5 Unconventional Ways Companies Are Using LLMs for Process Automation

You know what everyone's talking about these days: AI, ChatGPT, Large Language Models (LLMs). And honestly, they aren't wrong to be excited. But here's the thing—most people are still stuck thinking about LLMs as fancy chatbots. Sure, they make great conversationalists, but that’s like saying your smartphone is just for making calls. It barely scratches the surface.

I’ve had the privilege of working closely with companies exploring the full breadth of LLM capabilities, and let me tell you, the use cases I’ve seen go way beyond answering customer queries. We’re talking about transformative process automation, unconventional workflows, and business operations that feel like they’re straight out of a sci-fi movie. Imagine a system that’s not just working for you, but is learning with you—anticipating your next need, refining itself, and augmenting what your team can accomplish. These aren't far-off dreams anymore; they're happening right now.

In this post, we’ll dive into five lesser-known, yet impactful ways companies are using LLMs to take automation to the next level. These aren't your run-of-the-mill chatbot scripts. We're talking about automations that reduce friction, save millions, and create entirely new ways of working. Let’s go beyond chat and peek into what’s really possible.

1. Contract Analysis and Negotiation: Automating Legal Bottlenecks

Contracts are the lifeblood of any business relationship—they tie everything together, from vendor agreements to customer commitments. But anyone who’s ever been involved in a contract review knows that these documents can be complex, dense, and downright headache-inducing. In fact, it’s estimated that up to 57% of business deals face delays due to contract negotiations, often because of the back-and-forth between legal teams.

Enter LLMs. Leading companies are now using LLMs to automate contract analysis and even negotiation prep. LLMs are trained on thousands of contracts and legal texts, giving them the power to understand intricate clauses, identify potential risks, and even suggest modifications. Unlike traditional automation tools that might flag keywords, LLMs can interpret entire paragraphs and identify inconsistencies or deviations from standard clauses that could be costly.

What’s particularly exciting is how LLMs can also generate contextual suggestions for negotiation. Picture this: your legal team receives a draft from a vendor, and before anyone reads it, an LLM has already flagged unusual payment terms, generated alternative phrasing for confidentiality clauses, and even summarized the key points in a one-page memo. By doing so, negotiation time is slashed in half, and legal professionals are freed to focus on complex, non-standard issues.

Take Ironclad, for example, a digital contracting platform that integrates LLM capabilities to speed up contract processing. They use LLMs to interpret contractual language, identify typical negotiation flashpoints, and automate first-round redlines. This not only reduces dependency on high-cost legal consultants but also empowers teams to manage contracts in real-time—without drowning in jargon.

2. Knowledge Management and Dynamic Documentation

Companies generate enormous volumes of knowledge—internal policies, product documentation, competitive intelligence, and more. Keeping this information organized and up to date has always been a monumental challenge. Knowledge bases often become obsolete the minute they're published, and employees end up spending valuable time hunting for information that should be readily available.

LLMs, when paired with process automation, are making dynamic knowledge management possible. Imagine an internal knowledge base that auto-updates itself whenever there's a change in your procedures or a new feature rollout. Think about a system that doesn’t require manual editing every time a policy is tweaked—instead, an LLM is working behind the scenes to keep everything in sync.

Companies like Notion and Confluence have begun experimenting with LLM-based integrations that make it possible to query internal documents more naturally and dynamically. Instead of sifting through folders or outdated files, employees can ask, “What’s the latest policy on remote work reimbursement?”, and get an answer that’s not only accurate but also reflects real-time changes.

Another standout example is ServiceNow, which uses LLMs to fuel their knowledge management systems. When an update is made in one part of the documentation, the LLM automatically revises other related documents, ensuring a cohesive and up-to-date knowledge base. This doesn’t just streamline internal processes; it also prevents costly errors that can occur when employees act on outdated information.

3. Coding as Collaboration: AI-Assisted DevOps

Traditionally, DevOps has required a high level of collaboration between development and operations teams—a delicate balance that ensures software deployments are both swift and reliable. LLMs are making this balance easier by automating the mundane parts of coding and infrastructure management.

At first glance, this sounds like another coding assistant story, but it’s so much more. Companies are using LLMs to interpret human requests into technical commands, essentially translating natural language directly into code. Picture a developer casually saying, “Spin up a staging environment similar to production but without user data,” and the LLM understands the intent and orchestrates the entire backend process.

GitHub Copilot, for example, has demonstrated how LLMs can assist developers by auto-suggesting snippets, but forward-thinking companies are integrating LLMs into their entire DevOps pipeline. Imagine LLMs that can proactively resolve merge conflicts, or identify redundant scripts and clean them up before anyone even notices. It's a level of automation that’s more collaborative than autonomous—a real-time coding partner that doesn’t just assist but learns and grows with the development team.

Take Shopify, for instance, which has integrated AI into its DevOps practices. Their LLM-based system helps with release note generation, by analyzing code changes and drafting notes that are informative and concise, saving precious hours during deployments. This isn’t just automating repetitive tasks; it’s fundamentally changing how DevOps teams collaborate.

4. Hyper-Personalized Customer Feedback Loops

We all know the power of customer feedback. It’s the cornerstone of innovation and customer loyalty. But gathering, analyzing, and acting on that feedback? That’s the tough part. LLMs are stepping into this arena by automating not only data collection but also the nuanced interpretation of qualitative feedback.

Companies are using LLMs to sift through thousands of customer comments—identifying trends, recurring pain points, and even subtle shifts in sentiment. This isn’t just about counting how many times a feature was requested. LLMs analyze tone, language complexity, and context to determine how customers actually feel about your product and how urgently they need certain changes.

Amplitude has started to use LLM-driven analysis for its feature development process. By correlating user feedback with product analytics, LLMs help identify which features would drive the most significant increase in customer satisfaction. This feedback then feeds directly into sprint planning, ensuring that product teams work on changes that maximize value.

More interestingly, LLMs are now being used to generate personalized responses. If you’ve ever left a piece of detailed feedback and received a generic, “Thanks, we value your input,” you know how unfulfilling it can be. With LLMs, companies can respond contextually—“We noticed your concerns about onboarding complexity, and here’s what we’re doing next.” This closes the feedback loop in a way that fosters trust and makes customers feel heard.

5. Financial Process Automation and Risk Management

Financial processes are often seen as the backbone of an organization—payroll, accounts payable, budgeting, compliance, and more. These workflows involve not just numbers but also a mountain of paperwork and verification. Many of these processes are rule-based and repetitive, making them perfect candidates for automation. But with LLMs, we're seeing companies move beyond mere automation to something more akin to strategic optimization.

Take the case of accounts payable. Traditionally, invoices are manually cross-checked with purchase orders, receipts, and delivery notes. It’s a process ripe for automation, but LLMs are taking it further by understanding the context of discrepancies and making judgment calls that would typically require human intervention. An LLM could flag a discrepancy not just because the numbers don’t match, but because it knows—from reading an email thread—that a supplier offered a discount that hasn’t yet been applied.

Consider JPMorgan, which uses its LLM-powered platform, COiN, to review commercial loan agreements. What took legal teams over 360,000 hours annually can now be done in mere seconds with AI-driven analysis that’s more accurate and less prone to human error. This isn’t just about processing speed; it’s about risk reduction and smarter decision-making. The LLM doesn’t only extract information—it also flags risky clauses and suggests mitigative actions.

In the insurance world, LLMs are also transforming claims processing. Insurance companies use LLMs to evaluate claims, identify fraudulent patterns, and even suggest alternative settlements—speeding up the entire process while minimizing risk. This new approach doesn’t just improve efficiency; it also drastically improves customer satisfaction by reducing the waiting time for claim settlements.

Beyond Chat, Toward a More Intelligent Future

The reality is, we’re only beginning to understand what LLMs can do. Beyond chat, beyond the straightforward Q&A, there lies an expansive field of potential applications that are already transforming industries. Contracts are being analyzed in seconds, knowledge is staying dynamic, development is getting collaborative, customers feel more understood, and finances are managed with a strategic lens.

We’re witnessing the start of a shift—one where companies leverage LLMs not just to cut costs but to add value in ways that were once impossible. The line between machine and team is blurring. LLMs are becoming co-workers, mentors, and tireless assistants, doing the heavy lifting and letting people focus on creative, strategic, and human-centric aspects of their jobs.

So, the next time someone talks about LLMs as glorified chatbots, remember this: Conversations are just the beginning. The real story lies in how these models are quietly reshaping the very fabric of our processes, redefining how we work, and expanding the boundaries of what’s possible.

1. How are LLMs different from traditional automation tools in contract analysis?
Traditional automation tools typically focus on keyword detection and rule-based workflows, which can be limiting when dealing with complex language. LLMs, on the other hand, can interpret nuanced clauses, identify risks, and provide contextual suggestions, which makes contract analysis more accurate and less time-consuming.

2. What specific value do LLMs add to dynamic knowledge management systems?
LLMs bring real-time updating capabilities to knowledge management systems. When a policy or procedure changes, the LLM can automatically revise associated documents, ensuring employees always have access to accurate information without relying on manual updates.

3. How do LLMs support DevOps beyond just writing code?
LLMs in DevOps are not limited to code suggestions. They assist in setting up environments, proactively resolve merge conflicts, and even generate release notes. This allows DevOps teams to move faster, focus on complex challenges, and reduce human error in the development process.

4. How do LLMs improve the handling of customer feedback?
LLMs analyze customer feedback beyond simple sentiment analysis. They detect recurring pain points, the urgency of issues, and even shifts in sentiment, enabling companies to act more effectively. LLMs also generate personalized responses that enhance customer engagement by making users feel heard.

5. Can LLMs make judgment calls in financial processes, such as accounts payable?
Yes, LLMs can make context-based judgment calls. For example, they can flag discrepancies by understanding context from emails or discount agreements, rather than merely comparing numbers. This leads to a more informed and efficient financial workflow.

6. What are some challenges companies might face while integrating LLMs into their workflows?
Challenges include ensuring the quality and relevancy of training data, avoiding biases, and the need for robust integration with existing systems. Additionally, LLMs require ongoing fine-tuning and supervision to provide accurate, context-sensitive results.

7. How does the use of LLMs in DevOps lead to faster releases?
LLMs automate repetitive tasks like code formatting, generating deployment scripts, and creating release notes. By reducing manual work and proactively resolving merge conflicts, LLMs enable teams to release new software versions faster while maintaining quality.

8. Can LLMs replace legal professionals entirely in contract analysis?
LLMs are not likely to replace legal professionals completely. Instead, they reduce the workload by handling standard, repetitive tasks. They assist in analyzing documents, identifying risky clauses, and generating preliminary edits, allowing legal experts to focus on complex legal issues that require nuanced understanding.

9. How do LLMs transform customer feedback loops into actionable insights?
LLMs categorize and analyze thousands of customer comments, identifying trends and pain points more precisely than manual analysis. They help correlate this feedback with product analytics, ensuring that product teams focus on features or improvements that will have the most significant impact on customer satisfaction.

10. Why are LLMs seen as strategic tools in financial process automation rather than simple automation solutions?
LLMs go beyond rule-based automation by analyzing the context and making recommendations that add strategic value. For example, in risk management, they don’t just highlight non-compliance but provide mitigative actions. This capability to contextualize data and suggest solutions is why LLMs are increasingly viewed as strategic tools.

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