If your product team is like most, it’s grappling with the best ways to incorporate artificial intelligence (AI) into its products.

After all, the options for using AI technology are seemingly endless: You can build everything from an enterprise AI search that answers users’ questions to AI agents that take all kinds of actions on behalf of users.

To help you identify, prioritize, and implement the best AI product ideas, we’ll walk you through several options across industries. 

Provide best-in-class support for your online store

Imagine you run an e-commerce store and want to improve the quality of customer support you provide at scale.

To help, you can develop an AI agent that can access and use transaction data in your ERP system to diagnose and resolve issues.

For instance, if a customer wants to modify an order, the AI agent can see whether the order was already shipped. Based on what it finds, it can decide on the appropriate outcome (e.g., allow the customer to modify the existing order) and craft a corresponding message that your support rep can use with the affected customer.

Related: A guide to setting product objectives

Empower PMs to analyze product issues and opportunities effectively

You can offer a product intelligence solution that can listen to customer conversations and, on a predefined cadence, use a large language model (LLM) to generate the key themes from those conversations. These reports can then get sent via Slack or email so that PMs can easily find and review them quickly.

To help PMs better understand which issues are more important and prioritize them accordingly, you can also integrate with their CRMs and pipe in certain data for a given report—such as the accounts impacted and their total annual recurring revenue.

Build an AI enterprise search that answers employees’ questions

Say you provide an intranet solution and you want to help users get any of their questions answered, quickly.

To help facilitate this, you can integrate with their file storage solutions and feed the documents to the LLM you use. 

The LLM can then use natural language processing (NLP) to understand and answer users’ questions through the file contents it's ingested. 

The LLM can even use access control levels (ACLs) to source from the files the user has access to. And the response can link to the source(s) in case the user wants to learn more.

Related: Common RAG use cases

Provide AI-powered sales recommendations

Imagine your platform serves sales teams and you want to help them source the best leads at scale.

To help you do just that, you can integrate with customers’ CRM systems and sync data on their opportunities to better understand the types of accounts they’ve recently closed, the ARR associated with those deals, the contacts who were champions, and more.

The LLM you use can then leverage these insights coupled with users’ inputs (e.g., “I want to target CTOs at enterprise SaaS companies in Europe”) to continually identify and recommend the leads that are most likely to close at a relatively high ARR.

Create and modify financial models for finance teams

Let’s assume you offer a financial planning and analysis (FP&A) solution that can help finance teams analyze and forecast key financial metrics, like their run rates and headcount costs.

To help your users analyze these metrics faster and more easily, you can integrate with their ERP solutions and sync financial data from documents like P&L statements.

You can then feed this data to the LLM you use, which can use it to auto-calculate the financial metrics users are interested in. And, if users want, the LLM can use the financial data to populate customizable models (e.g., Runway forecast).

Enable recruiting teams to identify thousands of candidates in seconds

Say you offer a recruiting automation solution and you want to help talent teams find top candidates for open roles quickly.

To that end, you can integrate with customers’ ATS solutions and display the customer’s open roles within their instance of your platform. 

Once they click on a role, the job description from the integrated ATS would get fetched and fed to the LLM you use, triggering the LLM to kickstart a candidate search with the job description (the LLM would go on to generate thousands of candidates in seconds).

Related: How startups can build AI product features

Answer finance teams’ questions in real-time

You can offer an AI assistant for accounting firms that can carry out several tasks on behalf of users by integrating with their ERP systems.

For example, the AI assistant can add transactions to a customer’s ERP system as needed; run reports to validate the accuracy of certain data; analyze the financial data and come up with key takeaways via plain text, and more.

Help clinicians avoid writing notes on patients 

Imagine you offer a healthcare platform that supports clinicians’ documentation on patients.

To allow clinicians to save time, you can offer an AI-based scribe that listens to conversations with patients and, based on the conversations, auto-populate medical notes in whatever format the clinicians use. The clinicians can then review the notes in your product, and once they sign off on them, they get pasted to the electronic health record automatically.

Power AI agents that complete tasks on behalf of customer-facing teams

Imagine you offer a platform that supports customer-facing users like account executives and customer support reps. To further help these users, you can add agentic AI capabilities that can reference user input and integrated data to retrieve information and perform relevant actions.

For example, a sales manager may want to create a custom sales dashboard to better understand how their teams are performing and how they’re tracking towards their sales goals.

To help the sales manager do just that, you can integrate with their CRM system and fetch data related to their opportunities, customers, reps, and more. The AI agent can then use the integrated CRM data and the sales manager’s input to populate the dashboard they want—and the agent can continually update it.

Enable people managers to support their team effectively

Finally, say you offer a performance management platform and want to help people managers identify the top areas that their direct reports need to work on and improve in.

To address this, you can integrate with customers’ HRIS solutions, feed the performance review data to an AI-driven chatbot in Slack, and let people managers ask the chatbots to craft development plans for specific individuals based on their most recent reviews. 

How to build differentiated AI products

To help you build long-lasting and impactful AI solutions, it's worth keeping the following in mind.

Analyze product usage behavior to identify the best AI opportunities

Given all the possibilities with AI, many companies try to build as many AI features and products as possible.

The reality is that your users and prospective users would only benefit from a handful of AI use cases in your product. And if you err on the side of building as much as possible, you’ll likely fail to address these use cases fast enough and effectively.

To help you pinpoint the most important AI functionality for your product, you can rely on a data-driven approach: Analyze product usage behaviors and look for patterns of when users get stuck or move away from your product. 

For example, if customers are slow to adopt your product, you may want to prioritize AI features that help customers realize a fast time to value.

Make your features available in the places your customers work in

As many of our examples showed, the AI products were available within tools like Slack and within function-specific applications (e.g., Zendesk for customer support).

This makes the process of adopting your AI products relatively seamless, which helps drive adoption and, in turn, improves the customer experience. 

With this in mind, it’s worth researching the platforms your users typically work in and would want your AI product to be compatible with before going on to build your solution.

Outsource your customer-facing integrations

Your AI product will likely need clean, up-to-date, and accurate customer data. In addition, it’ll probably need data from several types of solutions—whether that’s file storage platforms, CRMs, ATSs, ticketing tools, or ERP solutions.

The process of building these customer-facing integrations—let alone maintaining them—can take your developers hundreds of hours, which prevents them from focusing on the core AI development tasks they need to complete to help you bring a cutting-edge solution to market.

Your team can, instead, turn to a unified API solution, which allows you to build to a single, aggregated API to add hundreds of integrations to your product.

In addition, using Merge, the leading unified API solution, your engineers don't need to maintain the integrations and your customer-facing teams can access the tooling they need to manage each integration. Taken together, your customer-facing integrations will experience little downtime—which enables the LLM you use to gather all the data it needs over time.

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