Coté

On average, ChatGPT Enterprise users attribute 40–60 minutes of time saved per active day to their use of AI, with data science, engineering, and communications workers saving more than average (60–80 minutes per day).

That’s the headline grabbing piece from the recent ChatGPT for work study. The theory take-away from that is that the more you use ChatGPT, the more productive you are.

Also, the current use revolves around chat and coding. The use cases are chat applications with many of them being better search over inventory, how to for projects Lowes for that), and then searching over complex regulations and legal text. That is: sloppy search and text based content. This is fine! It gives you some strategic targeting: if you want to do an AI project, find text-heavy tasks to improve.

Doing more analysis is an area for growth. Even “Frontier firms (95th percentile)"¹ have about 20% headroom to work with there.

Of monthly active users, 19% have never used data analysis, 14% have never used reasoning, and 12% have never used search. Among daily active users, those shares drop to 3%, 1%, and 1%, respectively.

Also, as someone who likes to just capitalize the first letter of titles, dig them doing the same for the report title. I don’t know why I like this style. I suspect it’s because I like to give a winking “I know I’m messing with MLA-whatever” to people who notice.

Highlights

Here’s ChatGPT 5.1’s analysis of the report, and here are my highlights:

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OpenAI’s more than 1 million business customers provides a distinctive view into this shift

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Since November 2024, weekly Enterprise messages have grown approximately 8x in aggregate, with the average worker sending 30% more messages.

approximately 20% of all Enterprise messages were processed via a Custom GPT or Project

BBVA regularly uses more than 4,000 GPTs, indicating that AI-driven workflows are increasingly implemented as persistent tools embedded in daily operations

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Average reasoning token consumption per organization has increased by approximately 320x in the past 12 months, suggesting that more intelligent models are being systematically integrated into expanding products and services.

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Seventy-five percent of surveyed workers report that using AI at work has improved either the speed or quality of their output. On average, ChatGPT Enterprise users attribute 40–60 minutes of time saved per active day to their use of AI, with data science, engineering, and communications workers saving more than average (60–80 minutes per day). Time saved per message varies by function: accounting and finance users report the largest benefits followed by analytics, communicationsand engineering.

87% of IT workers report faster IT issue resolution 85% of marketing and product users report faster campaign execution 75% of HR professionals report improved employee engagement 73% of engineers report faster code delivery

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In absolute terms, ChatGPT Enterprise customers are most concentrated today in professional services, finance, and technology, sectors that were early adopters and continue to lead in their scale of AI usage. Healthcare and manufacturing started from a much smaller base but are now among the fastest-growing sectors, rapidly closing the gap

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To understand this growing divide more deeply, it is useful to compare frontier workers (defined as those in the 95th percentile of adoption intensity) to the median worker. Frontier workers generate 6x more messages than the median worker. Even among those who work in data analytics, frontier workers use the data-analysis tool 16x more than the median.

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A 2025 Boston Consulting Group (BCG) study found that over the past three years, AI leaders achieved 1.7x revenue growth, 3.6x greater total shareholder returnand 1.6x EBIT margin. They also outperformed on non- financial measures such as patent output and employee satisfaction, linking AI maturity to both financial and organizational strength. While this evidence is still earlyit suggests that AI adoption is correlated with improved financial performance and organizational outcomes

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Mylow is available on Lowes.com and in the award-winning Lowe’s mobile app. When customers engage with Mylow during their online visits, t he conversion rate more t han doublesMylow Companion is deployed in 100% of stores and answers hundreds of thousands of associate questions each week. Lowe’s is seeing customesatisfac tion scores increase 200 basis points when associates use Mylow Companion to help customers shopping in the aisle.

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In Mexico, BBVA must perform a legal check (also known as bastanteo) to confirm that a company representative has the authority to sign and act on behalf of the company before key transactions can proceed (e.g., opening accounts, signing contracts, issuing credit). Historically, this process relied on a specialist legal team responding to repetitive branch queries, creating delays, bottlenecks, and high demand for scarce legal capacity

BBVA built a generative AI chatbot that provides instant access to standardized, pre-validated legal FAQs and documentation guidance for common signatory-authority questions. The content was developed and reviewed by BBVA’s Legal Services team, reducing manual handling of daily inquiries and making approved legal guidance consistently available.

automates more t han 9,000 queries annually and has enabled BBVA to redeploy the equivalent of 3 FTE’s toward producing over 11,000 bastanteos per year, delivering 26% of t he Legal Services division’s annual savings KPI.

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Oscar developed a pair of member-facing chatbots to answer member benefits, costs and general health questions, on-demand and in realtime. Unlike general-purpose AI chatbots, these are integrated with Oscar systems and dataallowing them to draw from medical recordsclaims, and customer service interactions to personalize responses. Their chatbots can also assist with common tasks, including finding in- network doctors and refilling prescriptions

Their platform answers 58% of benefits questions instant ly and is able to handle 39% of benefits messages wit hout any human escalation. Today, they now have t he foundation for future capabilities, including appointment booking, voice interactions, and condition- specific management

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teams can spend more time pressure-testing trade-offs and making higher-quality decisions earlier in the TPP creation processModerna reports that a core analytical step in this process has been reduced from weeks to hours in some cases, and believes that each day gained in early TPP planning can help t he company deliver for patients more quickly.