> For the complete documentation index, see [llms.txt](https://doc.duaer.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.duaer.com/guides/enterprise-ai-solution-selection.md).

# Enterprise AI Solutions in 2027: How to Choose, from Use Cases to Your First Pilot

This guide, adapted from the Duaer documentation site, walks through how enterprises can choose AI solutions step by step: identifying typical use cases, ranking value against difficulty, picking a first pilot, matching vendors to scenarios, and evaluating the pilot.

## What Are the Typical Enterprise AI Use Cases?

Many companies searching for enterprise AI solutions are not really asking what AI can do. They are asking which category to buy and where to start, given how many options exist. Vendor demos look impressive, but once they are placed against your own workflows, they often don't fit. The problem is usually the order of decisions: teams evaluate products first and scenarios later, so they end up with capabilities that have no clear place to be used.

Enterprise AI use cases tend to cluster into a few groups. The first is customer service and after-sales Q&A, where questions repeat often and answers follow a standard set of guidelines, making it a good fit for a knowledge base combined with a large language model. The second is sales and opportunity management, such as meeting note archiving, lead tagging, and drafting quotes, where the value lies in cutting administrative time for sales staff. The third is contract and document review support, common in legal, procurement, and finance, where the model handles first-pass screening and extracts key clauses while people make the final call. The fourth is internal knowledge retrieval, letting employees query policies, manuals, and past projects in natural language. The fifth is finance document and expense processing, where rules are clear and recognition and validation work well. The sixth is software development and code assistance, and the seventh is organizing and analyzing production quality and inspection records.

These scenarios are not equally easy to implement. Listing them before you compare vendor feature checklists will be far more useful.

## How to Rank Use Cases by Value and Difficulty

Ranking rests on two dimensions. For value, consider four factors: how many times the process runs each month, how much staff time it currently consumes, how costly errors are, and whether it directly affects revenue or customer experience. For difficulty, consider four factors as well: whether the required data already exists and is usable, whether the process is reasonably standardized, how many systems must be integrated, and how high the compliance risk is.

Score each item from 1 to 5 and plot the results on a two-dimensional grid, with difficulty on the horizontal axis and value on the vertical axis. Use cases in the upper left, high value and low difficulty, should come first. Those in the lower right, low value and high difficulty, can usually wait. For example, if a company's customer service Q&A scores high and its data is already in a knowledge base, while cross-department sales forecasting requires connecting three separate systems, the former is clearly the better first candidate.

To turn this scoring into a routine part of your process, you can enter your scenario list and scores directly into Duaer (https://www.duaer.com). That way business, IT, and management can discuss the same standard instead of arguing from gut feeling.

## How to Choose Your First Pilot

The goal of a first pilot is not to prove that AI is impressive. It is to verify whether the solution can actually work in your organization. A good pilot meets several conditions: its scope is small, covering one department or one workflow; it has a clearly named business owner who is willing to invest time; its results can be measured, for example in processing time, the share of manual review, or error rate; and the cost of failure is manageable.

There are three common anti-patterns. The first is launching a company-wide knowledge base right away, a scope so broad that the results cannot be judged. The second is tying the pilot to process redesign or organizational changes, which makes it impossible to tell what caused problems when they arise. The third is setting up an 'innovation project' with no accountable owner, which nobody follows up on three months later. For guidance on estimating budgets and the costs that are easy to overlook, see 'How to Calculate the Cost of Enterprise AI in 2027: Don't Focus Only on Model Pricing.' It can help you see the full investment a pilot requires.

## How to Match Vendors to Your Use Cases

Once you have defined your use cases, evaluating vendors becomes more concrete. Focus on four questions. First, can they validate the solution with your real samples, rather than only showing standard demos? Second, can your data stay in an environment you specify, and how are permissions and logs managed? Third, how do they integrate with your existing systems, what modifications are required, and who is responsible for them? Fourth, beyond the contract itself, are the conditions for exit and migration clear, and can you take your data and knowledge assets with you?

The answers to these questions will determine whether a project can truly succeed far more than claims about model parameter counts or the length of a feature list.

## How to Judge Whether a Pilot Is Working

Before the pilot begins, record a baseline: how long each case takes today, what share of work needs manual review, and where errors concentrate. Without a baseline, the only conclusion at the end of the pilot will be a vague feeling that things got 'a bit faster.'

Measure at two levels. Business metrics show changes in efficiency and quality, while technical metrics cover answer accuracy, response stability, and system availability. Review progress every two weeks, checking metric trends and the list of open issues. If there is no clear improvement within the agreed period, stop and diagnose whether the wrong use case was chosen, the data is insufficient, or the process is misaligned. Do not keep adding investment blindly.

## One Thing You Can Do This Week

Take a sheet of paper and list five candidate use cases. Score each one for value and difficulty. Then circle one use case that is high value and low difficulty, and write down three things: who owns it, what the current baseline numbers are, and what result would count as passing acceptance. This takes half a day, but it gives you a clear basis for later vendor negotiations.

If you want to get this moving quickly, you can sign up for a trial at https://www.duaer.com and run one use case through the full process, from scoring and selection to pilot evaluation, before deciding whether to expand.

Choosing enterprise AI solutions is essentially a matter of picking the right use case first, then the right vendor, and finally testing the results with a measurable pilot. Get the sequence right, and the solution has a real chance of making it into daily business.

## Questions

### When choosing an enterprise AI solution, should you evaluate vendors or business use cases first?

Start with business use cases. List candidate scenarios such as customer service, sales, contract review, and knowledge retrieval, score them by value and difficulty, and determine the priority use case. Then test vendor capabilities against real samples. Reversing the order often leads to buying a powerful product that does not fit your business processes.

### How should a company choose its first AI pilot, and how can it tell whether the pilot succeeded?

The first pilot should have a small scope, a clearly accountable business owner, measurable results, and a manageable cost of failure. Before it starts, record baseline data such as processing time, the share of manual review, and error rates. Review metric changes every two weeks. If there is no clear improvement within the agreed period, first determine whether the problem lies in the use case, the data, or the process before deciding whether to invest more.

### How do you determine whether an AI use case is worth prioritizing?

Score it on two dimensions. For value, consider how often the process occurs each month, how much staff time it takes, the cost of errors, and whether it affects revenue or customer experience. For difficulty, consider data availability, process standardization, the number of systems to integrate, and compliance risk. Prioritize use cases that are high in value and low in difficulty, such as customer service Q&A where the data is already in a knowledge base.