  • Bryan suggests building a “brand avatar” that holds the brand’s soul.
  • Human input can train AI on voice, tone, and values.

5. Experimentation Fuels Innovation

  • Big Leap runs dozens of simultaneous experiments, without assuming the right answers in advance.
  • Testing helps discover new formats, messages, and channels.
  • Speed of iteration becomes a competitive edge.

6. SEO Is Evolving, not Static. But it’s still SEO.

  • Traditional keyword search is shifting toward knowledge exploration.
  • Brands must optimize for questions, not just clicks.
  • Bryan emphasizes helpfulness over hacking the algorithm.

7. Trust Is the Foundation of Modern Marketing

  • Metrics like engagement – a mechanical idea – must be reframed as emotional outcomes.
  • Marketing returns to its roots: relationships, trust, value.
  • AI helps, but human touch builds brand love.

8. Big Companies Must Embrace “Venture Mode”

  • Startups iterate fast—enterprises must learn to do the same.
  • Bureaucracy can’t keep pace with AI-enabled shifts.
  • Big Leap helps large firms act with entrepreneurial agility.

9. Clients Want Impact, Not Just Efficiency

  • AI enables better brand perception, faster results, and meaningful insights.
  • Bryan notes a shift away from pure efficiency to effectiveness.
  • Value creation now trumps cost-cutting.

10. Brand Monitoring Must Extend Beyond Owned Media

  • Teams now track Quora, Reddit, and other forums for consumer and customer insights.
  • Presence in conversations requires both listening and participating.
  • Tools + human review ensures brand is represented “lovingly.”

11. Communities Can Be Built Intentionally

  • Brand communities don’t have to be organic only.
  • Bryan discusses how to nurture them with content, interaction, and value.
  • AI helps scale presence—but humans spark connection.

12. Optimism in a High-Speed World

  • Bryan believes we should welcome change—and prepare for it.
  • Culture, systems, and mindset help Big Leap adapt.
  • Relationship-based marketing is the stabilizing force amid AI disruption.

68. Steve Phelan Explains Why Entrepreneurial Intelligence Beats Artificial Intelligence

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Artificial Intelligence promises cognitive augmentation for business practitioners. Professor Steven Phelan’s research reveals that Entrepreneurial Intelligence is far more important and far more likely to influence business success. He explains Entrepreneurial Intelligence and why it will always beat Artificial Intelligence on this week’s episode.

Key Takeaways & Actionable Insights

What is Entrepreneurial Intelligence?

For Steven Phelan, “It’s all about the spark” — the moment of inspiration in combining disparate elements together to develop a new solution. Humans draw on “the fringes of consciousness” to create new constructs.

Entrepreneurs also take risks, investing time, talent and treasure in their venture in hopes of gain, yet understanding that they could lose something of value to them in the endeavor.

Entrepreneurial Intelligence vs Artificial Intelligence

Click on the image to view the full PDF.

How do we contrast Entrepreneurial Intelligence and Artificial Intelligence?

First, we need to differentiate between the narrow and general forms of AI. Narrow AI is software that can solve problems in a single domain. For example, a Nest thermostat can raise the temperature or lower it in a room according to a pre-set rule. “If this, then that” is the general rule for this kind of intelligence. The parameters are designed by the programmers.

For the unstructured problems of life and business, a truly intelligent computer would have to figure out for itself what is important. Part of the problem is that understanding or predicting human motivations — as entrepreneurs do — requires a “theory of mind”, an understanding of what makes humans tick. Entrepreneurs need empathic accuracy — unavailable to AI — to anticipate the needs of consumers. A sentient computer would need self-awareness or consciousness to truly empathize with humans, and have a set of values with which to prioritize decisions.

What’s the role of machine learning?

If you work in a business that generates a lot of data, it can be mined by data scientists for patterns, and those patterns might indicate a better way to respond to customer needs. The richest source of data is behavioral — like choosing songs to listen to on Pandora. Machine learning can detect a pattern of what kinds of sings a user chooses most. A human interpreter can translate those patterns into preferences — in other words, motivations are embedded in behavior and machine learning can help entrepreneurs extract them.

So, the entrepreneur’s best resource is entrepreneurial intelligence.

The psychologist Howard Gardner helped us to recognize many types of intelligence, including math, language, spatial, musical and social. There are two types that might be indicative of entrepreneurial intelligence: EQ (Emotional intelligence) might be associated with intensified empathic skills and empathic accuracy; CQ (Curiosity Intelligence) is linked to the kind of creativity that finds solutions by combining elements on the “fringes of consciousness”, as Hubert Dreyfus puts it.

Can entrepreneurs and business owners assess their own entrepreneurial intelligence?

There are scales to measure EQ and Creativity. Here’s a link to an entrepreneurial quotient assessment: Mises.org/E4E_68_QA

And here is a more action-oriented self-assessment we developed for E4E: Mises.org/E4E_68_SA

The bottom line:

Entrepreneurs need knowledge of how to profitably satisfy customer preferences given the resources at hand. This is not a trivial requirement. It is not possible to pre-state all of the uses for a given resource nor to compute the payoff for a given application. Current computational methods are thwarted without a complete list of entrepreneurially valid moves and the payoffs from such moves. No amount of growth in processing power, data communication, or data storage, can solve this problem.

The late Steve Jobs is often held up as the epitome of a successful entrepreneur. His founding of Apple, ousting by his own board, and subsequent return to rescue the company, and then make it the most valuable publicly traded company in the world is the stuff of legend. One of the apparent secrets of his success was to understand that “people don’t know what they want until you show it to them. That’s why I never rely on market research. Our task is to read things that are not yet on the page.”

This ability to “read things that are not yet on the page” lies at the heart of the concept of empathic accuracy. Empathic accuracy is “the ability to accurately infer the specific content of other people’s thoughts and feelings”. Until AI can do this, Entrepreneurial Intelligence is a better tool for the innovating entrepreneur.

Free Downloads & Extras

Insights Statement Template: Our Free E4E Knowledge Graphic
Marking Platform Tool: Our Free E4E Knowledge Graphic
Understanding The Mind of The Customer: Our Free E-Book

Start Your Own Entrepreneurial Journey

Ready to put Austrian Economics knowledge from the podcast to work for your business? Start your own entrepreneurial journey.

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8. Will Dinkel on Everyday Applications of Artificial Intelligence

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We talked to Will Dinkel, CEO of Nova.ai, an intelligent platform for outbound sales and marketing – and a great example of A.I. as a tool for everyday tasks of everyday businesses of all kinds.

Show Notes

AI has come a long way in a short time. 10 years ago, we always had to have a “human in the loop” for any task that could be made more productive with software. It could never be so productive as to not use human labor. And often that labor was very inefficiently deployed. Will cited the example of tracking labels and numbers on shipping containers – software could record the data, but humans still had to interpret it.

AI is available and relevant for entrepreneurs and small businesses today. Emerging technologies – including AI, Platforms, Apps and Global Exchanges – augment the capacity of individual entrepreneurs: AI is a business tool and a creative tool for entrepreneurs right now.

Outbound sales and marketing is a practical application of AI in a critical everyday activity. The specific area of application we talk about is personalization – which increases engagement and results. Personalization can generate as much as a 10X increase in sales effectiveness. Without AI it’s very labor intensive – 94.2% of the typical enterprise sales team’s budget is labor. With AI, personalization is very much less labor-intensive, very effective, and potentially self-improving over time.

Personalization of sales messaging via AI is an example of bringing machine intelligence to empathy. In episode 5, Peter Klein explained the pivotal role of empathy in entrepreneurial success. With AI – in combination with the empathic entrepreneur – we can make empathy work for us more intelligently, more intensively and with greater analytical rigor.

Machine learning accumulates data over time and, via regression, uses it to make better decisions. When Netflix recommends “British mid-century dramas with a strong female lead” for your viewing enjoyment, it has accumulated your input data (searching, for example), and your output data (what you actually watch) and identified the most dominant co-varying themes in order to identify a recommendation you are highly likely to accept. Initially, the model needs a human in the loop to help it become accurate, but over time it can operate autonomously.

Nova.ai is an example of an application of AI that has become much more broadly capable over time at helping humans perform better. Initially, it was able to identify snippets of sentences and information that were effective in increasing outbound e-mail sales productivity by +40%. Now it can focus on the much broader role of the seller – in a process called Intelligent Customer Management – by sifting through all the data a salesperson has to deal with, identifying the major time sinks associated with it, and lifting the burden by providing analyses and recommendations for the most productive actions.

The future increase in AI productivity will come from it knowing more about the individual user. Currently, AI can sort through data intelligently, but it knows far less about the human user of the data. When that gap is closed, AI productivity will ascend to a new level. Imagine a nutrition bot that knows all your personal health and eating and exercise data. When scanning data in front of your eyes – like a menu or a deli counter – it will be able to make truly personalized, and perhaps life-extending, recommendations.

A.I. productivity will be available to all businesses, big and small.  A.I. will be very egalitarian. Everyone can access it, and the upfront cost is low. In the first industrial revolution, capital intensiveness limited access to opportunity. Not many had enough capital to build a railroad or a steel mill. In the era of AI, we can all access training in coding and AI and machine learning on Udemy or Coursera or one of many other learning platforms.

A good place to start is to open a Github account. GitHub is free at the basic level, and anyone can search for AI applications in any subject of interest. Everyone should have a fundamental programming education and Github is a great place to explore. Nova.ai is the place to find out about Intelligent Customer Management.

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