8 Компаний, доказывающих, что Искусственный интеллект может обеспечить реальную рентабельность инвестиций

Большинство компаний инвестируют в ИИ. Гораздо меньшее число компаний могут доказать, что это действительно приносит свои плоды.

Исследование Массачусетского технологического института показало, что только пять процентов проектов, связанных с созданием ИИ, приносят реальную отдачу. Gartner прогнозирует, что к 2027 году 40% проектов будут отменены из-за неясной ценности для бизнеса, в то время как BCG утверждает, что только пять процентов компаний по всему миру достигли того уровня, когда они постоянно получают существенную отдачу от ИИ.

Эти цифры рисуют довольно мрачную картину. Тем не менее, некоторые компании уже используют искусственный интеллект для сокращения расходов, увеличения доходов, улучшения качества обслуживания клиентов и решения проблем, которые было бы чрезвычайно сложно решить другими способами.

Так что же они делают по-другому?

От Walmart и IKEA до Bank of America и UPS - вот восемь компаний, создающих ощутимую ценность с помощью ИИ, и те уроки, которые их успех дает всем остальным.

Американский гигант розничной торговли внедрил инструмент искусственного интеллекта для своей команды разработчиков в 2024 году. Целью компании было повышение качества каталогов продукции; внутренние базы данных использовались для всего: от помощи клиентам в поиске доступных товаров до управления логистикой и запасами. Это включалоcreating or updating 850 million data points. Completing this manually would have involved expanding the size of the team tenfold. Thanks to their AI, the task was completed on time, delivering improved customer and employee experience, in-store and online.

UK energy supplier Octopus has rolled out a number of generative AI initiatives through its Kraken platform. They include Arlo, an autonomous agentic platform built for handling routine customer service inquiries, credited with lifting customer satisfaction rates from 73 to 76 percent. The success of Arlo and other genAI projects prompted the company to spin out Kraken as an independent entity, providing AI services to other energy companies and credited with generating £422 million in revenue in 2025.

Latin America’s largest e-commerce provider used tools built around OpenAI’s GPT-4 to screen online product listings for potential fraud and counterfeiting. Its system checks every product listed against more than 5,000 variables to determine their authenticity in under a second, protecting customers from online scams or buying fake products. This has enabled it to achieve fraud detection accuracy of almost 99 percent. Another genAI system enabled it to increase the speed at which it can catalog products by 100x over a two-year period.

The largest food delivery platform in the US worked with AWS to build a voice-activated solutions portal for customers, merchants and delivery drivers. A specific problem they aimed to solve was the need for drivers to access help while on the job without having to stop making deliveries. This was achieved by cutting the number of calls requiring transfer to human agents by 49 percent. The Customer Connect system now handles hundreds of thousands of calls every day through a self-service portal credited with achieving $3 million in year-on-year operational savings.

The US banking giant was one of the first businesses to launch a dedicated customer service chatbot, way back in 2018. Erica helps customers manage their accounts and get quick answers to questions instead of being left waiting on hold. In 2025, it surpassed three billion client interactions and was credited with cutting the number of service desk calls by 50 percent, meaning BoA’s human agents could spend their time on trickier or sensitive issues requiring an expert human touch.

When the Swedish furniture designer deployed its own AI customer assistant, Billie, it was able to autonomously resolve 47 percent of incoming queries. However, rather than downsizing its human workforce, it retrained 8,500 customer service agents as remote interior design consultants. By offering this new service to customers, it was able to generate $1.4 billion in new sales.

Radisson used AI to create and optimize digital advertising for its hotels, automatically adapting images and messaging for different languages, markets and cultural preferences. This cut time spent on creating ad material by 50 percent while also driving a 22 percent increase in ad-driven revenue and improved ROI on ad spend by 35 percent.

Changes to global tariff regimes in 2025, specifically around US imports, have forced many delivery and logistics companies to adopt new approaches in order to avoid long delays. UPS’s response to this challenge was to integrate agentic AI into its brokerage and documentation systems. In June 2026, it announced it had increased the number of small packages clearing customs in one day without manual intervention from just 21 percent to 97 percent, delivering solid customer experience benefits.

The common thread running through these success stories is surprisingly simple: they started with a business problem, not with AI.

Walmart needed to improve hundreds of millions of product data points. DoorDash wanted to reduce the burden on customer support. Mercado Libre needed to detect fraud faster. UPS had to respond to a sudden increase in customs complexity.

In each case, AI was applied to a clearly defined challenge where success could be measured in revenue, cost savings, productivity, customer satisfaction or speed.

That is an important lesson at a time when so many AI initiatives are struggling to demonstrate value. The companies getting the strongest returns are not asking, "Where can we use AI?" They are asking, "What important business problems could AI help us solve better?"

For business leaders, that may be the most useful place to start. Find the problem, define what success looks like, and then decide whether AI is the best tool for solving it. That approach will dramatically improve the odds that your AI investments end up among the success stories rather than the 95 percent that fail to deliver.