# Should enterprises consider implementing large language models?

URL: https://www.getdynamiq.ai/post/should-enterprises-consider-implementing-large-language-models

> Should your enterprise implement LLMs? Where they pay off, the risks and costs, when not to use them, and a step-by-step plan from first use case to scale.

Implementation

Should your enterprise implement LLMs? Where they pay off, the risks and costs, when not to use them, and a step-by-step plan from first use case to scale.

Maria-Elena Tzanev · AI strategy · May 23, 2024 · 6 min read · Updated September 29, 2026

In short

Yes, for most enterprises the question is no longer whether to use large language models but where they pay off and how to run them safely. LLMs, usually as agents that use tools and company data, work well in customer service, document-heavy operations, compliance, knowledge work and software engineering. Start with one measurable process and put guardrails in place before you scale.

Yes. For most enterprises the question is no longer whether to use large language models (LLMs), but where they pay off and how to run them safely. LLMs work best today inside agents that use your tools and data, in processes with high volumes of text: customer service, document review, compliance preparation, internal knowledge and software engineering. The practical path is one measurable process at a time, with data controls, evaluations and human approvals in place before you scale.

This guide covers where LLMs deliver value, when not to use them, the costs and risks, and a step-by-step implementation plan.

## Should your enterprise implement LLMs?

Implement LLMs where three things are true: the work is mostly language (reading, writing, searching, classifying), it happens at volume, and you can measure the outcome. That describes a large share of work in banking, insurance, healthcare, telecom and the public sector.

Do not use an LLM where it adds risk without value:

-   **Deterministic, stable processes.** If fixed rules already handle a task reliably, keep the rules; they are cheaper and fully predictable.
-   **Decisions you cannot explain or review.** Keep a person on decisions such as credit approvals, where the outcome must be justified.
-   **No access to the data.** An LLM that cannot reach your systems or documents will guess, and guesses are expensive.
-   **No way to measure success.** Without a baseline, you cannot tell whether the system helps.

The [AI agent readiness assessment](https://www.getdynamiq.ai/resources/ai-agent-readiness-assessment) helps you check whether a process and your team are ready.

## Where do LLMs deliver value in enterprises?

| Function | What LLM agents do | Typical measure |
| --- | --- | --- |
| Customer service | Resolve routine requests, hand complex ones to people with context | Resolution rate, handle time |
| Back office | Match records, prepare exceptions and reports for approval | Cases per person, error rate |
| Document review | Extract and check fields in contracts, claims and loan files | Review time per file |
| Compliance | Prepare KYC and AML cases, track regulatory change | Case preparation time |
| Knowledge work | Answer questions from internal documents with citations | Time to answer, accuracy |
| Software engineering | Write code, tests and documentation for review | Cycle time |
| Sales and marketing | Research accounts, draft content for review | Selling time, content cost |

Customer service is often the first production use case because volumes are high and outcomes are easy to measure. An Asian neo-bank [automated about 85% of support inquiries](https://www.getdynamiq.ai/case-studies/automating-customer-support-at-scale-how-a-neo-bank-saves-1-5m-a-year-with-ai) and went live in 30 days.

## What are the benefits and risks of LLMs for enterprises?

| Benefit | Matching risk | How to manage it |
| --- | --- | --- |
| Handles unstructured text at scale | Wrong answers stated confidently | Ground answers in your documents with citations; evaluate before release |
| Automates multi-step work through agents | Agents acting beyond their remit | Least-privilege tools and approvals on consequential actions |
| Works with your internal data | Data leaving your control | Self-host where needed; detect PII before text reaches a model |
| Quick to prototype | Pilots that never reach production | Pick measurable use cases and plan for integration early |
| Many models to choose from | Lock-in or sudden model changes | Keep models swappable behind one gateway |
| Natural-language interface for everyone | Prompt injection and misuse | Input detectors, monitoring and clear usage policies |

Regulation adds structure rather than a ban. In the EU, the [AI Act](https://www.getdynamiq.ai/post/how-the-eu-ai-act-will-impact-your-business) requires chatbots to disclose that they are AI and sets stricter obligations for high-risk uses such as credit scoring, while GDPR continues to govern personal data.

## What does it cost to implement LLMs?

-   **Model usage:** per-token fees for hosted models, or GPU capacity for self-hosted ones.
-   **Platform:** orchestration, retrieval, guardrails, evaluation and tracing, built or bought.
-   **Integration:** connecting agents to your systems of record, usually the largest effort.
-   **People:** a product owner, engineers, domain experts to review outputs, and change management.
-   **Operations:** monitoring, evaluation runs and updates as models change.

Measure cost per completed case, not cost per token. Our guide to [GenAIOps build vs. buy](https://www.getdynamiq.ai/post/genaiops-for-enterprises-the-build-vs-buy-dilemma) covers the platform decision in detail.

## What are the risks of waiting?

Waiting has a cost: competitors that automate support and operations serve customers faster at lower cost, and teams without hands-on experience take longer to judge vendors and risks. Rushing has a cost too: an unsupervised chatbot that misstates a policy can create legal and reputational damage. The answer is not speed or caution alone but a controlled first use case that proves value and builds the capability.

## How should an enterprise implement LLMs?

1.  **Pick one process with a baseline.** Choose high volume, clear rules and a measurable outcome, and record current cost and quality.
2.  **Check data readiness.** Confirm the agent can reach the documents and systems it needs, with permissions that match your roles.
3.  **Choose the deployment model by data sensitivity.** Managed cloud, self-hosted in your cloud or data center, or fully isolated for the most sensitive work.
4.  **Select models by testing.** Compare two or three models on your own cases for quality, latency and cost.
5.  **Build the agent.** Give it instructions, tools, knowledge retrieval and memory, and keep the model swappable.
6.  **Add guardrails and approvals.** Screen inputs for PII and prompt injection, validate outputs and require approval before consequential actions.
7.  **Pilot against your KPIs.** Run real cases alongside the current process and compare.
8.  **Scale with monitoring.** Trace every run, score live traffic, train the people who work with the agent, and reuse what works in the next process.

The [AI agent ROI calculator](https://www.getdynamiq.ai/resources/ai-agent-roi-calculator) helps you estimate the value of a first use case before you start.

## How does Dynamiq help enterprises implement LLMs?

Dynamiq is one platform to build, run and govern LLM agents. Teams build on a visual canvas or with the open-source Python SDK on the same engine, connect to a large catalog of app integrations and your own databases, and ground agents in knowledge bases with permission-aware retrieval. Guardrails detect PII and prompt injection, approvals gate any tool step, evaluations run on test sets and live traffic, and every run is traced with cost and latency.

The platform runs in Dynamiq Cloud or [self-hosted](https://www.getdynamiq.ai/deployment/self-hosted) on AWS, Azure, GCP, IBM Cloud, Red Hat OpenShift, any Kubernetes cluster or on-prem, and our forward-deployed engineers can build the first use cases with your team.

## FAQ

### What is a large language model?

A large language model is an AI model trained on large amounts of text to understand and generate language. In enterprises, LLMs usually run inside agents that combine the model with instructions, tools and company data to complete tasks.

### Should we build our own LLM?

Rarely. Training a model from scratch is expensive and seldom necessary. Most enterprises use pre-trained hosted or open-weight models, ground them in company data with retrieval, and fine-tune only for narrow needs such as a specific output format.

### Is it safe to use LLMs with company data?

It can be, with the right controls: a deployment model that matches the data's sensitivity, permission-aware retrieval, PII and prompt injection detection, approvals on consequential actions and full tracing. Check each vendor's data terms and certifications.

### How long does it take to implement an LLM use case?

A prototype can be built quickly; production takes longer because of integrations, testing and security review. With a focused use case and experienced engineers, production in weeks is realistic: the neo-bank above went live in 30 days.

### Which departments should adopt LLMs first?

Start where volume is high, rules are clear and outcomes are measurable: customer service, finance and operations, document review, compliance preparation, IT and HR service desks, and software engineering.

Maria-Elena Tzanev

AI strategy

Writes on agentic AI strategy and enterprise adoption at Dynamiq.

On this page

1.  [Should your enterprise implement LLMs?](https://www.getdynamiq.ai/post/should-enterprises-consider-implementing-large-language-models#should-your-enterprise-implement-llms)
2.  [Where do LLMs deliver value in enterprises?](https://www.getdynamiq.ai/post/should-enterprises-consider-implementing-large-language-models#where-do-llms-deliver-value-in-enterprises)
3.  [What are the benefits and risks of LLMs for enterprises?](https://www.getdynamiq.ai/post/should-enterprises-consider-implementing-large-language-models#what-are-the-benefits-and-risks-of-llms-for-enterprises)
4.  [What does it cost to implement LLMs?](https://www.getdynamiq.ai/post/should-enterprises-consider-implementing-large-language-models#what-does-it-cost-to-implement-llms)
5.  [What are the risks of waiting?](https://www.getdynamiq.ai/post/should-enterprises-consider-implementing-large-language-models#what-are-the-risks-of-waiting)
6.  [How should an enterprise implement LLMs?](https://www.getdynamiq.ai/post/should-enterprises-consider-implementing-large-language-models#how-should-an-enterprise-implement-llms)
7.  [How does Dynamiq help enterprises implement LLMs?](https://www.getdynamiq.ai/post/should-enterprises-consider-implementing-large-language-models#how-does-dynamiq-help-enterprises-implement-llms)
8.  [FAQ](https://www.getdynamiq.ai/post/should-enterprises-consider-implementing-large-language-models#faq)

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