Identify business needs and define AI use cases

  • 5 minutes

You should prioritize your use cases to focus on ones that have the most business impact and are the most cost efficient. Consider the following factors:

A diagram showing icons that represent business needs and AI use cases.

Reduce overhead

Focus on use cases that minimize manual labor, administrative burden, or redundant processes.

For example, you could create a patient intake assistant: an AI agent that interacts with patients via a secure web or mobile interface to collect, validate, and structure intake information before the patient's appointment. Before implementing this solution, receptionists would manually enter patient details from paper forms into electronic health records (EHR), often duplicating effort, and introducing errors. With the AI agent, patients fill out forms online, the agent clarifies ambiguous responses with additional questions, data is validated in real time via API integrations, and the structured data is automatically pushed to the EHR system.

Streamline resource allocation

You should identify use cases that optimize resource efficiency.

You should focus on use cases with a precisely designed scope to avoid over-engineering, potentially using multiple, modular, agents rather than one complex, monolithic, agent. You should consider use cases that use lightweight models and task-specific models, where appropriate, rather than always turning to large general-purpose models, to increase resource efficiency.

For example, you could deploy multiple, modular factory line optimization agents. In manufacturing, resource efficiency is critical, not just in energy or materials, but in compute and operational overhead. Instead of deploying a single, monolithic AI agent to manage the entire production line, companies are increasingly using modular, task-specific agents that each handle a distinct function.

Improve scalability

Select use cases that enable growth without proportional increases in cost or complexity.

You should focus on use cases that can automatically scale on demand and that continually learn and adapt, without the need for retraining.

For example, insurance claims processing agents could handle the entire claims workflow, from First Notice of Loss (FNOL) to verification and settlement recommendations, without requiring constant retraining. The agents could adapt through feedback loops and real-time data ingestion to automatically scale during high-claim periods, for example, after a natural disaster.

Drive productivity gains

Target areas where AI agents can accelerate task completion or enhance employee output.

You should consider use cases that automate repetitive tasks and agents that apply intelligent decision-making to improve efficiency.

For example, AI agents for automated expense management could streamline the entire expense reporting workflow, from receipt capture to policy compliance checks and reimbursement, without human bottlenecks. The agents would combine automation with intelligent decision-making to eliminate repetitive tasks like manual data entry and receipt matching, apply policy logic to flag anomalies or noncompliant expenses, learn from corrections to improve future accuracy without retraining, and integrate with ERP systems.

Enhance customer satisfaction

You should consider use cases that enhance customer satisfaction. For example, you might choose to enhance customer satisfaction by delivering improved response times, creating agents that are personalized to the user, consistent and accurate, and available 24/7.

Support revenue growth

Consider use cases that enable upselling, cross-selling, or better customer retention.

For example, you could consider agents that use machine learning to proactively identify customers who are most likely to benefit from an upselling or cross-selling proposal. Also consider sales support agents that suggest products that are often bought together, or which conditions might prompt an upgrade. Furthermore, investigate use cases that detect signals that might result in churn, such as reduced product use or increased service desk tickets, allowing an intervention before the customer is lost.

Examples of cost-effective AI agents

Here are two examples of AI agents, one, which is cost effective and one, which isn't:

IT helpdesk agent

This AI agent handles common IT issues such as password resets, software installation guides, VPN setup, and printer troubleshooting.

This is cost effective because it has:

  • Low training cost Use pretrained models fine-tuned on internal documentation.

  • 24/7 availability There's no need for shift work or overtime pay.

  • Quick ROI Deflects 50% of tier1 support tickets, reducing wait times and allowing support staff to deal with more complex issues.

  • Scalability The chatbot can be adapted to new products, tasks, languages, or markets.

Predictive AI system to manage stock levels

As a small specialist bookstore with 500 titles, you invest in an AI agent to predict future demand and manage stock levels.

This is not cost effective because it has:

  • High development and maintenance costs Custom AI solutions can be expensive to build and require ongoing tuning.

  • Overkill for the task Basic spreadsheet tools or rule-based systems could do the job as well.

  • Low ROI The complexity doesn't translate into meaningful savings or performance gains.