From FAQs to absorbing chats. Developing the capabilities of AI agents

We’re in a completely different place now, though. Combining natural language processing (NLP), machine learning (ML) and large language models (LLMs) has enabled AI agents to carry out multi-threaded conversations, understand context and even read users’ emotions. These are no longer passive tools for handling straightforward questions, but business partners that support sales, client/customer services, HR and IT.

Introducing simple chatbots to answer FAQs does make sense. They rapidly reduce the number of repeated questions and provide a measurable return on investment. However, in a world where clients and customers expect smooth 24/7 service, that’s merely the first step. 

The chief limitations of FAQ chatbots are:

• their inflexibility; they only work within the framework of the questions and answers they’re programmed with;
• their lack of memory; they treat every question separately, without remembering previous exchanges;
• the absence of process support; they are incapable of leading a user through a complex process such as returns or onboarding;
• their passivity; instead of proactiveness, they wait until users ask questions, rather than taking the initiative.

The result? Frustrated users, low effectiveness and a sense that the company is using technology more for show than to offer a genuinely enhanced experience.

The latest generation of agents operate completely differently. With their deep understanding of language and capability of integrating with back-end systems, they form an integral part of an organisation. They can:

·     hold multi-stage conversations while maintaining context;

·     personalise communication on the basis of data and interaction history;

·     perform a range of tasks, from updating data in a CRM to handling reservations and returns;

·     analyse moods and adjust the style of their responses accordingly;

·     operate proactively; one example would be suggesting a solution before the user asks for help.

Smart assistants are evolving in the direction of personal advisors who do more than respond to questions, actively learning a user’s preferences and building a personal experience for them. Multimodal interfaces are developing in tandem with this. Conversation with AI isn’t limited to text, but also includes voice, image and video, creating an interaction as natural as contact with a person. 

The automation of processes is going even further. AI agents will initiate and coordinate complex tasks between systems and teams independently, becoming a real business workflows ‘operator’. This represents a huge opportunity for companies in the form of services that are created faster, processes that are more efficient and new sources of competitive advantage. The question is no longer whether or not to deploy smart agents, but how best to leverage their potential.

1.  Improved natural understanding of language (NLU)

o Identifying users’ intentions

o Capturing crucial data, such as order numbers and location

o Emotional analysis

o Context memory

2.  Multithreaded conversations

o Designing cohesive dialogue paths

o Handling digressions and returns to the main topic

o Mechanisms for escalating to a person as and when necessary

3.  Integration with a company’s systems

o Connecting with the company’s CRM, ERP and ticketing systems

o The capability of performing actions in real time

o Security and compliance with regulations such as the GDPR, for instance

4.  Proactiveness

o Organising conversations on the basis of a user’s behaviours

o Intelligent recommendations and suggestions

o Preventing problems before they develop into reports

5.  Ongoing improvement

o Analysing logs and KPIs

o Collecting feedback from users

o Regular training on models

·     Client/Customer onboarding: taking them through the process step by step, including dealing with documents and questions

·     Returns and complaints: handling the entire procedure in one place

·     Arranging meetings: full integration with calendars and reminders

·     Updating accounts: changing data securely via conversation

·     Upselling and cross-selling: recommendations tailored to the client/customer

·     In-house IT support: resetting passwords, systems access and solving typical problems

It’s worth understanding that the difference between a simple FAQ chatbot and a smart assistant is much more than just a matter of technology. It’s a change in the way we think about client/customer service and business processes. The first chatbots to be deployed were seen as a fast solution to the problem of growing numbers of queries. They were intended to relieve staff of the burden, operating as a digital ‘answers database’. Nowadays, though, that model is becoming an anachronism, because it’s incapable of keeping up with client/customer expectations and business dynamics. A state-of-the-art AI assistant doesn’t just answer questions. It understands context, is capable of combining data from various sources and actively supports users as they make decisions. 

These agents are a breakthrough. AI is becoming part of the client/customer experience and a crucial aspect of organisations’ growth strategies. Those that embrace this transformation are building not only more efficient services, but, primarily, more personalised, absorbing and profitable client/customer interactions.

The shift from simple FAQ chatbots to advanced AI agents goes beyond being a technological change. It’s also a strategic decision that has a real impact on the efficacy and quality of a company’s services and its users’ experience. Organisations that invest in developing their agents gain cheaper service processes, greater client/customer loyalty, higher efficiency among their teams and a definite competitive edge.

For more information, see MakoLab | News | AI chatbots with RAG webinar