Artificial Intelligence is no longer just for large enterprises with massive IT budgets. Today, even the smallest businesses can automate repetitive work, improve customer service, and reclaim valuable hours using AI.

Chapters:

00:00 Introduction to Anne Cantera
01:12 The Role of AI in Small Businesses
03:43 Identifying Problems in Small Businesses
06:41 Implementing AI Solutions
10:24 Tech Stack and Feasibility for Small Businesses
12:30 Driving Towards Workable AI Products
13:43 Adoption Challenges and Experiences
15:43 Measuring ROI in AI Implementations
18:07 Advice for Small Business Owners
19:40 Educating Yourself on AI
22:43 Public Perception of AI
24:09 Conclusion and Contact Information

Episode # 146

She is an AI and voice technology expert, conversation designer, and founder of Elementyl Intelligence

• Website: Elementyl Intelligence
• LinkedIn

What Listeners Will Learn:

• Why small businesses may adopt AI faster than large enterprises
• How to identify business problems that AI can solve
• A practical framework for assessing AI opportunities
• When AI is the right solution and when it isn't
• How voice agents can automate customer interactions
• The benefits of AI-powered inbox and workflow automation
• How custom AI solutions are becoming affordable for SMEs
• Why ROI should drive every AI investment
• The importance of AI literacy for business owners
• How to become a better AI operator.
• Why monitoring and continuous improvement matter after AI deployment.
• Common mistakes businesses make when implementing AI.
• How to avoid misinformation and AI hype.

• Elementyl Intelligence
• Voice of AI - Free AI Education Platform
• Elemental Intelligence - AI Consulting for Small Business
• Reuven Cohen - Trusted AI Educator

Artificial Intelligence has completely transformed software development. What started with autocomplete has evolved into AI coding agents capable of building entire applications from simple prompts. But as organizations adopt these tools, new challenges emerge: cost, security, vendor lock-in, enterprise governance, and the future role of software engineers.

Today's conversation isn't about one product. It's about understanding where software development is heading over the next few years.

Joining me is Emilie Schario, VP of Engineering at Kilo, where she works on agentic engineering and multi-model AI developer tools. We discuss how enterprises should think about AI coding assistants, why relying on a single AI model may not be the future, how engineering teams should prioritize product development, and what both junior and senior developers need to do to stay relevant.

Whether you're a developer, engineering leader, CTO, startup founder, or simply curious about AI transforming software engineering, this episode is packed with practical insights.

Let's get started.

Chapters:

00:00 Introduction to Emily Sherio and Kilo Code
04:13 The Role of Kilo in Developer Productivity
07:52 Choosing the Right AI Models for Development
12:24 Building Features that Matter
16:38 The Importance of Code Review and Feature Management
19:24 The Future of Development and AI Integration
25:59 The Necessity of Learning Coding Principles
30:16 Enhancing Security in Coding Tools

Episode # 194

She works on agentic engineering and multi-model AI developer tools

Guests Links:

• LinkedIn

What Listeners Will Learn:

• Why AI coding is moving toward multi-model architectures.
• The advantages and risks of proprietary AI coding tools.
• How organizations can reduce AI infrastructure costs.
• Why vendor lock-in is becoming an AI challenge.
• The future of developer productivity.
• Why code review is becoming more important than writing code.
• Product management lessons every startup should know.
• When to remove features rather than add more.
• Enterprise AI security challenges.
• How organizations can adopt AI without compromising security.
• Why prompt engineering is becoming a fundamental skill.
• Advice for junior developers entering the AI era.
• The importance of mentoring the next generation of engineers.

• Kilo - AI Developer Platform
• Prompts for Mere Mortals by Florian Hines

One lesson I've learned throughout my career, from enterprise architecture to cloud transformation and now Generative AI, is this.

Every technology revolution follows the same pattern.

At first, companies invest in technology.

Later, they realize they should have invested in people.

I've seen organizations spend millions on software while allocating almost nothing to learning.

Then six months later they ask,

"Why aren't people using the new platform?"

The answer usually isn't difficult.

People don't resist technology.

People resist uncertainty.

When employees understand how AI helps them become better, not replaced, they become curious.

Curiosity leads to experimentation.

Experimentation builds confidence.

Confidence creates innovation.

That's how real transformation happens.

Today's conversation is about something that many organizations overlook.

Not AI models.

Not software.

Not hype.

We're talking about AI capability, the skill that will define the winners of the next decade.

Let's begin.

Episode # 193

John Munsell is the co-founder of Bizzuka, an AI consulting firm focused on artificial intelligence strategy and implementation. With a career spanning over 25 years in marketing, software development, financial services, and sales, John brings a wealth of experience to the AI industry.

• Website: Bizzuka

What Listeners Will Learn:

• Why AI success depends more on people than technology
• The importance of AI skills across an organization
• Why giving employees AI tools isn't enough
• How organizations can measure AI capability
• The hidden cost of poor AI adoption
• Why AI training delivers long-term business value
• Common mistakes leaders make during AI transformation
• Practical ways to improve workforce AI readiness
• Why experimentation matters more than perfection
• How AI can increase productivity across every role

• Bizzuka