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The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. In most companies, generative AI is full of contradictions. On one hand, 67% of business leaders predict that GenAI will transform their organization in 2025, according to a KPMG survey. On the other, just 36% of executives say their company has a well-defined vision for AI. The core issue: Nearly 2.5 years after ChatGPTs introduction, most companies are still stuck in what I call prototype purgatory. Theyve bought and attempted to adopt off-the-shelf GenAI tools and developed pet project prototypes. But despite big promises from vendors or demos, theyve generated little more than incremental valuefar from the AI revolution that was promised. I see this constantly when talking to enterprise execs. Theyre frustrated. And the data bears this out, too. Recently at A.Team, we surveyed 250 senior tech leaders responsible for AI initiatives at their companies and found that only 36% of organizations have successfully deployed AI to production. (The majority of respondents came from enterprise companies.) The rest remain caught in an endless cycle of proof of concept projects and pilotsor havent gotten started at all. Its not hard to see why this is happening. The space is moving at whiplash speed, disrupting itself weekly. Its impossible to upskill your full-time employees on all things AI, which makes it difficult to make crucial technical decisions. At this stage of the game, locking into one platform is highly premature. But amidst these struggles, some companies are breaking through. The most fascinating part of our research was what AI leaders do differently than AI laggardsand it’s not what you might expect. The talent equation: Blended teams win The most striking finding from our research was that organizations that use blended teamsa model that integrates specialized freelance talent with full-time employeesare twice as likely to reach advanced stages of AI innovation. These companies find that this model helps alleviate the AI talent crisis that most companies are experiencing. Ninety-four percent of the tech leaders we surveyed said talent constraints are their primary barrier to innovation, with 85% having delayed critical AI initiatives due to talent shortages. [Graphic: A.Team 2025 State of AI Innovation Report] Theyre finding that traditional hiring can’t solve this problem89% said the traditional recruitment model is broken. Two-thirds of respondents said it takes at least 4 months to hire top engineering talent. These protracted hiring cycles are particularly problematic in AI development, where technology evolves at a breakneck pacerendering traditional workforce planning obsolete as new possibilities emerge and roadmaps change. In 2025, its hard to know the exact skills you will need in six months. Successful organizations that have escaped prototype purgatory have found a different approach with blended teams, and they report stunning improvements from incorporating freelance or fractional talent into their teams: 99% enhanced innovation capability 98% improved project success rates 96% accelerated speed of delivery [Graphic: A.Team 2025 State of AI Innovation Report] Build versus buy: A third way may be the answer For the past 2.5 years, Ive watched build vs. buy become one of the dominant discussions in executive boardrooms. While off-the-shelf AI tools like ChatGPT Enterprise and GitHub Copilot deliver obvious value, it now looks like the build approach is winning. Among companies that have successfully deployed AI to production, 93% say building custom solutions delivers more value than off-the-shelf tools. But that might not be the whole story. The most successful organizations aren’t building everything from scratch, however. They’re taking an “assemble” approachleveraging the explosion of open-source building blocks (we’ve seen a 60% boom in open-source GenAI contributions on GitHub in the past year alone) while customizing solutions for their specific needs. The assemble model is built for speed; integrated components can be easily updated or swapped out, which is crucial when the shelf life for state of the art AI is shorter than a jar of organic marinara sauce. It allows you to keep the most crucial part in place: developing these GenAI components into existing workflows that empower your employees and customers, giving you a true data moat. When you look at where the senior tech leaders in our study are making their investments, it reflects this kind of foundational approach: 50% are increasing spending on AI safety and monitoring tools 49% are prioritizing AI development platforms 41% are investing in data infrastructure [Graphic: A.Team 2025 State of AI Innovation Report] Theyre not investing in the models themselves but in everything needed to turn them into production-grade systems: data pipelines, testing frameworks, monitoring tools, and integration capabilities. Want ROI? Start with AI-powered automation One of the biggest questions about generative AI is: Are companies seeing ROI? And if so, where? We got the answer by asking AI leaders their expected ROI timeline across four key areas of focus: Custom AI product development AI-powered automation Customer-facing AI features Internal AI tools [Graphic: A.Team 2025 State of AI Innovation Report] Not surprisingly, AI-powered automation had the highest ROI rate already achieved, at 14%. Surprisingly, customer AI product development came in second, at 12%. Perhaps most surprisingly, most leaders expect to see ROI across every use case this year. [Graphic: A.Team 2025 State of AI Innovation Report] Our research suggests that a significant portion of that investment will go into custom AI product development and customer-facing AI features. While the dominant AI discussion has focused on cost cutting, more respondents said they were focusing on generating ROI through revenue generation (46%) over cost cutting (30%). [Graphic: A.Team 2025 State of AI Innovation Report] Its been said a million times, but it bears repeating: This will be a critical year for AI development inside most companies, with many Fortune 500 players at risk of falling behind. And while there have been whispers of a trough of disillusionment, tech leaders remain bullish: 96% plan to increase AI investments in 2025, with over half planning increases of 51% or more. The challenge isn’t a lack of ambitionit’s execution. Most AI initiatives fail at the last milenot because the technology isn’t viable but because organizations underestimate the complexity of productizing AI and dont have the right talent with the right mindset inside their organization. Companies that embrace these challenges and think differently will escape prototype purgatory. The rest may find themselves in limbo for years to come. Raphael Ouzan is cofounder and CEO of A.Team.
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The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. AI is no longer a side project. It now sits at the heart of how companies grow, compete, and make decisions. Yet many leaders still struggle to separate hype from value and wonder how to invest wisely without wasting time or resources. A key challenge lies at the top: a lack of AI literacy among executive teams. Research covering nearly 7,000 executives across 645 firms shows a clear pattern: Companies led by AI-literate teams are more likely to identify where AI can create value and act on it. Rethink responsibility Many executive teams still treat AI as a tech issuesomething for IT or data teams to figure out. But AI is a leadership issue. It belongs on the agenda of every CMO, CFO, CHRO, and CEO. More importantly, its not about a single role. Its about the collective literacy of the top team. Research rooted in upper echelons theory confirms this: AI-literate leadership teams are more likely to build strategic visions that integrate AI and translate that vision into tangible action, from capability building to execution. So appointing a chief AI officer (CAIO) without a broader shift in understanding wont be enough if the rest of the executive team cant grasp the art of the possible and actively shape the direction AI takes in the business. As one leader put it, Hiring a CAIO is like hiring a pilot for a crew that doesnt believe in flying. The cost of poor AI literacy As MIT Sloan Management Review points out, The overall low literacy rate is a problem for todays executives, who will face more and more processes or products that claim to be powered by AI. Making informed decisions about these AI tools requires leaders to understand how they align with strategy and operationsand to know which questions matter. Without a clear understanding of what AI can door where it breaks downexecutive teams fall into familiar traps: Buying into hype they cant evaluate Investing in tools without understanding their fit Setting expectations AI (or teams) cant meet Focusing on flashy pilots instead of long-term capability building The result is often pilot purgatory, or initiatives that stall. Missed opportunities. And in some cases, the slow decline of companies that once dismissed digital as a passing trend. From confusion to competence: The AI literacy ladder To help executive teams assess where they stand and what to do next, we use a five-step model: the AI literacy ladder. Think of it as a five-step staircase representing the typical journey executive teams take as they build fluency in AI, moving from scattered perspectives to a shared understanding and strategic alignment: Confusion: AI feels like a buzzword. Theres no shared understanding or agreement on relevance. Curiosity: Interest is rising, but views are fragmented. Theres little clarity on where to begin. Comprehension: The team develops a common language around AIs potential and risks. Confidence: Teams ask sharper questions and align on use cases that matter. Competence: AI becomes part of strategic planning and decision making. [Graphic: Philippe De Ridder, CEO at BOI] Why AI-literate teams outperform When executive teams build AI fluency together, they unlock a dynamic we call the AI fluency flywheel: Teams that move beyond confusion and start learning together gain momentum. They stop treating AI as an isolated initiative and start treating it as a core strategic capability. Over time, this fluency allows them not just to respond, but to lead. [Graphic: Philippe De Ridder, CEO at BOI] So where do executive teams learn AI? Despite the flood of AI training programs, few are built for leadership teams. Most are either too technical, too long, or designed for individuals. Whats missing is a shared learning experience. One that helps leadership teams: Understand whats possible and whats not Cut through noise and inflated promises Align on use cases worth pursuing Build a common language across roles Closing the gap starts at the top As AI reshapes how organizations operate and compete, executive teams cant afford to stay on the sidelines. The journey toward AI maturity isnt about becoming technical experts. Its about building shared fluency across the leaership team. It starts with honest reflection: Where are we on the AI literacy ladder? What will it take to move forward, together? The first step is simple but powerful: Make space for the conversation. Invite different perspectives. Commit to learning together. Teams that do this wont just keep up. Theyll help shape whats next. Philippe De Ridder is founder and CEO of BOI (Board of Innovation) and AUTONOMOUS. Laura Stevens, PhD is managing director, Data & AI at BOI.
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The Lyrid meteor shower is one of the most well-known stellar displays, occurring once a year in April. It’s also one of the oldest meteor showers that we know of, with records dating back to 687 BCE from Chinese astronomers. Unlike many meteor showers, the Lyrids are relatively short: In 2025, the event runs a little more than a week, from April 17 to April 26. It will peak in the nighttime hours of April 21 to 22. Typically, you can expect to see 10 to 20 meteors per hour at the peak, though the Lyrids have been known to outperform and deliver up to 100 meteors per hour. If you’d like to catch the show this year, here’s what to know about the 2025 Lyrids meteor shower peak. What’s the best time to see the Lyrid meteor shower peak? The Lyrids will be most visible after midnight and before the dawn hours. That’s as the moon will be relatively dim in its waning crescent phase and wont rise until the early morning hours, around 3 a.m. local time. It’s best to target this window of time between midnight and 3 a.m. Where should I look to see the Lyrids? The Lyrids are viewable from the Northern Hemisphere. To see them, find the bright star Vega, which is a bluish white star that will rise in the northeast in the evening hours. Its one of the brightest stars in the night sky and is easily visible, even from light polluted areas (aka, excessive artificial lighting). Vega is located in the constellation Lyra. Lyra is the radiant of the meteor shower, which means that the meteors will appear to originate from this constellation (hence the name, the Lyrids). To get the best view of the meteor shower, try to avoid areas with lots of light pollution. What causes the meteor shower? The Lyrid meteor shower may look to us like it originates from the constellation Lyra, but it’s actually the product of Earth passing through the trail of the comet C/1861 G1 Thatcher, which takes 415 years to orbit the sun. As the comet proceeds through the solar system, it leaves dust and debris in its wake. When Earth intersects this trail, thats what produces the meteor shower.
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