Leveraging AI

Are you using AI to create more content—only to end up with generic output that sounds nothing like your company?
The problem isn’t necessarily the AI. The problem is often the context you give it. When AI understands your brand voice, audience, goals, expertise, and processes, it can produce dramatically more relevant and consistent work.
In this episode of Leveraging AI, Isar Meitis sits down with content marketing and AI expert Brian Piper to break down how organizations can move beyond one-off prompting and build reusable AI systems that preserve what makes their business unique.
Brian walks through a practical process for auditing your existing brand voice, building detailed prompts with AI, comparing results across different AI tools, and turning successful workflows into reusable skills.
The bigger opportunity goes far beyond marketing. The same approach can be applied to repeatable business processes across an organization.
In this session, you'll discover:
Why generic prompting often leads to mediocre “AI slop.”
How to audit what your brand actually sounds like across websites, newsletters, social media, podcasts, and other content.
How to use the CRIT prompting framework to give AI context, assign a role, and have it interview you.
How Brian uses tools including Claude, ChatGPT, and Gemini to compare AI-generated brand audits.
How to turn a successful prompt into a reusable AI skill.
How AI interviews can capture subject-matter expertise instead of replacing it with generic information.
How organizations can build libraries of brand voice, personas, stories, research processes, and other reusable knowledge.
Brian Piper is a content marketing expert who has worked across large corporations, small businesses, consulting, and academia. In recent years, he has focused extensively on helping organizations use AI more effectively while maintaining their expertise, identity, and brand voice.
Connect with Brian Piper on LinkedIn:
https://www.linkedin.com/in/brianwpiper/
About Leveraging AI
Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/
YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/
Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ 
Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events
If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

What happens when the infrastructure powering the AI boom becomes politically toxic—just as businesses are becoming more dependent on AI?
That tension is quickly becoming impossible for business leaders to ignore. AI data centers are facing growing public and political opposition, model prices are dropping fast, competition between the major AI labs is intensifying, and companies are getting more choices about where—and how cheaply—they can access intelligence.
For business leaders, the message is simple: don’t just follow which model is “best.” Pay attention to the economics, infrastructure, standards, and public sentiment shaping where AI goes next.
In this episode of Leveraging AI, Isar Meitis breaks down the most important AI developments of the week and, more importantly, connects the dots around what they could mean for businesses.
In this session, you'll discover:
Why AI data centers have suddenly become a bipartisan political issue in the United States—and why public opposition could have much broader economic consequences.
Why the backlash against data centers may have less to do with servers, water, and electricity than with Americans’ underlying concerns about AI and jobs.
How slowing data center development could affect U.S. competitiveness, investment, access to compute, and ultimately the economy.
Why AI inference prices are falling rapidly and how the competition between OpenAI, Anthropic, Google, and Chinese AI labs is reshaping the market.
Why businesses should stop assuming every task needs the most expensive frontier model.
How testing cheaper models against your actual workflows could substantially lower the cost of enterprise AI.
The other important AI releases and developments from a packed week in artificial intelligence.
The AI race is no longer just about who builds the smartest model.

About Leveraging AI
Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/
YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/
Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ 
Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events
If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

What if the biggest thing stopping you from building your next great idea is a limitation that AI has already made obsolete?
Until recently, turning an idea into a working application meant developers, product teams, infrastructure, budgets, and plenty of technical expertise. Today, AI tools are dramatically lowering that barrier—and Bryce Rattner Keithley is proof.
Bryce had no software-development background when she started experimenting with a simple idea: an app that would give her one exercise every day and track 100 repetitions. Using tools including Replit, Lovable, and Claude, that experiment became Daily100, an application now available in Apple’s App Store.
The lesson for business leaders goes well beyond building apps.
You don't necessarily need to understand how every piece of technology works to start creating with it. You need to know what problem you're solving, ask good questions, exercise judgment, and be willing to iterate.
In this session, you'll discover:
How Bryce went from a simple personal problem to a working application without knowing how to code.
How tools such as Replit and Lovable can turn plain-English instructions into functioning software.
Why a beginner's mindset can actually become a competitive advantage when working with AI.
Why asking AI to question you can dramatically improve your product requirements and decisions.
How Claude helped her work through the process of preparing her Replit application for Apple's App Store.
Why AI can get you to 80% remarkably quickly—and why the final 20% still requires human judgment.
Why screenshots, sketches, and visual references can sometimes communicate your vision to AI better than another 500 words of prompting.
Bryce Rattner Keithley has spent much of her career in talent and recruiting, including technical and design-related recruiting. Her experience working alongside technologists—without being a software developer herself—helped shape the beginner's mindset she brought to building Daily100.
Connect with Bryce on LinkedIn:
https://www.linkedin.com/in/brycerattner/

About Leveraging AI
Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/
YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/
Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ 
Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events
If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

What happens when AI stops being impressive in demos—and starts helping us fight cancer, transform how companies operate, and attract hundreds of billions of dollars in investment?
That shift may already be underway. This week brought some of the strongest signals yet that AI’s impact is moving beyond better chatbots. From personalized cancer treatments and dramatically earlier detection to autonomous business workflows and AI-powered scientific research, the conversation is increasingly about measurable outcomes.
For business leaders, there’s an equally important takeaway: the competitive advantage may no longer come from choosing the “best” AI model. It may come from giving AI the right context about your business.
Anthropic’s own sales team provides a striking example. By connecting Claude to systems including Salesforce, Apollo, Common Room, and Gong, the company reports cutting manual work by 70%. The lesson is simple: smarter models help, but AI becomes dramatically more useful when it understands your data, workflows, processes, and preferences.
And that’s only the beginning.
In this session, you'll discover:
Why new developments in personalized mRNA cancer treatment could represent an important milestone for AI-assisted healthcare.
How AI is helping researchers detect and understand cancer earlier and with greater precision.
How Anthropic is using AI workflows to reduce manual sales work by 70%.
How AI systems are beginning to learn the way people work and turn repetitive activities into automations.
How increasingly capable open models could dramatically change the cybersecurity threat landscape.
How AI is accelerating drug discovery and complex scientific analysis.
How AI-assisted coding and agentic development continue to change software creation.
Why an extraordinary amount of capital is flowing into AI infrastructure and applications—including a proposed $500B financing platform around NVIDIA infrastructure, Databricks' $5B raise, and major funding rounds across the ecosystem.
About Leveraging AI
Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/
YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/
Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ 
Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events
If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

What if you could build a working business app—and an entire team of AI agents—in minutes, just by explaining what you want in plain English?
That’s the promise of GrokBot. And after putting it to work on two very different real-world problems, the combination of simplicity, autonomy, and always-on AI agents is hard to ignore.
In this episode of Leveraging AI, Isar Meitis puts GrokBot through its paces. He builds a personalized travel-management app that checks his calendar and email for upcoming trips, identifies missing bookings, and helps research travel options. Then he goes considerably further: creating a multi-agent AI team to research, develop, and execute a marketing campaign.
No elaborate prompt engineering. No dedicated Mac Mini. No complicated agent orchestration setup.
But there are important catches—including cost, security questions, and the quality of the initial outputs.
In this session, you'll discover:
Why always-on AI agents could become an important tool for business leaders
How GrokBot compares with the experience of running tools such as OpenClaw
How I created a personalized travel-management application in less than five minutes
How multiple AI agents can collaborate on research, strategy, content creation, publishing, and performance
How an autonomous AI marketing team identified target audiences and developed campaign assets
Why creating custom apps through plain English could challenge the traditional App Store model
Why GrokBot’s initial output quality still leaves room for human guidance and refinement
If you’re a business leader wondering what comes after AI chatbots, this episode offers a practical glimpse at a world where you don’t just chat with AI - you give it a goal and let a team of agents get to work
About Leveraging AI
Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/
YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/
Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ 
Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events
If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!