Artificial intelligence (AI) is now a cornerstone of modern business operations. Among the most transformative new uses case is Agentic AI – or AI agents – which are autonomous systems performing tasks, making decisions, and continuously learning from their environments.
As industries across the globe integrate these agents into their workflows, measuring their impact is essential for understanding their value and driving strategic growth.
But how do you know if you are adding value or adding distractions?
AI agents are revolutionizing how businesses operate. By automating routine tasks, they free up human talent for higher value work. These agents also analyze vast amounts of data in real time, providing insights and enabling better decision making. For industries like finance, technology, and healthcare, this means improved operational efficiency, reduced errors, and enhanced customer experiences.
The versatility of AI agents across these domains highlights their critical role in shaping the future of work.
The productivity gains driven by AI agents stem from their ability to handle complex, time-intensive tasks at scale. They boost productivity in several ways, including:
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Automation of Repetitive Tasks |
Real-Time Decision Making |
Continuous Learning |
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AI agents handle mundane tasks like data entry, scheduling, and compliance checks, allowing employees to focus on strategic initiatives. |
By processing large datasets quickly with both structured and unstructured data, AI agents enable faster, data-informed decisions. |
Feedback loops ensure that AI agents improve over time, adapting to new challenges and optimizing processes. |
In global industries, these productivity improvements translate to cost savings, faster service delivery, and higher-quality outcomes.
While AI agents are powerful, their true potential is unlocked when they operate within a framework of process understanding with process specific to your enterprise. Businesses must have a deep grasp of their workflows to deploy AI agents effectively. This involves:
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Mapping Processes |
Identifying Bottlenecks |
Designing for Integration |
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Understanding end-to-end flow of tasks within an organization |
Pinpointing where inefficiencies or errors occur |
Ensuring AI agents seamlessly integrate into existing workflows |
Process understanding ensures that AI agents are not just automating tasks but transforming them according to your business. They go far beyond generic LLMs found on the internet.
Process intelligence is especially critical in industries like finance, where compliance and accuracy are paramount, or healthcare, where patient safety is at stake. A poorly understood process can lead to suboptimal AI deployment, undermining its potential benefits.
To fundamentally understand process, you need a holistic view of how people work, not a silo of one or two applications. This is where Skan AI is leading the way to help enterprises move towards an AI-enabled workforce.
Skan AI’s process intelligence platform uses proprietary AI to capture data from the desktops of your workforce. By observing actual work, Skan AI can stitch the data together to create a Digital Twin of Operations so you can fully understand how people work across applications to fully understand where AI agents can add the most value.
To assess the value of AI agents, organizations should focus on measurable outcomes aligned with their strategic goals. Key metrics include:
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Efficiency Gains |
Reduction in time spent on tasks and overall process cycle times |
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Cost Savings |
Lower operational costs due to automation and error reduction |
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Accuracy Improvements |
Enhanced precision in data analysis, decision-making, and execution |
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Employee Productivity |
Increased output and engagement of employees freed from repetitive tasks |
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Customer Satisfaction |
Improvements in service quality and speed, as reflected in customer feedback and retention rates |
A combination of quantitative and qualitative measures provides a holistic view of AI agents' impact, enabling businesses to refine their strategies and maximize returns on AI investments.
When it comes to AI Agents in healthcare and insurance, this technology can streamline claim processing by verifying patient eligibility, detecting fraudulent claims, and automating reimbursement processes. These capabilities reduce administrative burdens and accelerate service delivery. For example, an AI agent can instantly cross-check a patient’s medical records with policy coverage to approve or deny claims, ensuring compliance and reducing errors.
AI agents also contribute to personalized customer experiences. By analyzing patient and policyholder data, they offer tailored recommendations for insurance plans. This proactive approach fosters customer trust and loyalty while improving health outcomes.
For traditional financial and banking services, AI agents will be able to help keep staff on procedure. Banks invest heavily into anti-money laundering (AML) processes. AI agents with knowledge of company-specific procedures will be able to help detect non-compliant activities in real-time. This new level of controls monitoring will be crucial for financial services companies.
For customer-facing operations, agentic chatbots handle routine inquiries such as balance checks, loan applications, and account updates, enabling banks to deliver faster and more efficient service. Moreover, AI agents detect anomalies in transaction patterns to prevent fraud, safeguarding both institutions and their customers.
By automating these processes, financial organizations not only reduce costs but also improve accuracy and speed, leading to better customer experiences and stronger financial performance.
AI agents are becoming indispensable for managing complex systems and driving innovation. These agents support software development by identifying bugs, predicting system failures, and automating testing procedures. Additionally, they assist in IT operations, optimizing resource allocation and managing cybersecurity threats in real-time.
Agentic AI also enhances customer support through advanced chatbots that can learn and evolve, as opposed to the current generation with pre-programmed responses. Virtual assistants that provide 24/7 service, resolve common issues, and escalate complex problems to human agents when necessary will learn faster than ever. This not only improves customer satisfaction but also reduces operational costs.
While AI agents hold immense potential, implementing them effectively is no simple task. Organizations often face hurdles such as technical complexity, integration challenges, and resistance to change. Without a well-planned strategy, AI initiatives can falter, leading to suboptimal outcomes or even project failure.
One of the biggest challenges is “teaching” an AI model your standard process. LLMs like ChatGPT learn from the public internet, but your process is not publicly available. It’s private information. Therefore, you need to be able to document exactly how to achieve the processing time SLAs you want the AI agent to support.
AI agents need to integrate into existing workflows. Legacy systems, incompatible data formats, and siloed operations can hinder smooth adoption. Additionally, AI agents require high-quality data to function effectively, making data cleansing and preparation critical steps in the implementation process.
Agentic AI solutions must scale with business needs, whether it involves handling increasing data volumes, adapting to new use cases, or supporting growing customer bases. Achieving this requires robust infrastructure and ongoing optimization.
Organizations must also address ethical considerations, such as data privacy and bias in AI algorithms. Ensuring compliance with regulatory requirements and fostering trust among stakeholders are essential for successful deployment.
Given these complexities, partnering with the right technology provider is crucial. An experienced partner like Skan AI can offer:
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Strategic Guidance |
Technical Expertise |
Process Understanding |
Support & Maintenance |
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Help organizations define clear objectives, align AI initiatives with business goals, and create a roadmap for implementation. |
Provide the tools, platforms, and frameworks needed to build, train, and deploy AI agents effectively. |
Assist in mapping workflows, identifying bottlenecks, and designing AI solutions tailored to specific needs. |
Offer ongoing monitoring, updates, and troubleshooting to ensure long-term success. |
As AI agents become more sophisticated, their influence on industries will deepen. Finance firms will leverage them for enhancing compliance and risk management, while healthcare providers will deploy them for personalized care and smoother operations. In technology, AI agents will drive innovation in product development and user experience.
However, success in this AI-driven future starts with an organization’s ability to understand and optimize its processes. Those that invest in process intelligence today will be ready to harness the transformative power of AI agents tomorrow.
AI agents represent a paradigm shift in how businesses operate, offering new opportunities to enhance productivity and drive innovation to gain a competitive edge. By focusing on process understanding first and adopting a robust framework for measuring their impact, organizations can ensure these agents deliver sustainable value.
The path to an AI-enabled workforce starts with Skan AI. Let us show you how we’re helping enterprises bridge the gap to the AI-powered future.