Lessons learned: The top 10 enterprise use cases for LLMs

 A conversational assistant, neatly packaged inside a text box, that can answer questions, draft emails, or generate ideas on demand. It's the demo that captured headlines, boardroom conversations, and entire investment theses.

But for enterprises, chat is not the endgame. It's a user interface, and not always the right one. The real power of LLMs is emerging in less visible but far more consequential spaces: knowledge management, decision support, compliance, and operations. These are the areas where organizations feel the most pressure, where inefficiency or failure carries real cost, and where generative AI is quietly changing the way business gets done.

The question is no longer "Should we build a chatbot?" but "Which parts of our enterprise workflows can LLMs augment to create measurable value?"

Here are ten enterprise use cases for LLMs that move beyond the hype of chat, backed by real-world examples already delivering results.

Every organization struggles with knowledge sprawl. Decades of documents sit in SharePoint folders. Institutional memory leaves when people retire. Search engines return long lists of links when what people really want is a contextualized answer.

This is where LLMs, especially when paired with retrieval-augmented generation (RAG), are redefining knowledge management. Instead of static repositories, companies can now build living systems that surface relevant insights in real time.

Take McKinsey's internal assistant, Lilli. Tapping into over a century of the firm's intellectual capital, Lilli gives consultants instant access to reports, frameworks, and case studies. More than 70% of McKinsey's 45,000 consultants now use it about 17 times per week, saving around 30% of the time they'd normally spend digging through documents (Business Insider). That's not a gimmick. That's a material productivity gain at scale.

One of the myths about generative AI is that it's meant to make decisions for us. In reality, the strongest applications are not about replacement but augmentation, helping executives and specialists cut through complexity and make better, faster, more confident calls.

Mercy Corps offers a compelling example. Operating in more than 40 countries, often in crisis conditions, the humanitarian aid organization developed Methods Matcher. This tool uses LLMs to match field workers with the right methodologies and evidence-based practices for their situation. It doesn't make decisions for them; it empowers them to make better ones, with context they can trust (Business Insider).

It's a subtle but critical distinction. AI isn't the decider. It's the co-pilot.

Compliance is often described as the immune system of the enterprise. It keeps organizations safe, but it's perpetually overwhelmed by regulatory complexity. Policies evolve across jurisdictions. Contracts pile up. Audits demand endless documentation.

LLMs are making it possible to shift from reactive compliance to proactive monitoring. Salesforce, for example, has built a generative AI assistant for its legal-ops team. It streamlines contract review, redlining, and compliance documentation, work that would normally demand outside counsel. The result? More than $5 million in annual savings (GAI Insights).

This is where AI moves beyond "productivity tool" into strategic enabler: reducing risk exposure while cutting cost.

Contracts are another classic pain point. Dense, inconsistent, and full of hidden risks, they're slow and expensive to review at scale. LLMs can flag non-standard clauses, highlight potential liabilities, and surface points that need human attention.

Pfizer's Charlie platform takes this even further. Originally designed as a marketing workbench, it automates content creation and bakes in compliance checks. By embedding legal and regulatory oversight directly into the workflow, Pfizer speeds up approvals without sacrificing compliance, no small feat in one of the most tightly regulated industries in the world (Pfizer Case Study PDF).

The lesson here is that LLMs are most powerful when integrated, not bolted on.

Few areas demonstrate the value of proactive intelligence like supply chains. A single disruption can ripple globally. Historically, organizations have been reactive, scrambling to adapt after an event.

Pfizer, in collaboration with AWS, flipped that model. By integrating AI-driven monitoring systems, they were able to detect the potential impact of Hurricane Ian before it disrupted operations. The system generated alerts, allowing Pfizer to adjust in advance and maintain continuity of critical medicine delivery (AWS Case Study).

That's not "nice to have." That's resilience as a competitive advantage.

Employee ramp-up is a hidden drag on enterprise productivity. It can take months for a new hire to become fully effective, especially in regulated industries with complex SOPs.

Luxury retailer Tapestry (owner of Coach, Kate Spade) tackled this by deploying a generative AI knowledge management system on AWS. Employees now access SOPs, policies, and institutional knowledge through a contextual AI assistant. The result: faster onboarding, more consistent training, and reduced friction across the workforce (AWS Case Study).

In this model, AI isn't replacing training. It's accelerating it.

Analysts are another group drowning in information. Synthesizing market reports, competitor filings, and industry research can take weeks. LLMs cut that down to hours or even minutes, highlighting patterns humans might miss.

At McKinsey, consultants are already using Lilli for precisely this purpose, not just accessing internal knowledge but also integrating external research, dramatically reducing prep time before client engagements (Business Insider).

This isn't about replacing analysts. It's about freeing them to focus on strategy instead of synthesis.

In industrial contexts, downtime is measured in millions. Maintenance teams rely on streams of sensor data to anticipate failures, but the volume is overwhelming. LLMs can summarize anomalies, generate human-readable diagnostics, and recommend actions before failures occur.

Siemens has been pioneering this space, applying agentic AI to monitor industrial systems and predict breakdowns. Their results: up to 25% reduction in unplanned downtime (Wikipedia).

When AI moves into operations, it stops being abstract and starts delivering dollars.

Customer service is often associated with chatbots, but the real opportunity lies behind the scenes. Service agents spend precious minutes digging through CRM notes and past interactions while customers wait.

Uber, working with Google Cloud, solved this by using LLMs to summarize past communications and surface recommended actions directly in the agent's dashboard. The AI doesn't talk to the customer; it empowers the human agent to resolve the issue faster (Google Cloud).

The distinction matters. The AI isn't replacing human service; it's augmenting it.

Enterprises constantly need new content, marketing, training, and customer education. But in regulated industries, every piece must pass through rigorous legal review, slowing everything down.

Pfizer's Charlie addresses this by combining generative content creation with compliance guardrails. The system automates first drafts and routes them through built-in regulatory checks before human review, accelerating production while reducing risk (Pfizer Case Study PDF).

It's not just about speed. It's about trust.

The lesson across all these examples is clear: the most valuable applications of LLMs are not chatbots. They are embedded systems that quietly eliminate friction, accelerate decision-making, and reduce risk.

A CFO doesn't want to "chat" with their balance sheet; they want AI-generated risk models in their dashboard. A compliance officer doesn't want banter; they want a draft report that aligns with the latest regulations. An engineer doesn't want a chatbot; they want a predictive alert that prevents a million-dollar failure.

Chat is the door through which generative AI entered the enterprise conversation. But it's not where the real story ends. The companies that will win are the ones that see LLMs not as conversational novelties, but as infrastructure, powering knowledge systems, compliance engines, and operational intelligence.

The right question isn't "What chatbot should we build?" but "Where can AI augment our workflows to create resilience, trust, and measurable advantage?"

Those who answer that question now are the ones who will set the pace for the decade ahead.