Leveraging AI

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What if the reason your AI automations keep falling apart isn’t the AI, it’s the way you’re defining the work?
Most businesses are still using AI as a smarter search engine or an occasional assistant. But if you want AI to reliably take work off your plate, you need something much more structured.
In this episode of Leveraging AI, Isar Meitis sits down with AI entrepreneur and former Facebook AI team member Rachel Woods to break down the concept of AI playbooks — repeatable, detailed processes that allow AI to execute business tasks consistently and reliably.
Rachel shares the framework she uses to turn everyday business processes into AI-powered systems. The idea is surprisingly simple: define the trigger, inputs, steps, and expected output. Then break complex workflows into smaller pieces that AI can execute extremely well.
In this session, you'll discover:
Why most AI automations fail when you give AI too much freedom
How to turn existing business processes into AI playbooks
The four components every AI playbook needs
Why breaking complex workflows into smaller tasks dramatically improves AI performance
How Rachel uses more than 100 playbooks to help run a 10-person company
How AI can manage email triage, content creation, project planning, and other operational workflows
Why you should “own the playbook and rent the tech”
How to build reusable AI processes that aren't dependent on ChatGPT, Claude, or any specific platform
Why your first AI playbook probably shouldn't be built by AI
How individual playbooks can eventually work together like an entire AI team
Rachel also walks through real examples, including her LinkedIn content workflow and a project-planning system that helped her team dramatically increase the number of projects they can execute.
Connect with Rachel Woods on LinkedIn:
https://www.linkedin.com/in/woodsrach/
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!

Learn more about Multi-agent orchestration Course and use LEVERAGINGAI100 to get $100 off! https://multiplai.ai/multi-agent-orchestration-course/ 
What happens when AI stops being a tool you open… and starts becoming a workforce that never logs off?
That transition is happening faster than most business leaders realize. AI agents are moving into the cloud. They can work continuously, collaborate with other agents, access company data, write code, research, take actions, and increasingly operate without a human sitting in front of a computer.
And while the capabilities are accelerating, the safety debate is getting significantly more serious.
This week, Jensen Huang and Bill Gates presented dramatically different views of AI risk. OpenAI unveiled more than 20 announcements at DevDay. Google returned to the frontier-model race with Gemini 4. Anthropic launched Claude Sonnet 5.5. Meta pushed deeper into enterprise AI. Microsoft reshaped its Copilot strategy. And nearly every major player is converging on the same idea: always-on AI agents running in the cloud.
For business leaders, the takeaway is bigger than another round of model upgrades.
Start thinking beyond chatbots.
The emerging AI operating model is persistent agents, shared workspaces, company knowledge, automated triggers, and humans supervising increasingly autonomous systems.
In this session, you'll discover:
Why Jensen Huang believes AI safety is fundamentally an engineering problem — and why Bill Gates strongly disagrees.
The growing concern around AI-enabled cybersecurity attacks and the ability to remove model safeguards.
Why the debate over AI regulation is becoming much harder to ignore.
Jensen Huang's argument that AI will create more jobs — and the counterargument that this technological revolution may be fundamentally different from every one before it.
Bill Gates' proposal to tax AI-generated labor as machines replace human work.
NVIDIA's new approach to monitoring AI agents with independent hardware-level safeguards.
What OpenAI's massive DevDay reveals about where the company believes AI work is heading.
Why OpenAI Dots could make persistent, always-running AI agents mainstream.
Why cost per token is becoming a misleading way to compare AI models.
How Gemini 4, GPT-6.1 Sol, Claude Sonnet 5.5, and Meta Muse compare in an increasingly crowded frontier-model market.
How realistic AI video participants could soon join human meetings.
The AI race is no longer just about who has the smartest model.
It's becoming a race to build the operating system for how humans and AI work 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!

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/
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!

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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!