AI agents are popping up everywhere, and their promise is hard to ignore. Unlike traditional AI, which offers insights or flags risks, AI agents take action, approving expenses or routing invoice exceptions to the right person automatically. How you bring them into your organization can make or break their speed to deployment, accuracy, safety, and long-term impact.
In finance, mistakes aren’t an option.
So, what’s the smartest way to integrate AI agents into your team’s workflow? Let’s explore the differences between AI and AI agents, then break down the pros and cons of three key approaches: build, borrow, or buy.
AI vs. AI agents: What’s the difference?
Artificial intelligence (AI) is a set of models or algorithms that make predictions, classify data, or surface insights. But AI can’t do anything with that information by itself. AI Agents, on the other hand, are like digital assistants. Not only can they understand documents or instructions and respond to requests, they can independently take action. Give them a job to do, and they plan and execute the steps to complete it, all on their own.
Manual processing and traditional automation are giving way to AI agents that can verify, validate, and approve invoices, expense reports, and other financial documents with very little human intervention. Given the amount of investment still pouring into AI infrastructure, it’s clear this is not a fad.
The AI agent dilemma facing finance leaders
Finance leaders are watching their back office tech stack transform seemingly overnight. That means their teams are changing, too. Companies like Salesforce are creating entire cadres of AI agents to improve automation by assisting their customers in completing tasks and resolving problems.
If you’re trying to get a grasp on how to make your teams more efficient while still maintaining safety and compliance, here’s an exploration of three routes to AI agents organizations typically take–and why one consistently delivers faster, safer, more cost-effective results:
1. Build: Creating custom AI agents in-house using large language models (LLMs)
2. Borrow: Using generic AI/IT platforms (such as workflow tools with AI capabilities)
3. Buy: Adopting a purpose-built AI platform specifically designed for finance operations (like AppZen)
Each approach varies in its accuracy, speed to value, and maintainability. Understanding these differences is crucial for making the right investment and maximizing ROI for your organization.
Building AI agents in-house, using generic LLMs
As large language models (LLMs) and artificial intelligence tools grow easier to access and deploy, AI development is speeding up. Programmers and non-programmers alike are employing techniques like “vibe coding,” using natural language tools and LLMs to quickly build code.
Organizations with strong IT and data science teams might consider developing their own AI agents using large language models or other AI frameworks. This DIY approach offers maximum flexibility, as the AI can be tailored to the company’s specific invoice layouts, expense policies, and approval rules. Over time, a custom agent could incorporate unique business logic that off-the-shelf solutions might not handle.
The downside
Custom development gives complete control but requires substantial investment and patience before delivering value. This path comes with significant challenges and risks:
Extensive development spanning 12-24 months before seeing ROI
High costs for specialized AI talent and ongoing maintenance
Data privacy concerns when using external LLM APIs, which may expose financial data to third-party systems
Hallucinations, where the AI is incorrect in its predictions but reports them as correct in a very confident way, are a significant risk in financial contexts, with studies showing up to 41% hallucination rates in finance-related queries
Borrowing from generic platforms with AI capabilities
Another option is extending existing IT automation platforms or service management tools with added AI capabilities. Platforms like ServiceNow increasingly advertise AI or ML add-ons that can be applied to various business processes. These general-purpose tools may already handle workflows in IT or HR departments and seem like natural candidates for finance automation, promising a unified solution.
The downside
While this approach can provide some automation, “one size fits all” leads to significant limitations in specialized finance use cases. Generic platforms offer familiarity but rarely deliver the deep finance expertise needed for effective accounts payable and expense auditing automation. The nuances of finance data (e.g., tax calculations, merchant matching, regulatory flags) are hard-won knowledge that generic tools typically haven’t mastered.
Lower accuracy in detecting non-compliance and potential fraud due to a lack of finance-specific intelligence
Only 30% automation with generic AI often, versus 60-80% with specialized solutions
Slow implementation as a result of extensive customization and rule-building
Poor user experience and low adoption, as users must constantly switch between insufficiently integrated systems
Buying purpose-built finance AI platforms
While custom solutions offer flexibility and generic platforms promise integration, a ready-made platform purpose-built for finance, like AppZen's Mastermind AI Automation Platform, strikes the perfect balance of flexibility, fusion, and fine-tuning. These specialized AI agents deliver quick wins without compromising enterprise-grade governance. You get all the benefits of AI with none of the development headaches.
Pre-trained on millions of financial documents, the Mastermind Platform immediately understands invoices, receipts, and expense policies, giving you high-accuracy automation with strong safeguards from day one. Its AI Agents (with a capital “A”) have been refined through deployments at numerous enterprises, ensuring deep domain expertise that generic solutions simply can’t match. Payoffs include:
Rapid implementation measured in weeks rather than months or years
Elimination of hallucinations and strong accuracy controls
Robust governance features, including audit trails and explainable decisions
Proven scalability across multiple countries and ERP systems
Out-of-the-box finance intelligence pre-trained on financial documents for higher accuracy
The downside
The challenges you’ll face purchasing pre-built, finance-focused AI agents depends mainly on your long-term goals. Developing your own AI agents might be a worthwhile investment if you plan to build a more comprehensive AI ecosystem. Althoughe unique or complex requirements might seem to necessitate in-house development, an experienced AI provider can also create bespoke solutions that are perfectly tailored to your needs.
Making a strategic choice
When evaluating AI options for finance operations, organizations must weigh speed to value, accuracy requirements, and governance needs. Custom solutions offer flexibility. Generic platforms promise integration with existing systems. Purpose-built AI agents deliver immediate accuracy with minimal risk. For finance teams facing increasing pressure to do more with fewer resources, AppZen combines the speed of pre-packaged solutions with the specialized knowledge and control needed for finance operations at scale.
Whether your goal is to streamline expense audits, increase invoice processing accuracy, or enhance compliance, AI agents are your foundation for the future. By carefully assessing your organization’s specific needs against the strengths and limitations of each approach, you can select an implementation path that delivers both immediate efficiency gains and sustainable long-term value.
Figuring out which approach best aligns with your business is just a conversation away. If your team is ready for a more in-depth discussion, contact us. We’re always ready to explore with you how AI agents can help your finance team.
