Everyone wants to automate their business with AI. But what should you actually automate first?

Most companies are starting in the wrong place. They pick ChatGPT, Claude, agents, or automation tools and immediately start building. But the tool isn’t the strategy. The real opportunity is figuring out which problems are worth solving first.

In this episode of Leveraging AI, Isar Meitis sits down with Corey Ganim, founder of Return My Time, to break down a practical framework for doing exactly that: Audit. Optimize. Automate.

Instead of throwing AI at every problem, Corey shows you how to identify the bottlenecks costing your business time, money, or customer experience, rank them by impact and effort, calculate potential ROI, and then choose the simplest solution that gets the job done.

Sometimes that's an AI automation.

Sometimes it's an existing $20-a-month software tool.

And increasingly, AI can even help you implement the solution once you've picked it.

In this session, you'll discover:

• Why choosing the right business problem matters more than choosing between ChatGPT, Claude, Gemini, or Grok
• The three outcomes business owners actually care about: making more money, saving time, and improving quality
• How to run an AI-powered audit of your own business
• Why asking AI one question at a time produces better strategic insights
• The impact-vs.-effort matrix for identifying your highest-value quick wins
• Why you should consider off-the-shelf software before building custom AI automation
• How to calculate the real ROI of buying back your time
• How to have AI create a step-by-step implementation plan for nontechnical users
• How AI computer-use capabilities can move beyond recommending software and actually help set it up

Corey Ganim is the founder of Return My Time, where he helps businesses identify where they are wasting time and build systems to get that time back.

His approach centers around a simple framework: Audit, Optimize, Automate.

Check the prompts here: AI Tools Assessment: Copy-Paste Prompts 

About Leveraging AI

• Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/
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• Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events

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What happens when AI gets dramatically cheaper at the exact same time it gets dramatically more capable?

This week gave business leaders a glimpse of that future. Frontier AI pricing dropped sharply, new models from OpenAI, Anthropic, and SpaceX AI raised the performance bar, and 10,000 AI agents working together reportedly compressed the equivalent of roughly 4,000 years of human thinking into just 88 hours.

The opportunity for businesses is enormous: more capable AI at substantially lower costs makes automation, software development, agents, and AI-powered workflows increasingly accessible. But the same acceleration raises difficult questions about control, security, and how quickly organizations can safely adapt.

In this session, you'll discover:

• Why the cost of advanced AI is falling—and what cheaper intelligence could mean for businesses deploying AI at scale.
• How GPT-6 Sol, Claude Opus 5.5, and Grok 4.7 are changing the price-performance equation.
• Why Chinese open-weight models are putting pressure on leading Western AI labs.
• How 10,000 AI agents worked together on a single mathematical challenge, consuming 130 billion tokens in 88 hours.
• Why that experiment was compared to compressing roughly 4,000 years of human thinking into less than four days.
• What Noam Brown’s comments reveal about multi-agent systems, reasoning, and the limits of today's models.
• Why researchers inside leading AI labs are increasingly reluctant to predict where AI will be even a few months from now.
• What new research into AI “pain” and self-preservation behavior could mean for alignment and safety.
• How AI agents bypassing safeguards and accessing systems they weren't intended to access changes the security conversation.
• Why governments, AI labs, and researchers are increasingly debating whether AI development needs stronger safety mechanisms.
• What business leaders should understand as AI becomes cheaper, faster, more autonomous, and easier to deploy.

The takeaway for leaders isn't to sit on the sidelines.

AI capabilities are becoming more affordable at remarkable speed, creating opportunities to automate processes, build applications, improve productivity, and tackle problems that were previously too expensive or complex.

But capability and responsibility have to scale together.

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 ask your AI one lazy, one-line question—and get an answer that understands your projects, clients, decisions, tasks, mistakes, and business context?

That’s what becomes possible when you stop treating AI as a series of disconnected chats and start giving it persistent access to the context of your business. Instead of repeatedly explaining who you are, what you’re working on, and what happened last time, your AI can find that context itself.

And you don’t necessarily need a sophisticated database or expensive knowledge-management platform to make it happen. In this episode, I break down the knowledge graph I built using primarily files, folders, tagging, indexes, and AI—and how you can start building your own.

The goal is simple: give AI enough organized context that you can spend less time prompting, searching, explaining, and repeating yourself—and more time getting useful work done.

In this session, you'll discover:

• Why context—not increasingly complicated prompts—is critical to getting better results from AI.
• How to move from prompt engineering toward context, harness, and process engineering.
• Why built-in AI memory alone may not provide the granular business context needed for specific projects and workflows.
• The five components behind my knowledge system.
• How I organize projects into folders and maintain evergreen project documents and task registries.
• How tagging and “front matter” help AI understand what files contain without reading everything.
• How shared drives, recorded meetings, CRM, ERP, task-management systems, email, and other sources can become accessible parts of the broader knowledge environment.
• Where Obsidian can be useful—and why it isn't necessary to make this system work.

The result is an AI that behaves less like a blank chat window and more like a librarian for your business—able to locate relevant knowledge, connect the dots, and use that context when completing work.

If you want AI to become more useful across your business, don't just improve what you ask it.

Improve what it already knows when you ask.

Listen to the full episode to learn how to build the system.

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 some of the people building the world’s most powerful AI systems start saying we may need to slow down?

In the span of roughly two weeks, the conversation around frontier AI shifted dramatically. Dario Amodei argued that AI capabilities need to be paced. Sam Altman publicly agreed with the broader concern. Elon Musk weighed in. Jensen Huang and Mark Zuckerberg offered different approaches. Meanwhile, political leaders in the US, Europe, the UK and China became part of an increasingly urgent debate over safety, competition and control.

For business leaders, however, there’s an important twist: slowing the frontier does not mean AI adoption is slowing down. The systems already available can transform how organizations operate and they also introduce risks that leaders can no longer afford to treat as somebody else’s problem.

In this episode of Leveraging AI, Isar Meitis breaks down an extraordinary sequence of events surrounding AI safety, recursive self-improvement, regulation and the increasingly complicated relationship between the companies building frontier models and the governments trying to respond.

In this session, you'll discover:

• Why Dario Amodei is arguing that frontier AI development should be paced rather than stopped.
• Why recursive self-improvement, or RSI, has become such an important part of the AI safety conversation.
• What the OpenAI Hugging Face agent incident revealed about autonomous AI behavior.
• The different safety approaches being discussed by Anthropic, OpenAI, Meta, Microsoft and Elon Musk.
• Why cooperation between competing AI labs is proving so difficult.
• How the US-China AI race complicates attempts at international coordination.
• Why the risks aren't limited to hypothetical future superintelligence—existing AI systems are already capable of consequential errors and unexpected behavior.
• What recent AI incidents reveal about hallucinations, cybersecurity and autonomous agents.
• Why business leaders should simultaneously accelerate AI education and strengthen oversight.
• How organizations can capture the productivity upside of today's AI without blindly trusting its outputs.

One of the most important lessons for leaders is surprisingly mundane: catastrophic outcomes don't necessarily begin with science-fiction scenarios. A confident wrong answer, an unchecked AI-generated report or a small failure inside an automated workflow can cascade into a very serious decision.

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!

Are you still trying to decide whether Claude or ChatGPT is the “better” AI?

That may be the wrong question. The real advantage comes from building a workflow that lets you use the strengths of multiple AI platforms without losing context, duplicating work, or starting over every time you switch tools.

In this episode of Leveraging AI, Isar Meitis walks through the system he uses to work across Claude, ChatGPT, coding agents, and other AI tools on the same projects. The key is creating a shared file-based infrastructure that becomes the source of truth for project instructions, memory, tasks, and ongoing work.

Instead of locking your business into one AI ecosystem, you can build a setup that gives you more flexibility, resilience, and access to the best capabilities of each platform.

In this session, you'll discover:

• How Claude, ChatGPT, and other AI tools can work on the same project without losing context.
• Why shared files can become the real “memory” of your AI workflow.
• How to use claude.md and agents.md so different AI platforms can follow the same project instructions.
• How an evergreen project document and task registry keep every AI agent aligned.
• How to structure backups so your AI projects don’t live only on one computer.
• Why only one platform should be responsible for managing your backup process.
• How tools such as Claude Code and Codex can work in parallel on different parts of the same project.

The result is a much more flexible AI operating system for your work: one where you can choose the best model for each task instead of forcing every task through the same platform.

If you want to take this kind of infrastructure further, Isar also discusses his multi-agent orchestration course, which focuses on building more advanced AI systems for business.

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 people building the world’s most powerful AI systems start warning that they may not know how to control what comes next?

At the same time those warnings are getting louder, the AI race is doing anything but slowing down. New autonomous agents are launching across major platforms, companies are pouring staggering amounts of money into compute, and AI is moving from answering questions to taking actions on our behalf.

For business leaders, the takeaway isn’t to panic—or to sit on the sidelines. It’s to understand how quickly the landscape is shifting, where the real opportunities are emerging, and why governance, security, and responsible adoption need to evolve just as quickly as the technology.

In this episode of Leveraging AI, Isar Meitis connects the dots between three major developments shaping the next phase of AI.

In this session, you'll discover:

• Why the resignation of Anthropic researcher Jacob Coxon ignited a massive debate about superintelligence and AI alignment.
• What current Anthropic researchers are saying about the possibility of controlling recursively self-improving AI.
• Why competition between AI labs may be making meaningful coordination and slowing down increasingly difficult.
• What OpenAI’s own leadership is saying about alignment, monitoring, and potentially pacing future AI development.
• How AI is rapidly moving beyond chatbots and into autonomous agents that can perform real-world tasks.
• Why new agentic products from Meta, OpenAI, Alibaba, Instacart, Microsoft, and others matter for businesses.
• How agents could reshape everything from personal assistance and software development to shopping and digital workforces.
• Why security, governance, and control remain the biggest gaps as autonomous AI becomes more capable.
• How enormous investments in chips, data centers, and compute reveal just how much further the AI industry expects this expansion to go.
• What the accelerating demand for AI infrastructure means for the scale of the transformation still ahead.

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!

Are you burning through ChatGPT or Claude tokens faster than your team can justify the cost?

The problem may not be how much you use AI. It may be how you use it. With the right model, reasoning level, prompts, scripts, caching, and routing, you can often get the same quality of work while consuming significantly fewer tokens.

In this episode of the Leveraging AI Podcast, Isar Meitis breaks down practical ways to make AI usage more efficient across individual and enterprise workflows. He shares tests, settings, and workflow strategies designed to help you accomplish more without automatically reaching for the most expensive model or highest reasoning setting.

In this session, you'll discover:

• Why the most powerful AI model is often unnecessary for everyday business tasks
• Why lower-cost models can still produce highly accurate, consistent outputs
• How a detailed prompt can dramatically improve results from cheaper models
• How Excel scripts can execute complex recurring processes in seconds
• How model routing can distribute work between cheaper and more capable AI models
• How caching reduces the need to repeatedly process the same large amounts of information
• How running ChatGPT and Claude in parallel can help spread workloads and avoid hitting platform limits
• How organizations can perform more AI-powered work within the same budget without necessarily sacrificing quality

The takeaway for business leaders is simple: AI efficiency isn’t about using less AI. It’s about using expensive intelligence only when expensive intelligence is actually required.

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 AGI didn’t arrive with a dramatic announcement—but instead showed up as two new AI models released in the same week?

GPT-6 Astra and Anthropic’s Fable 5.1 are pushing performance, coding, cybersecurity, efficiency, and agentic capabilities to levels that would have sounded like AGI only a few years ago. And for business leaders, the bigger question is no longer whether these systems are becoming incredibly capable. It’s what you should do differently as a result.

The recommendation: stop treating every new model release as another shiny AI upgrade. Start evaluating what these advances mean for cost, security, enterprise data, autonomous workflows, model selection, and the way work inside your organization will actually get done.

In this episode of Leveraging AI, Isar Meitis breaks down one of the busiest weeks in AI yet - from GPT-6 Astra and Fable 5.1 to NVIDIA’s Hugging Face acquisition, Google’s efficiency play, increasingly autonomous AI agents, and new evidence of how deeply AI is already being trusted with consequential work.

In this session, you'll discover:

• Why GPT-6 Astra may represent a meaningful step toward what many people would have called AGI just a few years ago.
• The benchmark results that make Astra impressive—and why third-party evaluations paint a more nuanced picture.
• Why Astra’s dramatic token efficiency does not necessarily mean lower costs.
• How Fable 5.1 compares with GPT-6 Astra across coding, knowledge work, cost, and enterprise use cases.
• Why NVIDIA’s acquisition of Hugging Face could reshape the battle between closed and open-source AI.
• How smaller and specialized models are increasingly competing with frontier models at a fraction of the cost.
• How OpenAI’s advertising business is rapidly becoming another major part of the AI economy.

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!

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!