Unleashing the AI Octopus: Reshaping Enterprise with Structured Data and Decentralized Decisions
<video style="width: 100%;height: auto" controls><source src="https://d2qu7nggdaeihr.cloudfront.net/output/d81d7dd6-7bed-4727-ad86-00d0203debf0-1794493765.mp4" type="video/mp4"></video>
<aside>(This article was generated with AI and it&#8217;s based on a AI-generated transcription of a real talk on stage. While we strive for accuracy, we encourage readers to verify important information.)</aside>
<img decoding="async" src="https://web-summit-engine.imgix.net/fb58fcepntffx7fxg5d5p36ccgco?auto=format&amp;fit=fill&amp;fill=solid&amp;fill-color=white&amp;w=600&amp;h=600&amp;fm=jpg&amp;dl=Jim Edwards.jpg" alt="Jim Edwards" align="left" hspace="10" width="200" style="float:left;margin-right: 15px">
Fundamental is developing AI to understand and predict from a company&#8217;s most valuable structured data, including customer, operational, inventory, and transaction information. This enables precise predictions for customer churn and operational risk, where traditional AI often falls short. Mr. Max Schireson, a Partner at Battery Ventures, highlighted their investment, foreseeing AI bringing the &#8220;Chat GPT revolution&#8221; to structured data, reducing reliance on extensive data science teams. He clarified that while current large language models (LLMs) interpret tables within documents, they struggle to analyze terabytes of raw, structured enterprise data for subtle patterns due to limited context windows.

Mr. Greg, from Fundamental, demonstrated this with their work for AEG (LA Galaxy, LA Live), where AI analyzes customer and sponsorship data to identify potential season ticket churners and convert regular ticket holders. Fundamental&#8217;s generalized AI model offers a quicker, more effective alternative to traditional data science. Mr. Schireson emphasized the stark contrast between &#8220;AI-native&#8221; companies, which embed AI at their core for 50-200x productivity gains, and those merely augmenting human tasks for 2-3x gains. He cited Mirandel, an &#8220;AI-native&#8221; startup that built a competitive model in 3.5 months for $8 million with one researcher, a task typically costing billions.

Mr. Greg defined this as &#8220;superhuman&#8221; AI, capable of tasks beyond human capacity, analyzing billions of rows of complex oil platform data for insights. Extending this concept, Mr. Schireson discussed AI&#8217;s potential in medicine, particularly radiology. AI can not only boost radiologist productivity but, critically, improve diagnostic accuracy by detecting cancers earlier and reducing false positives. Mr. Greg then introduced the &#8220;AI octopus&#8221; organizational model, where AI empowers every individual to make predictions. In this structure, lower organizational &#8220;tentacles&#8221; make decentralized decisions, while leadership (the &#8220;brain&#8221;) focuses on strategic vision and guardrails, fostering nimbler and more efficient operations.

Mr. Schireson explained that leaders in this new paradigm will empower those closest to decisions with AI-driven insights and strategic context, not centralizing decision-making. While acknowledging potential for initial challenges, Mr. Greg anticipates flatter, more productive organizations. Regarding enterprise software, Mr. Schireson predicted &#8220;margin pressure&#8221; from AI, as high renewal costs might incentivize companies to rebuild systems. Mr. Greg stressed Fundamental&#8217;s commitment to delivering immediate, measurable &#8220;proof of value&#8221; to clients, addressing problems and often surpassing benchmarks.

For middle management roles in finance, operations, or analytics, Mr. Schireson identified both threat and opportunity. Those who leverage AI to add significant value will see their roles become more crucial, while those focused on routine report production may find their jobs commoditized. Addressing concerns about AI errors, Mr. Schireson humorously noted that human errors also exist, suggesting the focus should be on monitoring AI error frequency and trends over time.

AI’s octopus era

Closing remarks

Western tech: All is not lost