Rohit Gupta is the CEO and Co-Founder of Auditoria.AIa pioneer in AI-powered automation solutions for enterprise finance teams.
While much of the lay conversation around AI has focused on marketing assets and app building, finance teams have also wondered how AI could streamline their work.
Many financial teams and leaders have set their sights on the emergence of AI teammates and agents, in particular.
To be clear, both AI teammates and agents can serve critical functions within an organization and provide a range of benefits. That said, their roles, interactions, and scope of 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” may be unfamiliar to some, these applications make up the vast majority of GenAI tools that most people have used. They are also representative of the first wave of GenAI, which was primarily about creating new content and streamlining the processes for distributing it.
AI agents are personalized assistants designed to interact with users on a one-to-one basis, like ChatGPT for example. Operating primarily through chat-based interfaces, these agents leverage natural language processing to understand and answer user questions in a conversational manner. If your social media marketer needs to shorten a LinkedIn post into a tweet that’s consistent with the message, they might provide the AI agent with a prompt asking, “Please optimize this post for X to under 260 characters.” This prompt-based input-output model largely defines AI agents, making them useful in some cases but ill-suited for others.
That said, these solutions are not fundamentally contrary to the goals of finance teams, even if they are most often associated with other use cases. Let’s examine the pros and cons of AI agents in the context of finance.
Benefits
• Accessibility: These agents rely heavily on natural language messages, 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 driving widespread adoption.
• Efficiency: By automating routine tasks, AI agents free up time for finance professionals to focus on more strategic activities.
• Precision: AI agents minimize the risk of human error in data retrieval and processing, ensuring that the information used for decision-making is reliable.
Weaknesses
• Data security and privacy issues: Finance teams may be concerned about the risk of data breaches and the need to ensure AI systems comply with strict data protection regulations.
• Reliability issues: There are concerns that AI agents could make mistakes due to flawed data, rely on poor-quality data, or lack the nuanced judgment needed to make complex financial decisions.
• Cost and implementation challenges: High initial investment costs, ongoing maintenance, and difficulties integrating with existing systems make AI adoption difficult, especially if you’re not working with a vendor whose AI agent is specifically designed for financial processes.
Understanding AI Teammates
Unlike AI agents, AI teammates are designed to support broader business processes and multiple users. These solutions are the result of the second wave of GenAI, which shifted the focus of the technology from retrieving information and enriching it with generated content to owning and executing business workflows.
These solutions are therefore tailor-made to integrate holistically into the workflow of an entire team or organization. Rather than addressing individual needs based on individual interactions, they are designed to provide collaborative support. This multi-user support prerogative means that AI teammates support multiple users simultaneously, synthesizing and optimizing a team as a whole (as opposed to the priorities of a single user). This makes them a good option for collaborative environments where multiple stakeholders need to access and interact with shared data.
AI team members can interact with internal teams and external partners, creating a seamless interface for communication and collaboration to meet everyone’s needs in a coordinated manner. 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 team member can do so without the leader having to manually send information to each affected party.
Let’s explore the pros and cons of AI teammates.
Benefits
• Scalability: AI teammates extend their support across the entire finance organization, handling increased workloads without compromising 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 accelerate the completion of complex workflows, reducing the time it takes to execute financial processes and improving overall productivity.
Weaknesses
I won’t go into the weaknesses point by point here, as they are largely similar to those of AI agents. General concerns about the safety and accuracy of AI abound, and those who develop AI teammates are well aware of them. In many cases, AI teammates make companies more nervous because a potential problem could impact an entire department rather than a single individual. That said, because AI teammates are often more centrally monitored (and don’t rely on individual input), problems can be easier to spot and fix quickly.
What is the best choice?
In the title of this article, I asked a somewhat misleading question, suggesting that finance teams would be better off with an AI agent solution or an AI teammate solution. The answer is that both can serve a purpose.
While AI agents provide personalized, one-on-one interaction and execute specific processes with precision, AI teammates provide collaborative support, synthesizing workflows and improving overall efficiency. Additionally, as we enter the second wave of generative AI applications, characterized by workflow synthesis and process execution, the strategic integration of AI agents and AI teammates can unlock new levels of productivity, innovation, and strategic decision-making within the CFO’s office. These AI applications can elevate both individual contributions within finance teams, as well as the productivity and efficiency of the finance department as a whole.
By adopting these AI technologies, CFOs are not only streamlining their operations, but also positioning their organizations for long-term success in an increasingly complex and competitive business environment.
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