Artificial intelligence has quickly become a priority for finance leaders across industries, driven by big promises of faster processes, better insights, and greater efficiency. According to a recent KPMG report, 75% of organizations now actively use AI in the finance function, up from just 30% in 2024.
But in many cases, the first generation of AI hasn’t delivered on those promises. Instead of transforming the way finance teams work, AI has largely functioned as a chatbot. Sure, it can summarize information and provide generic suggestions, but it can’t understand the broader business context or connect to the systems where finance work is actually happening. So while adoption is high, it’s not surprising that just 23% of organizations feel AI is exceeding their expectations.
Fortunately for finance leaders, the chatbot era of AI is coming to an end. What will take its place will be AI that has the right context, system connectivity, and governance needed to become an active participant in financial operations.
Standalone AI is missing the mark in finance
For most finance teams, the first wave of AI adoption took the form of a chat interface from frontier models like ChatGPT and Claude, where data was exported from existing technology stacks and cut & pasted into a chatbot. Meanwhile, software developers rushed to add AI features to existing platforms and startups promised an “AI native” experience.
ChatGPT and Claude can certainly improve productivity, especially for tasks such as summarizing documents and other simple tasks. But that’s not exactly the picture of transformation finance leaders had in mind when they made their initial AI investments.
The problem with standalone AI is that it lives outside of the systems where finance work is actually done. It can’t access AP workflows, vendor records, invoice data, payment histories, which provide critical context that fuels meaningful recommendations. What’s more, because standalone AI operates separately from other tools in the finance tech stack, it can’t take action on the recommendations it provides.
For example, one standalone AI tool may analyze a discrepancy, but it can’t investigate it. Another can recommend an approval, but it can’t route the invoice to the right people at the right time.
Open protocols like MCP are the next era of AI
In the past few years, finance teams have heard plenty of big promises about how AI will transform the way they work. But for many, the first generation of AI hasn’t lived up to that hype. Open protocols like the Model Context Protocol (MCP) are starting to change that.
Simply put, MCP is a standardized way for AI to securely connect to the systems where finance work happens. Instead of operating as a standalone chatbot, AI can access the right data, understand business context, and perform approved actions in real time.
This is a notable shift for the finance function. AI was once limited to answering questions. But with open protocols, it can become an active participant in financial processes and finally start to live up to those big promises.
Let’s say a supplier submits an invoice to the buyer’s AP team, and a price doesn’t match the purchase order. A standalone AI tool could explain the difference or suggest next steps. But AI connected through an open protocol like MCP can take an active role in the investigation and resolution. It can zero in on the right purchase order and vendor history, compare current pricing to past invoices, identify the likely cause of the mismatch, route the invoice to the right approver, notify the appropriate stakeholders, and record the resolution. All of this happens within the organization’s established permissions, approval policies, and workflows. As a result, manual effort goes down, but the team is always in control.
While standalone AI tools may increase productivity in some ways, they’ll never truly transform the way finance teams work. But when AI has the right context and connectivity, it can finally have the impact finance leaders were promised.
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The impact is especially pronounced in finance and AP
Each day, finance teams create and manage massive amounts of operational data, including invoices, purchase orders, vendor records, payment histories, contracts, and approval workflows. Collectively, this data can provide the context AI needs to make better decisions and take impactful action. But at most organizations, financial data is scattered across disconnected systems and governed by strict controls.
Open protocols like MCP provide structure so AI can connect to that financial data securely. Teams no longer have to manually cut & paste data between disconnected tools because AI can access the right information at the right time, while staying within the guardrails of the organization’s existing permissions, approval policies, and audit requirements. As a result, finance can save time and move faster, while maintaining security and control.
While open protocols can have a significant impact across business functions, it’s especially evident in accounts payable where a lack of context often leads to late vendor payments, unidentified fraud, and overspending. For example, resolving an invoice discrepancy might require finding the right purchase order, checking historical pricing, confirming goods were actually received, and identifying the appropriate approver. Or, navigating a vendor dispute might involve searching through countless emails, payment records, and supporting documentation. All of these tasks take time, and slow resolution can cause real business risk.
With open protocols like MCP, these formerly labor-intensive investigations can often be completed in minutes. Issues get resolved faster and teams have more time to dedicate to higher value work.
With open protocols in place, AI evolves from a nice-to-have productivity feature to foundational finance infrastructure. The organization is equipped to make better, more timely decisions that move the business forward, without sacrificing accuracy, governance, or control.
Open protocols add governance where there was none
As AI becomes increasingly ingrained in business operations, many finance leaders remain hesitant. Security is often a top concern, and it’s a well-founded one. Financial systems contain highly sensitive information, and leaders must ensure any technology that touches that data meets the highest security standards.
Fortunately, open protocols don’t mean wide-open access to financial systems. In fact, it’s quite the opposite. Open protocols like MCP establish secure, role-based connections between AI and finance systems that respect existing user permissions, approval policies, and audit requirements. Every interaction can be scoped, monitored, and logged.
In fact, this approach is often far more secure than what’s currently happening at many organizations. Today, employees often feed invoices, contracts, or other sensitive financial data into public AI tools. These informal workflows aren’t properly governed, and when employees use them, they open the door to security and compliance risks.
Open protocols provide a safer, more secure alternative. Instead of employees working around existing systems, organizations can give AI secure, auditable access to the right information while maintaining the governance, accountability, and control finance teams require.
What’s next for finance AI
The chatbot era of AI in finance is giving way to a new generation where open protocols provide AI with the context and connectivity needed to deliver real business value. Finance teams that adopt this approach early on will see significant efficiency gains, as well as a lasting competitive advantage that continues to grow.
The longer these protocols are in place, the more context AI has to learn from. As a result, its ability to streamline routine tasks, surface insights, and support decision-making continues to improve. Organizations that delay adoption will find it difficult to catch up with these early adopters.
At the same time, open protocols are changing the role of finance. When organizations have a financial operating system that’s equipped to manage routine work in the background, teams have more time to focus on higher-value work, analysis, and decisions that truly require human effort.
The financial operating system isn’t defined by a single tool or platform. It’s the direction the industry is moving, where AI has the context, connectivity, and governance needed to become an active participant in everyday financial operations.
About Ottimate
Ottimate is an AI-powered Accounts Payable automation platform that empowers finance teams to reduce costs, prevent overpayments, detect fraud, and enforce policy compliance across the entire invoice-to-payment lifecycle.
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