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Scaling AI Startups – Hacker Noon

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Not so long ago, AI startups were the new shiny object that everyone was getting excited about. It was a time of seemingly infinite promise: AI was going to not just redefine everything in business, but also offer entrepreneurs opportunities to build category-defining companies. A few years (and billions of dollars of venture capital) later, AI startups have re-entered reality. Time has come to make good on the original promise, and prove that AI-first startups can become formidable companies, with long term differentiation and defensibility. In other words, it is time to go from "starting" mode to "scaling" mode.


Not another AI post

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Federico Antoni is managing partner at ALLVP, an early-stage VC based in Mexico. He is a lecturer in management at the Stanford Graduate School of Business. "Over the last couple of years, a billion new people have joined the super-connected world. Billions more around the developing world, now, walk with a high-speed computer in their pockets. And yet, they don't have a bank account, a formal education or access to most of the services we take for granted in the U.S. Imagine the possibilities… imagine how you can change the lives of billions of people."


After this COVID winter comes an AI spring

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During boom times, companies focus on growth. In tough times, they seek to improve efficiency. History shows us that after every major economic downturn since the 1980s, businesses relied on digital technology and, specifically, innovations in software technology to return to full productivity with fewer repetitive jobs and less bloat. The years I've spent as a VC have convinced me that this is the best time to start an AI-first enterprise, not despite the recession, but because of it. The next economic recovery will both be driven by artificial intelligence and accelerate its adoption.


Six AI Strategies – But Only One Winner

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Summary: The results are in. There is only one demonstrably successful strategy for creating big wins for AI-first companies. We'll briefly summarize the other contenders that have fallen by the wayside and then lift the curtain on the winner. For the last three years we've been close observers of exactly what makes a successful AI/ML strategy. In addition to our own observations we've been listening closely to VCs and how they describe their internal process for deciding who to fund.


5 steps to AI transformation and survival

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If an operating model defines how an organization positions people, process, and technology to deliver customer value, then companies with an AI-first operating model are those that prioritize the use of AI to weave more intelligence and automation into the firm's products, processes, and experiences. Data gathered from 100 global CIOs at the Metis Strategy Digital Symposium in July 2020 personifies the trend toward AI-first operating models: 66% of CIOs stated that they have teams focused on identifying AI use cases, conducting pilots and scaling those cases that improve outcomes. Of the CIOs who do not currently have resources focused on this, roughly 60% indicated it is on their roadmap. In our work with Fortune 500 companies, we have identified common characteristics among organizations that successfully navigate the shift to AI-first. Below are a series of smart first steps digital leaders can take to initiate, accelerate, or course correct their AI transformation.