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One-Third of interactions with GenAI services will use action models & autonomous agents for task completion

960 640 Stuart O'Brien

One-third of interactions with generative AI (GenAI) services will use action models and autonomous agents for task completion by 2028.

Autonomous agents are combined systems that achieve defined goals without repeated human intervention, using a variety of AI techniques to make decisions and generate outputs. They have the potential to learn from their environment and improve over time, enabling them to handle complex tasks.

“In the future, human interactions with GenAI may evolve from users prompting large language models (LLMs) to users interfacing directly with autonomous intent-driven agents, which could allow for a higher degree of autonomy and much better alignment with human goals,” said Arun Chandrasekaran, Distinguished VP Analyst at Gartner.

Autonomous Agents Will Impact Several Business Sectors
Autonomous agents can perform a variety of tasks, such as chaining different types of models, verifying the output of a model before inputting into another model and running in a continuous loop to process streaming inputs. These tasks can translate into capabilities such as accessing the internet and using applications, controlling model output and automating complex business processes based on human intent.

“Autonomous agents can reduce the need for human intervention when interacting with LLMs and reduce the burden on business users across many sectors, as they are able to spend less time on advanced prompt engineering,” said Chandrasekaran.

Autonomous agents will have an impact across several sectors:

  • Healthcare: Autonomous agents can help medical professionals in areas such as disease diagnostics, treatment planning and patient care.
  • Education: Autonomous agents can offer personalized learning experiences and adapt teaching methods to the needs of individual students.
  • Gaming: Autonomous agents can observe and interact with human players and provide more immersive and realistic experiences.
  • Insurance: Autonomous customer service apps can handle most policyholder interactions through voice and text, and can assist with claims, fraud, medical service, policy and repair systems. They can have a dramatic impact on resolution, with responses and actions taking minutes rather than days or weeks.

Use Clear Objective Functions as the Foundation for Autonomous Agents

“Autonomous agents need a clear objective function so that their behaviors can be controlled in a meaningful way to deliver value,” said Chandrasekaran. “The tasks autonomous agents can perform, such as verifying the output of a model before inputting into another model, has the ability to control model output and automate complex business processes based on human intent. But this can only be achieved with a clear objective function.”

In order to achieve this, Gartner suggests that organizations:

  • Identify use cases in which action models and autonomous agents can add value by reducing the amount of human effort and skill needed.
  • Build an architecture to enable autonomous agents to thrive. Do so by providing tool integration and access to knowledge repositories and long-term memory, enabling agents to demonstrate expanded reasoning and expertise.
  • Acknowledge that action models and autonomous agents aren’t a substitute for prompt engineering — their ultimate potential remains tied to the quality of the prompts they receive.
  • Balance between autonomy and control through extended pilots and rigorous agent monitoring.

Analyst Report: AI Helps Align Agent Performance with Customer Expectations

960 640 Abby Monaco

By Abby Monaco, Senior Product Marketing Manager, NICE

Using AI capabilities to deliver agent guidance in real time, at the exact moment it’s needed, helps contact centres maximize agent performance, delight customers and reduce costs. That’s the finding of a report by Aberdeen, which surveyed more than 300 contact center leaders across industries around the world.

Aberdeen found that the No. 1 priority of contact centres, regardless of industry, is to improve the customer experience, followed by reducing service costs to drive operational efficiency.  In the report, “The ROI of Real-time Agent Guidance: How AI Helps Align Agent Performance with Customer Expectations,” Aberdeen Vice President and Principal Analyst Omer Minkara details opportunities for improvement for contact centres considering implementing AI.

Aberdeen defines contact center AI as encompassing:

  • Artificial intelligence: Automated reasoning and decision-making capabilities based on insights uncovered through machine learning algorithms.
  • Machine learning: Technology applications that learn by themselves by analysing a pattern of historical and recent data.
  • Prescriptive guidance: Tools used to analyze structured and unstructured historical data to make predictions and suggest decision options.
  • Predictive analytics: Tools to predict future behavior of customers.
  • Automation: Tools used to automate the execution of tasks such as customer routing, agent scheduling and quality assurance.

AI enables organizations to monitor and analyse 100% of customer interactions in real time and give agents the contextual guidance they need to turn the conversation around in the moment. Supervisors gain relevant and accurate insights that help boost their productivity and inform the one-on-one coaching and guidance they offer to agents. In fact, firms using AI report a 2.9% annual improvement in the amount of time supervisors spend helping agents, compared to a 0.1% worsening by contact centers without AI capabilities.

The benefits of AI in the contact center extend to customers as well. According to the report, contact centres leveraging AI capabilities like speech analytics, text analytics and journey analytics enjoy superior CX performance improvements that include:

  • A 10.5% increase in customer retention (compared to a 3.2% improvement for contact centers not using AI).
  • A 3.5x greater improvement in customer satisfaction (10.1% vs. 2.9%).
  • An 8.8% improvement in customer effort score (compared to 1.1%).

“Modern contact centers leverage AI capabilities to take quality assurance to the next level,” Minkara wrote. “Instead of periodic reviews and subsequent agent coaching and guidance, they use AI capabilities to review all interactions in real time – using the resulting insights to provide agents with real-time contextual guidance.”

“This helps shorten the time to make necessary improvements in agent skills and activities to address evolving client needs,” he added. “It also minimizes customer frustration and churn due to inefficiencies in service delivery activities.”

Minkara concluded the report with a strong recommendation that contact centers not currently using AI to boost agent productivity and performance consider doing so. AI models can learn from and be trained using an organization’s historical interaction data, enabling quick results. Read the full report here.