Executive Summary

In this updated White Paper, FourNet considers how Artificial Intelligence and Automation has developed significantly in the last few years and how the latest technology can be utilised to enhance customer experience and customer journeys. We look at how the technology can improve internal and external processes and drive further efficiencies. And we examine how it can be used to assist employees, both in the workplace and with hybrid working practices.

Artificial Intelligence allows organisations to automate many of the more repetitive tasks in the customer support journey leaving agents to focus on more complex enquiries. The newest technology can now cope with more complex queries particularly thanks to the very latest conversational AI. This ensures customers are left with higher levels of satisfaction.

Chatbots allow customers to serve themselves and often remove the need for mundane and repetitive tasks by agents in the contact centre. For more urgent or complex issues where customers want to engage with an agent, AI can be used to route an enquiry to an agent with the right skills, thereby increasing first contact resolution, improving agent efficiency and outcomes in the contact centre.

Thanks to FourNet’s sector specific expertise and our highly experienced Customer Experience (CX) consultancy team, we are able to address the most common issues for different industry sectors.

Our 2023 White Paper considers the business drivers and how to
build the business case for investing in Artificial Intelligence. We share best insights on how to introduce AI and Automation to ensure more streamlined and efficient processes, job satisfaction for employees and improved business outcomes.


AI technology has moved on apace in the 2020s, and in this updated whitepaper we look at how AI and automation can be used to significantly improve customer experience (CX), improve employee and agent efficiency and drive improved outcomes across an organisation and in the contact centre.

We look at the business drivers and how to build the business case for investing
in Artificial Intelligence and Machine Leaning (ML) technology; we also share our insights on how to get started with AI and highlight how FourNet’s market-leading and ‘Best of British’ AI solutions and CX Consultancy team can help.

A growing list of AI-powered solutions can improve efficiency, produce cost savings and improve CX, so we have grouped them into four logical categories:

Customer Self Service

Chatbots, Standard and Conversational Interactive Voice Response (IVRs) and voice authentication give the customer the opportunity to answer their own query without needing to speak to an agent. All self-service options need a seamless process to handoff the query to an agent.

AI Enabled Assisted Service

This category of solutions uses AI to provide an agent with more information to handle queries and deliver quicker resolutions to customers. These Assisted Service solutions can be particularly relevant for inexperienced agents or where agents are working remotely.

AI for smarter routing an operational insights

AI enabled contact centres can harness the huge processing power of AI to route queries to the right agent. The technology also helps make more streamlined decisions and uses sentiment analysis to predict behaviour and improve outcomes.

Driving Agent Efficiency with AI

Artificial Intelligence powered Workforce Optimisation and Quality Management offers huge potential to improve agent performance. Robotic Process Automation (RPA) can automate non-value-added tasks; speeding wrap-up time, increasing the accuracy of data entry and thereby freeing agent time to focus on more value-added tasks.

Why has AI become so important?

Artificial Intelligence (AI) has become an omni-present fact of life. From Microsoft’s Chat GPT which can not only answer questions but ask them, to Google’s competitor Bard, through to apps which use AI to pinpoint empathy in text, or perhaps less helpfully to create fake images, fake news and clone people’s voices without their knowledge.

Artificial intelligence comes in all shapes, sounds and sizes. It is a myriad of different technologies working together to enable machines to act with human-like levels of intelligence. It’s already writing essays for students, diagnosing illnesses, designing clothes, even replacing guidedogs.

AI adoption is growing faster than ever and is transforming the way we work, the way businesses operate, and the way we interact, daily, with each other at home and in business. Whether it’s the voice in your smart speakers, the navigation system in your car, the robot cleaning the floor, the drone overhead, or the virtual assistant on your desk.

According to McKinsey’s ‘Global Survey on AI’, published in December 2022, adoption has more than doubled since 2017 with 50% of businesses using the technology in at least one business area.

Percentage of respondents who say given Al capability is embedded in products or business processes in at least one function or business unit2

Robotic Process Automation or RPA is among the most common utilisation along with a huge rise over the last five years in the use of conversational AI and natural language text understanding. According to McKinsey however the top use cases over the same period have remained relatively stable – with service optimisation at the top of organisational priorities for each of the past four years.

AI has long been regarded as a potential source of business innovation, but it is now helping to revolutionise organisations which hold an unprecedented amount of data.

Gartner research predicts that 10% of agent interactions will be automated by 2026, an increase from an estimated 1.6% of interactions using AI in August 2022. The same report suggests that conversational AI will reduce agent labour costs by around £65 billion globally by 2026.

Whether it’s a regular interaction, a fresh real-world enquiry or a complaint, AI can deal with – or assist an agent to handle – more customers, more efficiently, than ever before.

Based upon a customer’s history, the context of a call, what they want to know
or do, and previous patterns of similar interactions, AI can predict the most appropriate action to take, potentially handling a tricky customer who it considers likely to get upset. With sentiment analysis, AI can be programmed to head off that anger, perhaps with a sales offer or a new service.

As AI technologies have developed, more human-like traits have been added, such as avatars and highly realistic conversational speech. That means, for instance, that AI can be programmed to deal with customer enquiries through a personalised chatbot, which expresses human-like emotions and can tackle real time enquiries as if a human is at the other end.

These experiences can both improve customer experience and enhance the reputation or perception of an organisation.

AI expands the capability of the contact centre to perform better. It can help to improve efficiency and increase productivity, while allowing staff or contact centre agents to be switched to more relevant tasks. In turn this can help to reduce agent or employee attrition.

Post pandemic AI

Developments in all areas of AI, and uptake of the technology, was accelerated by COVID-19. Even before the pandemic, analysts were predicting that conversational AI would become as important for an organisation as its own website. Once coronavirus struck, the functionality offered by AI and automation took three of the top five slots in Tech Target’s Customer Experience IT Priorities surveys.

Fast forward three years, and Tech Target’s latest global survey of 3,500 technology and business buyers suggests that more than 75% of businesses expect 2023 to be the big year of AI and Automation.

While the pandemic accelerated digitisation – it also changed the way we live our lives and from where we work. The switch to hybrid working and the so-called Great Resignation meant that contact centres have had trouble meeting customer expectations, with a shortage of agents and rising costs.

Contact centre analytics firm CallMiner estimates that customer waiting times have tripled since the pandemic while ContactBabel, in its 2023 Decision-Makers’ Guide, suggests that the average speed to answer has increased from 106 to 120 seconds, call abandonment rates have risen from 8.2% to 9.1% and call duration rates have soared to an average 7 minutes and 6 seconds – the highest for 20 years.

Technology can provide the solution to many of these problems. With the pace of technological developments in AI, it’s likely to mean an even broader range of uses to which we put artificial intelligence to work, not only replacing even more complex tasks in future but creating more attractive work environments, processes and roles.

Consumer attitudes to AI

Even prior to the pandemic, the ability to hold a ‘conversation’ with technology had changed our habits for good.

With the growth in daily use of voice assistants like Apple’s Siri and Google’s Alexa at home, customers have become more comfortable with regular interactions with AI when dealing with businesses and brands. That trend is likely to continue as conversational AI technology develops.

A study conducted by customer experience researchers, PSFK, suggests that nearly three quarters (74%) of consumers prefer chatbots when they are looking for answers and those companies using the technology, particularly in retail, are seen by customers as efficient (47%), innovative (40%) and helpful (36%).

There are wide variations by age in the preferred method for contacting a company but according to FourNet’s partners, NICE, 81% of customers want organisations to offer more options to self- serve.

The 2023 ContactBabel Decision-Makers’ Guide suggests that while a fifth of B2C customers would prefer to speak to a human, more would choose automation if the outcome was the same.

And when it comes to the effect that AI and chatbots has on customers, the study’s findings are overwhelmingly positive.

However, the more complex and urgent the enquiry and the subject matter, the decision-makers’ survey suggests that most consumers would still like to talk to a human agent.

So while processes may be streamlined, and AI takes on a greater degree of the workload, human agents will remain a key part of the contact centre for the foreseeable future.

Technology can be used to assist the agent for the benefit of the employee, their team and the organisation.

Current Adoption of AI in the Contact Centre

But the latest research from Contact Babel still indicates that less than 30% of UK Contact Centres are using AI or Machine Learning and fewer than 30% plan to introduce it in the next 12 months.

Business Benefits of AI in the Contact Centre

Operational Efficiency and Cost Savings

Artificial intelligence can allow organisations to automate some of the more repetitive tasks in the customer support journey. Chatbots can provide customers with answers to basic questions so that they can serve themselves before seeking the assistance of an agent.

Natural Language IVR can enable customers to self-serve via the phone. RPA can automate the completion of basic tasks such as updating a customer’s address across all systems.

For more complex issues where customers want to engage with an agent, AI can be used to route the enquiry to the agent with the right skills to address the issue thereby increasing first contact resolution.

After many years of comparable costs for phone calls and digital channels, the figures from hundreds of UK contact centres in 2023 suggests that digital channels are becoming considerably cheaper than phone contact.

Every contact centre will have their own metrics for measuring the cost per inbound interaction. But Contact Babel’s latest research on mean average cost per inbound contact shows how quickly an investment in a web/app self-service chatbot or Natural Language IVR can pay off.

Cost per inbound interaction

Source: Contact Babel Inner Circle Guide to AI, Chatbots and Machine Learning

Improve the Customer Experience

Customers have long had the option to vote with their feet (or their keyboard mouse) and switch to a different provider if they don’t benefit from a great experience. Improving the Customer Experience in the post-pandemic world is up in the top three priorities for most C-level Execs.

Customers want to know that you understand and care about them.

Artificial Intelligence can help you to provide that personalised experience by serving agents with the relevant information to provide a great experience.

As well as ensuring that a call goes through to the person with the best knowledge to handle that call, AI can also provide agents with contextual information about a caller. This means that your agents will be able to see what the customer has had issues with before, which products they’re calling about and more. That way, your customer doesn’t have to repeat their story endless times to get a result.

AI powered sentiment analysis can help agents identify when a conversation is moving in the wrong direction and provide guidance on how to get control of the discussion again.

Improve Agent Performance

AI powered Workforce Optimisation not only enables organisations to accurately predict demand and schedule resources accordingly but can also supercharge Quality Management processes by enabling the monitoring and scoring of 100% of calls automatically rather than just random samples.

Machine learning can then use this huge pool of data to analyse patterns of agent behaviour and characteristics connected with best outcomes, to develop performance and training programs right down to the individual agent level. Gaps in agent knowledge or capabilities can be identified and addressed based on thousands of calls, rather than relying upon manual evaluations which can only process a handful of calls from each agent.

These capabilities are especially important with some agents regularly working from home.

Predict Future Needs

Beyond improving current performance in the contact centre AI can also use the data gathered from day-to-day conversations to make predictions about what a customer might want or need from your company.

AI can analyse huge volumes of data rapidly and can spot trends and offer predictions or forecasts that we wouldn’t be able to come up with on our own.

AI Solutions in the Contact Centre

In this section we look at the four main categories of AI-enabled contact centre solutions:

Customer Self Service

Most businesses will offer at least some form of customer self- service, for the most part this is a simple FAQ section on the website or an automated IVR. The main objective for AI in most contact centres consists of projects to increase the capability for customers to self-serve. This not only has the benefit of dramatically reduced costs but also an improved customer experience because of higher real first-contact resolution rates using the customer’s channel of choice.

Chatbots or Virtual Assistants

AI powered chatbots make it simple for organisations to automate customer service interactions and deliver a faster, more personalised experience for customers. Customers can interact with organisations from their preferred device through speech, messaging or visual interfaces and receive an instant, rich and consistent experience. Chatbots connect customers with the answers they need through embedded knowledgebases, from documents, from business applications, and from employees. A chatbot can answer questions and action requests at scale with human levels of understanding and deliver instant answers.

Chatbots, or Virtual Assistants, can deliver immediate responses to customer enquiries 24:7. Utilising AI & Machine Learning an AI powered chatbot can understand conversations no matter how a customer may phrase their enquiry and provides instant responses through automation and integration to live customer service applications.

AI powered conversational chatbots are light years away from the earliest versions of chatbots which could be frustrating and time- consuming for customers. Early chatbots were often only able to respond to very specific input, they were essentially a text-based equivalent of interacting with the old “phone tree” IVR call menus.

How do AI powered chatbots work?

Understanding humans isn’t easy for a machine but it’s getting easier by the day, as Microsoft’s Chat GPT4 and Google’s Bard illustrate very clearly.

The subtle and nuanced way humans communicate is a very complex task to recreate artificially, which is why chatbots use several natural language principles including Natural Language Processing (NLP), Natural Language Understanding (NLU) and Natural Language Generation (NLG).

These principles enable chatbots to mimic human conversation. The chatbot can identify the underlying intent behind the text a real person types or speaks, then deliver a response that matches that intent. Chatbots with NLP can now “learn” from past conversations and improve their ability to provide appropriate responses and solutions.

On a simple level, a human interacts with a chatbot. If voice is used, the chatbot first turns the voice data input into text (using Automatic Speech Recognition (ASR) technology). Text only chatbots such as text-based messaging services skip this step.

The chatbot then analyses the text input, considers the best response and delivers that back to the user. The chatbot’s reply output may be delivered in any number of ways such as written text, voice via Text to Speech (TTS) tools, or perhaps by completing a task.

Conversational AI bridges the gap between human and computer language and makes two-way communications more natural, without having to configure specific words or phrases. The intelligence comes from the system learning the many different ways that customers communicate their requests to improve the results and understand more.

The most immediate potential of AI powered chatbots in the contact centre is in handling digital enquiries: webchats tend to take longer than phone calls due to agent multi-tasking, and many email response rates take days.

When the chatbot has low confidence that it has returned the correct result, it is able to escalate the customers query seamlessly to a live chat agent, who then has access to the self-service session history, enabling a greater chance of a successful resolution without repetition.

Contact Babel’s average cost per inbound contact see figure 9 suggests that webchat is now significantly cheaper than voice contact.

But whereas only 5% of webchats had any automation involved in 2015, this has grown to 50% in 2022. There is still considerable room for automation, increasing efficiencies and lowering costs.

Impact after 3 months

The time taken to realise the benefits of implementing an AI powered chatbot can be extremely swift. The stats highlighted below show the monthly impact of implementing a chatbot for one of FourNet’s small housing association customers.

Natural Language IVR

Of those contact centres offering telephony self-service currently, a mean average of 18% of inbound enquiries see figure 9 were handled without requiring an agent, according to Contact Babel’s 2023 research; however, a typical IVR solution can only handle a small number of common enquiries and only works well in cases where the caller has a simple request.

A Natural Language IVR can simply be viewed as a chatbot that customers communicate with through voice, rather than typed text. It needs the capability to handle natural language, to use artificial intelligence to determine intent and provide solutions, and to convert speech to text and back again.

Natural Language IVRs can be used as triage to decide who gets automation and who is directed to an agent based on the profile of the customer, their requirements, circumstances and past outcomes.

As with all forms of customer self-service, an IVR must offer the customer an easy way to opt-out and request to be transferred to a live agent, ensuring all the data that has already been gathered is handed over.

In addition to the standard benefits of enabling customer self-service (24:7 availability, cost reduction, taking pressure off agents etc.) encouraging customers to use Natural Language IVR rather than to press numbers on a keypad generates large amounts of data that can be used to further train AI models.

Voice authentication

Voice authentication is a way to confirm a customer’s identity based on a unique characteristic – their voice. A voice is unique as a fingerprint and consists of a combination of characteristics such as dialect, pitch and speed. Voice authentication is even harder to spoof than fingerprints and can’t be hacked like passwords, making it an extremely secure method of authentication.

Alongside voice authentication, AI can also be used to power “phoneprinting” which focuses on preventing fraud. Phoneprinting collects information about the call being made, such as Calling Line Identifier (CLI), location, the type of phone being used, the phone number’s history, levels of voice distortion, etc. These factors are then scored and the score will then determine the security processes and questions that the agent is required to ask the caller, speeding up the process for genuine callers, and focusing the tightest levels of security on potentially fraudulent calls.

Once the system has a voiceprint, the customer gets a better experience when they call customer service. For example, the interactive voice response (IVR) system can recognise and authenticate their identity, which enables the customer to access self-service tasks like checking account balances. If the customer chooses to speak to an agent, the authentication information can be passed to the agent, which saves time and improves the customer experience because they don’t need to repeat any information.


  • Seamlessly voiceprint customers during the course of normal calls
  • Securely authenticate customers with zero customer effort
  • Reduce average handle time, no need for numerous security questions
  • Significantly reduce fraud risk

Building a Knowledge Base

The beauty of AI and Machine Learning powered self-service applications means that they continuously learn and improve based on customer interactions. However, to get started self-service tools need to be trained and fed.

Working with a solution provider with sector specific knowledge can help shortcut a great deal of the front end of this process. FourNet’s sector specific expertise means we can help automate a large volume of simple queries using our sector specific templates that consolidate the most frequently asked questions or address the most common issues by industry sector. Responses to enquiries may include content that rarely changes where pre-trained answers from FAQ’s or workflows will guide customers and resolve their query accurately.

For more complex or tailored queries, the underpinning AI technology can be used to access more complex and dynamic content found in documents, knowledge bases, databases, product manuals, and business applications such as CRM & Service Desk.

Where answers are dynamic and constantly changing, the system can be trained to search through specific content that exists in different formats that are typically challenging for traditional search technology to interpret (PDFs, Excel tables, PowerPoint). With embedded AI, information can be intelligently labelled and indexed within your enterprise document library (headers, footers, content, images, tables) enabling smart discovery of precise answers from within bodies of text.

Where dynamic content is stored within databases, it will search information and content found in business applications such CRM systems, Service Desk, HR systems, databases or industry specific systems. Data can be retrieved to help identify customers for Identity and Verification (ID & V), and look-up content to provide users personalised responses.

Assisted Service

AI-assisted service provides agents with information that is tailored and relevant to the customer interaction they are having real-time whether by phone, chat or email. AI assistance is particularly useful for helping inexperienced agents, for remote working scenarios where asking colleagues is more difficult and for more complex interactions which may require multiple systems and databases to be accessed.

AI assisted service offers an opportunity to provide timely and effective support to every agent while the call is happening. AI can provide the agent with suggestions about next best action, pull up relevant information from the knowledge base, make suggestions based on customer history and use sentiment analysis to warn when a conversation is going off track. Real-time sentiment analysis can flag

a supervisor remotely who can break into the conversation or whisper coach. These tools have a positive impact on first-contact resolution as well as customer experience.

AI assistance can help inexperienced agents where stupervisors or experienced colleagues aren’t available, they improve the outcome of the interaction in real- time rather than waiting for post-call reviews.

Using natural language processing in real-time speech analytics can identify when an agent is experiencing stress, becoming emotional, or in need of whispered or direct intervention on a call.

Assisted Service technologies became hugely important during the pandemic where a high proportion of agents were working remotely for the first time. The positive impacts on performance, sickness and attrition for those agents who adapted well to homeworking means that many contact centres have pursued a hybrid work strategy with some agents in the office and some working remotely. This has made AI Assisted Service technologies critical for businesses.

Smarter Routing & Operational Insights

Intelligent Routing

AI enabled contact centres can harness the huge processing power of AI to route queries to the right agent. The technology also helps make more streamlined decisions and use sentiment analysis to predict behaviour and improve outcomes.

Intelligent Routing utilises AI to automatically send inbound customer communications to the right place, so that best resource to deal with that customer query is allocated, thereby ensuring higher rates of First Contact Resolution. This can happen on any channel, whether phone, chat, text, email, or social media messaging. Not getting to the right resource the first-time results in agent handoffs which can require the customer to repeat themselves, all of which negatively impacts the customer experience.

AI Intelligent Routing is powered by data. With information about the customer and their query, you can route that enquiry to the best resource available, rather than waiting for the first agent available. Data sources to inform intelligent routing can include customer profile data from your CRM, Customer Journey Data which identifies what steps a customer has already taken and initial discovery questions either via chatbot or IVR.

A customer may begin engaging with the chatbot or IVR for self-service, but if they hit a dead end or need help from an agent, the chatbot or IVR leverages the data it has already gathered to route the customer to the best agent to resolve the issue.

Platforms based on machine learning ensure that the AI that drives intelligent routing learns from every customer interaction. This happens through feedback from both customers and agents, asking them how satisfied they are with how the customer was routed.

Sentiment Analysis

Sentiment analysis is a way of quantifying customer and agent emotions within interactions, whether on the phone or through an alternate channel. Sentiment analysis captures and analyses every interaction, which is then scored on a sentiment scale from highly positive to highly negative.

This can then be used to identify processes, behaviours and situations which cause strong levels of positive or negative sentiment that impact business outcomes and customer experience and enable root cause analysis to address and resolve the issue.

Using analytics and large data sources, datasets can be searched to identify and inspect the types of interaction that have major impacts on customer sentiment.

Uses of Sentiment Analysis

  • Root cause analysis: by analysing thousands or millions of interactions, sentiment analysis is able to show the products, processes and topics which most often provoke the strongest negative or positive reactions.
  • Quality assurance: sentiment analysis plays a part in quality management. Analysing metadata such as the topic under discussion should indicate whether this negativity arises from a specific agent performance or is more likely to be linked to the subject matter.
  • Employee wellbeing: sentiment analysis can be used to understand and track agent morale and motivation. This is particularly relevant with the increase in remote working during the pandemic.
  • Fraud detection: sentiment analysis can identify stress in real-time, which may be an indicator that fraud is taking place, prompting the agent to take the caller through more detailed levels of security in order to prove their identity.

Agent Efficiency

In today’s hybrid working environment, technology has become increasingly important to assess how agents are performing when working remotely.

Without the correct tools, supervisors have little direct oversight into an agent’s conversations, and it can be challenging to personalise coaching and understand which behaviours can be improved.

AI for Quality Management is playing a huge role in managing agent performance. RPA is being harnessed in these post COVID times to free agent time and increase operational efficiency.


Robotic Process Automation (RPA) can dramatically improve agent efficiency. RPA uses digital agents or ‘robots’ to handle repetitive, rules-based tasks with high accuracy and consistency, for example: assisting agents with change of address requests, ticketing processes, document reviews, and validating customer account information. RPA allows human agents to concentrate on more complex tasks.

Unlike AI, RPA robots carry out their tasks in a consistent manner each time, neither altering nor learning from their behaviour or past outcomes. RPA does not replace existing systems, instead they sit on top of existing logic and applications, using them in the same way that a human contact centre agent, chatbot or back-office worker would do. As such, RPA does not require complex integration, meaning roll- out of the robots can be relatively quick and flexible.

Most contact centres require agents to switch between multiple applications; hard enough in itself, let alone doing it while interacting with customers at the same time. RPA supports agents to assist customers by optimising the agent desktop. RPA- assisted integrated desktop solutions can remove the need for agents to log into multiple applications and can help them navigate between applications within the call. An RPA integrated desktop can ensure that customer data is gathered from the correct places and written back to any relevant databases without the need to navigate through multiple systems, rewrite systems or integrate deeply with multiple applications and databases. This both increases accuracy of data entry and frees the agent to focus on giving a great customer experience.

Workforce Management and Advanced Scheduling

AI powered forecasting and scheduling tools proved invaluable during the pandemic and have grown in importance as businesses adapted to hybrid working practices and a shortage of agents.

AI powered Workforce Management tools harness the processing power of AI to accurately forecast demand and schedule the appropriate levels of resource. They are powerful enough to cope with on-the-fly rescheduling that might be triggered by unexpected illnesses, childcare or domestic requirements from employees or peaks in demand from customers.

AI enabled forecasting and scheduling:

  • Enables forecasts and schedules to be run more frequently, easily and quickly; so managers can respond more quickly to peaks in demands and staffing fluctuations.
  • Provides the option to reschedule easily, without having to start from scratch as would need to be done with a spreadsheet.
  • Helps contact centre leaders to better assess forecast accuracy via visual reporting and rectify situations where the centre is over- or understaffed.
  • Enables intraday changes to immediately improve the service level by making instant adjustments, such as optimising breaks and lunches.
  • Makes it possible for supervisors and analysts to identify at a glance the best times to schedule off-phone activities to minimise the potential negative impact on customer service levels.
  • Provides built-in communication services such as app notifications or SMS alerts to agents if schedules change.

Quality management

AI powered Quality Management empowers the contact centre to analyse and track agent performance across 100% of customer interactions. Insights and data can be used to inform general and individual training programs, mitigate compliance risk, and uncover business drivers for leadership.

Traditionally QM only analyses 1-2% of recorded voice calls; the manual processes and subjective scoring impacts on the quality of agent feedback and misses opportunities to improve the overall customer experience. With poor quality automation, call scoring, and as a result, suboptimal training programs, contact centres are missing out on the wealth of insights around customer sentiment and agent performance.

AI enables QM managers to analyse and score every voice call that takes place. With reduced manual input required, combined with machine learning intuitively surfacing critical insights and identifying gaps, QM teams can focus more on providing feedback and creating more relevant training programs.

Transcription services can transcribe hundreds of calls in minutes, enabling 100% call quality assurance and monitoring. Transcription services that are based
on machine learning get better over time, and their capabilities grow as they encounter new types of data.

Transcription services can transcribe hundreds of calls in minutes, enabling 100% call quality assurance and monitoring. Transcription services that are based on machine learning get better over time, and their capabilities grow as they encounter new types of data.

Building the business case

Digital business is accelerating interest in artificial intelligence at a pace that has left many CIOs hurrying to build an AI strategy and investment plan appropriate for their enterprise.


The impact of the pandemic accelerated digital transformation at a pace never seen before. When digital became the default, many organisations realised that their self-service experiences weren’t up to scratch. The shift to hybrid working and new business practices has continued to challenge organisational processes.

The need to cope with high levels of demand with reduced levels of available agents has meant that organisations have been forced to look at how they
can automate both for efficiency and to maintain the customer experience. As referenced in see figure 8 , three of the top five drivers in investing in contact centre technology are driven by AI and automation objectives.

In this section we’ll help you articulate how investing in AI can help your contact centre save money, grow revenue, increase operational efficiency, innovate products and improve the customer experience.

Quantitative and Qualitative Benefits

Most contact centres will have comprehensive dashboards which measure their most important operational KPIs; we also recommend focusing on the human experience. Interview or work shadow your agents to understand their pain points and identify where they are spending their time.

FourNet Business Analysts can help with this process, they can identify quick wins within the constraints of your current systems and provide you with the quantifiable potential benefits of streamlining these processes in terms of operational efficiency.

The impact on employee engagement generated by enabling them to spend their time on helping customers rather than repetitive, inefficient processes is less easy to quantify but will have measurable results in terms of job satisfaction and retention.

When building the business case, prioritise the KPIs that executive decision-makers care about and connect them to financial value. For example, what would the financial impact be if assisted service could deliver the right content at the right time to your agents so that the average case handle time decreases by 10%? What would your average cost per contact look like if you could enable self-service on an additional 5% of inbound calls using natural language IVR? How much agent time could you save by handling 50% of your webchats using AI chatbots?

Save Money

Enable self-service via chatbots or voicebots;

  • Recent projects have seen up to a 43% reduction in the volume of live chat requests that can now be handled by AI virtual assistants.
  • Quantify the number of simple queries that could be answered by chatbots or voicebots, identify your contact centre’s cost per inbound contact and model the impact that this switch in types of contact has on your overall cost to serve.

Increase Operational Efficiency:

Reduce Average Handle Time (AHT);

  • AI powered Assisted Service for Agents can reduce the average handle time per call by suggesting next best action or pulling up relevant information from the knowledge base.  This reduces the need for screen switching and streamlines the call.  We have seen reductions in Average Handle Time of up to 30% by implementing Assisted Service Solutions.

  • Robotic Process Automation (RPA) can have a dramatic impact on Average Handle Times.  Using RPA-assisted integrated desktop solutions saves agent time switching between screens during the call and reduces Call Wrap-Up times post call by ensuring data is written back to any relevant databases.

  • Average Handle Time for webchat can be reduced to zero agent hours for those queries that can be handled by AI chatbots.

    Increase First Contact Resolution (FCR);

  • Intelligent Routing ensures that queries are directed to the right agent to handle the customer’s specific issue.  Making AI powered decisions based on the caller identity, customer journey input and data already gathered via chatbot or IVR can route the contact to the agent best suited to resolve the issue.  We have seen increases in FCR of up to 20% after implementing AI powered Intelligent Routing solutions.

  • Assisted Service can have a dramatic impact on FCR rates as well as Average Handle Times, we’ve seen improvements of nearly 20%.

Grow Revenue:

Increased retention rates;

  • There are multiple research sources that have proven the link between increased Customer Satisfaction and higher retention rates. According to Invesp, improving customer retention rates by just 5% can increase profits by between 25% and 95%. Using the average Life Time Value (LTV) of a customer you can model the impact that even small percentage increase in retention would have on the bottom line.

Suggest cross-sell or upsell options;

  • Assisted Service can use AI to suggest relevant cross-sell or upsell options to the agent while on the phone.

Improve the Customer Experience

A study by Aurora Market Research into 1,000 UK consumers commissioned by Contact Babel in 2023 identified the most important factors to a customer when contacting an organisation

Both businesses and consumers agree that first-contact resolution is the most important single factor impacting upon customer experience when contacting a business. A short wait time for a response is also seen as a vital factor. AI and automation can help on both counts.

Getting started

The great thing about AI in the age of cloud and as-a-service technology is that you don’t have to take an all or nothing approach to the trend.

In fact, we strongly recommend most organisations make decisions with the long term in mind but start with small projects. By this we mean that for those organisations looking to upgrade their overall contact centre infrastructure, now is the time to look at solutions built with AI at the heart which will provide a framework on which to build for the future. But for organisations undertaking large contact centre system upgrades and those organisations with existing estates, we recommend you start with a small self-contained project for a clearly defined business issue or process e.g. chatbots to replace digital enquiries.

It is important that the boundaries of the project are clearly defined and understood, with relevant baseline metrics captured before the project, and clear and achievable outcomes defined so the success of the project can be clearly measured. A limited, low-risk use case that can be simply implemented can demonstrate a quick win to build confidence in using AI within the contact centre.

While quick wins are essential to get buy- in, it is essential to place the project in the context of a longer-term vision. We recommend building a roadmap of linked businesses cases that layout a long-term vision for the strategic use of AI across all customer facing parts of the organisation.

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