Author: IRPA AI Senior Analyst, Kieran Gilmurray

Agentic AI systems differ from traditional AI because they are goal-oriented, capable of using tools, and can perform autonomous, multi-step reasoning. For businesses, this offers a significant opportunity to tackle a longstanding and expensive issue: the inefficiencies associated with legacy automation.

Why Businesses Waste Money on Legacy Automation

Traditional automation, which relies on rigid rules and predefined workflows, has long been a cornerstone of business operations. However, its limitations are becoming increasingly evident in today’s fast-paced technological landscape.

Legacy systems are often fragile and tend to fail when faced with unexpected situations. They function in isolation, unable to collaborate across different business functions, which forces employees to fill the gaps. This results in wasted resources, as employees must continuously manage the limitations of these automated systems instead of focusing on higher-value tasks. A Salesforce report found that salespeople spend as much as 71% of their time on non-selling activities, highlighting the inefficiency of these processes.

In contrast, agentic AI offers a more adaptive solution. These AI agents can learn from new data, adjust to changing conditions, and work collaboratively to achieve shared objectives. This level of autonomy and intelligence leads to significant cost savings and improvements in efficiency

5 Ways Agentic AI Slashes the Budget
 

  1. Context-Aware Process Automation: Agentic AI systems can understand the context of a task and make decisions based on that understanding. For example, in manufacturing, AI agents can analyze data related to order volumes, machine availability, and material inventory to optimize production schedules. This advanced level of intelligent automation has achieved significant results, such as Siemens reducing unplanned downtime by 30% and cut maintenance expenses by 20% through the use of predictive maintenance agents.
     
  2. 24/7 Self-Improving Agents: Agentic AI systems are dynamic and evolve over time. They learn and improve over time. In customer service, AI agents learning from each interaction to improve their responses and resolutions. This ongoing improvement significantly impacts business performance. For instance, H&M deployed a virtual shopping assistant that resolved 70% of customer queries autonomously, leading to a 25% increase in conversion rates.
     
  3. Adaptive Knowledge Workers: Agentic AI enhances human teams by handling repetitive and time-consuming tasks. In the healthcare sector, AI agents are used to automate clinical documentation, which reduces the time physicians spend on administrative work by up to 60%. This frees professionals to focus on higher-value activities that require their unique expertise.
     
  4. Cross-Functional Collaboration without Human Bottlenecks: One of the most significant advantages of agentic AI is its ability to facilitate seamless collaboration between different business units. For instance, in supply chain management, AI agents can analyse data across sales, logistics, and inventory to optimise the entire process. Fujitsu’s Azure AI agent improved sales proposals by 67% in productivity, freeing teams to serve more clients. Similarly, DHL implemented an AI logistics agent that improved on-time delivery rates by 30% and reduced fuel and route optimisation costs by 20%.
     
  5. Data-to-Action Loop Closure: Agentic AI can not only analyse data to provide insights but also act on those insights autonomously. In retail, AI-powered systems track inventory in real-time and automatically place orders to prevent stockouts and reduce overstocking. Levi Strauss, for instance, used agentic AI for demand forecasting and inventory optimisation, leading to increased full-price sell-through rates and reduced waste.

The ROI Curve: When Agentic AI Breaks Even

The return on investment for agentic AI can be remarkably swift. Some businesses have reported an ROI of up to 300% within months of implementation. The key is to start with well-defined use cases where the potential for cost savings is clear. For instance, one healthcare provider was able to save $750,000 per year by using agentic AI to reduce the time spent on data labelling by 52%. Additionally, global energy company AES achieved a staggering 99% reduction in audit costs by using agentic AI to automate and streamline safety audits.

Warnings: Where AI Still Falls Short on Cost Savings

Despite its potential, agentic AI is not a panacea for every business challenge. The initial setup costs can be significant, and there are areas where AI has limitations. Tasks that involve high complexity, nuanced judgment, and high-stakes decisions with substantial ethical implications are best managed with human oversight. Additionally, the success of any AI implementation relies heavily on the quality of the data it is trained on.

Experiment, Measure, Iterate

The journey to unlocking the full potential of agentic AI begins with a single step. Businesses should start by identifying a specific, high-impact process that could benefit from intelligent automation.

By implementing a pilot program, they can assess the impact of agentic AI on key metrics such as cost, efficiency, and customer satisfaction. This iterative approach allows for continuous learning and refinement, ensuring that the technology provides real, tangible value.

The evidence is clear: companies willing to embrace this new era of automation can reap transformative rewards.

The question remains; are you open to being one of them?

About the Author: Kieran Gilmurray 

Senior IRPA AI Analyst & Advisor, Kieran Gilmurray is a certified executive coach, intelligent automation & digital transformation thought leader & content guru focused on helping solve complicated problems others can't.  For the past 25+ years, he has driven business digital transformation programs across a range of industries like digital technologies, intelligent automation, data analytics, social media and robotic process automation, having generated millions of dollars of value.

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Originally posted in the IRPA AI Network — Announcements & Updates

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