Become a $1M/yr FDE (Full Course)

The podcast discusses the growing role of forward-deployed engineers in driving AI transformation within businesses, particularly those owned by private equity firms. These engineers are tasked with re-engineering outdated workflows by mapping complex, often undocumented processes across multiple systems and locations. Rather than simply automating tasks, they focus on deep process optimization - identifying inefficiencies, eliminating unnecessary steps, and redesigning workflows to integrate AI agents effectively. A key insight is that successful AI implementation requires more than just deploying technology; it demands a thorough understanding of existing operations, including where critical knowledge resides in employees' minds rather than formal systems.

AI tools are applied across various business functions, such as finance, customer service, and legal, with measurable impacts like reducing invoice processing costs by 80% or cutting month-end close times from weeks to days. The discussion highlights the importance of using AI not to replace humans outright but to remove low-value work and improve accuracy and speed. Different types of automation are categorized: fully automated (deterministic) steps, agentic workflows requiring judgment, and high-risk tasks needing human oversight. Success depends on integrating AI within existing systems like Salesforce or NetSuite rather than introducing new platforms, ensuring faster adoption and minimal disruption. The financial upside is significant, with improvements in gross margins and operational efficiency leading to substantial increases in company valuation over time.

• What if you audited one SMB's invoice processing workflow this week?

  • Move: Identify a small business (e.g., local accounting firm or e-commerce store) and map their current invoice workflow - list all systems used, steps taken, and pain points via a 30-minute interview.
  • Why Now?: Most SMBs have undocumented, inefficient processes; early mapping creates immediate leverage for automation and cost savings (e.g., reducing $31 invoice cost to $6).
  • Expected Upside: Uncover 5 - 10 steps of waste; position yourself to build a deterministic or agentic solution, potentially charging $5K - $20K for optimization.

• What if you deployed a background AI agent into your own workflow this month?

  • Move: Use open-source models (e.g., GPT, Muse) to build a silent agent that automates a repeatable task in your stack - e.g., transcribing voice notes (Gemini 3.5) or auto-filing expenses into NetSuite.
  • Why Now?: Tools like Gemini Live and Microsoft Cop are production-ready, and solo devs can replicate PE-style efficiency plays at zero marginal cost.
  • Expected Upside: Free up 5 - 10 hours/month; demonstrate ROI to potential clients via before/after metrics (cycle time, accuracy), creating a reusable sales asset.

• What if you positioned yourself as a profit-sharing FTE for a portfolio company?

  • Move: Pitch a private equity - owned business (e.g., one with 3+ systems like Salesforce + NetSuite) to co-implement AI agents in exchange for 0.5% of the profit delta from efficiency gains (e.g., 30% faster close).
  • Why Now?: PE firms are actively reengineering workflows across 50+ companies - solo operators who can map, reengineer, and deploy offer rare multidisciplinary value.
  • Expected Upside: Turn a single $10K project into a $100K+ outcome via shared upside; build a scalable playbook across NetSuite or Salesforce-heavy portfolios.

• Conduct a personal workflow audit by listing all apps and communication tools used, then map inefficiencies to identify automation opportunities.
• Document and dissect one recurring business process (e.g., invoicing or expense tracking), categorizing each step as deletable, deterministic, agentic, or requiring human oversight.
• Offer free process optimization for a single workflow to an SMB, using the audit and categorization method to demonstrate value before scaling to paid engagements.
• Test and benchmark multiple AI models (e.g., GPT, Claude, open-source alternatives) on real tasks to determine the most cost-effective and accurate model for specific use cases.
• Focus AI integration within existing systems (e.g., Salesforce, NetSuite) rather than introducing new platforms, ensuring faster adoption and minimizing operational disruption.