AI in Operations
AI applied to internal workflows, back-office automation, finance, legal, HR, procurement, logistics, and cross-functional business processes.
Enterprise Agentic AI Adoption Is Still Below 1 Out Of 10
EY global consulting chief Errol Gardner argues that enterprise agentic AI remains far earlier than the market narrative suggests, rating adoption at less than 1 on a 0-to-10 scale. In a conversation with Craig Smith, Gardner says the main obstacle is not model capability but the difficulty of changing large organizations: aligning leaders, managers, workers, data controls and governance around redesigned workflows. He expects agentic AI to matter, but says scaled adoption will be slowed by human resistance, regulation, workforce displacement concerns and unresolved questions about who captures the value.
Startups Should Build Recorded, Queryable Operations That AI Can Improve
YC general partner Tom Blomfield argues that startups should not treat AI as a copilot bolted onto existing org charts, but as the basis for a company that records its work, exposes its tools, and improves through recursive loops. In his batch talk, he says founders should make company knowledge legible to AI, spend more on tokens rather than headcount, and rebuild operations around systems that can detect failures, update themselves, and reduce the need for human coordination.
Ivan Zhao Says AI Makes Companies Flatter, Not Hierarchy-Free
Notion founder and CEO Ivan Zhao argues that AI will not make companies hierarchy-free, but can reduce the amount of human routing that makes hierarchy slow. In a conversation with Brian Halligan, Zhao describes Notion’s answer as “jazz mode”: a deliberately decentralized company that still has structure, but relies on high-agency people, ex-founders and model-enabled teams to improvise as product and market conditions change. His broader case is that AI-era leaders have to refound around the technology itself, not just bolt it onto the old SaaS operating model.
Kled Founder Alleges Luel Copied Its Human Data Marketplace
This Week in Startups put two founder arguments side by side: Mercury chief executive Immad Akhund said the fintech’s new $200mn round is meant to create strategic flexibility for a profitable company seeking a bank charter, while Kled founder Avi Patel argued that an alleged copycat in the human-data marketplace category threatens trust in a business built on consent and compliance. Jason Calacanis treated Patel’s dispute with Luel, Y Combinator and General Catalyst less as an intellectual-property case than as an ethics and diligence signal for investors.
Google’s AI Assets Are Becoming a Product Coherence Problem
John Coogan and Jordi Hays read Google’s I/O as evidence that the company’s AI advantage is becoming a product-navigation problem: it has data, distribution, models and hardware partnerships, but its demos and product names left questions about coherence and pace. Across the source, that same pressure appears in more operational forms, as AI pushes companies to turn technical capability into usable workflows, secure software dependencies and faster product systems. Tae Kim’s Nvidia argument and the expected SpaceX IPO make the capital-market version of the question explicit: whether investors will keep paying for scarce infrastructure, extreme scale and growth curves that may take years to prove out.
AI’s Value Is Shifting From Model Demos to Distribution and Measurement
Google’s problem at I/O, Jordi Hays argued, was no longer proving that its AI models are impressive, but making Gemini useful rather than redundant across products investors now increasingly view as part of a full-stack AI business. The TBPN discussion extended that framing across the rest of the show: AI’s value, the hosts and guests argued, depends less on model spectacle than on distribution, workflow integration, economics and adoption by institutions. That distinction ran from Google’s risk of crowding users with Gemini entry points to SendCutSend’s physical capacity constraints, Commure’s push to automate healthcare administration, and METR’s effort to turn frontier-model risk into something auditable.
Zepto Is Building India’s Urban Grocery Supply Chain Around Quick Commerce
Zepto co-founder and CEO Aadit Palicha argues that the company is not mainly a quick-commerce app but a grocery infrastructure business built around dark stores, supply-chain control and the promise of 10-minute delivery. In a Startup School India conversation with Jared Friedman, Palicha traces Zepto’s path from a COVID-era WhatsApp grocery group in Mumbai to a platform handling millions of daily deliveries, saying the decisive moves came from staying close to dissatisfied customers and working backward from speed, quality, selection and price.
Serval Bets Boring IT Controls Will Unlock Enterprise AI
Serval founder and CEO Jake Stauch argues that enterprise AI will be won less by giving models broad autonomy than by constraining them inside permissions, approvals, audits and workflows that companies can trust. In a conversation hosted by Sequoia’s Pat Grady, Stauch describes Serval as a ServiceNow-like system rebuilt for AI: an admin agent generates workflows from natural language, while a help desk agent can act only through tools IT has explicitly approved. He says that same logic extends to Serval’s operating model, where customer insight and “fewer, better” hiring matter more than model access in a market that may force products to be rebuilt every few months.
Seven Eight-Figure Businesses Run on a Relationship Operating System
Gary Vaynerchuk told Sam Parr and Shaan Puri on My First Million that his portfolio of seven eight-figure businesses depends less on software or formal process than on a long-built system of people. He described an operating model built around trusted lieutenants, compressed meetings, personal-brand distribution, and non-transactional relationship work, while acknowledging that the same instinct made him slow to deliver candid feedback and fire underperformers. His case is that disciplined generosity, long-tenured operators, and long-horizon bets are the leverage behind the “juggler” role outsiders often mistake for motivational branding.
UK Government Tests an Insurgent Model for In-House AI Delivery
Eoin Mulgrew of the Number 10 data science team argues that the UK state’s AI problem is less a shortage of use cases than a shortage of technical people with the access, mandate, and proximity to build inside government workflows. In a talk on the No. 10 Innovation Fellowship, he presents the model as a deliberate hack around normal civil-service constraints: market-rate pay, outside recruitment, a highly selective technical process, and authority to enter departments and ship tools that remain with the teams using them.
ServiceNow Says Agentic AI Lifted HR Capacity and Automated Support Work
ServiceNow executives Jacqui Canney and Kellie Romack argue that agentic AI is already changing workplace operations by creating measurable capacity rather than simply replacing jobs. In a ServiceNow-sponsored interview, they point to the company’s internal deployments — including faster commission answers, autonomous IT service-desk resolution, and large-scale support automation — as evidence that AI’s value depends on redesigning workflows, tracking the capacity created, and redeploying employees into higher-value work. Their case is that managers now have to govern both people and agents, with visibility, skills assessment, and explicit choices about what work should be automated.
Drones and Sensor Networks Are Turning Policing Into Real-Time Response
David Ulevitch’s a16z conversation with Arizona DPS director Jeffrey Glover and Flock Safety’s Rahul Sidhu argues that public safety technology is moving from record-keeping and faster response toward earlier situational awareness. Sidhu describes drones, license-plate readers and gunshot detection as a layered system for proactive response, while Glover says agencies are building broader technology ecosystems that also monitor officer wellness, analyze body-camera footage and share intelligence across jurisdictions. Both argue that founders need direct exposure to field work if they want to build tools that departments can actually use.
Incident.io Uses Coding Agents to Debug Its AI SRE
Lawrence Jones, founding engineer at Incident.io, argues that complex AI products now require debugging tools built for agents as well as humans. In a talk on Incident.io’s AI SRE system, which runs hundreds of prompts across telemetry and code during production investigations, Jones describes how the team moved from human trace inspection to agent-addressable evals, downloadable file-system traces, and parallel analysis pipelines to find and fix failures that had become too large to debug manually.
ChatGPT for Excel Adds Audit Trails to Finance Workbook Reviews
A demo of ChatGPT for Excel shows how finance teams could review a CFO performance workbook before it reaches leadership. The case it makes is constrained: ChatGPT inspects the model in Excel, flags tie-out breaks, stale source data and variance issues, applies only mechanical cleanup, and creates workbook tabs for the issue log, fixes, remaining risks and owner questions. The source presents the tool less as a substitute for financial judgment than as a way to put a documented audit trail and readiness verdict inside the file itself.
Legacy Infrastructure Is Slowing Enterprise Agentic AI Adoption
Kris Lovejoy, global strategy leader at Kyndryl, argues that enterprises are not being held back from agentic AI mainly by model capability or startup speed, but by the difficulty of running agents securely and reliably inside legacy infrastructure. In a conversation with Craig Smith, she says pilots are widespread but scaled deployments remain rare because agents need context, governance, compliance controls and modernized IT foundations before they can touch core systems. Her near-term prediction is narrower than much of the hype: by about 2031, agentic AI may handle roughly half of traditional line-one and line-two IT administration tasks, with humans still supervising the loop.
AI’s Value Is Moving From SaaS Margins to Hardware Capacity
PwC technology, media and telecommunications leader Dallas Dolen argues that the AI boom is a real infrastructure and business-model shift, but one constrained by chips, construction labor, telecom capacity, copper, power and enterprise economics. In a PwC-sponsored interview, he says value is moving from SaaS toward hardware, software margins are compressing, and most companies are less limited by compute access than by token costs, security rules and measurable return on investment. Dolen’s view of enterprise AI is practical and bounded: agents are working in defined back-office, sales and legal tasks, while broader automation will depend on cost, governance and human oversight.
AI and Robotics Will Make Today’s Hospitals Look Archaic
BD chief executive Tom Polen argues that AI and robotics will change hospitals so substantially over the next decade that today’s practices will look archaic. In a Bloomberg interview with Caroline Hyde, he described BD’s approach as an operational transformation: predictive AI for intensive-care patients, robotics to take non-clinical work off nurses, more care delivered at home, and supply chains built for resilience rather than just efficiency.
Codex Is Moving From Code Generation to Delegated Knowledge Work
Codex is moving from a coding assistant toward an agent for delegated knowledge work, according to Thibault Sottiaux, OpenAI’s head of Codex. In an OpenAI Forum conversation with Chris Nicholson of OpenAI Global Affairs, Sottiaux argues that as models have become more reliable and better connected to workplace context, Codex is being used to research, organize information, create files and presentations, coordinate across tools, and run background tasks. That shift, he says, makes delegation, trust and access controls central as agents act across files, communications tools and company systems.
AI Predicted the Supreme Court’s Questions, but Human Persuasion Won
In a TED talk, Supreme Court lawyer Neal Kumar Katyal argues that AI helped him prepare for a historic tariff case, but did not win it for him. Katyal says a legal AI system trained on decades of justices’ questions and writings anticipated major lines of attack in a challenge to a president’s tariff program, including concerns that later appeared in argument and opinions. His central claim is that prediction is not persuasion: the case was won by combining AI-assisted foresight with human judgment, listening, composure and the ability to answer the person in front of him.
AI Companions Are Tempting Because They Make Relationships Too Easy
Joanna Stern, author of I Am Not a Robot, argues on Big Technology Podcast that AI’s most plausible near-term role is not as a standalone gadget or replacement professional, but as a second layer on devices, workflows, and relationships people already use. Drawing on a year of trying to put AI into daily life, she says the tools can be genuinely useful in wearables, medical interpretation, and solo work, while chatbot companionship exposes a more troubling risk: systems that are always available, agreeable, and easier than human relationships.
SAP Says ERP Context Will Make AI Agents Reliable for Business
SAP chief executive Christian Klein used Bloomberg Technology to frame the company’s new autonomous enterprise platform as a bet that AI agents need business context more than proprietary models. He argued that SAP’s advantage is its access to ERP data and process knowledge, which can make agents reliable enough to coordinate work across finance, commerce, inventory, procurement and supply chains. Pressed on competition from partners such as AWS, Klein said SAP’s role is to provide the enterprise context layer while working with hyperscalers and data platforms to harmonize data beyond SAP systems.
AI Will Commoditize Legal Work Product, Not Legal Judgment
Harvey co-founder and chief executive Winston Weinberg argues that AI will commoditize much of the routine work product in law while increasing the value of judgment at the point where legal decisions are made. In a Knowledge Project interview with Shane Parrish, Weinberg describes how Harvey grew from a GPT-3 test on landlord-tenant questions into an $11bn legal AI company, and explains the operating discipline behind it: faster decisions, sharper prioritization, and a team built to withstand repeated failure.
Long Lake’s $6.3 Billion Amex GBT Deal Tests AI-Led Buyouts
Long Lake Management co-founder and CEO Alexander Taubman argues that AI can change the economics of services businesses when the buyer owns the workflow, not just the software layer. In a conversation with Elad Gil about Long Lake’s announced $6.3bn take-private of American Express Global Business Travel, Taubman presents the firm’s model as acquiring trusted services companies, embedding its Nexus AI platform into day-to-day operations, and using productivity gains to drive growth, customer service and employee retention rather than short-term cost cuts.
Freight Automation Starts With Platforms, Not Just Autonomous Trucks
Einride chief executive Roozbeh Charli argues that the shift to electric and autonomous freight will be led by software orchestration rather than by vehicles alone. In an interview with Bloomberg’s Tom Mackenzie, he says large shippers need a platform to coordinate electric trucks, autonomous systems, routing, charging and operational handoffs, while regulation and human supervision remain critical to making the model work at scale.