Guide to Agentic AI and the Smarter Way to Automate

Artificial Intelligence (AI) comes in many shapes and sizes and the applications are seemingly endless. But AI is now shifting from hype to practical deployment, especially in customer service.

Automation has been around for years within customer services, but according to James Brooks, Head of Practice – Automation & AI at FourNet, previous automation technology was "artificial but not intelligent". This is down to early automation technology being driven by scripts and rules which can easily hit limitations when you consider the complexities of customer services.  

Whereas modern AI powered customer service technology can interpret intent within conversations and have the ability to make decisions based on data and adapt in real time. Conversational AI technology can also improve the experience for customers interacting with it, ending the days of clunky Chatbots sending you round in circles, offering up the same self-service guide over and over. Customers are now able to hold human-like conversations with AI around the clock, performing tasks and getting the information they need.

In this guide, we share an indepth look at using AI technology, including AgenticAI and Conversational AI to transform your customer service processes. We also discuss FourNet's AI-infused consulting approach, our IntellAIgent platform, the use cases of these tools, designing effective automations and optimising agent workflows. 

As James explains, "every business is becoming an AI business" and the new norm is intelligent customer interactions at scale. This guide will help you achieve that.

AI comes in many forms, and one of the latest you may not have come across yet is Agentic AI. Most people are familiar with Generative AI tools like ChatGPT or Google Gemini. These tools are great at holding conversations, generating content, and supporting quick research or small tasks. But where they fall short is taking action; particularly when it comes to working across wider business systems like Customer Relationship Management (CRM) or telephony platforms.

That's where Agentic AI steps in. Think of it like a digital employee that understands what needs to be done, uses your existing systems, and acts independently to complete tasks. Rather than waiting for prompts, Agentic AI tools are designed to carry out autonomous tasks based on goals set by the user, whether that's resolving a customer issue, sending a follow-up email, or updating a CRM record.

The real difference is in the decision-making. While Generative AI can create content and answering questions, Agentic AI focuses on making decisions and executing tasks. It assesses the context, explores all the available tools and routes, and chooses the best path forward; without needing human input at every step. It's like onboarding a new member of the team, only this employee doesn't need sleep or training refreshers.

How Does Agentic AI Work?

Agentic AI follows a clear four-step cycle: it perceives, reasons, acts, and learns.

It starts by gathering data from your systems and tools, understanding what's going on in real time. Then, using advanced Large Language Models (LLM), it decides how to act by pulling in the right data, generating a plan, and choosing the best next step. From there, it connects with your business systems to take action, whether that's updating a record or sending a response.

Every interaction also feeds back into the loop, helping the AI fine-tune decisions and improve outcomes over time, while staying within clear rules and guardrails.

You may be wondering how this Conversation AI differs to current Chatbot solutions and the self-serve applications. Well, think of the messaging pop-up as a way of powering the AI, it may look the same but the technology underneath the hood is far more powerful.

Traditional Chatbots are driven by rules and scripts which can easily lead customers to a dead end. Alternatively, Conversational AI leverages Large Language Models (LLMs) to understand the context of the conversation and questions from customers, considering intent and emotion. Conversational AI can also be leveraged across various platforms, not just your website, meaning that customers can engage with you through their preferred channel.

The goal of Chatbots and Conversational AI are the same, "digital deflection" as FourNets Director of CX Consulting, Oliver Bareham, explains. However, Conversational AI is much better at achieving this goal due to its dynamic capabilities to understand context, review information and serve up appropriate answers; solving issues without agent involvement by guiding customers across digital journeys.

This technology gives you the ability to meet growing customer expectations to give instant answers to questions and resolve queries faster than traditional bots.

Robotic Process Automation (RPA) has long been the go-to tool for automating repetitive, rule-based tasks in contact centres. It’s efficient for structured processes like data entry and form handling, reducing handling time by up to 40% and cutting operational costs by 30%. However, RPA’s rigidity becomes apparent when processes evolve or exceptions arise, often leading to failures that require manual intervention.​

Artificial Intelligence (AI), on the other hand, brings adaptability to the table. It can handle unstructured data, interpret intent, and make decisions in real-time, making it ideal for dynamic customer service environments. Unlike RPA, AI can explain its actions and suggest fixes when issues occur, enhancing transparency and trust.​

The integration of AI into contact centres marks a shift from batch-based automation to intelligent automation, where systems can analyse customer behaviours and personalise interactions on the fly. This evolution not only improves customer satisfaction but also empowers agents by handling routine tasks, allowing them to focus on more complex issues.

When most people think of the advantages of AI, they instantly go to cost savings, which is a key driver for many organisations; especially in today’s unpredictable economic climate. But we want to highlight the opportunities AI brings in creating a more personalised experience for customers and the advantages for agents, freeing them from manual tasks to work on complex customer engagements where the 'human touch' is needed.

AI enables hyper-personalisation, allowing you to tailor messages and services to each customer based on real-time data. By analysing customer preferences and behaviours, AI can ensure that your communications are relevant, timely, and impactful. This level of personalisation creates a better customer experience (CX), which, in turn, leads to higher satisfaction, repeat business, and improved brand loyalty.

Personalisation goes beyond just addressing customers by their names. It can extend to mimicking the tone, preferences, and communication style of individual customers, making interactions feel more human and engaging. This deeper level of connection enhances the overall brand experience, encouraging customers to advocate for your services. After all, if a customer loves the service they receive, they'll tell their friends and family.

AI also helps to drive first-time resolution, which is crucial in reducing the number of repeat contacts and rework in contact centres. With AI handling routine tasks and providing agents with accurate information, customers are more likely to get the answers they need on the first call, leading to a more efficient and satisfactory service experience.

In summary, the integration of AI not only delivers impressive cost savings and boosts automation but also significantly improves customer retention through a personalised experience. The return on investment (ROI) from AI is clear, as it enhances operational efficiency and strengthens customer relationships over the long term.

AI isn't just for tech companies or digital-first brands. It's delivering real, measurable results across sectors, from government and finance to utilities and logistics. In this section, we breakdown some of the applications that are being used already.

Utilities are using AI in contact centres to send pre-emptive outage alerts via SMS, email, or voice, based on real-time grid data. This significantly reduces inbound call volumes and reassures customers before they even have to ask. In fact, one energy provider saw a 25% drop in outage-related calls after rolling out proactive alerts. This is a prime example of AI in utilities reducing pressure on human agents.

For instance, Anglian Water implemented the AI to send over 200,000 proactive notifications annually, resulting in an estimated £100,000 in annual operational savings and improved customer experience. ​

Logistics firms are automating delivery updates, but also layering in smart personalisation. If a delivery is late, the system can generate a goodwill voucher or offer a discount to reduce churn. These contact centre use cases combine customer experience (CX) automation with customer retention strategy.

Retail is where AI really shines with hyper-personalisation. Retailers are using AI to nudge customers towards loyalty programmes, sending tailored offers based on browsing behaviour or past purchases. This AI in retail tactic not only boosts conversion rates but builds long-term loyalty. 

Public Sector organisations are using AI to help citizens complete complex forms, providing real-time, plain-language guidance. AI can guide users through government applications or services, reducing barriers for those who might struggle with jargon or confusion. This has huge potential for AI for government services, particularly in regulated environments where accessibility is key.

Finance is using AI for proactive customer outreach. If a customer is at risk of missing a payment, AI can step in to provide a personalised reminder or even offer a tailored repayment plan, selecting the best communication channel (SMS, email, or voice). This is helping financial institutions reduce defaults and improve customer relationships, which is a huge win for AI in finance.

Training an AI model isn't a once and done task, it's a continuous process of refinement and improvement. You want to refine these models to avoid “hallucinations” which is where AI generates irrelevant or incorrect responses as it has to give a response; even if it doesn't have the information it needs. 

The key to preventing hallucinations is ensuring the AI uses trusted, curated data from reputable sources. AI platforms like IntellAIgent use reinforcement learning to improve responses based on customer feedback. For example, a thumbs-up/thumbs-down system lets customers rate their experience, giving the AI immediate feedback for improvement.

Additionally, AI also learns based on whether a customer takes action after interacting with it. Did they follow through with a payment after a reminder? Did they resolve an issue after chatting with the bot? These signals help the AI adjust its behaviour for better future outcomes, leading to more accurate decision-making in future interactions.

The platform is also self-learning, meaning it doesn't just rely on humans for improvement. As AI interacts with customers and gathers more data, it can adjust its responses over time, fine-tuning to suit the specific needs of each business.

Good data is crucial for getting the right results from AI. Poor data equals poor results, and that goes for Agentic AI as well.

We recommend that all organisations spend a lot of time to test their 'data readiness' before implementing new AI tools. This means that data is in a format that is organised and easily accessible, and most importantly, correct. For instance, in a contact centre, data could include customer interaction histories, service details, and product info. If this data is spread out across different platforms, it can be hard for AI to piece things together. But when everything is connected and structured properly, AI can work more effectively and provide better insights. 

One of the key benefits to using AI is that, even if data isn’t accessible or organised, it will feed back and suggest ways to improve it. Tools like IntellAIgent are designed to help identify gaps in your data, flagging when it encounters missing info, allowing businesses to address these issues before they become a problem.

To get your data in the right place for AI to work effectively, start by consolidating it into a centralised system. Use CRM or data management platforms to bring together customer information, service logs, and any other relevant data sources in one accessible location. It's essential to standardise formats and remove inconsistencies, so data is uniform and easy for AI systems to process. Clean up duplicates and fill in any missing details wherever possible. 

You should also consider tagging and categorising data to help AI quickly identify and make use of it. Regular audits and updates to your data will ensure it stays relevant, helping your AI solutions perform at their best. Lastly, work with experts who can structure and organise your data effectively, ensuring it's ready for automation with minimal friction.

Learn more about utilising data in our new guide.

Many of the organisations that we work with at FourNet have to adhere to strict regulations and provide tailored support for vulnerable customers. So a question we get regularly when discussing AI projects is 'how can we ensure that quality is maintained?'

The answer is that you need to continue monitoring quality, just like you would with a human employee, performing sample-based checks to monitor that AI is meeting standards. You can also leverage AI to assess the work of other AI, meaning that you get 100% coverage of performance, rather than small sample sizes. With pass/fail logic, AI interactions are evaluated with complete transparency, making it easy to spot any gaps in access or prompt design.

Additionally, by incorporating real-time monitoring and feedback loops, you can continuously optimise the AI's performance, ensuring it remains in line with your organisation's goals and regulatory requirements. This ongoing evaluation not only helps maintain quality but also ensures that your AI solutions remain adaptable to changing needs.

There are also AI powered tools like Speech Analytics that provide support and guides in real-time to help support agents; whether its meeting compliance, identifying vulnerability or simply improving sales conversation rates.

Conversational AI brings huge potential, but also new risks that need managing. Prompt injection is one example, where a user tricks the AI into giving out unintended responses. Others include attempts to provoke harmful or off-brand answers, or pulling inaccurate information from unreliable sources. Without the right controls, these issues can cause serious damage.

There are a number of methods that you can use to mitigate these risks through strict data access and conversational controls. We recommend limiting AI knowledge to pre-approved sources like internal FAQs and compliance documents, and use techniques like retrieval-augmented generation to ground responses in real-time, validated data.

It's also good to set clear conversational boundaries. Filters block toxic language or sensitive topics, and intent validation can flag risky queries and pass them to a human advisor when needed. If someone types “how do I bypass security,” for example, the system will spot and divert it instantly.

To prevent abuse, implement rate limits to stop repeated malicious queries and fallback protocols if confidence in a response drops too low. In sensitive sectors like healthcare or public services, apply additional safeguards like data masking and full audit trails to meet GDPR and compliance standards.

With these layers in place, businesses can unlock the value of AI safely and responsibly.

We have all worked with that one colleague that seems to have the answers to every question. We describe AI as that person, except they are always available and no request is too much. AI hasn't been designed to replace humans, it is designed to support and supercharge their efforts. 

Think of AI as the ultimate assistant, helping agents focus on what they do best; having meaningful conversations with customers. While agents are engaged with the customer, AI can take care of the background tasks that might otherwise slow them down. For example, it can automatically generate offers, navigate systems, or handle call wrap-up tasks.

Once the call is over, AI continues to support the agent by providing feedback, such as suggesting they “slow down your speech” or reminding them to clarify points that may have been missed. AI can also send post-call offers, prepare follow-up tasks, and even suggest the next steps to ensure a seamless customer journey.

AI acts like a coach or performance assistant, providing valuable insights and feedback that help agents improve over time. As a result, agents become more confident, productive, and efficient and aren't as burdened by having to build relationships, remain compliant, and hit KPIs without support. The outcome is happier agents, better call results, and a more consistent service delivery. By augmenting contact centre agents with AI, organisations can ensure they're not just meeting but exceeding customer expectations.

In the conversation around AI's role in contact centres, a common concern we often hear is whether AI will replace human agents. However, AI is here to augment, not eliminate, the workforce. Far from taking jobs, AI helps by handling repetitive tasks and optimising workflows, allowing human agents to focus on engaging with customers in meaningful ways.

As contact centres face growing volumes of customer interactions, AI can take on the heavy lifting of routine tasks like data entry, basic inquiries, and customer contact generation. This means there's less need to expand agent headcount, even as customer demand increases. In fact, AI's ability to work 24/7 without fatigue, and never forget a detail, makes it the perfect support tool for agents.

Think of AI as your best agent, always available and never missing a beat. While human agents bring empathy, intuition, and critical thinking, AI complements their abilities by streamlining processes and increasing efficiency. This combination of human skill and AI power is the future of contact centres, where AI isn't replacing jobs but enhancing the workforce. The myth of AI replacing humans fades away when we see how it enables agents to deliver better experiences faster, with more accuracy and consistency.

In today’s global marketplace, providing customer support in multiple languages is no longer a luxury, it’s a necessity. AI-powered multilingual support enables real-time translation between agents and customers, ensuring clear and effective communication regardless of language barriers. This is particularly beneficial in regulated environments like UK Government services, where compliance with language requirements, such as providing services in Welsh, is mandatory.​

Implementing AI for multilingual customer support not only enhances accessibility but also reduces the need to hire multilingual staff, ensuring consistent customer experiences across different markets. Studies show that 75% of customers are more likely to purchase from companies that offer support in their native language . By leveraging AI language translation, businesses can meet this demand efficiently and effectively.​

We believe that to get the most out of AI-powered automation and customer support tools like IntellAIgent, they need to be managed by the people and teams responsible for customer services; not only IT teams. That's why our platform is designed as a low to no code platform, meaning business users can design and deploy their own automation workflows without needing a technical background. With intuitive, drag-and-drop tools, creating complex workflows is as simple as piecing together a puzzle.

Of course, if deeper technical involvement is required, organisations like FourNet can provide full deployment and ongoing support, making the process seamless for you. This means that business-led automation is now a reality, and the teams closest to the process can take charge of their automation needs.

AI doesn't need to sit with IT anymore. It encourages decentralised innovation, allowing various business units to create and refine workflows that best suit their needs.

Unlike traditional software, contact centre AI isn't priced with a flat monthly licence. Instead, costs are shaped by how many customer contacts the AI will handle, and the time the AI will be processing requests. That's why every project starts with understanding your current volumes and workflows.

At FourNet, we assess your existing customer journeys to identify where AI can make the biggest impact. We look at the type of queries coming in, how long they take, and which channels they appear on, and we use this data to build a pricing model based on real usage, not guesswork.

For Conversational AI, pricing typically falls between 50p and £1 per interaction, but the exact cost really depends on where the conversation is happening and how complex it is. A straightforward web chat that resolves a simple query will naturally cost less than a voice call that mirrors a detailed, multi-turn conversation with a human agent. If the AI needs to handle something that would normally take a person ten minutes on the phone, such as guiding a customer through a detailed process or retrieving information from multiple systems – then the programming and processing involved will push the cost higher.

That's why it's difficult to pin down a flat price across the board, because the value and demand for the tool varies based on what you need it to do. The more complex the requirement, the more effort goes into building and maintaining that AI interaction.

FourNet's proposals for example, come with a clear ROI analysis, showing you how long a process would usually take with a human, how much faster and more scalable it becomes with AI, and what you'll save in the long run. That way, you're not just investing in a tool, you're investing in measurable performance and proven outcomes.

At FourNet, we bring years of expertise working across a broad range of sectors, from public services to finance and utilities. We understand the distinct needs and challenges that different industries face, which is why we focus on delivering tailored AI solutions that truly optimise customer experience.

We've been at the forefront of AI innovation, having worked with GPT-2 long before ChatGPT gained mainstream attention. This early experience gives us a unique advantage in helping you leverage AI's full potential. Our team doesn't just look for any AI solution; we identify the processes that can be automated with minimal effort and maximum impact.

As mentioned in earlier sections, organised and accessible data is essential for AI to work effectively and FourNet's Customer Experience Data Diagnostics can help you get there. This service assesses all of your contact centre data, spots gaps and implements new tools and processes to ensure that effective data management is maintained.

We then continue to support our clients through the entire lifecycle, from consulting and scoping, to deployment and ongoing optimisation. This ensures that we continuously improve the solutions, helping you stay ahead in a fast-evolving landscape. With FourNet, you're not just getting a platform; you’re getting a strategic partner with deep knowledge of operations and customer experience, driving real, measurable results.