Intuit brings AI agentic to the mid-market and saves organizations up to 20 hours per month

Sign up for our newsletters and get only the information that matters to enterprise leaders in AI, data, or security. Subscribe Now Medium-sized businesses are among the fastest growing companies, but they also face a technology dilemma. They have outgrown the small-business solutions, but are still too small to utilize more robust enterprise software. This is the “mid-market” segment. Intuit defines large enterprises as companies that generate between $2.5 million and $100 million in annual revenues. They tend to operate differently than small businesses. Small businesses may run on seven applications. As they grow, mid-market companies are often juggling 25 or more software tools. Mid-market companies often lack the resources to tackle complex system integration projects.

It creates a unique AI implementation challenge. How can you deliver intelligent automation to fragmented and multi-entity businesses without requiring expensive platform consolidating? Intuit is working to solve this challenge. The company behind popular small-business services such as QuickBooks, Credit Karma and Turbotax, among others, wants to find a solution.

Intuit announced in June the launch of a series AI agents that will help small businesses operate more efficiently and get paid faster. Intuit Enterprise Suite is introducing a new set of AI agents to meet the needs mid-market companies.

The enterprise suite introduces four key AI agents – finance, payments, accounting and project management – each designed to streamline specific business processes. The finance agent can, for example, generate monthly performance summaries. This could save finance teams 17-20 hours a month.

This deployment is a case study for addressing the needs in the mid-market segment. It reveals that mid-market AI needs fundamentally different approaches from those required for small businesses and enterprise solutions.

Ashley Still, Intuit’s executive vice president and GM, mid-market, told VentureBeat that “these agents are really about AI combined human intelligence.” “It is not about replacing people, but enhancing their productivity and enabling better decisions.”

GenOS is Intuit’s AI platform that has been under development for several years.

This foundation is built on large language models (LLMs), prompt optimizing and a data cognition level that understands various data types. Since 2024, the company has been developing agentic AI for automating complex business processes.

Mid-market agents are built on this foundation and address the specific needs for mid-market organizations. Mid-market organizations may have multiple lines of business, as opposed to small businesses that might only have a single line of operation. These agents do not require platform consolidation, nor are they isolated point solutions. They work across multiple business structures and integrate deeply with existing workflows.

This approach is exemplified by the Finance Agent. It does more than automate financial reporting. It creates monthly summaries which understand entity relationships and learn business-specific metrics. It also identifies performance differences across different parts of an organization.

Project Management Agent addresses a mid-market specific need: real-time profit analysis for project-based business operating across multiple entities. Still explained that construction companies, for example need to understand profitability on a per-project basis and see this as early as possible in the project’s life cycle. This requires AI to correlate project data with entity specific cost structures and revenue-recognition patterns.

Implementation without disruption accelerates AI Adoption

Many mid-market companies want to use AI, but don’t want the complexity.

As businesses grow, they add more applications, fragment data, and increase complexity,” Still said. “Our goal is simplify that journey.”

The experience is critical to success and adoption. Still explained that mid-market AI capabilities are not a part of an external tool but rather an integrated experience. It’s not just about using AI because it’s the latest technology. It’s about making complex tasks faster and easier to complete.

While agentic AI experiences may be the most exciting new capabilities, AI-powered ease-of-use begins at the very beginning, when users migrate from QuickBooks or spreadsheets to Intuit Enterprise Suite.

“When managing everything in spreadsheets, or different versions QuickBooks, the first step, where you create your multi-entity system, can be a great deal of work because you’ve managed things all over the place,” Still explained. “We have a done for you experience, it does that for us, and creates your chart of accounts.”

According to Still, the onboarding process is an example of a situation where it’s important that users don’t know that it uses AI. The user is only concerned with a simple, functional experience.

What it means for enterprise technology

Technology leaders evaluating AI strategies for complex business environments can use Intuit’s approach as a frame of reference to think beyond traditional enterprise AI deployment.

1. Prioritize AI solutions that fit within existing operational complexity instead of requiring business restructuring to accommodate AI capabilities. Focus on AI that understands the relationships between business entitiesand not just data processing. To minimize disruption and risk, consider integrating workflows rather than replacing platforms .
2. Assess AI ROI in terms of strategic enablementand not just task automation metrics.

Due to the unique needs of the mid-market segment, AI deployments that are most successful will deliver enterprise-grade Intelligence through small-business implementation complexity.

For companies looking to be at the forefront of AI adoption, this means that they must recognize that operational complexity is not a flaw, but a feature. Instead of demanding simplification, look for AI solutions that can work within the complexity. The fastest AI ROI comes from solutions that enhance and understand existing business processes, rather than replacing them.