AI-Driven Business Shifts: Leverage Charts, Data Moats, and Distribution - Episode Hero Image

AI-Driven Business Shifts: Leverage Charts, Data Moats, and Distribution

Original Title:

TL;DR

  • Businesses will shift from hierarchical org charts to leverage charts, where one person directs AI and automation to achieve departmental outcomes, replacing large teams with a single individual amplified by technology.
  • The future of work requires transitioning from "doer" roles to "director" roles, focusing 80-90% of time on vision and creative solutions, leveraging AI for execution rather than performing tasks.
  • Competitive advantage will move from feature-based product development to data-based moats, utilizing proprietary customer data to train AI for personalized feedback loops and faster innovation.
  • The back office will become autonomous, with AI agents handling finance, HR, and legal tasks, allowing leaders to audit exceptions and focus on strategic oversight rather than operational details.
  • Business success will pivot from development advantage to distribution advantage, prioritizing audience reach and brand building through owned channels and partnerships over complex technical development.

Deep Dive

The discussion begins by introducing the imminent and widespread impact of artificial intelligence on businesses, noting that many are unprepared for these changes. The speaker, who actively launches AI companies, plans to outline five key shifts and actionable strategies for navigating them.

The first shift discussed is the transition from organizational charts to leverage charts. In the traditional model, departments were structured with many people, and problems were solved by adding more staff. The new paradigm emphasizes a single person owning an outcome within a department, utilizing AI, agents, and automation to achieve results. The focus shifts from "who do I hire" to "what can we build," suggesting that one salesperson, augmented by AI, can now perform the work of ten people.

Next, the conversation addresses marketing, moving from a "hamster wheel" approach to AI-driven strategies. AI can now identify content to create, generate outlines, and produce emails, copy, and entire campaigns automatically. The source highlights that AI can curate and personalize content based on global expertise, making it more effective for reaching ideal customers than human efforts alone. The concept of an "AI Ops person" is introduced as a future role responsible for optimizing workflows and leveraging AI to free up team members' time from busywork, allowing them to focus on direction.

The second major shift presented is the move from being a "doer" to a "director." The speaker explains that current AI limitations stem from users not knowing how to direct or craft its use. In the past, individuals spent about 90% of their time doing the work and 10% on vision and creative solutions. This is now reversed, with 80-90% of a team's time dedicated to directing AI, understanding its possibilities, and leveraging automations and agents for execution. The speaker notes the disruption of jobs, such as those in call centers, and references a robot sorting packages at an UPS store as an example of current automation. Adapting to this shift requires viewing one's business through the lens of a director, similar to a movie director orchestrating players, resources, and set designs.

The third shift discussed is the move from feature-based competitive advantages to data-based ones. Traditionally, companies competed on features, with a 3-6 month window before competitors could copy them. The source argues that since AI can build features rapidly, the true competitive advantage now lies in proprietary data. This data informs AI, enabling faster learning and creating feedback loops. The speaker uses ChatGPT as an example, noting its improvement with user interaction and data collection. Businesses are encouraged to collect customer preferences and use this data to inform AI. A company called Precision.ai is mentioned as an example of a business built around this concept, integrating various data sets to identify industry benchmarks, prioritize projects, and unlock business bottlenecks. Building a data-based moat involves three steps: cleaning all data, using AI to analyze data and make correlations, and having AI suggest next steps to address bottlenecks.

The fourth shift is the "autonomous back office." In the past, finance, HR, and legal departments required full-time staff. Now, these functions can be handled by AI-driven policy agents that execute requests, close loops, and provide updates. An example is given of an AI agent reviewing contracts in real-time, a task that previously required waiting for a human legal professional. To implement this, one connects financial data to AI tools like HelloFrank.ai for analysis, auditing, and reporting. Leaders then audit for exceptions, with AI handling the remaining 98% of tasks. A hack for implementing this involves using automation tools like Make or Zapier to codify business rules. The principle is that exceptions require people, while patterns and repeatable systems can be handled by code.

The final shift discussed is the transition from a development advantage to a distribution advantage. The speaker states that in the past, success depended on large development teams and skilled coders. Today, distribution is paramount, and AI is making advanced coding accessible to many, including 12-year-olds using tools like Lovable, which allows app creation through voice commands. AI is described as the first technology coded in English, making it universally accessible. The speaker shares their own strategy with Martel Ventures, partnering to build AI tools and launching them through existing audiences like YouTube, email lists, and online communities. The value is in distribution and customer access, not just the code. A three-step process for building distribution is outlined: building distribution into the business model by growing an owned channel, attaching a brand to a clear problem and promise that focuses on outcomes, and pre-selling the tool before significant investment. The speaker suggests partnering with individuals or entities that already possess an audience as a way to gain distribution.

Finally, the speaker addresses skepticism about AI replacing jobs. They argue that AI will eliminate tasks people don't want to do, allowing humans to focus on areas AI cannot disrupt, such as vision, taste, and caring for people. These tools are seen as enabling teams to act as artists and creators, leading to increased productivity and earnings. The consequence of not adopting AI is that others will, and AI will not take jobs, but individuals using AI will.

Action Items

  • Build leverage charts: Replace traditional org charts with AI-driven outcome ownership for 3-5 departments.
  • Create AI Ops role: Hire one person per 5-10 teams to identify and automate workflows, reclaiming hundreds of hours monthly.
  • Implement data moat strategy: Clean and analyze customer data using AI to inform personalized outputs, creating a competitive advantage.
  • Design autonomous back office: Codify rules for finance and HR functions into AI agents, with leaders auditing exceptions (1-2 per week).
  • Develop distribution partnerships: Identify 1-2 partners with established audiences to co-promote AI tools, focusing on reach over development.

Key Quotes

"The first shift i see coming is moving from org charts to leverage charts the old world was having departments plus people and yeah structure and hierarchy and people when there were problems they just threw bodies at them the new world is completely different where one person owns a specific outcome of that department let's say finance and then use ai and all the different tools from agents to automation to robotics to get the results for the team we got to stop asking who do i hire and instead start asking what can we build the days of needing 12 plus people to build a sales team are gone now with one closer and then using ai to give that closer leverage that person can do the job of 10 people for example the infinite sdr role essentially sales development rep can do outbound for that sales rep can qualify inbound can take phone calls can personalize emails can rearrange the calendar the whole thing is automated through ai massive leverage using the calendar so the sales rep only talks to people that are actually going to buy"

Dan Martell argues that the traditional organizational structure of departments and hierarchies is being replaced by a "leverage chart" model. This new approach emphasizes one person owning a specific outcome and utilizing AI and automation tools to achieve results, rather than relying on large teams or simply adding more people to solve problems. Martell suggests this shift allows a single individual, empowered by AI, to achieve the output of many.


"the second shift i see coming is going from doer to director the reason ai doesn't blow people's minds to the level it should is because they don't know how to use it they don't know how to direct it they don't know how to craft it in the old world you spent only about 10 of your time talking with team about like vision and creative solutions and ideas and kind of like pushing the art form and then 90 of your time sitting there doing the work think about it for my executive assistant she used to spend 90 of her time just processing my inbox and adding stuff to the calendar and coordinating things with other people and doing massive research projects now in this new world it's flipped 80 90 of my team's time is on directing it's trying to understand what is freaking possible when you look at the automations the agents the connectors these things are taking care of the last mile of actually executing and booking stuff and coordinating things not just the creative output"

Dan Martell explains that the future of work involves transitioning from being a "doer" to a "director." He posits that AI's true potential is unlocked when individuals learn to direct and craft its capabilities, rather than simply performing tasks. Martell illustrates this by noting that his team now spends the majority of their time directing AI and exploring possibilities, with AI handling the execution and coordination of tasks.


"the third shift i see coming is going from feature based modes to data based modes a moat essentially protects you think of a castle in the middle of a field it's got a moat around it because it needs to protect itself from the bad guys coming in to try to take the castle just like the water around the castle is your moat the future is data in the old world you would compete on features you would launch something your competitor would have three to six months to copy and that was the rat race that you got stuck into the future since the ai can actually build the features faster than you could ever think of them is that the proprietary data of how you do your business or what you collect about your customer and using that to inform the ai to learn faster to create feedback loops that is the competitive advantage it's not about what you deliver the features it's about how you deliver it so that your customer can trust that you're the innovator in the space"

Dan Martell identifies the third major shift as moving from "feature-based moats" to "data-based moats." He uses the analogy of a castle's moat to explain that in the past, competitive advantage came from product features, which competitors could quickly replicate. Martell argues that the future competitive advantage lies in proprietary data, which can be used to train AI, create feedback loops, and inform business operations, making it harder for competitors to catch up.


"the fourth shift i see coming is the autonomous back office in the old world you had finance and hr and legal as full time positions in the new world they're ai they're literally policy driven agents that take the requests execute the work close the loop reply to everybody on your team with no bottlenecks in the past when i sent a contract over to my legal department i had to wait until that person had the time to review it to give me the red lines now i have an ai agent that does it for me in real time"

Dan Martell describes the fourth shift as the "autonomous back office," where functions like finance, HR, and legal are handled by AI agents. He contrasts this with the old model where these departments required full-time human staff and could create bottlenecks. Martell highlights that AI agents can now execute requests, close loops, and provide real-time responses, such as reviewing contracts instantly, which significantly streamlines operations.


"the last shift i see coming is going from development advantage to distribution advantage see the old world was about building the biggest development teams the smartest people coding stuff today it's about distribution the doing of the business is no longer her you're like oh i've got 17 years experience in this industry nobody gives a shit well i know how to code really advanced algorithms nobody gives a shit all of these things are now done by 12 year olds using ai the other day i saw an ad by a product called lovable where it had a kid in the ad build an app and deploy it and start monetizing it it took everything the database the interface the whole workflow and logic i guess i'm an engineer now sure and the cool part is he built it all through voice he talked it he didn't type anything see ai is the first technology that's coded in english which makes it available to every human on earth that is completely different"

Dan Martell asserts that the final shift is from a "development advantage" to a "distribution advantage." He explains that in the past, expertise in coding and development was paramount, but now AI can handle these tasks, even being coded in natural language like English. Martell emphasizes that the true advantage now lies in the ability to distribute products and reach customers, as AI has democratized the creation of sophisticated applications.

Resources

External Resources

Books

  • "Buy Back Your Time" by Dan Martell - Mentioned as his new book.

Articles & Papers

  • "Figure Two" (Source not specified) - Mentioned as an example of a robot sorting packages.

Tools & Software

  • Hello.frnk.ai - Mentioned as a financial analysis tool that connects financial systems and other tools to perform heavy lifting.
  • Make - Mentioned as an automation tool for codifying business rules.
  • Zapier - Mentioned as an automation tool for codifying business rules.

People

  • Dan Martell - Host of "The Martell Method" podcast, founder of Martel Ventures, and author of "Buy Back Your Time."
  • Matt (Last name not specified) - Mentioned as a friend who built a company called Precision.co.

Organizations & Institutions

  • Martel Ventures - Mentioned as Dan Martell's AI incubator.
  • Precision.co - Mentioned as a company that analyzes data sets to identify business priorities.

Websites & Online Resources

  • go.danmartell.com/4ihuhHw - URL for a free AI Company Operating System.
  • martelmethod.com - URL for The Martell Method Newsletter.
  • bit.ly/3XEBXez - URL for The Martell Method Newsletter subscription.

Other Resources

  • AI Company Operating System - Offered as a free resource.
  • The Martell Method Newsletter - Offered as a newsletter to elevate mindset, fitness, and business.
  • AI Ops - Mentioned as a role focused on looking at workflows and creating leverage for teams.
  • Infinite SDR role - Mentioned as an example of AI leverage in sales development.
  • AI Ops person - Mentioned as a hire for workflow and automation tasks.
  • AI - Discussed as a technology changing business through shifts in organizational structure, marketing, data analysis, back-office functions, and distribution.
  • Leverage charts - Mentioned as a new organizational structure replacing org charts.
  • Agents - Mentioned as a component of AI used for automation and execution.
  • Automation - Mentioned as a key component of AI for executing tasks.
  • Robotics - Mentioned as a component of AI used for automation.
  • AI incubator - Mentioned as a structure for launching new AI companies.
  • Data-based moat - Described as a competitive advantage derived from proprietary data informing AI.
  • Feedback loops - Mentioned in relation to AI learning from customer interactions.
  • Autonomous back office - Described as AI-driven policy agents handling finance, HR, and legal functions.
  • Policy-driven agents - Mentioned as AI entities that execute work based on policies.
  • Make or Zapier - Mentioned as automation tools for codifying business rules.
  • Exceptions deserve people, patterns deserve code - A principle for distinguishing tasks for human oversight versus automated systems.
  • Distribution advantage - Described as the primary competitive edge in the current business landscape, surpassing development advantage.
  • Crowdfunding - Mentioned as a market where companies pre-sell products to fund development.

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