The rise of Generative AI has highlighted where it can make the most impact with Customer Support operations consistently ranking as a front runner. Much of the discussion surrounding GenAI adoption in CX right now is focused on two key areas: Agentic AI and AI Agent Copilots.
Why all the buzz?
Agentic AI is the driving force behind the 10x productivity and efficiency agents are experiencing because it immediately contextualizes customer information in real-time and acts on it…without any human input or direction required.
Agentic AI refers to autonomous AI agents that can adapt, make decisions, and take actions independently based on real-time data and learning.
What’s equally as important to point out is that not all Copilots have Agentic AI at their core.
While some AI copilots have Agentic capabilities, many are designed as assistive tools that operate within defined parameters set by human input, focusing on augmenting human tasks rather than acting autonomously.
Breaking Down The Types of AI Copilots
Across CX right now, nearly every company is offering a Copilot. But, not all are the same. For example, here are 3 varying types of Copilots in the market.
1. Rule-Based Copilots: These operate based on predefined rules and workflows, without significant autonomy. They support users by suggesting actions, retrieving information, or streamlining tasks, but they do not adapt independently.
2. Assistive AI Copilots: These copilots offer contextual help and suggestions, often using natural language processing (NLP) and machine learning. They assist human decision-making but still rely heavily on human oversight.
3. Agentic AI Copilots: These advanced copilots are designed to take autonomous actions and learn from real-time data. They can make decisions and evolve their strategies without direct human intervention, reflecting the agentic nature.
The key question to ask as you explore all of the Copilot offerings in the market is, ‘Which ones actually leverage Agentic AI and which ones are still dependent on rule based and NLP?’
Read more on our AI Agent Copilot here.
Exploring Agentic AI Further
Since you’ll continue hearing the term ‘Agentic AI’ as adoption skyrockets, we’ll define it here once more.
Agentic AI refers to artificial intelligence systems that operate with a level of autonomy, acting with little to no intervention on behalf of humans while performing tasks, making decisions, or solving problems. It is self-correcting and powerful.
Unlike passive AI tools that simply provide recommendations or summarizations, Agentic AI can handle complex cases and actions. It works based on predefined goals, rules, or learned behaviors. Agentic AI can be thought of as having a degree of “agency,” meaning it can independently initiate actions to achieve specific objectives, often in dynamic and complex environments.
The key characteristics of Agentic AI include:
- Autonomy: Agentic AI can function independently, making decisions and taking actions without constant human intervention. This autonomy can vary in degree, from simple automation to more complex decision-making capabilities.
- Goal-Oriented Behavior: Agentic AI is designed to achieve specific goals, whether that’s optimizing a process, responding to customer inquiries, or managing resources. It has memory and can adjust its strategies based on feedback, learning and changing conditions to meet these goals.
- Adaptability: Because it has memory, Agentic AI can learn from interactions and experiences, enabling it to adapt to new situations and improve its performance over time. This learning can be based on machine learning algorithms, reinforcement learning, or other AI techniques.
- Contextual Awareness: Agentic AI can understand and interpret the context in which it operates, allowing it to make more informed decisions. This might include understanding user preferences, the current state of a system, or external factors that impact its tasks.
- Interactivity: Agentic AI often interacts with humans or other systems, providing suggestions, taking actions based on input, or even collaborating with human users to achieve a shared objective.
Simply put, Agentic AI moves beyond basic automation by adding layers of decision-making and adaptability, enabling it to function as a fully functioning assistant and collaborator across various domains including customer support.
With this in mind, it should now be clear why not all “Copilots” are created equal.
Agentic AI and The Enterprise
Enterprise companies are increasingly exploring Agentic AI based Copilots for their support teams for various reasons including:
Efficiency Gains: Agentic AI can automate repetitive and low-value tasks, freeing up agents and managers to focus on more strategic and critical activities. This leads to significant efficiency improvements and allows companies to do more with fewer resources.
Scalability: As companies grow globally and remotely, the need for consistent, high-quality output increases. Agentic AI can standardize processes and ensure that knowledge workers have access to the same level of support and information, regardless of location, training or scale.
Innovation and Competitive Edge: Organizations leveraging agentic AI can innovate faster by accelerating R&D, improving product development feedback loops, and enhancing customer experiences. This helps them stay ahead of competitors in rapidly evolving markets.
Employee Experience and Retention: Agentic AI can reduce the cognitive load on employees by handling a plethora of tasks without their involvement, leading to a more fulfilling work experience. This, in turn, helps reduce burnout and increases retention among agents.
Cost Optimization: By automating routine tasks and improving agent efficiency, enterprises can optimize costs, reducing the need for additional hires or, alternatively, ensure that new hires can ramp faster and perform at the same levels of their tenured peers.
Personalization and Customization: Agentic AI can tailor information, tools, and workflows to the specific needs and preferences of customers, enhancing their effectiveness and satisfaction.
Future-Proofing the Workforce: As Agentic AI becomes more integrated into business processes, companies that adopt it are better positioned to future-proof their workforce, ensuring that employees are equipped to work alongside advanced technologies.
Read how Gainsight was able to boost efficiency and save agents 300 hours per month with TheLoops AI Agent Copilot.
AI Agent Copilots: A Wide Spectrum, Choose Wisely
Given the popularity of Copilots across all aspects of business, it’d be safe to assume that what one does, the other does as well. But as this blog suggests, that’s just not true.
A clear understanding of Agentic AI and choosing a Copilot built on it can be the key to maximizing your ROI when implementing AI in CX operations.
According to TheLoops CTO and Co-Founder Ravi Bulusu, who holds 26 patents for AI and machine learning, “Copilots in general are typically made up of 3 core elements: responses, AI search and knowledge. However, when it comes to Customer Support, how you respond (empathetically, friendly, positive, upbeat) has nothing to do with the actual steps required to solve a customer’s problem. This is why Agentic AI is essential.”
To resolve customer issues with precision and without compromising quality, your agents need a Copilot that is Agentic, one that can provide them with the immediate steps required to solve complex issues while also giving managers direct understanding as to where the agent may fall short through AutoQA.
You also need an AI Agent Copilot that has access to more than one knowledge source.
Agentic AI can scan and scour your help articles, past case resolutions, FAQs and so much more–no matter how vast or varied these assets may be.
As customer expectations evolve and the demands on your support teams increase, it’s vital to understand the differences between basic, assistive AI Agent copilots, extendable Copilots and advanced AI Agent Copilots such as TheLoops.
Basic Copilots
- Basic AI copilots in customer support begin with summarization capabilities. This version can quickly condense one source of customer data into concise summaries, helping agents grasp the essence of a conversation instantly.
- Summarization is useful when agents need to handle multiple cases at one time. But the onus is still on agents to reply to the customer and decide what steps to take to resolve their issues and challenges.
- Another fundamental function of basic copilots is that of response generation. Basic copilots can assist agents by suggesting possible replies based on predefined templates, tones or other insights.
- However, these responses are often generic and focused more on maintaining positive, grammatically correct communication rather than resolving the underlying issue and required steps to help the customer.
The next level of Copilots that Support leaders can choose from are extendable Copilots.
Extendable Copilots
- Moving beyond basic Copilot functionalities, extendable Copilots do provide automation capabilities that can increase agent productivity.
- These Copilots can pick up more than one knowledge source while also triggering automated workflows based on conditions, such as escalating a case when a certain keyword is detected or automatically updating customer records after a conversation.
- By reducing the need for manual discovery and multitasking, extendable Copilots free up agents to focus on more complex tasks that require human judgment.
- Extendable Copilots also bring personalization into the mix. They tailor responses and actions based on the customer’s history, preferences, and past interactions with the Support team. They can also discern when a customer is asking for a feature request vs flagging a major incident.
- This level of personalization improves the customer experience by making interactions feel more relevant and prescriptive. To be clear, extended Copilots do provide summarization and response generation while also understanding the customer’s needs and searching multiple knowledge sources to resolve them.
When it comes to case resolution, there is still heavy human oversight with extendable copilots.
Advanced Copilots with Agentic AI such as TheLoops
- Advanced AI Agent copilots shift the focus from just assisting with responses and handling simple requests to actively driving resolutions particularly in complex scenarios.
- They can perform all of the functionalities of basic and extendable Copilots while also analyzing past cases and resolutions, providing agents with step-by-step recommendations on how to solve current issues and complex cases, and learning how to improve with feedback from their human agent counterparts.
- At their core, advanced AI Agent copilots can understand the symptom of a customer case, provide a resolution and simultaneously trigger an action all on their own.
- By leveraging historical data and AI, advanced copilots guide agents toward the most effective solutions, reducing resolution times and significantly improving customer satisfaction.
- This resolution-centric approach is a key differentiator, addressing the core of customer support by focusing on solving problems rather than simply acknowledging and pacifying them.
- Moreover, Advanced AI Agent Copilots go beyond just resolution summaries; when paired with AutoQA, they offer real-time coaching and quality improvement guidance.
What Else Makes Advanced AI Agent Copilots Like TheLoops Beneficial
Agentic AI is powerful and often known as being “multi-modular.”
Examples of components or modules commonly found in modular AI systems include:
Natural Language Processing (NLP) Modules: Handles tasks related to understanding and generating human language, such as text classification, sentiment analysis, named entity recognition, and language translation.
Image and Video Processing Modules: Processes visual data to perform tasks such as object detection, image recognition, facial recognition, and video analysis.
Speech Recognition Modules: Converts spoken language into text, enabling applications like voice commands, transcription, and voice-based interfaces.
Recommendation Engine Modules: Provides personalized recommendations based on user behavior, preferences, and data, often used in e-commerce, content streaming, and social media platforms.
Decision-Making Modules: Uses algorithms and models to make decisions based on data inputs, often employed in areas like automated trading, risk assessment, and strategic planning.
Data Integration and ETL (Extract, Transform, Load) Modules: Manages the integration, cleaning, and transformation of data from various sources, preparing it for analysis and use in AI models.
Predictive Analytics Modules: Uses statistical models and machine learning algorithms to predict future trends, behaviors, or outcomes based on historical data.
Anomaly Detection Modules: Identifies unusual patterns or outliers in data that could indicate fraud, errors, or other significant events.
Chatbot or Conversational Agent Modules: Facilitates interactions with users through conversational interfaces, providing automated responses and support.
Because it is built on Agentic AI, TheLoops AI Agent Copilot is multi-modular providing Support teams with flexibility, adaptability, and ease of management. Each separate, interchangeable module is responsible for specific functions or tasks and provides an even clearer understanding of the customer.
In TheLoops AI Agent Copilot, these modules can be combined, customized, or replaced independently to create a versatile and adaptable AI system that evolves and scales with your changing needs.
Distinguishing Prompting vs Planning and Process Automation: Final Thoughts On Agentic AI
Thanks to the widestream adoption of ChatGPT, the majority of Support leaders are familiar with Large Language Models and AI prompts. Agentic AI goes way beyond that.
Agentic AI has memory and understands what’s been done in the past and learns how to suggest a more productive path forward. It also has built-in governance to ensure it adheres to any privacy, data or other protocol to adhere to. Agentic AI Copilots Are Your Path Towards Productivity In The Enterprise.
With just one look at a Google search, you will see that the AI Agent Copilot landscape is vast, varied and hard to discern.
While many options and providers do capitalize on assisting agents, very few offer all features, robust functionality and Agentic AI.
However, TheLoops AI Agent Copilot does. Ensure your team’s productivity and efficiency through our unique blend of combining AutoQA with our advanced AI Agent Copilot. Need a little more convincing? ; )
Set up a call here.
As we close this out, we’ll recap the points made throughout this blog:
- Selecting the right Agentic AI and AI Agent Copilot for your team is not an easy lift. You want to invest in a solution that will take you and your team forward and leapfrog your results vs crawl along.
- It’s essential to select a Copilot option that seamlessly creates a continuous improvement loop, provides immediate steps to your customers resolutions and provides real-time coaching to prevent mishaps.
- As a bonus, you’ll want a Copilot that can generate and update knowledge with each case closing and provide your agents the ability to ASK the Copilot questions.
