Enterprise LLM Adoption in 2027: How to Pick Use Cases by Value and Difficulty

Typical Enterprise Use Cases

Enterprise LLM use cases generally fall into a few categories:

Knowledge Q&A and document understanding: retrieving and summarizing contracts, policies, and product documentation;

Content generation and rewriting: first drafts of marketing copy, customer service replies, and weekly report drafts;

Decision support: ticket classification, complaint root-cause analysis, and initial screening of sales leads;

Workflow assistance: form filling, data extraction, and aggregating information across systems.

The common thread in these use cases is that a human stays in the loop: the model produces a result, and a person confirms it before it is used. Fully automating an entire process with a model is usually a long-term goal, not a sensible first step. For more on how to narrow down scenarios, see How to Choose Enterprise AI Use Cases: Three Typical Scenarios to Start With.

Ranking Use Cases by Value and Difficulty

Use two dimensions when ranking, scoring each item from 1 to 5.

For value, ask how large the processing volume is, how long each task takes, how costly errors are, and whether the task directly affects revenue or cost.

For difficulty, ask a different set of questions: Is the required data already structured? Does it require connecting multiple systems? How demanding are the compliance and permission requirements? Are the business rules clear?

Once the use cases are placed on a two-by-two matrix, the strategy becomes clear. Prioritize high-value, low-difficulty items. Treat high-value, high-difficulty items as phase two, to be pursued once the infrastructure is in place. Low-value, low-difficulty items can serve as practice projects, but should not receive significant budget. Shelve low-value, high-difficulty items altogether.

For example, an automated ticket classification and reply drafting tool in customer service usually has high volume and relatively clear rules, which makes it a good first project. By contrast, automated cross-department approval decisions may be very valuable, but permission boundaries, accountability, and data pipelines are complex, so they belong later.

How to Choose Your First Pilot

Your first pilot should meet three conditions at once:

1. It has a clearly named owner who can make decisions and is willing to accept the results.

2. It has a baseline to compare against, such as the average time it currently takes a person to handle one ticket and the approximate error rate.

3. The data can be obtained within one or two weeks, rather than spending a year on data governance first.

Write the pilot goal as a single sentence that can be verified, for example: 'The first-response drafting time for a certain type of ticket is cut in half compared with manual handling, and the rate of manual edits stays below the agreed threshold.' A goal like 'improve efficiency' cannot be verified, so at the end of the pilot you can only judge it by feel. Most stalled pilots are not stalled because the model fails. They stall because no success criteria were defined in advance.

When calculating costs, do not look only at model call fees. Integration, maintenance, and employee training often make up a larger share. To include all of these items, see How to Calculate the Cost of Enterprise LLM Adoption: Look Beyond Model Pricing.

One Thing You Can Do This Week

Don't discuss model selection this week. Do just one thing: list the ten most time-consuming and most repetitive tasks your department handled over the past month, and note the volume, the manual time each takes, and the consequences of errors. Then use the two-dimension scoring above to pick the highest-scoring item, and write the pilot goal and baseline data on a single page.

The starting point for enterprise LLM adoption is not the model itself. It is a use case that can be costed out and verified.