 |
|
|
|||||
Every C-suite executive I meet asks the same question: Why is our AI investment stuck in pilot purgatory? After surveying over 200 AI practitioners for our latest research, I have a sobering answer: Only 22% of organizations have moved beyond experimentation to strategic AI deployment. The rest are trapped in what I call the messy middleburning resources on scattered pilots that never reach production scale. In my 20-plus years helping companies solve complex problems with open-source AI and machine learning, I’ve watched this pattern repeat across industries. Companies get excited about AI’s potential. They fund pilots. They hire data scientists. But when it comes to production deployment and measurable ROI, they hit the same wall: over 57% take more than a month to move from development to production. That’s not innovation velocitythat’s friction eating your competitive advantage. The problem isn’t enthusiasm or investment. The problem is they’re building on quicksand. Without shared standards, every team reinvents the wheel. Tools fragment. Governance gaps widen. Trust erodes. What should take days stretches into months. Here’s what business leaders need to understand: The companies escaping this trap aren’t using better AI models. They’re using better foundations by using open-source software. Standards create a competitive advantage Standards might sound like bureaucracy, but in AI they separate companies that scale from companies that stall. Our research reveals the real barriers: 45% of teams cite data quality and pipeline consistency as their top production obstacle. Another 40% point to security and compliance challenges. These aren’t technical problemsthey’re coordination problems. When every team speaks a different technical language, you can’t share work, build trust, or scale effectively. Think about it this way: Imagine if every department in your company used different email systems that couldn’t talk to each other. That’s essentially what’s happening with AI tools today. Open standards solve this by creating shared languages for AI development. When everyone uses compatible tools and formats, collaboration becomes automatic. Integration that used to take months happens in days. The result? Faster deployment cycles and measurable ROI. Companies are starting to get the message: 92% of AI practitioners use open-source tools, and 76% say their organization has increased its open-source priority this year, according to our research. Three standards that drive results Not all standards matter equally. Based on what I’ve seen transform organizations, here are three that deliver immediate impact: Ways to move AI models between systems without rebuilding. Standards like Open Neural Network Exchange prevent vendor lock-in and eliminate reworkthe silent killer of innovation velocity. When teams can deploy the same model across different environments, development speeds up dramatically. Protocols that let AI services communicate seamlessly. Instead of building custom integrations for every new tool, teams can assemble complex AI systems from standard components. This turns months of integration work into days of configuration. Frameworks for responsible AI governance. With 53% of organizations lacking comprehensive AI policies, standardized approaches to model documentation and validation turn governance from a blocker into an accelerator. Teams move faster because they know exactly what compliance looks like. The pattern I see repeatedly is this: Each standard reduces friction. Together, they create an ecosystem where innovation compounds instead of fragmenting. Open source is your competitive edge Some executives worry that open source means chaos. They think standards need central authority. But AI moves too fast for traditional standardization. By the time a formal standards body publishes specifications, the technology has evolved. Open source solves this through evolutionary design. Standards emerge from real-world use, spread through community adoption, and adapt at market speed. This keeps them relevant in ways top-down standards can’t match. There’s another crucial factor: Transparency builds trust. Our research shows less than half of AI practitioners feel confident explaining model decisions to executives or regulators. When standards are open, you can inspect how they work, verify their claims, and adapt them to your needs. This transparency accelerates adoption and regulatory approval. What surprised me most in our research was the community insight: People distinguish between using open-source software and building on open-source foundations. True acceleration requires shared standards that let teams move independently while still moving together. Escaping the messy middle Here’s my core advice for C-suite leaders: Stop treating AI as a technology problem and start treating it as a systems problem. The messy middle exists because organizations approach AI as isolated projects. Teams pick different tools, build separate pipelines, and create individual governance processes. This works for pilots but kills scalability. Strategic AI requires a foundation built on compatibility. Here are three ways to achieve it: 1. Simplify your toolchain around core platforms that work together. You don’t need 47 different AI tools. You need a unified approach where teams can share models, data pipelines, and deployment processes without starting from scratch. 2. Choose solutions you can inspect and verify. This reduces risk and builds stakeholder confidence. Trust accelerates adoption, and adoption accelerates value creation. 3. Measure deployment cycles, not just model accuracy. Track time from prototype to production. Track how many AI projects deliver measurable business outcomes. These metrics reveal whether your foundation is working. Our work with large corporations shows that organizations moving from fragmented approaches to unified platforms see dramatic improvements: faster deployment, higher success rates, and clearer ROI measurement. Standardization and innovation are partners The gap between strategic AI deployers and pilot-trapped organizations will only widen. The winners won’t be those with the most experiments; they’ll be the ones who turn experiments into value fastest. According to McKinsey research, organizations are seeing material benefits from AI deployment, with a majority reporting cost reductions and revenue increases in busines units using the technology. The good news? The foundations you need are being built right now by the open-source community. Your job as a leader is recognizing their strategic value and committing to building on them. This means making architectural decisions that prioritize compatibility over proprietary lock-in. It means investing in platforms that combine the innovation velocity of open source with the governance requirements of enterprise deployment. Most importantly, it means understanding that in AI, standardization and innovation aren’t oppositesthey’re partners. Standards create the stable foundation that lets innovation flourish at speed. Start with one diagnostic question: Can your teams share AI models and data pipelines across projects without rebuilding them? If not, you’re building on quicksand. The companies that can answer yes will set the competitive pace for the next decade. Peter Wang is cofounder and chief AI and innovation officer at Anaconda.
Category:
E-Commerce
Is this the beginning of the end for Bitcoin? A Bitcoin (BTC) sell-off on Thursday is sending the cryptocurrency down again today by 3% to $86,410.50 in midday afternoon trading, after a rally had it above $93,000 earlier in the day. It’s now hit its lowest level since April. It’s part of an overall decline in the crypto market that also saw closely watched digital asset XRP (XRP-USD) falling below $2.00 per token during the day, while Ethereum (ETH-USD) shed nearly 3% and was trading at $2,832 in the late afternoon at the time of this writing. Both stock market and Bitcoin investors were briefly riding high on chip maker Nvidia’s third-quarter earnings report, which came in after Wednesday’s market close and beat the street’s expectations. Revenue came in at $57.01 billion, and adjusted earnings per share (EPS) at $1.30, both well above Wall Street analyst estimates. But as the day progressed, stock markets turned negative again, and Nvidia (Nasdaq: NVDA) found its own stock falling down over 2% as investors remain spooked at the prospect of an AI bubble. Some experts, including Wall Street oracle Michael Burry, head of hedge fund Scion Asset Management, predict the bubble resulting from inflated valuations and inflated earnings. Burry, who both predicted and then shorted the 2008 housing bubble, has suggested that massive spending by tech companies like Meta, Nvidia, and OpenAI, which are pouring record-amounts of money into artificial intelligence (AI) while also laying off scores of employees, are overstating their profits by artificially boosting earnings through aggressive accounting practices.
Category:
E-Commerce
NBC recently debuted a new show: Stumble, a comedy about a former cheer coach leading a team of misfits. Some earlier reviews of the show, which premiered on November 7, called it “hilarious” and “full of sharp writing.” But the new sitcom, starring Kristin Chenoweth, Jenn Lyon, and Taran Killam, has audiences chuckling at more than just cheer squad antics. It also makes narcolepsy, a serious neurological disorder, into a recurring joke. Narcolepsy, a chronic condition that can lead to major challenges for sufferers, takes center stage in the show as a character named Madonna (Arianna Davis) has the condition. Due to the disorder, Madonna collapses without warning, sometimes even mid-routine, casting her as a talented-yet-awkward misfit due to the illness. The dramatic and tough-to-miss depiction may be similar to what many who know little about the condition believe it looks like. Per Johns Hopkins Medicine, a person with narcolepsy may experience symptoms like excessive daytime sleepiness, sleep paralysis, disrupted sleep, and sometimes, cataplexy (sudden muscle weakness). It can also lead to memory loss, difficulty concentrating, and depression. But according to experts, it doesn’t look at all the way it often is depicted onscreenand thats problematic for a number of reasons, they say. [Photo: Jocelyn Prescod/NBC] Julie Flygare, the founder of Project Sleep, is a leading narcolepsy spokesperson and award-winning author who also has been diagnosed with the condition. In an online petition to NBC, Flygare wrote that narcolepsy is not what Hollywood so often makes it out to be. “In reality, during an episode of sleepiness, a person with narcolepsy feels sleepy, has an urge to sleep, similar to an urge to use the bathroom,” Flygare wrote. “This sensation of sleepiness generally allows individuals some amount of time (from several to 30 minutes) to find a place to nap. Even in extreme moments, a person with narcolepsy would lay down, sit down, or support their head and body to avoid injury.” While the moments when Madonna collapses onscreen make for a laugh-out-lough character quirk, Flygare tells Fast Company that those very inaccurate depictions of neurological conditions can be dangerous, given they can lead to delays in real diagnoses, “by showing someone experiencing symptoms that are so other-worldly or not biologically possible.” Flygare says that depiction is part of the reason why it can take around eight to 15 years to get an accurate diagnosis. A missed opportunity for Hollywood As a person with narcolepsy, Flygare says NBC is missing an opportunity to show people what the condition actually looks like. “I believe Hollywood is our biggest public health educator, yet it’s not a role they always seem to want to take on,” says Flygare. “If you’re going to use a real condition name that impacts 200,000 Americans and 3 million people worldwide (including many children), I think it’s important to do your homework and work with medical experts and leading community groups.” That’s why Flygare’s petition calls on NBC to take a few steps to do better, like collaborating with the narcolepsy community to represent the real symptoms of narcolepsy, and even provide educational resources for viewers. Fast Company reached out to NBC to ask if it had seen the petition or had plans to address the concerns, but did not hear back by the time of publication. Flygare says that knowledge is massively important when it comes to living a productive life with the condition. According to the online petition, “Once diagnosed, people with narcolepsy manage their symptoms with medical treatments, lifestyle adjustments, naps, and social support.” But currently, she says, widespread misconceptions promoted in the mainstream canand dostand in the way.
Category:
E-Commerce
All news |
||||||||||||||||||
|
||||||||||||||||||