Official translation of OpenAI's material on how companies use AI to boost productivity, automate work and improve products.
Reading time: 12 min. Lessons from seven leading companies. As an AI research and deployment company, OpenAI prioritizes partnerships with global companies because our models perform best in complex, interconnected workflows and systems. We see AI delivering meaningful, measurable improvements in three areas:
Increasing employee productivity - helps people achieve better results in less time.
Automation of routine operations - frees employees from repetitive tasks so they can focus on creating value.
Product enhancement - delivers a more relevant and responsive user experience.
The greatest success goes to companies that treat AI as a new way of working. This leads to experimental thinking and an iterative approach that delivers value faster and drives greater engagement from users and stakeholders.
. OpenAI is organized around three teams: Research Team - advances the fundamental foundations of AI by developing new models and capabilities. Applied Team - turns those models into products such as ChatGPT Enterprise and our API. Deployment Team - brings these products into companies to solve the most urgent problems. We use iterative deployment to learn quickly from real-world cases and accelerate product improvements.
This means frequent updates, feedback gathering, and improved performance and security at every stage. Result: users get early and frequent access to the latest AI advances - and their feedback shapes future products and models.
Seven lessons for adopting AI in the enterprise:
Start with evaluations - use a systematic approach to measure how models perform on your tasks.
Embed AI into products - create new customer experiences and more relevant interactions.
Start now and invest early - the sooner you begin, the more compounding effect you will gain over time.
Customize and fine-tune models - adapting models to your specific use cases can significantly increase value.
Give experts access to AI - those closest to the process understand best how to improve it with AI.
Unblock developers - automating the development lifecycle multiplies the value of AI.
Set bold automation goals - most processes include routine work that can be automated. Aim high. Next, we will look at each lesson in more detail using customer examples.
. As a global leader in financial services, Morgan Stanley is relationship-driven. It is no surprise that questions arose across the company about how AI could add value in such a personal and sensitive line of work. The answer was to run rigorous evaluations for every proposed AI use case. An evaluation (eval) is a tightly structured process for measuring how an AI model performs on a specific task against defined metrics.
It is also a way to continuously improve AI solutions with experts involved at every stage.
03 Expert comparison - comparing AI outputs with responses from professional advisors, assessing accuracy and relevance. These and other evaluations gave Morgan Stanley confidence to bring AI use cases into production.
. Today, 98% of Morgan Stanley advisors use OpenAI daily; access to documents has grown from 20% to 80%, and time spent searching for information has dropped significantly. Advisors spend more time with clients thanks to task automation and faster insights. Advisor feedback has been overwhelmingly positive.
They became more engaged, and actions that used to take days now happen in hours. - Caitlin Elliott, Company-wide Head of Generative AI What is an evaluation (eval)? An evaluation is the process of checking and testing the outputs produced by your model. Rigorous evaluations lead to more stable and reliable applications that are resilient to change.
They are built on tasks that measure the quality of model output against a reference: is it more accurate? does it meet requirements? is it safe? Key metrics depend on what matters in your specific case.
How Indeed makes job matching more human.
When AI automates and speeds up boring, repetitive work, employees can focus on what only people can do.
Thanks to its ability to process vast amounts of data, AI can create customer experiences that feel more personalized and human. Indeed, the world's #1 job search site, uses GPT-4o mini for new ways of matching job seekers and openings.
Simply suggesting the right job is not enough - it is important to explain why that job was recommended. Indeed uses GPT-4o mini's data analysis and text generation capabilities to craft such explanations in emails and messages.
Popular feature "Invite to apply"
now includes reasons why the candidate is a strong fit for the role, based on past experience and skills.
Results after introducing AI into recruiting:
20% increase in started job applications -
A 13% increase in end success (getting hired). With over 20 million messages per month and 350 million site visitors, this means a significant business impact.
To improve efficiency, OpenAI and Indeed jointly fine-tuned a smaller model that delivers similar results but uses 60% fewer tokens.
Chris Hyams, CEO: "Matching the right people with the right jobs is a deeply human outcome. The Indeed team uses AI to connect people with work faster - that's a win for everyone."
. AI is rarely a plug-and-play solution - real-world use cases become more complex and valuable through iteration. The earlier you start, the faster and greater the benefits you get from compounding improvements. Klarna, a global payments network and commerce platform, introduced a new AI assistant to streamline customer service. Within a few months, the assistant was handling two-thirds of all chats, doing the work of hundreds of agents and reducing average response time from 11 minutes to 2.
This project is expected to generate $40 million in profit, while satisfaction metrics have remained at the level of human support. And none of this happened overnight. Klarna achieved these results through continuous testing and improvement of the assistant. Equally important, 90% of Klarna employees use AI every day. Widespread familiarity with AI made it possible to launch internal initiatives faster and continuously improve the customer experience.
By investing early and encouraging broad adoption, Klarna is seeing accelerating returns from AI across the business. - Sebastian Siemiatkowski, Co-Founder and CEO: "This AI breakthrough in customer interaction means better service at a better price, more interesting work for employees, and higher returns for investors."
The companies that see the greatest success with AI are those that invest in internal adaptation and training models on their own data. OpenAI has invested heavily in the API to make customization easier, both for self-serve use and with support from OpenAI.
We worked closely with Lowe's, a Fortune 50 home improvement company, to improve search in its online store. With thousands of suppliers, Lowe's often deals with incomplete and inconsistent product data.
The key was improving product descriptions and tags, plus understanding how shoppers behave when searching, which differs across product categories.
Fine-tuning models is especially important here.
Results of fine-tuning on Lowe's data:
20% improvement in product tagging accuracy -
60% improvement in error detection efficiency - Nishant Gupta, Senior Director of Data, Analytics, and Computational Intelligence: "The team was thrilled when they saw the results of fine-tuning GPT-3.5 on our product data. We knew this was our win!"
Product note: OpenAI launched Vision Fine-Tuning to further improve product search and address tasks in medical imaging and autonomous driving.
If a GPT model is an off-the-rack suit, fine-tuning is bespoke tailoring: adapting the model to your data and needs.
Higher accuracy - a model trained on your data, such as catalogs or FAQs, produces more relevant, brand-aligned responses -
Industry expertise - the model better understands professional terminology, style, and context -
A consistent tone and style - whether legal references or brand descriptions - everything is formatted the same way -
Faster results - less manual editing, and employees can focus on what matters
. Employees know the company's internal processes and pain points best - and they are often the ones most capable of finding the right AI solutions. Putting AI in the hands of these experts can be far more effective than building one-size-fits-all solutions from the top down. BBVA, a global banking leader, has more than 125,000 employees, each facing unique tasks and opportunities.
The company decided to give employees around the world access to AI, working closely with legal, compliance, and IT security teams to ensure responsible use. They deployed ChatGPT Enterprise across the company and then let people find their own use cases. - Elena Alfaro, Global Head of AI Adoption at BBVA: "Usually, even to build a prototype, our business requires technical resources and time.
With custom GPTs, it became easy - anyone can build an app for their own task."
- Employees have created more than 2,900 custom GPTs - Many of them reduced project and process timelines from weeks to hours Use cases: - Credit risk team - assesses creditworthiness faster and more accurately - Legal team - handles 40,000+ requests a year on policy, compliance, and other matters - Customer support - automates sentiment analysis in surveys
NPS AI tools are also widely used in marketing, risk management, operations, and other departments - all because employees themselves found ways to apply AI in their work. - Elena Alfaro: "We see investing in ChatGPT as investing in our people.
AI amplifies our potential and helps us be more effective and creative." Product note: ChatGPT can perform deep research. You ask a question, and it synthesizes hundreds of sources to create detailed, expert-level reports - in just minutes. Internal evaluations showed that this type of research saves an average of 4 hours per complex task.
. In many companies, developers are the main bottleneck and growth constraint. When engineering teams are overloaded, innovation slows and ideas pile up in the backlog. Mercado Libre, the largest e-commerce and fintech company in Latin America, worked with OpenAI to build a development platform powered by GPT-4o. That led to Verdi, a platform layer that helps Mercado Libre's 17,000 developers speed up and standardize the creation of AI applications.
Verdi combines language models, Python nodes, and APIs into a single scalable system where natural language is the primary interface. Developers can now build high-quality applications faster without diving into source code. Security, routing logic, and guardrails are already built in.
- Higher cataloging throughput: GPT-4o mini Vision helps tag and describe products, making catalog population 100 times faster - Fraud detection: AI processes millions of product listings, reaching up to 99% accuracy on suspicious cases - Product description localization: translation and adaptation for regional language nuances in Spanish and Portuguese - More orders: automatic review summaries help shoppers
understand the essence faster - Notification personalization: adapting push notifications increases engagement and recommendation quality - Sebastian Barrios, Senior Vice President of Technology: "We built our ideal AI platform on GPT-4o mini with a focus on reducing cognitive load and enabling the entire company to build and launch innovation."
. At OpenAI we work with AI every day and constantly look for new ways to automate routine processes.
. Our support teams spent too much time accessing systems, analyzing context, drafting responses, and taking the necessary actions on behalf of the customer. So we built an internal automation platform that runs on top of our existing processes and systems. It automates routine work and speeds up getting insights and taking actions.
. The platform accesses customer data and relevant articles, then uses the results: - to draft an email reply - to take actions such as updating an account, creating a ticket, and more
Result: teams became more effective, faster, and more customer-focused - the system now handles hundreds of thousands of tasks each month, freeing people for higher-value work. And all of this became possible because we set ambitious automation goals from the start instead of accepting inefficient processes as a "cost of doing business."
As the previous examples show, every company has opportunities to apply AI for better results.
Use cases may differ by industry and scale, but the principles remain universal.
AI delivers the greatest return when an open, experimental mindset is combined with rigorous evaluations and safeguards. Companies that succeed do not rush to put AI into every process - they start with simple but high-value use cases, learn from them, and then apply that experience to new areas.
A more personalized customer experience -
More meaningful work - employees do what people do best
We now see companies integrating AI into complex processes, often using tools and agents to achieve results.
We will keep sharing observations from the front lines so you can apply this knowledge in your strategy.
Product note: Operator is an example of OpenAI's agentic approach.
Its own virtual browser lets it navigate sites, click buttons, fill out forms and collect data the way a human would.
It can also carry out processes across different systems and tools - without integrations or APIs.
Examples of use in companies:
Automated software testing - Operator works like a real user, identifying bugs in the interface -
Updating record systems on behalf of users - with no technical instructions or APIs Result: end-to-end automation that frees teams from routine work and improves overall efficiency.
For our enterprise customers, security, privacy and control matter most.
Your data stays yours - we do not use your content to train models; your business retains full ownership.
Enterprise-grade compliance - data is encrypted in transit and at rest.
SOC 2 Type 2, CSA STAR Level compliance
Flexible access control - you decide who can view and manage data.
This ensures compliance with governance requirements.
Data retention settings - flexibly configure logs and retention to match your organization's policy.
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