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John Phelan
What it takes to rewire a CPG company to outcompete in digital and AI Interesting article from McKinsey showing CPG manufacturers are in the lowest quartile of sectors engaging with digital transformation, while retail is near the top of the pile. ie, the CPG manufacturers are at a massive disadvantage when engaging with retailers on digital/data conversations. McKinsey recommend Six questions for CPG companies to ask, with my own simplified observation below: 1. Where is the value? · Efficiency (automating the mundane data jobs and freeing up resources to add value) and/or effectiveness (using data to become category leaders, product drivers and outperform the market at revenue and margin level) 2. Are leaders from the business side actively part of the transformation? · Champions from the top down 3. Are you an attractive long-term employer for digital talent? · Making hero’s of your talent · Give them the tools to do their jobs. They will excel, add exponential value to the company and are far more likely to stay 4. Are you deploying your technology investments to optimize for reuse? · Base Layer, Data Layer, Use Case (point solutions for departments, RGM, Category Management, Sales, Finance etc) and Market Layer (integration with end systems) 5. Are you developing data products? · All data sources democratised to the whole company (no silo’s) 6. Are you anticipating and preparing for the most critical scaling challenges? · One View of the Truth See www.adviseCPG.com for your “One View” of Category Intelligence, Revenue Growth Management, Sales Performance, Field Sales Intelligence, Supply Chain Control Tower, Price & promotion analytics, Price & Promotion predictive simulators… See original article: https://lnkd.in/eKdUg7g6
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Nicole Fourgoux
I love this! Thank you to Rachel Brown from Beauty Independent to kick-off this exchange with Andrew Ross' provocative thesis and have us all react to it. I truly enjoyed reading everyone's thoughts on the topic of the shortening lifecycle of beauty brands. Here is a snippet of mine: "Because beauty brand lifespans are shortening, it is important that we are able to differentiate brands with staying power from brands that will end up being a flash in the pan. There are much more of the latter than the former in the market right now, and if a brand is not well-positioned, it will be challenging for that brand to survive over the long-term. That’s why we focus on understanding each brand’s differentiators and long-term relevance." See more here: https://lnkd.in/esVbwnu2 and you will discover what Kevin Murphy, Odile Roujol, Rich Gersten, Tina Henry Bou-Saba, Oliver Nordlinger, Claire Chang, Manica Blain, Sonya Brown, George Birman had to say. #beautyindustry #beautyinvesting Stride Consumer Partners
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Atul Tiwary
Great analysis by the AGC team on SaaS public company comps. It's worth a look as we recalibrate mid-way through earnings season. The analysis aligns with the broader Nasdaq equity performance, where the Mag 7 (or fab 4 now :-)) have shown more resilience than the rest of the market. #AGC #SaaS #EarningsSeason #Nasdaq #EquityPerformance
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Stephane Eyraud
🌐 The $600 Billion AI Question: A Revolution in the Making? 🚀 At the core of the AI debate is a monumental question: Can AI bridge the $600 billion revenue gap necessary to sustain the massive current investment? This issue, highlighted by David Cahn at Sequoia, underscores the tension between the high hopes placed on AI and the significant challenges it must overcome to fulfill those expectations. But let’s pause and reconsider. Amara’s Law reminds us that we tend to overestimate the impact of technology in the short term and underestimate it in the long term. This principle lies at the heart of the AI debate, making it challenging to distinguish real potential from inflated expectations. Generative AI may be the most capital-intensive technology Silicon Valley has ever encountered. OpenAI is reportedly losing $5 billion annually, with Sam Altman suggesting he’d be willing to spend ten times that amount if necessary. The line between visionary investment and reckless spending is becoming increasingly blurred. This gap between short-term hype and long-term impact is fueling skepticism and talk of an AI bubble. Yet this pattern mirrors every modern tech cycle. We’re likely somewhere between the “inflated expectations” and “disillusionment” phases of generative AI. If history is any guide, this doesn’t spell the end for AI—quite the opposite. Here’s the optimistic view: Every technological revolution has its failures, like Pets.com, but it also produces its Amazons, Googles, and Salesforces. Major technology shifts always demand substantial upfront investment. Recall the billions invested in laying fiber optic cables during the dot-com boom or the immense costs of building out cellular networks. These investments seemed risky at the time, but they laid the foundation for the digital, cloud-based world we now take for granted. According to Goldman Sachs, generative AI could boost U.S. productivity by 9% and GDP growth by 6.1% over the next decade. It’s easy to forget how long it takes for new technologies to achieve widespread adoption. The first PCs hit the market in the mid-1970s, but it wasn’t until the late 1990s that they became household staples. By 2000, 51% of U.S. households had a computer; today, that number stands at 95%. AI is on a similar path, but it’s advancing much more rapidly. The capabilities of AI systems aren’t growing linearly—they’re accelerating. Humans are notoriously poor at thinking exponentially. As LLMs scale, they reveal new, unexpected capabilities. Upcoming breakthroughs in model efficiency and hardware innovation promise to speed up progress even further. The pressing questions are: How deep will that transformation be, and how quickly will it unfold?🚀 Stéphanie Baruk Charles Dally Olivier Garcia Benoit Genest #AIRevolution #TechInvesting #FutureOfWork #GenerativeAI #AI #Innovation #Investment
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