Openai published a complete 24 -page document entitled AI in the companyeOffering a pragmatic framework for organizations that navigate the complexities of the deployment of large -scale AI. Rather than focusing on abstract theories, the report presents seven implementation strategies based on information tested in the field of collaborations with leading companies, notably Morgan Stanley, Klarna, Lowe's and Mercado Libre.
The document can be read less as promotional material and more as an operational guide – a systematic isolation assessment, preparation for infrastructure and specific integration in the field.
1 and 1 Establish a rigorous evaluation process
The first recommendation is to initiate the adoption of AI by well -defined (“Evals”) evaluations which write the performance of the model against targeted use cases. Morgan Stanley applied this approach by assessing language translation, summary and recovery of knowledge in financial advisory contexts. The result was measurable: improvement of access to documents, reduction of research latency and wider adoption of AI among the advisers.
Evals validate not only deployment models, but also help refine workflows with empirical feedback loops, improving both the safety and alignment of models.
2 Integrate AI into the product layer
Rather than treating AI as an auxiliary function, the report insists on integration directly into user -oriented experiences. For example, in fact, used GPT-4O Mini to personalize the matching of work, completing the recommendations with “why” contextual declarations. This has increased user engagement and hiring success rates while maintaining profitability thanks to refined and optimized token models.
The point to remember key: The performance of the only model is insufficient: impact scales when AI is integrated into the logic of the product and adapted to the needs specific to the domain.
3 and 3 Invest early to capture aggravated yields
Klarna's early investment in AI has generated substantial gains in terms of operational efficiency. An assistant fueled by GPT now manages two thirds of support cats, reducing resolution times from 11 minutes to 2. The company also reports that 90% of employees use AI in their workflows, an adoption level which allows rapid iteration and organizational learning.
This illustrates how early engagement improves not only tools but accelerates institutional adaptation and compound value capture.
4 Take advantage of the fine adjustment for contextual precision
Generic models can provide high baselines, but adaptation of the domain often requires personalization. LOWE has made significant improvements in the relevance of product research by GPT models refined on their internal product data. The result: an increase of 20% of the precision of the marking and an improvement of 60% of the detection of errors.
OPENAI highlights this approach as a low latency path to achieve the brand's consistency, domain mastery and efficiency between content and research generation tasks.
5 Empower internal experts, not just technologists
BBVA illustrates an adoption model of decentralized AI by allowing non -technical employees to create tools based on personalized GPT. In just five months, more than 2,900 internal GPTs have been created, meeting legal, compliance and customer service needs without requiring engineering medium.
This upward strategy allows experts in the matter to iterate directly on their workflows, produce more relevant solutions and reduce development cycles.
6. Rationalize the workflows of developers with dedicated platforms
The engineering bandwidth remains a bottleneck in many organizations. Mercado Libre tackled this by building VerdiA platform powered by GPT-4O Mini, allowing 17,000 prototyper developers and deploying AI applications using interfaces in natural language. The system incorporates railings, APIs and reusable components – ranging faster and standardized development.
The platform now supports high-value functions such as fraud detection, multilingual translation and automated content marking, demonstrating how the internal infrastructure can speed up the speed of AI.
7 Deliberately and systematically automate
OPENAI emphasizes the definition of clear automation targets. Internally, they have developed an automation platform that fits into tools like Gmail to write support responses and trigger actions. This system now manages hundreds of thousands of tasks monthly, reducing manual workload and improving responsiveness.
Their broader vision includes OperatorA browser agent capable of interacting independently with web interfaces to complete the processes in several stages – indicating an evolution towards automation without API based on agents.
Final observations
The report ends with a central theme: the effective adoption of AI requires iterative deployment, an interfunctional alignment and a desire to refine strategies through experimentation. Although the examples are on a business scale, fundamental principles – to start with evals, in -depth integration and personalization with the context – are largely applicable.
Data security and governance are also explicitly processed. OPENAI reiterates that company data is not used for training, offer SOC 2 and CSA Star compliance and provides granular access control for regulated environments.
In an increasing AI landscape, the OPENAI guide serves both as a mirror and a card – reflecting current best practices and helping businesses trace a more structured and sustainable path.
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Asif Razzaq is the CEO of Marktechpost Media Inc .. as a visionary entrepreneur and engineer, AIF undertakes to exploit the potential of artificial intelligence for social good. His most recent company is the launch of an artificial intelligence media platform, Marktechpost, which stands out from its in-depth coverage of automatic learning and in-depth learning news which are both technically solid and easily understandable by a large audience. The platform has more than 2 million monthly views, illustrating its popularity with the public.
