Over the past years, integrating AI in the workplace has become increasingly common, especially among enterprises. We’ve seen chatbots used in customer service, AI-based product recommendations, smart filters, and categorization assisting users with email correspondence, and numerous other examples of AI in the workplace.
Most recently, the surge of generative AI has encouraged businesses to automate more complex tasks, for example—managing enterprise knowledge or streamlining document processing. To understand how this becomes possible, we took the large language model (LLM) that runs under the hood of ChatGPT and tested its capabilities applied to enterprise data management.
Further in this blog post, we share our thoughts on automation and AI in the workplace, provide a comprehensive understanding of LLMs and their capabilities, and reveal the potential of ChatGPT for document management.
What is the potential of generative AI in the workplace?
According to Goldman Sachs, artificial intelligence is likely to replace about 300 million jobs globally. In the US solely, generative AI can automate up to two-thirds of all occupations.
In their recent report “Superagency in the workplace”, McKinsey estimates that generative AI (gen AI) could contribute up to $4.4 trillion in productivity gains across corporate use cases over the long term—not just incremental improvements but a sweeping opportunity to transform how work is done.
In the same report, they highlight that today’s AI rivals the transformative power of the steam engine, driven by several key advances that accelerate its impact in the workplace:
- Enhanced reasoning and intelligence enable modern models like GPT-4 and Gemini 2.0 Flash to perform near-human tasks such as acing bar exams and answering complex medical questions with high accuracy.
- Agentic AI is empowering systems to take autonomous actions, from managing entire customer interactions to processing payments, ushering in the era of AI agents deeply embedded in workflows.
- Multimodality allows AI to understand and generate across text, audio, images, and video, enabling richer and more dynamic applications.
- Hardware acceleration through advances in GPUs, TPUs, and cloud infrastructure supports rapid, large-scale deployment of these powerful models—even in real time.
- Improved transparency and explainability enhance trustworthiness, with models increasingly meeting benchmarks that make AI systems more auditable and reliable.
These breakthroughs collectively open a new chapter in how organizations operate, making AI a true collaborator rather than just a tool.
With these capabilities at the forefront, one of the most recognizable and widely adopted applications of generative AI in the workplace today is ChatGPT—an AI system that exemplifies many of these advances and is rapidly reshaping how professionals work, communicate, and innovate.
Zoom in: what ChatGPT is capable of in the workplace
In November 2022, the world was stirred up by OpenAI releasing ChatGPT, an artificial intelligence chatbot capable of understanding natural human language and generating human-like replies. In just a few months, the tool has reached 100 million monthly active users, and in less than a year was reported to be used by companies like Coca-Cola, Snap.inc, Slack, and Salesforce.
Becoming the most successful AI project in the world so far, ChatGPT has proudly broken into digital workplaces and become a reliable ally to customer support teams, marketers, content creators, software engineers, sales managers, and recruiters.
Recent studies show that ChatGPT delivers a substantial productivity boost across various roles. Software engineers using ChatGPT have been shown to complete 126% more projects per week. Customer support agents can handle 13.8% more inquiries per hour, while business professionals are able to draft 59% more documents per hour. These impressive improvements highlight the transformative potential of ChatGPT beyond basic tasks.
Still, the question remains—how to make ChatGPT practical if we go beyond using the web app for casual data search and email text generation? What’s more, how can enterprises unlock its full value at scale, integrating it deeply into their unique workflows and complex business environments?
To understand the potential of ChatGPT for enterprise document management, and evaluate its capabilities in relation to specific organizations and business challenges that exist within them, let’s zoom in on the technology that works under the hood of the tool.
How does ChatGPT work?
Tools like ChatGPT represent large language models (LLMs), computer algorithms that process inputs in natural language and generate outputs based on their knowledge. Trained on huge amounts of data available on the internet, the models are capable of providing diverse information once they are given a prompt or a question.
In detail, the process of user interaction with ChatGPT looks as follows:
- The user provides ChatGPT with a prompt (input).
- A deep learning algorithm that runs under the hood analyzes the input to detect keywords and phrases that provide a context for the question.
- ChatGPT uses natural language to generate a response that would be grammatically correct and relevant to the context.
- ChatGPT generates the response (output) to be displayed to the user.
The importance of prompt engineering
While ChatGPT’s language model is powerful, the quality and relevance of its responses depend heavily on the prompts it receives. In enterprise settings, crafting precise and context-rich prompts—often referred to as prompt engineering—is essential for extracting accurate insights from large volumes of complex data.
Effective prompt design helps tailor the AI’s output to specific business needs, reduces ambiguity, and enables more actionable results. Enterprises that invest in developing prompt engineering capabilities can unlock greater value and efficiency from their AI tools.
How can enterprises benefit from ChatGPT for data management?
If an LLM is capable of learning from data available on the internet, it can potentially learn from any data, including unique business- and industry-specific knowledge and documentation that creates major challenges in enterprise workplaces.
- Using deep learning technology, data extraction tools can also pull out text from different file formats, including PDF and JPEG. This allows enterprises to analyze the diversity of enterprise documents, from digital tax reports to paper invoices.
- Moving further, LLM-based tools allow users to interact with multiple enterprise documents at a time by transforming bits of data into embeddings, specific word representations used to categorize text bits based on their semantic meaning.
Integrating ChatGPT in the workplace is associated with vector stores, dedicated databases for storing documents and their embeddings. This means that any data uploaded is broken into chunks and stored in a vector format, and the same applies to user queries. Using semantic search, the system calculates the distance between vectors to determine the ones that are located close to the query—this information is considered the most relevant in the given context.
Using vector-based document content search as a basis, enterprises can proceed with developing “chat with your document” capabilities and create a ChatGPT-like virtual assistant trained on the company information.
How does that work in practice?
Let’s say an Account Executive needs to find information about a specific client the company has been working with for the past 30 years. Apparently, part of that documentation is still located in offline archives, while the other part is stored locally in the form of Word documents, Excel spreadsheets, and PDF files. In addition, pieces of information about the client may be scattered across more generic sources, like annual corporate reports or historic databases.
Without an AI-based solution, analyzing such data may take months or even years. Here is what it looks like when the information is processed and analyzed with an LLM-based tool.
Step 1. Uploading files that would make an internal database.
Step 2. Providing input. For example, the client’s name.
Step 3. Using prompt engineering to “сhat with the documents” allows the worker to fully manage the way LLM analyzes data and get all the necessary information in a blink of an eye.
Step 4. Discovering a few more ways to work with documents. Once given a prompt, LLMs can perform more useful actions. For example, total amount calculation and data summarization.
These steps are just a few examples of how LLMs like ChatGPT apply to document management within a specific client-related enterprise role.
Acknowledging the new scale of automation
Solutions like this can help to streamline the work of employees from different departments in many industries.
- Accountants can dramatically decrease the time they spend processing financial documentation, invoices, and payrolls.
- HR department and talent acquisition teams can benefit from automating employees’ and candidates’ data screening.
- Data analysts can get insights into a business's customers faster and automate rebuilding historical data.
- Logistics and transportation workers can streamline supply chain tracking and identify the reasons for supply chain delays.
- Healthcare professionals can streamline the analysis of patient data by summarizing medical histories.
- In construction, civil engineers can get precise information about cost estimates and view historical data about the structures and infrastructures they manage.
In fact, by introducing AI-enabled document search and “chat with document” capabilities, businesses get a document management studio with an LLM running under the hood. Using the power of prompt engineering companies can further adapt it to solve a number of tasks. Some open source LLMs can also be trained on custom data, which makes the expected scale of automation even more massive.
Understanding the enterprise landscape for AI in the workplace
After seeing the powerful impact AI can have in real enterprise scenarios, it’s important to step back and look at what it takes to bring these solutions to life across entire organizations. Introducing tools like ChatGPT into the workplace isn’t just about technology—it’s about working with people, data, and systems in a way that delivers real value while managing risks and challenges.
Here’s what enterprises need to consider when adopting AI at scale.
Data privacy and security: protecting what matters most
Enterprises work with sensitive information every day—customer details, financial records, intellectual property, employee data, and more. Any AI tool needs to handle this data carefully to protect privacy and comply with legal standards.
- Strict access control: Only authorized users and systems should have access to sensitive data.
- Encryption: Data must be encrypted both while stored (at rest) and during transmission to prevent leaks.
- Audit trails: Maintaining logs of who accessed what and when helps with accountability and regulatory compliance.
- Regulatory compliance: Different industries require adherence to specific rules, like GDPR in Europe or HIPAA in healthcare, which must be factored into AI deployments.
Ignoring these security and privacy concerns not only exposes companies to risks but can also lead to loss of trust from customers and partners.
Integration with legacy and modern systems: bridging old and new
Most enterprises don’t have the luxury of starting fresh. They run a mix of legacy software and newer platforms, often with data spread across multiple databases, applications, and formats. This diversity can make it difficult for AI systems to get a unified view or interact seamlessly.
- Data format challenges: Older systems may use outdated or proprietary formats that aren’t immediately compatible with AI tools.
- Lack of APIs: Many legacy applications don’t have modern interfaces (APIs) that allow smooth integration.
- Performance and scalability: Legacy infrastructure might struggle to handle the volume or speed AI requires for real-time processing.
Overcoming these hurdles usually means building middleware to connect systems, modernizing key platforms step-by-step, and carefully planning data flows to avoid disruptions.
User adoption and training: bringing people along
Technology alone doesn’t guarantee success. AI tools like ChatGPT will only deliver value if employees actually use them and trust their outputs.
- Fear of replacement: Some workers worry AI might take their jobs, leading to resistance.
- Lack of skills: Not everyone is familiar with AI or how to incorporate it into their workflows.
- Workflow disruption: New tools can seem intrusive or add complexity if they don’t fit naturally with daily tasks.
The solution is clear communication about AI’s role as a helper, not a replacement, and investing in hands-on training tailored to different teams and roles. Designing AI to integrate seamlessly with existing processes also lowers the barrier to adoption.
Maintaining data quality and model performance: keeping AI useful over time
AI’s effectiveness depends heavily on the quality and relevance of the data it’s trained on and uses to generate responses.
- Data cleanliness: Enterprises must continuously clean and update datasets to remove errors, duplicates, and outdated information.
- Monitoring and retraining: AI models need regular checks and retraining to adapt to changing data patterns and business conditions.
- Managing data drift: As market trends, customer behavior, or internal processes evolve, AI must keep pace to avoid providing inaccurate or irrelevant outputs.
Without ongoing maintenance, AI tools can quickly lose their value and frustrate users.
Organizational culture and leadership: the human side of AI
Successful AI adoption isn’t just about technology or data—it’s also about the people and culture within the organization.
- Leadership buy-in: When executives actively support AI initiatives, it sets a positive tone and secures necessary resources.
- Cross-functional teams: Bringing together IT, data science, and business experts encourages collaboration and faster problem-solving.
- Transparency and trust: Being open about AI’s capabilities and limits builds trust with users and reduces fear or unrealistic expectations.
Building an AI-ready culture helps organizations move faster and adapt more effectively.
What’s next for ChatGPT in the workplace?
We’ve already mentioned some roles that are going to benefit from implementing AI, and LLMs in particular. However, the real potential of generative AI is yet to be discovered. Therefore, limiting the capabilities of tools like ChatGPT to specific industries and user roles would be a mistake.
What’s clear so far is that humans will still play a key role in managing emerging technology in the workplace, and the results companies achieve with AI will depend on how fast they tame the innovation.
Apart from that, organizations should already start thinking about the other side of automation—managing the risks, determining the skills and capabilities to acquire, and rethinking core business processes in line with each new level of AI enablement.
Get maximum value from generative AI with a tailored solution
Implementing AІ in the workplace requires a deep understanding of business-specific processes and workflows. At Trinetix, we leverage a strategic, discovery-first approach that helps enterprises not only embrace technology to elevate their business outcomes but become future-ready. If you are interested to learn how cutting-edge technology can help you achieve measurable productivity results, let’s chat and make it real!
