by Rohit Gupta, Forbes Councils Member
Forbes Finance Council
Rohit Gupta is the CEO and co-founder of Auditoria.AI, a pioneer in AI-driven automation solutions for corporate finance teams.
While much of the layman’s conversation around AI has focused on marketing assets and application building, finance teams have also been asking how AI could streamline their work.
Many financial teams and leaders have set their sights on the emergence of AI teammates and AI agents, specifically.
To be clear, both AI teammates and AI agents can serve critical functions within an organization and provide a range of benefits. That said, their roles, interactions and the scope of their applications differ significantly, and failing to understand these differences could lead to choosing the wrong tool for the wrong purpose.
Understanding AI Agents
While the term “AI agent” might be unfamiliar to some, these applications make up the vast majority of GenAI tools that most people have used. They are also indicative of the first wave of GenAI, which was predominantly about creating new content and streamlining processes to deliver it.
AI agents are personalized assistants designed to interact with users on a one-to-one basis—think ChatGPT, for example. Working via predominantly chat-based interfaces, these agents leverage natural language processing to understand and reply to user questions in a conversational manner. If your social media marketing manager needs to shorten a LinkedIn post into a message-consistent tweet, they might provide the AI agent with a prompt requesting, “Please optimize this post for X at fewer than 260 characters.” This prompt-based, input-to-output model largely defines AI agents, making them useful in some cases, but poorly suited for others.
That said, these solutions are not fundamentally anathematic to finance teams’ goals—even if they are most commonly associated with other use cases. Let’s look at the upsides and downsides of AI agents in the context of the finance department.
Benefits
Accessibility: These agents rely heavily on natural language prompts, allowing users to interact with them as they would with a human assistant. The natural language interface makes it easy for users to interact with AI agents, reducing the learning curve and promoting widespread adoption.
Efficiency: By automating routine tasks, AI agents free up time for financial professionals to focus on more strategic activities.
Accuracy: AI agents minimize the risk of human error in data retrieval and processing, ensuring the information used for decision making is reliable.
Weaknesses
Data security and privacy concerns: Financial teams may worry about the potential for data breaches and ensuring AI systems comply with stringent data protection regulations.
Reliability issues: Concerns exist about the potential for AI agents to make mistakes based on erroneous inputs, rely on poor data or lack the nuanced judgment needed for complex financial decisions.
Cost and implementation challenges: The high costs of initial investment, ongoing maintenance and integration difficulties with existing systems make AI adoption challenging, especially if you’re not working with a provider whose AI agent is specifically designed for financial processes.
Understanding AI Teammates
In contrast to AI agents, AI teammates are designed to support broader business processes and multiple users. Such solutions are the result of the second wave of GenAI, which changed the technology’s emphasis from retrieving information and augmenting it with generated content to owning and executing business workflows.
As a result, these solutions are custom-made to integrate holistically into the workflow of an entire team or organization. Rather than serving individual needs built upon individual interactions, they’re built to provide collaborative support. This multiuser support prerogative means that AI teammates assist several users simultaneously, synthesizing and optimizing a team as a whole (as opposed to the priorities of one single user). This makes them a good option for collaborative environments where multiple stakeholders need to access and interact with shared data.
AI teammates can engage with both internal teams and external partners, which creates a seamless communicative and collaborative interface to address everyone’s needs in concert. For example: If a CFO wants to automate the exchange of financial data with external auditors or coordinate with other departments to ensure compliance with financial regulations, an AI teammate does so without that executive having to send the information manually to every relevant party.
Let’s explore the upsides and downsides of AI teammates.
Benefits
Scalability: AI teammates scale their support across the finance organization, handling increased workloads without compromising on performance.
Collaboration: By integrating into business processes, AI teammates foster a collaborative environment, enabling all aspects of finance to work together more effectively.
Speed: AI teammates expedite the completion of complex workflows, reducing the time required to execute financial processes and improving overall productivity.
Weaknesses
Here, I will hold off on the point-by-point weaknesses, as they are largely similar to those of AI agents. General concerns about AI’s security and accuracy abound, and those developing AI teammates are well aware of them. In many cases, AI teammates make businesses more nervous because a potential glitch could impact an entire department rather than just one individual. That said, because AI teammates are often more centrally monitored—not beholden to the inputs of individuals one at a time—problems might be easier to spot and fix promptly.
Which Is The Better Choice?
In the headline of this article, I posed a bit of a cheat question, suggesting that finance teams were better off with either an AI agent solution or an AI teammate solution. The answer is that both can serve a purpose.
While AI agents provide personalized, one-to-one interaction and execute specific processes with precision, AI teammates offer collaborative support, synthesizing workflows and enhancing overall efficiency. Moreover, as we move into the second wave of generative AI applications, characterized by the synthesis of workflows and the execution of processes, the strategic integration of AI agents and AI teammates can unlock new levels of productivity, innovation and strategic decision making in the CFO’s office. These AI applications can elevate both the individual contributions in finance teams, as well as the productivity and efficiency of the finance department as a whole.
By embracing these AI technologies, CFOs not only streamline operations but also position their organizations for long-term success in an increasingly complex and competitive business environment.
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Rohit Gupta is the CEO and co-founder of Auditoria.AI, a pioneer in AI-driven automation solutions for corporate finance teams. Read Rohit Gupta’s full executive profile here.
