Look, I've been tracking enterprise AI adoption pretty obsessively for the past couple of years, and I have to tell you — what's happening with ChatGPT Enterprise right now is absolutely wild. We're not talking about a few tech startups experimenting with AI anymore. We're talking about 92% of Fortune 500 companies actively using this technology, with over 1 million businesses worldwide paying for OpenAI's services.
That's not hype. That's a fundamental shift in how work gets done.
I spent the last few weeks diving deep into case studies, speaking with industry analysts, and researching what's actually working (and what's not) for companies deploying ChatGPT Enterprise. What I found surprised me — not just the scale of adoption, but the creative ways these organizations are using AI to solve real business problems.
In this guide, I'm going to walk you through everything: the companies leading the charge, the specific use cases that are driving ROI, the security considerations that matter, and the practical lessons you can apply whether you're at a Fortune 100 giant or a 50-person startup.
Let's get into it.
What Is ChatGPT Enterprise and Why Does It Matter?
Before we dive into the case studies, let's make sure we're on the same page about what ChatGPT Enterprise actually is — because it's not just a fancy version of the chatbot you might use at home.
OpenAI launched ChatGPT Enterprise in August 2023, and it was specifically designed to address the concerns that made corporate IT departments nervous about consumer AI tools. Think about it: when employees started using ChatGPT at work, security teams everywhere had mild panic attacks. Where was that proprietary data going? Was it being used to train AI models? Could competitors access it?
ChatGPT Enterprise was OpenAI's answer to those concerns. Here's what makes it different:
Your data stays your data. OpenAI explicitly commits to not training its models on your business data, conversations, or usage patterns. This was a huge deal for companies worried about intellectual property.
The platform offers enterprise-grade security. We're talking SOC 2 Type 2 compliance, data encryption at rest (AES-256) and in transit (TLS 1.2+), single sign-on integration, and the ability to choose data residency in regions including the US, Europe, UK, Japan, and more.
There are no usage caps. Unlike the consumer version where you might hit limits, Enterprise customers get unlimited access to GPT-4 (and now GPT-5) at faster speeds.
Organizations can build custom GPTs — specialized AI assistants tailored to their specific workflows, trained on their own documentation and processes.
Advanced data analysis is included. The ability to upload files, analyze data, and generate insights — previously known as Code Interpreter — comes without limits.
The pricing isn't publicly disclosed (you have to talk to their sales team), but from what I've gathered through various sources, it runs somewhere in the range of $30-60 per user per month, depending on scale and specific requirements. For context, ChatGPT Enterprise seats have grown 9x year-over-year, so clearly companies are finding the investment worthwhile.
The Numbers That Matter: ChatGPT Enterprise by the Stats
Let me share some numbers that will give you a sense of the scale we're talking about.
As of late 2025, OpenAI reports over 1 million business customers actively using their platform. That includes organizations using ChatGPT Enterprise, ChatGPT Team, and their API services. ChatGPT Enterprise specifically has seen 9x year-over-year growth in seats.
Enterprise users report saving 40-60 minutes per day on average. That's not a typo. When you multiply that across thousands of employees over a year, the productivity gains are staggering.
According to a Harvard/MIT study, consultants using GPT-4 completed tasks 12.2% faster and produced 40% higher quality work than those without AI assistance. The productivity gains are real and measurable.
Companies report 75% positive ROI from AI tools, with early adopters seeing $3.70 in value for every dollar invested. Top performers are achieving returns as high as $10.30 per dollar invested.
The message from these numbers is clear: ChatGPT Enterprise isn't just an experiment anymore. It's becoming infrastructure.
Wall Street's AI Revolution: How Financial Giants Are Using ChatGPT Enterprise
If there's one industry that's gone all-in on ChatGPT Enterprise, it's financial services. And honestly, it makes sense. Wall Street firms deal with massive amounts of data, research, and documentation — exactly the kind of work where AI can have the biggest impact.
Morgan Stanley: The First Major Wall Street GPT Deployment
Morgan Stanley made waves when they became one of the first major financial institutions to deploy a ChatGPT-based solution at scale. And I mean serious scale — we're talking about a tool used by their roughly 15,000 financial advisors.
Here's what they built: An internal assistant called "AI @ Morgan Stanley Assistant" that gives financial advisors instant access to the bank's vast intellectual capital — approximately 100,000 research reports and documents.
Before this tool, advisors had to manually search through mountains of research to find relevant information for client conversations. Now? They just ask questions in natural language and get answers in seconds.
Jeff McMillan, Morgan Stanley's head of analytics, data and innovation, explained it this way: people want to be as knowledgeable as the smartest person in the firm. This assistant is like having their chief strategy officer sitting next to you when you're on the phone with a client.
The adoption numbers are remarkable: 98% of Morgan Stanley's financial advisor teams have adopted the chatbot.
But they didn't stop there. Morgan Stanley then rolled out "Debrief" — an AI assistant that sits in on client Zoom meetings (with consent), takes detailed notes, creates meeting summaries, and drafts follow-up emails. One advisor involved in the pilot said the program saves 30 minutes of work per meeting. When you're doing four to six meetings a day, that's significant.
Don Whitehead, a Houston-based advisor who tested Debrief, shared his experience: as a financial adviser doing four, five or six meetings a day, having the note-taking service built in through AI means you can really be invested in the meeting. You're actually a lot more present.
Goldman Sachs: Building an AI That Thinks Like a Goldman Employee
Goldman Sachs took a slightly different approach. Rather than just deploying ChatGPT out of the box, they're building an AI assistant that's designed to absorb the firm's culture and way of thinking.
Their program, called "GS AI assistant," has been rolled out to about 10,000 employees so far, with plans to expand to all knowledge workers. What makes their approach interesting is how they're training the AI to act like an experienced Goldman employee.
Marco Argenti, Goldman's Chief Information Officer, explained that for the AI to have a very specific identity that reflects the tenets, the values, the knowledge and the way of thinking of the firm is extremely important.
What does that mean in practice? Just as an experienced Goldman employee would know to double-check their work with multiple data sources or use a specific algorithm for a calculation, the AI is being trained to apply those same standards.
Goldman has also been using generative AI to help developers write code — with some developers able to write up to 40% of their code automatically using AI assistance.
Klarna: The AI-First Company That's Reshaping Fintech
If there's one company that's gone truly all-in on ChatGPT Enterprise, it's Klarna. The Swedish fintech company has become almost a poster child for aggressive AI adoption, and their results are worth paying attention to.
When ChatGPT launched in November 2022, Klarna's CEO Sebastian Siemiatkowski immediately reached out to OpenAI CEO Sam Altman with a promise: he wanted Klarna to be OpenAI's "best customer." That wasn't just talk.
Klarna was the first European company and the first fintech globally to launch a ChatGPT plugin. Then they became one of the earliest adopters of ChatGPT Enterprise, making it available to all 5,000 employees.
The adoption numbers are staggering: 90% of Klarna employees now use generative AI tools daily. Let me repeat that — 90% daily adoption. That's not just a few power users; that's an entire organization transformed.
What's particularly interesting is where the adoption is happening. You might expect engineering teams to lead, but the highest adoption rates are in non-technical departments: Communications (93%), Marketing (88%), and Legal (86%).
Klarna built an internal AI assistant called "Kiki" that has handled over 250,000 employee inquiries — about 2,000 per day. Their legal team uses ChatGPT Enterprise to create first drafts of contracts, dramatically reducing the time needed for document preparation.
But the customer-facing results are even more dramatic. Klarna's AI assistant has had 2.3 million customer service conversations in its first month — handling two-thirds of all customer service chats. The company estimates it's doing the equivalent work of 700 full-time customer service agents.
Customer resolution time dropped from 11 minutes to less than 2 minutes. Repeat inquiries dropped by 25%. And Klarna estimates the AI will drive $40 million in profit improvement for 2024.
Siemiatkowski has said they push everyone to test, test, test and explore. As Klarna continues to discover applications for OpenAI's tech, there's the potential to take the business to new heights.
Moderna: How a Biotech Giant Built 750+ Custom GPTs
Moderna's approach to ChatGPT Enterprise might be the most sophisticated I've seen. The pharmaceutical company, best known for their COVID-19 vaccine, has woven AI into nearly every aspect of their operations.
The numbers tell the story: Moderna now has 750 custom GPTs deployed across the company, with 40% of weekly active users having created their own GPTs. On average, each user has 120 ChatGPT Enterprise conversations per week.
Stephane Bancel, Moderna's CEO, explained that they're looking at every business process — from legal, to research, to manufacturing, to commercial — and thinking about how to redesign them with AI.
What I find fascinating about Moderna's approach is how they started. Before ChatGPT Enterprise, they built their own internal chatbot called mChat using OpenAI's API. Over 80% of employees adopted it. When it came time to decide whether to continue developing mChat or switch to ChatGPT Enterprise, they did extensive user testing comparing both options.
ChatGPT Enterprise won — and the migration was a success because employees were already comfortable with AI interaction.
Here are some of the specific GPTs Moderna has built:
The Dose ID GPT analyzes clinical trial data to help verify the optimal vaccine dose selected by clinical study teams. It applies standard dose selection criteria, provides rationale, references sources, and generates informative charts. This kind of specialized analysis would typically require significant expert time.
The Contract Companion GPT allows any function to get clear, readable summaries of contracts — making legal documents accessible to non-lawyers.
The Policy Bot GPT helps employees get quick answers about internal policies without searching through hundreds of documents.
The Earnings Call GPT prepares slides for quarterly earnings calls.
The Communications GPT converts complex biotech terminology into approachable language for investor communications.
Moderna's legal team has 100% adoption of ChatGPT Enterprise — the highest of any department.
Brad Miller, Moderna's Chief Information Officer, shared a powerful insight: 90% of companies want to do GenAI, but only 10% of them are successful. The reason they fail is because they haven't built the mechanisms of actually transforming the workforce to adopt new technology and new capabilities. The lesson? Technology alone isn't enough — you need a strategy for driving adoption.
Canva: Using AI to Build AI
Canva, the design platform with over 100 million users, received an invitation from OpenAI to test ChatGPT Enterprise in August 2023. Their experience offers useful insights for how creative and technology companies can leverage the platform.
Before Enterprise, Canva employees used the basic version of ChatGPT for common tasks: writing first drafts of marketing copy, brainstorming strategy documents, troubleshooting coding errors. Some employees reported that using the chatbot saved hours of time on certain tasks.
ChatGPT Enterprise amplified these gains with faster, more useful responses. Data analysts found they could run analyses more quickly. Teams used it to summarize long documents. Software engineers wrote higher-quality code faster.
But here's what I find most interesting about Canva's experience: they used ChatGPT Enterprise to build their own AI products.
Danny Wu, Canva's head of AI products, noted that the most surprising use case was actually using AI to help them build AI.
One concrete example: Canva's text-to-image generator is trained to block prompts that ask the AI to generate images in the style of specific artists. But occasionally, the tool would incorrectly block words that were part of an artist's name — like the word "war" (because "Andy Warhol" is blocked).
Engineers used ChatGPT Enterprise to review Canva's blocklist and flag artist names that were similar to dictionary words for human review. A task that would have taken a full day of engineering time took 10 minutes with ChatGPT.
What would typically take a developer several hours — generating code that connects different parts of Canva's tools together — could now take just one hour with AI assistance.
One important note from Canva: getting employees up to speed on how to use ChatGPT was a challenge. Before they developed an internal AI training program, some employees didn't know how to craft effective prompts. Teaching AI skills has been critical to getting value from the investment. Wu emphasized that these tools are not being used to replace anyone. They're used to help people.
BBVA: 2,900 Custom GPTs and 80% Time Savings
Spanish banking giant BBVA provides one of the most impressive case studies of ChatGPT Enterprise deployment at scale.
The bank started with 3,000 ChatGPT Enterprise licenses six months ago (as of late 2024), with plans to expand further in 2025. In that time, employees have created over 2,900 custom GPTs for specific tasks including legal, marketing, and finance functions.
The results? 80% of users report saving more than two hours of work weekly.
Think about that math for a moment: 3,000 employees each saving two hours per week. That's 6,000 hours saved weekly, or 312,000 hours per year. Even at conservative average hourly costs, that's massive value.
What made BBVA successful is that they empowered employees to identify their own use cases and build solutions. Rather than a top-down mandate about how to use AI, they gave people the tools and let creativity flourish. Nearly 2,900 GPTs didn't come from a central AI team — they came from employees solving their own problems.
PwC: From Customer to Reseller
PwC's relationship with ChatGPT Enterprise is unique: they've gone from being an early customer to becoming OpenAI's first resale partner.
The consulting giant is now distributing ChatGPT Enterprise to 100,000 employees across their U.S., U.K., and Middle Eastern offices. But they're also reselling it to their clients as part of broader AI transformation services.
Richard Hasslacher, OpenAI's global head of alliances and partnerships, explained that PwC is the first partner they are leaning into in this way. PwC becomes their largest customer, but they're also their first partner who's going to be reselling ChatGPT Enterprise.
This move aligns with PwC's broader push into AI, including a planned $1 billion investment over three years to expand and scale their AI capabilities.
Internal training engagement has been high, with a 90% engagement rate on PwC's AI training tools. This suggests strong internal adoption and the potential for significant impact on client services.
The Other Players: Cisco, T-Mobile, and Beyond
The companies I've covered so far are just the beginning. OpenAI lists dozens of major enterprises as ChatGPT Enterprise customers.
Cisco has rolled out OpenAI's Codex into their engineering workflows, reportedly cutting code review times by 50% and shrinking project timelines from weeks to days.
T-Mobile signed what was reported as a $100 million contract with OpenAI — one of the largest enterprise AI deals disclosed publicly.
Lowe's, Target, Booking.com, and Amgen are all listed as major enterprise customers.
Block (Square), Carlyle, The Estée Lauder Companies, and Zapier were among the earliest ChatGPT Enterprise adopters.
The breadth of industries is striking: financial services, retail, healthcare, technology, hospitality, manufacturing. This isn't an AI solution for one vertical — it's genuinely horizontal infrastructure.
Use Cases That Actually Work: Where Companies Are Seeing Real ROI
Based on my research, here are the use cases where companies are seeing the most tangible returns from ChatGPT Enterprise.
Knowledge Management and Information Retrieval
This is probably the single biggest use case. Companies have vast repositories of documents, research, policies, and institutional knowledge. Finding the right information at the right time has always been a challenge.
Morgan Stanley's assistant accessing 100,000 research documents is a perfect example. Moderna's Policy Bot helping employees find answers without searching through hundreds of documents is another. These aren't revolutionary concepts — they're just being executed at a level of quality that wasn't possible before.
The key insight: ChatGPT Enterprise shines when you can give it access to your organization's unique knowledge and let employees query it conversationally.
Content Creation and Communications
From drafting emails to creating presentations, content creation is where many employees first experience value from ChatGPT.
Klarna's Communications team has 93% adoption. Moderna uses GPTs to prepare earnings call slides and convert complex biotech terminology into investor-friendly language. Canva employees draft marketing copy and brainstorm strategy documents.
The pattern I see: AI doesn't replace human judgment about what to communicate, but it dramatically accelerates the process of turning ideas into polished content.
Software Development and Code Generation
Developers were early adopters of AI assistance, and the productivity gains are well-documented. Goldman Sachs developers are writing up to 40% of their code automatically. Cisco cut code review times by 50%.
Canva's example is particularly instructive: connecting complex bits of code that would take hours manually can now be done in a fraction of the time.
The caveat here is that AI-generated code still requires human review. These aren't autonomous systems — they're productivity multipliers for experienced developers.
Customer Service and Support
Klarna's AI assistant handling two-thirds of customer service chats is the most dramatic example. Resolution time dropped from 11 minutes to under 2 minutes.
But there are important caveats. Klarna's bot is carefully designed with guardrails. It hands off to humans when conversations go out of bounds. And a significant portion of its value comes from handling simple, repetitive queries — like telling customers that issues need to be resolved directly with merchants.
Customer service AI works best for high-volume, well-defined scenarios where you can build appropriate guardrails.
Legal and Contract Analysis
Legal departments are emerging as surprising power users of ChatGPT Enterprise. Klarna's legal team has 86% adoption. Moderna's has 100% adoption.
Common use cases include creating first drafts of contracts, summarizing complex legal documents for non-lawyers, and quickly answering questions about policies and procedures.
This makes sense when you think about it: legal work is highly text-intensive and often involves finding and synthesizing information from large document sets — exactly what large language models excel at.
Data Analysis
ChatGPT Enterprise's Advanced Data Analysis capabilities (the ability to upload files and analyze them) are being used across industries.
Moderna's Dose ID GPT analyzing clinical trial data is a sophisticated example. But simpler use cases are equally valuable: Canva's data analysts running analyses more quickly, financial analysts crunching market data, marketers analyzing survey results.
The democratization of data analysis is real. Tasks that once required specialized technical skills can now be done by anyone who can describe what they want in natural language.
Security and Compliance: What IT Leaders Need to Know
I know security is top of mind for many readers, so let me break down what ChatGPT Enterprise offers.
ChatGPT Enterprise has SOC 2 Type 2 Compliance. ChatGPT Enterprise, Team, Edu, and the API Platform have all been audited and certified. The most recent SOC 2 report covers security, availability, confidentiality, and privacy.
The platform holds ISO Certifications including ISO 27001, 27017, 27018, and 27701.
Data Encryption uses AES-256 at rest and TLS 1.2+ in transit.
OpenAI commits to No Training on Your Data. They explicitly state they do not use business data, conversations, or usage for model training.
Enterprise Key Management allows customers to control their own encryption keys.
Data Residency is available in US, Europe, UK, Japan, Canada, South Korea, Singapore, Australia, India, and UAE.
SSO and Admin Controls include SAML SSO integration, domain verification, role-based access controls, and comprehensive admin console.
HIPAA Compliance through Business Associate Agreements is available in eligible cases for healthcare organizations.
Audit Logs provide complete visibility into usage for security and compliance monitoring.
This is a materially different security posture than consumer ChatGPT. The fact that major banks, healthcare companies, and defense contractors are deploying ChatGPT Enterprise tells you something about the maturity of its security controls.
Lessons from the Leaders: What Successful Implementations Have in Common
After studying all these case studies, certain patterns emerge. Here's what the successful implementations have in common.
They Started with a Foundation Before Enterprise
Moderna built mChat before adopting ChatGPT Enterprise. Morgan Stanley tested with 300 advisors before rolling out widely. Klarna pushed employees to experiment from day one.
The companies seeing the best results didn't just flip a switch. They built organizational muscle for AI adoption before making major investments.
They Empowered Employees to Find Their Own Use Cases
BBVA's 2,900 custom GPTs came from employees, not a central team. Moderna's 750 GPTs were built by users across the organization. Klarna pushes everyone to test, test, test and explore.
Top-down mandates about how to use AI tend to fail. Bottom-up experimentation, with appropriate guardrails, tends to succeed.
They Invested in Training
Canva developed an internal AI training program after realizing employees didn't know how to craft effective prompts. PwC has 90% engagement on their AI training tools.
As Moderna's CIO put it, the reason most companies fail is because they haven't built the mechanisms of actually transforming the workforce to adopt new technology. Technology alone isn't enough.
They Connected AI to Company Knowledge
The most valuable use cases come from connecting ChatGPT Enterprise to proprietary knowledge — Morgan Stanley's research documents, Moderna's internal policies, Goldman Sachs's institutional knowledge.
Generic ChatGPT is useful. ChatGPT that understands your specific context and can access your specific information is transformative.
They Measured Results
BBVA can tell you that 80% of users save two hours weekly. Klarna knows their AI handles two-thirds of customer service chats. Morgan Stanley is tracking whether AI boosts advisor productivity.
You can't improve what you don't measure. The organizations seeing clear ROI are the ones that defined success metrics from the start.
The Challenges No One Talks About
It would be incomplete to only talk about successes. Here are the challenges I've observed.
Hallucinations Are Still a Problem
According to one study, 47% of enterprise AI users made at least one major decision based on hallucinated content in 2024. 77% of businesses express concern about AI hallucinations.
This is why every successful implementation includes human oversight. Klarna's customer service bot has extensive guardrails and hands off to humans when needed. Morgan Stanley has humans checking the accuracy of responses.
Adoption Is Uneven
Even at Klarna, which pushes AI harder than almost any company, Siemiatkowski noted at one point that only 50% of their employees use it daily. Even enthusiastic organizations struggle to achieve universal adoption.
The gap between leadership using AI at 33% and individual contributors at 16% suggests that making AI genuinely useful for everyone remains a challenge.
Most AI Projects Still Fail
Despite the success stories, 70-85% of AI projects still fail to deliver expected value. The case studies in this article are the winners — but for every winner, there are multiple failed experiments.
The companies that succeed treat AI adoption as an organizational change management challenge, not just a technology deployment.
ROI Takes Time
Most organizations achieve satisfactory ROI within 2-4 years — much longer than typical 7-12 month technology payback periods. This is a long-term investment, not a quick fix.
Morgan Stanley's Jeff McMillan said it will take at least a year to determine whether their AI technology is boosting advisor productivity. Patience is required.
Getting Started: A Practical Framework
If you're considering ChatGPT Enterprise for your organization, here's a framework based on what I've learned from successful implementations.
Step 1: Let people experiment first. Before investing in Enterprise, encourage teams to experiment with consumer ChatGPT or ChatGPT Team. Build organizational familiarity with AI. Moderna's pre-existing mChat adoption made their Enterprise transition smooth.
Step 2: Identify high-value knowledge repositories. What institutional knowledge do your employees struggle to access? Morgan Stanley's 100,000 research documents. Moderna's internal policies. BBVA's legal and finance documentation. Find your equivalent.
Step 3: Start with a pilot group. Morgan Stanley tested with 300 advisors before rolling out widely. Pick a department or team that's enthusiastic and has clear use cases. Let them discover what works.
Step 4: Invest in training. Don't assume people will figure out effective prompting on their own. Develop internal training programs. Share successful prompts and use cases across the organization.
Step 5: Empower custom GPT creation. The most successful organizations let employees build specialized GPTs for their own workflows. BBVA's 2,900 custom GPTs didn't come from IT — they came from end users solving their own problems.
Step 6: Measure and iterate. Define success metrics from the start. Track adoption rates, time saved, and business outcomes. Use data to continuously improve your AI strategy.
Step 7: Plan for the long term. This isn't a one-time deployment — it's an ongoing organizational transformation. Budget for continuous learning, iteration, and expansion.
Frequently Asked Questions
How much does ChatGPT Enterprise cost?
OpenAI doesn't publicly disclose pricing — you need to contact their sales team. Based on various reports, pricing appears to be in the $30-60 per user per month range, depending on scale and specific requirements. Volume discounts are available for large deployments.
What's the difference between ChatGPT Enterprise and ChatGPT Team?
ChatGPT Team is designed for smaller teams (starts at $25-30/user/month) with basic admin features. ChatGPT Enterprise is for larger organizations and includes additional features like SSO, advanced admin controls, data residency options, custom model training, higher usage limits, and dedicated support. Both offer the core privacy commitment that data isn't used for training.
Is my company data used to train OpenAI's models?
No. OpenAI explicitly commits that business data from ChatGPT Enterprise, ChatGPT Team, ChatGPT Edu, and the API is not used to train their models. This was one of the primary concerns that led to the creation of Enterprise-tier products.
What security certifications does ChatGPT Enterprise have?
ChatGPT Enterprise has SOC 2 Type 2 certification and is ISO 27001, 27017, 27018, and 27701 certified. Data is encrypted at rest (AES-256) and in transit (TLS 1.2+). They offer HIPAA BAAs for eligible healthcare organizations and data residency in multiple regions.
How do companies actually use ChatGPT Enterprise?
The most common use cases are knowledge management (accessing company documents and information), content creation (drafting communications, reports, and presentations), software development (code generation and review), customer service automation, legal and contract analysis, and data analysis. Most organizations see the biggest value when they connect ChatGPT to their proprietary knowledge bases.
What are custom GPTs and why do they matter?
Custom GPTs are specialized AI assistants that can be tailored to specific tasks, trained on company-specific documentation, and shared within an organization. Moderna has 750+ custom GPTs, BBVA has 2,900+. They're valuable because they turn ChatGPT from a generic tool into one that understands your specific context and workflows.
How long does it take to see ROI from ChatGPT Enterprise?
Results vary widely. Some organizations see immediate time savings (BBVA reports 80% of users saving 2+ hours weekly within six months). However, industry data suggests most organizations achieve satisfactory ROI within 2-4 years for comprehensive AI initiatives. Early adopters report $3.70 in value for every dollar invested, with top performers achieving $10.30 returns.
What's the biggest mistake companies make with ChatGPT Enterprise?
Based on my research, the biggest mistake is treating it as a technology deployment rather than an organizational change initiative. Companies that succeed invest heavily in training, empower employees to find their own use cases, connect AI to company-specific knowledge, and measure outcomes carefully. As Moderna's CIO noted, 90% of companies want to do GenAI, but only 10% are successful — usually because they haven't built mechanisms for workforce transformation.
Will ChatGPT Enterprise replace jobs at my company?
This is a nuanced question. Klarna's AI is doing the equivalent work of 700 customer service agents. But companies like Canva explicitly state that these tools are not being used to replace anyone. Goldman Sachs says AI will empower employees to do more, not necessarily result in the need for fewer humans. The reality is that AI changes job responsibilities rather than simply eliminating them. Tasks get automated, but new tasks emerge. The organizations that communicate openly about this and invest in reskilling tend to have more successful implementations.
How do I get started with ChatGPT Enterprise?
Contact OpenAI's sales team through their website. Before doing so, I'd recommend building some organizational experience with ChatGPT through consumer or Team plans, identifying high-value knowledge repositories that could be connected to AI, and selecting a pilot group that's enthusiastic and has clear use cases. The companies that succeed typically don't just flip a switch — they build organizational readiness first.
Can ChatGPT Enterprise integrate with our existing tools?
Yes. ChatGPT Enterprise now integrates with tools like Slack, SharePoint, Google Drive, GitHub, and more through their "company knowledge" feature. This allows ChatGPT to reason across your existing tools to get answers, do analysis, and take action. The integrations are expanding rapidly, so it's worth checking with OpenAI's sales team about specific tools you need.
Is ChatGPT Enterprise suitable for regulated industries like healthcare or finance?
Yes, but with appropriate safeguards. Major banks like Morgan Stanley and Goldman Sachs are using it. Healthcare companies like Moderna have deployed it extensively. OpenAI offers HIPAA Business Associate Agreements for eligible healthcare organizations. The key is implementing appropriate human oversight, maintaining audit trails, and ensuring compliance with industry-specific regulations. The security certifications (SOC 2, ISO 27001, etc.) provide a strong foundation, but you'll still need to work with your compliance team to ensure proper implementation.
How does ChatGPT Enterprise handle sensitive or confidential information?
ChatGPT Enterprise provides multiple layers of protection. Your data is encrypted at rest and in transit. OpenAI commits to not training models on your business data. You can control data residency (choosing where your data is stored). Enterprise Key Management lets you control your own encryption keys. Admin controls let you manage who has access to what. And audit logs provide complete visibility into usage. That said, most organizations still implement policies about what types of information should and shouldn't be shared with AI tools.
The Bottom Line
We're past the point of debating whether enterprise AI is real. It's happening. Over 1 million businesses are paying for OpenAI's services. 92% of Fortune 500 companies are using ChatGPT. Enterprise seats have grown 9x year-over-year.
The companies I've profiled in this article — Morgan Stanley, Goldman Sachs, Klarna, Moderna, Canva, BBVA, PwC, and many others — aren't just experimenting. They're deploying AI at scale and seeing real results: hours saved daily, customer service transformed, code written faster, institutional knowledge made accessible.
But the lesson from these success stories isn't just "buy ChatGPT Enterprise." It's that the technology is only part of the equation. The companies seeing the best results are treating this as an organizational transformation — investing in training, empowering employees to find their own use cases, connecting AI to proprietary knowledge, and measuring outcomes carefully.
As OpenAI's commercial officer noted, 2023 was the year of experimentation. 2024 was about solving specific business problems. 2025 and beyond is about scaling AI across entire organizations.
The question for your organization isn't whether to engage with this technology. It's how to do so strategically, securely, and in a way that creates real value.
I hope this guide has given you a useful framework for thinking about that question. The future of work is being written right now — and the companies I've covered are helping write it.
What's your experience with ChatGPT Enterprise? I'd love to hear what's working (or not working) for your organization. Drop me a note — I'm always looking for new case studies and lessons to share.
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