Brian Evergreen Discusses Autonomous Transformation, AI Education, And The Future of AI Agents In Business With Dinis Guarda
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Brian Evergreen, founder of The Future Solving Company, explores the evolving landscape of AI, the importance of AI education, and how autonomous transformation is reshaping business processes in the latest episode of the Dinis Guarda Podcast. The podcast is powered by Businessabc.net, Citiesabc.com, Wisdomia.ai, and Sportsabc.org.
Brian Evergreen is a recognised expert in artificial intelligence (AI) and strategy and founder of The Future Solving Company, where he helps organisations prepare for the future of AI. He was also included in Edelman’s Top 50 AI Creators You Need to Know in 2025. Brian has also provided advice to over a dozen Fortune 500 companies, leading firms like Accenture, AWS, and Microsoft.
He is also the author of ‘Autonomous Transformation: Creating a More Human Future in the Era of AI’, which has been praised as a "Must-Read" by the Next Big Idea Club and named one of Thinkers50’s Top 10 Best New Management Books for 2024.
During the interview with Dinis, Brian discusses initiatives that blend innovation, business, technology, and culture:
"The world tour aims to bring thought leaders together with senior executives. We’re focusing on the gap where senior executives often don’t have the opportunity to gather with others in similar roles in their local market.
We pair these leaders with thought leaders, like having someone like Rita McGrath, we’re partnering with companies to choose the right cities, topics, and people to bring together. It's an exclusive, VIP experience for senior leaders in the industry.”
'Autonomous Transformation': The shift from digital reformation
Brian discusses the core concepts of his book, ‘Autonomous Transformation’, which introduces frameworks designed to help leaders navigate the evolving landscape of AI, automation, and business transformation, he says,
"One key idea covered is systems thinking, which includes the concept of systemic design.
‘Fire was not discovered; it was designed,’ this quote from Harold Nelson captures the essence of systemic thinking, where you don’t just follow a process, but actively design the future, even if it’s on a small scale, with the agency to improve systems.
We’re starting to move beyond digital transformation toward this era of autonomous transformation... Digital transformation involves improving processes without changing their nature, but autonomous transformation is fundamentally about changing those processes.
Digital reformation is the process of improving something from analog to digital without actually transforming it... It's about increasing efficiency but not changing the underlying nature of the process.
Problem-solving is about getting rid of what you don’t want, while future-solving is about getting what you do want. They’re not the same thing.
We have to protect our mental models from logical fallacies. Just because someone is an expert in one field doesn’t mean they’re an expert in AI or blockchain."
Talking about the dangers of data-driven approaches, Brian says:
"Being data-driven is fundamentally unscientific. The scientific method involves conducting experiments, analysing results, and drawing conclusions, whereas in business we just gather data and make decisions based on it without real experiments.
Instead of measuring outcomes as pass or fail, we should measure whether a hypothesis is true or false and use that to make decisions.
We have to stop flattening everything to numbers and focus on what we’re trying to achieve as a company. It's not about the numbers, it's about the theory of what will make us successful.
If I say, 'I need groceries for a party,' and then the groceries show up without me needing to intervene, that’s the essence of autonomous transformation—where AI and agents are doing the work in a way we never imagined before."
AI education and careers for the next generation
Brian explains how AI can be a tool for empowerment rather than a threat to jobs:
"AI education is important, but the first thing we need to do is demystify AI and take the elephant out of the room of fear.
There are a lot of misconceptions about AI, and fear often comes from not understanding the technology. It’s not about fearing AI, but rather how we can use it to solve problems and create value."
Talking about the role of universities in AI education:
“Universities are making investments to integrate digital and AI education into their curricula, but we still have a gap. Students need to learn not just about the theory, but how to apply it practically."
On the reality of AI and job security, Brian says,
"We still need people. Companies that are focusing on replacing everything with AI will regret it. People still want that human connection, especially in customer service.
Even the companies that are pushing for full automation, like McDonald’s with their autonomous drive-thru, have realised that people still want to interact with humans, so they’ve pulled back on those initiatives."
Brian also shares the information about digital and AI learning resources:
"There are many resources out there, such as free courses on Coursera and Code Academy, which are great for gaining skills. I’ve also created a course on Maven called 'How to Get Into AI' that provides a series of interviews with AI experts.
You don’t need to rely on expensive degrees to learn AI. You can get started with free or inexpensive resources and build your expertise over time.
When I started at Accenture, AI wasn’t the focus of my role. I learned about AI on my own time and pitched it to leadership, which led to funding for a data scientist. That’s how I got started.
For students, the key is to get a job that supports their living, and then focus on building skills. Learning to work with peers and bosses is a valuable skill that many forget to focus on in school."
The future of AI Agents: Transforming business efficiency
Brian discusses his perspective on the future of AI, distinguishing between the hype surrounding LLMs and the true capabilities of AI agents:
"I do not think that we're on a path or that we've reached any form of AGI. I personally have not seen anything that would give me that impression. There's a lot of excitement, but I believe we're on an S-curve, and not everything is moving as quickly as some people think.
Generative AI, especially tools like ChatGPT, are exciting because they’re the first version of AI that anyone can easily access and experience. People who know nothing about AI can type into ChatGPT and see what it can do. That’s why it took off the way it did."
Talking about agentic AI and its potential, Brian says,
"People are getting excited about AI agents, especially with the focus on LLMs. However, I believe LLM-based agents are more like 'mouth-for-brain' AI because they lack true cognitive capabilities. It's not a brain; it's just a tool for decision-making.
Creating one AI agent to do everything is like a one-man band. It might impress in one context, but it’s not suitable for complex environments like enterprises where even 1% error can cause significant losses."
On the potential of AI agents in business, Brian says:
“In the future, AI agents will interact behind the scenes, blurring the lines between organisations. For example, if two lumber companies are sending trucks across the country with the same goods, AI agents will allow these organisations to interact and trade resources seamlessly, making it more efficient and cost-effective.
We’re going to see more value creation through these AI agents, adding more value than we could have created with just traditional models.
AI agents will be micotransacting and negotiating across different agents, taking care of tasks behind the scenes without us needing to be involved in every transaction. It’s an interconnected system that’s more efficient than anything we’ve seen before.”
Brian also discusses building better AI agents:
"AI agents should have neural networks trained on deep reinforcement learning, either through data-driven simulation or first principles. The only time an LLM should be involved is during training or as a tool to interact with the agent, not as the decision-maker itself.
There's an S-curve in technological progress. It's easy to assume that because AI has advanced rapidly in the past few years, it will continue on the same path, but we must recognise the natural progression from digital to autonomous systems.
In an enterprise context, we cannot afford errors or hallucinations from AI agents, especially in high-stakes environments like manufacturing. AI agents need to be trained specifically to develop skills, using simulations to improve their decision-making."
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