Agentic AI is taking the tech world by storm with the promise of completely transforming business operations. From increased automation to enhanced data visualization, AI agents are starting to provide real value to organizations when properly implemented and maintained. And as software vendors continue to invest heavily in the foundations for agentic AI, the systems will become even more mainstream and easier to use. However, truly realizing the value of agentic AI requires developing a clear agentic AI strategy focused on practical and achievable use cases.
Unlike many other complex software concepts, Agentic AI is exactly what it sounds like: artificial intelligence tools built to perform certain functions for your business, acting as autonomous “agents.” These AI agents can perform many different tasks, typically delivering automation around process-based work. AI agents can only perform tasks pre-built by your internal team based on your business’s data. Solutions are being developed to handle tasks like bank reconciliations and anomaly detection, but not necessarily complex decision-making (yet).
AI agents operate differently than other forms of artificial intelligence because they possess some level of autonomy. They can provide goal-directed planning, as well as suggest actionable solutions based on areas of concern within their workflow. AI agents can also be orchestrated or paired with other AI agents in a “stream” to complete complex processes across multiple business functions by prompting the other tools to launch the next step in the workflow.
Currently, AI agents are best deployed to take care of the “busy work” while freeing up human employees for the more thought-intensive tasks requiring judgement. And crucially, like most other artificial intelligence tools currently on the market, human intervention is still crucial in verifying information when utilizing agentic AI. At this time, the agents should only be set-up to suggest solutions or notify you of issues and concerns but not act on those tasks without user verification and acceptance. This human individual can be thought of as your AI coach or conductor over the agents, ensuring that nothing being done is based on a hallucination or inaccurate data.
While traditional AI is more reactive and rule-based, agentic AI orchestrates multiple components at once to achieve broader objectives and act in accordance with a business’s needs. AI agents go beyond simple command-response models, and can instead independently set goals, strategize, utilize other tools, learn from experiences, and execute complex tasks. The autonomous nature of AI agents also allows for less human oversight and intervention than more traditional AI tools, although it does not remove the need completely.
At this point, all major software vendors have an agentic AI strategy for current innovation and the future product roadmap. This means customers on modern software solutions have access to robust agentic AI tools, along with various studios for ongoing AI and workflow development. Through these innovations, the customization and development of AI tools to meet your business’s unique needs has never been easier. This is significant because traditional models required heavy intervention and expertise.
As these tools become more commonly adopted and offered, vendors are also beginning to change to more consumer-based pricing models, including hybrid models with flexible AI offerings, and value-based selling contracts focused on outcomes rather than features.
Most vendors offer an agent directly within their solutions, built more generically to meet basic operational needs. However, these agents are structured to work within the vendor’s architecture and workflows. Established vendors have formatted their agentic tools to function around common business workflows, building upon their pre-existing functionality. Alternatively, newer AI-based vendors are attempting a more fresh, ground-up approach to envision new ways of automating business processes with agentic AI at the core. The new generation of “AI-native” solutions have framed their architecture around Large Language Models (LLMs). As a result, these vendors’ tools can be trained easier, and their interfaces are more customizable at this time. Ultimately, each vendor is investing in the user experience (UX) and user interface (UI) to make interacting with the solution easier and allowing customers to speak in plain language, without the technical knowledge.
Additionally, integrations are becoming more efficient and effective across vendor platforms to ensure greater connectivity for the utilization of AI. Departments, locations, and business entities have never been more connected on a deep, database level.
We have also seen vendors competing to create “AI orchestration platforms,” allowing users to build their own AI agents and workflows in a “no code” or “low code” environment. This ease of use will deepen agentic AI’s unique capabilities and allow the agents to flex for niche business requirements.
“The future is ease of use. Whatever vendor gets there first will take a lot of market share…”
The most impactful way businesses are utilizing AI agents today is automation. Automating tasks and reducing human effort enables resources to dedicate their time to more value-added tasks, empowering strategy over manual work. Organizations are seeing automation used for key tasks surrounding data entry and analysis. The agent can handle previously tedious data entry tasks and can even provide analysis based on the data entered. Once the analysis is created, the AI agents can also provide better presentation of reports, making it easier to retrieve insights from your business’s data.
While it is always encouraging to see how technology’s capabilities have developed, agentic AI tools will not be widely adopted until value is being achived by early adopters. Record to report is becoming more automated, and the concept of a “zero-day financial close” is being realized by vendors such as Rillet. While the technology is not quite yet capable of closing the books in “zero days,” we are getting closer with every passing month. The agents are learning after each month-end close, providing high-level anomaly detection, and are requiring less human intervention. AI agents are also presenting recommendations for tasks regarding cash flow, vendor management, and account management, enabling financial executives to review recommendations and either approve or deny actions.
The businesses realizing value through agentic AI rely on their vendor’s offerings and flexing those solutions to match their business. Vendors drive the usage of your agents, and through supporting the application of these tools, vendors can deliver even greater, specialized value. To create value-added use cases in your business, you will need someone on your team who is invested in AI to head the initiative and begin the process with your vendor or partner. The agentic AI world is complex, so make sure you assess your people and find the right person to tackle this initiative!
With the value of agentic AI being established and realized, it is crucial to begin thinking about how your team will take advantage of the technology. Businesses can prepare for agentic AI by:
Overall, the most important thing your business can do to prepare for agentic AI is start!
AI agents bring exciting new possibilities to the world of enterprise software. But it is important to ensure that you have taken the correct steps to prepare your business for new technology and its impact on your employees. If you and your organization would like to leverage emerging AI solutions, but are not sure where to start, we can help. Schedule a free introductory meeting with our team today!