Impact of Agentic AI on Business Efficiency & ROI

In 2023, $67.2B was invested in GenAI alone in the United States, indicating optimism that this technology will significantly enhance productivity and innovation.

Combined with natural language processing (NLP), RAG and LLM fine-tuning techniques, artificial intelligence has proven increasingly useful in knowledge-intensive applications such as customer service, sales and marketing, law, and research. Capabilities to generate relevant content, images, and code for specific business functions are able to assist in automating routine tasks and improving employee productivity by as much as 40% according to an MIT paper.

Although generative AI is showing these benefits, it is still in its early stages and has limitations, such as having to automate tasks linearly and not being able to perform actions, thereby not fully realizing the ROI with AI and value to the business.

Recent developments in AI have led to the introduction of AI agents that plan, reason, decide, and automate tasks autonomously. These AI agents are often called Agentic AI because they work cooperatively with other agents with minimal human intervention to achieve specific goals.

A company that adopts agentic AI will gain competitive advantages in innovation, efficiency, and responsiveness while becoming more agile in its operations. Investments in AI agent projects are expected to yield orders of magnitude in ROI and business value as long as companies select high-impact use cases.

In this blog, we share a perspective on the ROI and business value of investments in Agentic AI.

>Agentic AI has the ability to perceive, adapt, and make decisions based on its environment, take actions to accomplish objectives, and learn from the results, resulting in business value measured in the following ways:

• Increased operational efficiency and productivity – Automating routine tasks with Agentic AI allows employees to focus on more valuable, complex tasks. Agentic AI improves efficiency and productivity by identifying inefficiencies in workflows and adjusting them in real-time. For example, routine IT customer support tasks such as password resets, application installs, and approvals can be automated to free up customer support personnel’s time, helping the operations team significantly.
• Enhanced decision making – Agentic AI’s ability to interpret data in real time, and predict future outcomes based on historical context and current data points gives an edge in real-time data-driven decision making. As an example, Agentic AI is able to predict the supply and demand of products, optimize logistics, and adjust pricing based on market conditions.
• Improved customer satisfaction – Agentic AI enhances customer satisfaction by automating customer interactions at every touchpoint through personalized experiences, faster service, proactive problem-solving, and continuous improvement. For example, Agentic AI can tailor customer support interactions for an individual customer or predict customer needs based on context and history.
• Innovation and competitive advantage – Agentic AI accelerates innovation cycles by speeding up research, development, experimentation, and testing. Using its autonomous decision-making and learning capabilities, businesses can unlock new levels of creativity, optimize processes, bring products to market faster, and gain a competitive advantage over the long run.
• Scalability and Adaptability – As business demand increases, Agentic AI can scale seamlessly. Unlike existing software which requires more resources, personnel, and infrastructure to grow, Agentic AI systems can scale without the proportional increase in cost or complexity. Agentic AI systems are also able to adapt their decisions and optimize workflows based on real-time data and learnings from every interaction. Automation of repetitive tasks, and scaling customer support to new product lines, domains, and geographies are examples of how Agentic AI can scale and adapt.

As Agentic AI evolves rapidly, measuring ROI poses the following challenges:

• Agentic AI systems have a learning curve before they reach peak potential and this needs to be factored in when calculating ROI.
• Improved decision-making quality is a soft benefit that is harder to quantify.
• The full value of Agentic AI can only be realized over an extended period as it learns and adapts.

Agentic AI offers significant benefits, but it is still a nascent field and its implementation comes with certain challenges:

• Privacy – Accessing and processing large amounts of data can raise privacy and security incidents concerns. Any Agentic AI system being implemented should be able to ensure data security, and privacy, and comply with regulations such as GDPR etc.,
• Transparency – Having the ability to explain how AI agents make decisions is crucial to establishing trust and transparency. Without it, it will be difficult to justify AI decisions.
• Technical Challenges
  • Data Quality – Poor data quality can lead to inaccurate predictions and decisions. Proper data curation and pre-processing should be an essential part of the implementation.
  • Integrations with existing systems – Integrating Agentic AI into existing enterprise systems and workflows can be complicated and time-consuming. By partnering with the right vendor who has pre-built integrations and workflow library, this challenge can be mitigated.
• Organizational readiness
  • Business Processes – Process discovery related to workflows may be challenging if existing processes are not documented or have multiple interconnected steps and decision points. Lack of expertise in organizations can make this process time-consuming and overwhelming, impacting critical operations within an enterprise.
  • Skills Gap – Many companies lack the skills and competencies to develop, deploy, and maintain Agentic AI solutions. By partnering with Aisera, businesses can access the latest Agentic AI product innovations, have staff training, and build competency.

The future of AI is promising. To realize the long-term value and high Agentic AI ROI, one of the key factors for organizations to consider is how to deploy AI agents. A logical grouping of agents by functional domains, such as IT, HR, Finance, Engineering, etc., enables enterprises to deploy agents finely tuned to specific departmental needs. Organizations can streamline workflows with this structure, enhance task accuracy, and ensure each task operates within the context it understands best.

This domain-specific organization of agents forms the backbone for the scalable and modular deployment of Agentic AI systems, which is emerging as a top AI trend. Coordination and interaction between agents is done through a reasoning and orchestration engine that is responsible for interpreting user requests, resolving ambiguities, decomposing tasks, and routing these tasks to appropriate agents.

As agentic AI revolutionizes industries and enterprises start deploying AI agents, it will bring a wide range of benefits to businesses, from operational efficiency and scaling to making better decisions, innovating faster, and improving customer service. This technology will position early adopters as leaders in their respective fields, ready to take on future challenges and generate a significant return on investment (ROI).

Now is the time to explore agentic AI. We at Aisera are here to help you incorporate AI agents into achieving your business goals and prepare your company to take advantage of the transformational potential of Agentic AI. Book a custom AI demo to experience the power of Agentic AI in action.