Fortune | FORTUNE 09月26日
数据限制阻碍AI发展,企业需警惕厂商锁定
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文章指出,尽管企业在AI领域投入巨资,但数据碎片化和供应商施加的数据限制正严重阻碍AI潜力的实现。作者以Salesforce对Slack的政策更新为例,说明了数据壁垒如何影响团队生产力和AI的有效性。文章强调,95%的AI项目失败率源于数据孤岛,而AI代理未来将承担更复杂的任务,届时数据鸿沟将更加明显。为应对这一挑战,企业应采取数据整合、应用精简、战略性供应商多元化以及拥抱开放系统等策略,并将厂商锁定和数据限制视为战略风险,从而确保AI能够充分发挥其变革性潜力。

📊 AI的真正潜力依赖于对所有业务数据的全面、统一访问。文章指出,目前大多数企业尚未实现AI的价值,主要原因是数据碎片化和供应商施加的限制,这些限制阻碍了AI获取完成任务所需的全部上下文信息,从而影响了生产力。

🚫 Salesforce对Slack的新限制被视为“数据战”的开端,它切断了实时沟通的上下文,而这对于团队做出更优决策和提高效率至关重要。这种限制与企业投入巨资发展AI的趋势形成鲜明对比,因为AI的有效性恰恰依赖于丰富的数据。

📉 近期MIT研究显示,高达95%的AI项目失败,其根源在于数据碎片化。随着AI代理未来将承担项目管理、预算重组等更复杂的任务,数据访问的障碍将变得更加明显,企业将为此付出沉重代价。

💡 企业应采取“融合”策略,即统一工作流程、数据和系统,以确保AI拥有完整的上下文。这包括审慎整合应用、战略性地实现供应商多元化,以及选择拥有强大API和互操作性的开放系统,从而掌控自身数据,避免被单一供应商的政策变化所掣肘。

⚠️ 企业必须将供应商锁定和数据限制视为最大的战略风险,这不仅是IT问题,更是需要董事会层面的关注。主动预见并应对数据壁垒,而非被动反应,是让AI发挥真正作用的关键。

I’m the founder of a $4 billion software unicorn, and I’ve spent years helping teams unify their data. I know firsthand that even seemingly minor restrictions can disrupt productivity—and completely undermine AI’s potential. 

Pay close attention to what Salesforce did with Slack. What looks like a routine policy update is actually the opening shot in a battle that will decide whether AI can truly revolutionize business. The data wars are coming fast.

Why restricting data is a shot fired

The promise of AI can only be realized with a complete, unified view of all context. For businesses, that means having 100% access to chats, meetings, projects, and files to boost productivity. Most people haven’t seen that value yet because they haven’t been able to bring all the pieces together. Emerging data restrictions will set them back even further.

The new Slack limits cut off the real-time communication context that helps teams make better decisions and get more done. Salesforce knows this, that’s surely why they did it. And while most business leaders don’t care yet, that will change quickly once hard lessons hit.

The irony is painful: Companies are investing millions in AI while at the same time allowing vendors to wall off the data that AI needs to be effective in the first place. 

The clock is ticking

Most enterprises are already struggling with AI value; recent MIT research revealed that as many as 95% of AI projects fail. Look closer and the reason is clear: data fragmentation. Systems can’t access the information they need to be productive, and vendor restrictions will only accelerate the silos that hold AI back.

Too many companies still underestimate the cost of these barriers because AI hasn’t even scratched the surface on potential value. But soon AI agents will begin managing projects, rebalancing budgets, and coordinating teams, and the gaps in context will be glaring.

What enterprises can do

How can companies avoid getting caught in the crossfire? The instinct might be to chase every shiny new AI tool. But that path leads to sprawl, not intelligence. The better answer is convergence: unifying workflows, data, and systems so AI can operate with full context.

That starts with intentional consolidation: cutting back on fragmented apps so AI can act seamlessly across the organization. It also means diversifying strategically so no single vendor’s policy change can derail your roadmap. And it requires betting on open systems with strong APIs and interoperability, so you remain in control of your data.

Above all, enterprises need to treat vendor lock-in and data restrictions as their greatest strategic risk. This isn’t just an IT problem; it’s a board-level issue.

The next enterprise data war is underway, and the restrictions we’ve seen so far are just the beginning. If business leaders anticipate the next walls, rather than wait to react, they will help AI reach its true potential. Otherwise, enterprise AI will be lost to the data war before it has a chance to win.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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相关标签

AI 数据限制 供应商锁定 数据碎片化 企业战略 AI potential Data restrictions Vendor lock-in Data fragmentation Business strategy
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