In June 2025, Walmart launched Sparky, an AI agent built into its app. Shoppers could use this to plan purchases, check local inventory, and compare prices. Adoption increased quickly after launch, with weekly active users quadrupling in the first quarter of 2026. Sparky users also spent 35% more money per order. By the fourth quarter, more than 50% of Walmart’s app users had opened Sparky at least once.

These results came from a focused use case. Unlike many companies, Walmart did not spread AI across multiple workflows at once. This choice helped Walmart show measurable results within a short period.

This article explains how to identify the right use cases for AI automation. This also shows where AI automation can create the most value in your business.

What Is AI Automation?

AI automation uses artificial intelligence to manage tasks, and workflows with minimal human input. This includes making decisions, interpreting unstructured data, and responding to changing conditions.

Traditional automation relies on predefined rules and structured inputs. AI automation can also work with emails, documents, images, and other unstructured information. This allows AI to classify data, determine the next step, and trigger actions within a workflow.

In practice, AI automation can route support requests, process documents, and coordinate multi-step workflows. These capabilities can connect directly with systems such as a CRM, inventory platform, or AI automation tools.

AI Automation Vs AI Tool

A basic AI tool, such as a chatbot, responds when someone asks a question. AI automation works differently. This can monitor a process, identify when a decision is needed, and take action within the system.

This difference can affect adoption. A standalone tool often requires people to open another interface, and change how they work. AI automation stays inside some familiar software, and completes the task there.

Why Do Most AI Automation Projects Fail?

MIT’s Project NANDA reviewed more than 300 public AI projects. The research found that only 5% produced a measurable impact on profit and loss. The stronger projects were tied to clear workflows, and designed around how people already work.

Gartner expects more than 40% of AI agent projects to be canceled by the end of 2027. High costs and unclear business value are among the main reasons. Companies need a clear problem, and a measurable goal before adding AI.

Where Does AI Automation Selection Go Wrong?

Picking by Ambition 

Leadership often favors the projects that sound most ambitious. These include automating customer service, or transforming the supply chain. Look for work that creates repeated friction. This could be a report built manually every week, or a form entered into several systems. These tasks are easier to define, automate, and measure. 

Automating a Task Instead of a Decision

Filling a form is a task. A decision is approving a claim, restocking an item, or picking what to recommend. Automating the task can save time, but the larger value often comes from automating the decision.

Who Owns the Exceptions?

Even a strong AI automation use case needs ongoing review. Walmart did not treat Sparky as a finished system after launch. Furner said response quality improved by 40% over the year through continuous tuning. That kind of improvement requires clear ownership.

Assign someone to track quality, review failures, and improve performance from day one. AI automation works best when accountability is defined before problems appear.

What Does a Wrong Answer Cost?

Some AI mistakes are easy to correct. A poor product recommendation may cost a click, while a wrong claims decision can create legal or financial risk.

Start with a use case where errors are easy to detect, and inexpensive to fix. Build a reliable review process first. Move to higher-stakes decisions only after that process has proven to be effective.

What Do Good First AI Automation Use Cases Look Like?

A first project does not need Walmart-scale data, or infrastructure. Some of the examples are listed in the table.

Can You Map the Automation From Start to Finish?

Before investing in AI automation services, map the full process automation workflow. A well-defined workflow should include five components:

Trigger → Context → Decision → Action → Review

The trigger starts the workflow, such as a support ticket, invoice, form submission, or system event. The context is the information the AI needs before it can make a useful decision. The decision is where AI interprets the context, and selects the next step. This is what separates intelligent processes, and automation from simple rule-based workflows. The action happens after the decision is made. The review happens when the system is uncertain, or when the decision carries more risk. An intelligent automation strategy specifies when the system can act independently, and when human approval is required.

E.g., Support request received → Customer and ticket data retrieved → AI classifies priority and issue type → AI automation routes the request, or drafts a response → Uncertain cases move to human review.

If you cannot clearly define any of these, the use case is not ready for automation in IT, or any other business functions.

How Do You Pick Your First AI Automation Use Case?

These five steps provide a practical framework for building an intelligent automation strategy in any department.

• List the decisions inside your process automation workflow: Review a typical week of your work, and look for repeated judgment calls. These are often the strongest starting points for automation in business. 

• Rank decisions by frequency and effort: Prioritize decisions that happen often, and consume significant time. AI automation creates more value when applied to work that repeats at scale.

• Map where the decision happens: Identify the exact step in the workflow where someone decides what happens next. AI automation needs a clear point of action.

• Check whether the right data exists: Look for past records, patterns, and outcomes that show how the decision is made today. AI automation works best when the system has reliable data. Sparky works because Walmart holds years of purchase history.

• Set the level of human review: Low-risk actions can often run automatically, while higher-risk decisions may need review before anything happens. This helps teams automate more work without giving up control where mistakes would matter most.

• Define success before building: Choose a business metric that elaborates the value added because of intelligent automation strategy. This could include time saved, lower costs, faster response times, or higher sales. Businesses like Data Prism, which provides AI automation services, help teams identify the right workflow, and define measurable outcomes before development begins.

The Bottom Line

Walmart’s success with Sparky came from focus, not scale. The company targeted a clear shopping decision, built the automation into an experience customers already used, and measured the business impact.

The best AI automation use case is often a frequent and well-defined decision. The right data should already exist, mistakes should be manageable, and success should be easy to measure. Start there, test the result, and expand only when the automation shows clear value.