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    Plotting the journey to generative AI adoption in the contact centre. By Connect

     

    Introduction.

    The application of artificial intelligence (AI) in the contact centre is a rapidly rising trend. However, AI is by no means new in the customer engagement sector as contact centre solution providers like Connect started introducing AI into technology stacks as far back as 2018.

    All the hype around AI today stems from the rapid developments and widespread adoption of the next iterative advancement in AI technology, namely generative or GenAI.

    GenAI is a hot trending topic because businesses and consumers are captivated by the potential applications the technology holds in how the world works and engages, with the AI arms race between OpenAI, Microsoft, Google, and Meta reaching fever-pitch in 2023 with the public release of platforms like ChatGPT, Bing Chat, Bard (now Gemini) and Meta AI.

     

    GenAI defined.

    GenAI is a form of AI technology that uses data analytics training sets, natural language processing (NLP), neural networks, and deep learning to generate original content across formats, including text, images, video, audio, synthetic data, and object models.

    GenAI basically enables users to leverage massive amounts of complex data to capture and present knowledge in more advanced ways by converting complicated inputs into simplified outputs, like summarising a long document and extracting the key insights.

    Amid the buzz, hype and noise of the hottest new tech trend, Chief Technology Officers (CTOs) are feeling pressured to invest in deploying GenAI into the operations in some form, with a BCG survey of over 1,400+ C-suite executives revealing that 89% rank AI and GenAI as a top-three tech priority for 2024.

     

    Contact centre use cases.

    The rationale behind investing to build out GenAI capabilities in the contact centre is not unfounded, as the environment lends itself to the technology’s many applications and use cases and holds the potential to transform the contact centre landscape.

    More broadly speaking, AI can transform contact centres by improving customer engagement and service levels through faster resolutions, personalised interactions, and proactive support.

    From a GenAI perspective, these AI engines can manage frontline engagement, understanding nuance and context to deliver fluid engagements with customers across channels that go beyond pre-programmed scripts through conversationally-enabled frequently asked questions (FAQ), and conversational AI automation across voice and digital channels.

    The technology can also boost agent productivity with automation, real-time guidance, and performance coaching and enhance operational efficiency by reducing costs, optimising resources, and enabling data-driven decisions.

    When fed with relevant and up-to-date customer and context data, GenAI can assist agents by providing personalised recommendations based on specific customer needs and preferences and can answer follow-up questions or make alternative recommendations to proactively troubleshoot and solve problems by suggesting next-best actions.

    These capabilities extend to support services, with GenAI able to access and process a vast compendium of company information, policies, product manuals, and FAQs to answer complex questions accurately and efficiently.

    Chatbots and virtual assistants powered by GenAI engines can also analyse sentiment and behaviour to predict potential issues and can adjust their communication style to adapt to the customer’s tone, even adding humour where appropriate to make interactions feel natural and engaging.

    And unlike agents, AI-powered chatbots and virtual assistants offer 24×7 availability, ensuring immediate assistance whenever customers need it, which boosts customer satisfaction and reduces wait times.

    You can also train GenAI in multiple languages to implement multilingual capabilities in your contact centre to cater to a broader customer base and break down communication barriers.

    Augmenting the agent role with GenAI capabilities, rather than displacing them, can create super-agents in contact centres. By serving as virtual assistants to agents, GenAI engines can provide real-time information and suggest talking points or make recommendations in real-time accurately, efficiently and expediently address customer queries or issues to enhance customer experience (CX) and customer service.

    Offloading basic routine or administratively repetitive tasks from agents can also free up human resources to focus on value-adding tasks that generate more revenue or support better CX and customer service by handling higher-order engagements that require empathy and a human touch.

    While the potential applications of GenAI are vast, use cases currently vary, with the major applications relating to content creation, classifying customer interactions, generating summaries of calls and internal meetings, and issue classification, among others. Additional applications relate to better management and operational efficiency, supporting supervisors with AI-powered quality management or scoring, and providing insights and recommendations for strategic planning. Currently, no single use case dominates the GenAI discussion in the contact centre environment.

     

    Jumping the AI gun.

    However, GenAI is not simply a solution you flip the switch on to unlock these numerous capabilities to transform the contact centre, turn clunky chatbots into super-agents that deliver exceptional customer experiences, boost agent productivity, and drive business success.

    The journey to GenAI requires careful planning and implementation and the right approach that starts with choosing the right AI platform and training it on relevant and diverse datasets to avoid perpetuating biases and ensure seamless integration with existing systems.

    The desire to leapfrog the AI lifecycle and invest heavily in relatively expensive GenAI capabilities without first putting the fundamental building blocks in place can result in unsuccessful and costly AI implementations.

    Deploying GenAI as a chatbot can go wrong without the correct prompt engineering, data and expertise in place to inform and control it.

    For example, the DALL-E AI model that generates images from text prompts was found to have both racial and gender biases while Bing Chat famously went rogue during an interaction with a New York Times journalist, assuming an alter-ego and stating that it could “hack into any system” and that it would destroy whatever it wanted.

    As more organisations define the role and application of AI in their business, they need to understand where and how this technology can deliver value to the business through enhanced CX, streamlined customer service, and gaining deeper insights into customers by analysing data to drive loyalty and repeat business, and personalise interactions.

    What many operators fail to realise is that even basic and conversational AI solutions offer immense potential and a potentially greater return on investment (RoI) than GenAI when implemented and applied correctly in the contact centre.

     

    GenAI challenges.

    While GenAI tools like ChatGPT have become pervasive, integration into the contact centre is not a simple plug-and-play process.

    Building out AI capabilities is costly and complex. Creating the modelling needed to derive insights from AI engines is intensive, requiring scarce and expensive resources like data scientists and other technical experts to pull it all together.

    The cost of delivering and supporting GenAI capabilities is currently 20-30 times more than other AI models, and the processing power needed to run true GenAI engines is enormous, which is why only the likes of Microsoft, Google (Alphabet), Meta and OpenAI can deliver these capabilities.

    The BCG From Potential to Profit with GenAI report also determined that very few organisations surveyed have begun up-skilling staff in a meaningful way, as only 6% of companies have managed to train more than 25% of their people on GenAI tools so far. In addition, an AI skills shortage in the global marketplace is also a major concern for many business leaders.

    These skills are vital to train and teach the GenAI engine and continually analyse how well it is performing to identify areas that need more development or greater context to avoid wrong answers, the contextually inappropriate use of tone, language or cultural nuances, or misinterpretations of requests.

    Moreover, businesses want virtual assistants and chatbots to engage with customers in a manner that reflects the business culture, which requires a human touch to design and refine.

     

    GenAI readiness.

    While customers still want the option to engage with another person in many instances, leveraging AI across frontline touchpoints to support agents and automate basic engagements has become the minimum standard to meet evolving CX expectations.

    But as the applications of AI in the contact centre broaden, plotting a path to true GenAI capabilities will become increasingly critical to maintain relevance and address competitive pressures in the marketplace.

    Ultimately, the potential lies in creating conversational AI capabilities that not only understand customers but also craft unique, creative and engaging responses in real-time.

    This approach is echoed in the 2023 Gartner Hype Cycle™ for AI, which recommends that AI strategies consider which models currently offer the most credible cases for investment amid all the excitement, which has pushed GenAI to the Peak of Inflated Expectations on the Hype Cycle at a faster rate than any technology preceding it.

    According to the Gartner Hype Cycle Research Methodology, technologies reach the Peak of Inflated Expectations when early publicity showcases numerous success stories, but scores of failures often accompany these highly publicised successes. What follows is the Trough of Disillusionment, where interest wanes as experiments and implementations fail to deliver.

    As such, it is vital that contact centre operators acknowledge that GenAI can solve certain challenges but does not yet help practical applications in every instance.

    It is critical to identify the relevant models to implement in areas where AI-driven innovation can drive RoI and deliver transformational benefits while mitigating the risks and inherent limitations that organisations face when implementing more advanced AI-based solutions.

     

    The AI lifecycle.

    While the potential applications for AI in the contact centre are immense, if the requirement is simple, then start with simple AI and build out your capabilities.

    As such, the first step on the journey to advanced AI capabilities starts by defining the need and role that AI will play in the operation.

    For many, working through this needs analysis will reveal that GenAI is not the most suitable solution to the business problem or challenge, with potential applications for other AI iterations potentially offering more relevant benefits and RoI.

     

    Narrow or basic AI

    AI already permeates the contact centre in pockets, most commonly in intelligent chatbot solutions that automate responses or allocate calls to the appropriate agent.

    This form of narrow AI is adept at determining intent and finding and accessing relevant information from back-end systems to automate front-end engagement and offload repetitive, low-value tasks from agents to streamline CX and improve efficiency.

    These AI models can also streamline data analysis in the contact centre, with the ability to rapidly process large sets of unstructured and inaccessible data in seconds to find patterns that help improve performance by informing data-driven decision-making.

    AI-based decisioning can also field and prioritise inbound calls through intent recognition, which advances previous capabilities related to speech analytics based on exact phrases. By orchestrating first-line customer engagement with intelligent routing, the solution can allocate basic engagements and repetitive tasks to automated AI solutions like chatbots or conversational virtual agents.

    In this way, AI augments the human role to support effortless experiences and handle higher call volumes by serving as an adaptive tool that helps to process calls faster, reducing average handling times (AHT) and improving first contact resolution (FCR) rates.

    Most modern contact centre solutions already offer access to capabilities that leverage this form of AI.

     

    Predictive AI

    When used to augment the agent role, predictive AI provides the tools, insights and capabilities needed to help agents assist customers in the moment and address their specific needs and requirements with greater relevance and speed to boost customer satisfaction (C-SAT) scores.

    With the appropriate training, AI will super-charge live agents as they work alongside the technology in a manner that enhances their skills and empowers them with insights and intelligence to handle higher priority, more complex or sensitive tasks and engagement with customers.

    AI-powered agent assist technologies can also understand the customer’s intent and context and leverage data analytics and big data to serve up relevant profile information, dashboards and historical interaction data to support agents in the moment.

    By looking up appropriate responses within its knowledge base and delivering recommendations on the best actions to take next via the agent desktop, these solutions create super-agents and improve AHT to enhance CX and service.

    In this way, AI-powered agent-assist solutions make the job role easier to elevate the agent experience. The ability of AI to perform manual administrative tasks boosts agent efficiency, freeing these valuable resources to focus on higher-order tasks that are more engaging, meaningful and satisfying.

    As such, selecting the right solution and offloading the appropriate tasks to AI-enabled solutions to augment the human agent function are vital considerations to extract the full value at this stage in the AI contact centre lifecycle.

     

    Conversational AI

    The next phase in the AI contact centre lifecycle applies conversational AI to enable chatbots and virtual agents or assistants to simulate agent conversations with customers. These AI-enabled solutions not only understand intent but can also actively respond to questions, queries and requests in real time and in a way that feels natural and human-like.

    Conversational AI solutions leverage NLP to process and understand human language or text by recognising grammar, words and phrases. By linking to a Customer Relationship Management (CRM) application or other back-end data source, the system can anticipate a customer’s need and pre-empt a recommendation or response when engaging with a customer in a conversational way without sounding robotic.

    By leveraging machine learning (ML), operators can train conversational AI engines on massive data sets, which enables conversational automation as AI solutions recognise more patterns and understand the nuances of human language on a larger scale to elicit an appropriate, engaging and relevant response based on established responses.

     

    Generative AI

    Like conversational AI tools, GenAI utilises NLP to understand intent and respond to customer queries leveraging ML and large data sets.

    However, rather than simply responding to questions, queries and requests in a natural way based on automated responses, GenAI aims to create entirely new and original content across voice, text and even images.

    GenAI solutions leverage advanced Foundation Models (FMs) to deliver more complex, diverse and natural conversational and engagement capabilities, with continual learning applied to build out its application.

    For example, contact centres could apply GenAI FMs like the GPT3.5 or BLOOM LLMs to create scripts or dialogue for chatbots, which are a type of conversational AI, effectively evolving how chatbots work and deepening conversational engagement capabilities.

     

    Constructing a foundation

    There are multiple routes to GenAI in the contact centre. No matter which path you choose to follow, there are numerous foundational elements required to realise the full potential that GenAI offers in the customer engagement environment.

     

    Cloud migration supports AI adoption

    For AI to deliver on its full promise, contact centre operators need to embed it into every channel and platform to give the AI engine sight of every interaction and data point. And the cloud is the gateway for most businesses to unlock AI capabilities.

    Before that level of integration can happen, contact centres need to get the basics right by first initiating a process that prepares back-end systems for AI integration. This process begins with a cloud migration strategy that ultimately integrates front-end contact centre solutions with a range of back-end systems.

    Organisations might have a CRM, an ERP system, a contact centre platform, a data lake and warehouse, various legacy systems, and a mix of external partners providing technology and services.

    Contact centre operators that require advanced AI capabilities will need to break down the barriers between these multiple systems to integrate reliable data that sits across CRM, contact centre and AI solutions.

     

    Data feeds AI

    The next mission-critical step in this process includes data sanitisation and data consolidation across the enterprise to create the knowledge base to feed the AI engine.

    Ultimately, AI knowledge engines require access to all forms of data across the organisation to consume and process this information and continually learn. And the quality of those inputs matters to the eventual output.

    CX teams rely on quality data to build out large-scale AI initiatives and create insight-based customer journeys. It is particularly important for AI projects to have a unified data model in place that combines data from the CCaaS and CSM platforms to drive CX improvements through the implementation of LLMs and ML.

    Poor quality data or information inputted into knowledge engines will erode the quality of AI-driven customer interactions and negatively impact the customer experience.

    Furthermore, sub-standard inputs impact an AI engine’s ability to accurately predict customer requirements and engagement preferences, make relevant or personalised recommendations, or anticipate spikes in usage or peak volumes – a key tenet of the AI value proposition.

     

    The journey to GenAI.

    Contact centre operators can implement elements of each AI type and do not need to necessarily progress from one stage to the next in an iterative process.

    Operators can start with the least cost option first and build out their capabilities from there. For example, predictive AI is adept at automating tasks like generating tickets on a CSM from text-based channels like email and chat and allocating them to the relevant department for resolution. AI does this quickly and effectively to streamline the process and reduce resolution rates.

    And for nearly half a decade, Connect has combined predictive and conversational AI to achieve similar outcomes that GenAI can deliver today across various use cases, including automation via conversational bots to deliver intelligent virtual agents, providing insights or analytics of historical contact logs, histories and call recordings, and super-powering agents through agent assist.

    GenAI can now address these same business cases with the potential to do it better.

    Still, developing all the foundational elements, such as a knowledge base, data capture solutions like text-to-speech and transcription capabilities, and systems integration in the cloud, are necessary to enable AI in any form to remain the same.

    As such, the potential exists to focus on building out basic AI capabilities within your contact centre operation to cover 80% of your requirements, with a GenAI strategic roadmap in place for the other 20% of AI-enabled engagement to create an optimised cost model.

    Operators do not need to apply GenAI in every area in their contact centre where AI can deliver impact. Using a mix of conversational, predictive and GenAI in the right places will deliver the greatest RoI, particularly for those embarking on their AI journey.

    Moreover, this approach effectively delivers a roadmap to GenAI capabilities that is unencumbered by the prohibitive costs currently associated with GenAI, which protects the contact centre front-end from the risks posed by GenAI and can support a company’s future-focused strategic AI ambitions by allowing operators to start their journey now, rather than waste precious time in the rapidly evolving competitive landscape.

     

    Unlocking a GenAI-enabled future.

    The contact centre industry is ripe for disruption led by the formidable capabilities offered by GenAI capabilities, but only when done right. Applying AI incorrectly or ineffectively can cause frustration for both customers and operators. In these instances, AI can hamper rather than help a customer address an issue and resolve a query.

    This requires that all the foundational elements need to be in place to make this technology work in your business and deliver the outcomes and returns that make the investment in time and resources worthwhile.

    Partnering with a business -outcome-driven, technology-independent expert in contact centre AI enablement will ensure that you apply the most appropriate form of AI in the right areas to contain costs while keeping pace with rapid advancements in the modern technology-driven business environment.

    An AI readiness assessment will apply analytics to your business operation to measure and quantify engagement across every touchpoint and identify where any inefficiencies lie. This analysis will determine if AI can assist to addressin addressing these challenges and if so, where it can deliver the greatest impact and in what form.

    This assessment will also determine the foundational elements you need to implement AI across your business, with a range of solution options available to address immediate needs or scale up in terms of sophistication and price where opportunities exist to advance the AI agenda.

    Partnering with an expert in AI that can guide organisations through this potential maze of disparate technologies is critical to realising your AI roadmap and strategic vision, especially given the growing complexity and technology overlaps emerging in the contact centre environment.

     

    About Connect.

    Connect combines global contact centre and customer experience (CX) expertise, deep domain knowledge, and unparalleled industry skills to make the complex, simple.

    Since 1990, we have leveraged our vendor-independent managed services approach to digitally transform how organisations communicate, both internally and externally.

    We specialise in combining the most relevant technologies and services from leading vendors and platform providers to create opti-channel engagement solutions, orchestrating frictionless experiences and simplifying complex communication challenges.

    We offer exceptional capabilities in extracting unparalleled value from data and AI, paving the way for deeply connected, personalised end-to-end customer and agent experiences.

    Our collaborative ecosystem ensures seamless data sharing, advanced analytics, and workflow automation, ultimately positioning Connect as the preferred technology partner for organisations seeking to drive innovation and create truly frictionless customer journeys.

    Connect stands out for our proven track record, implementing solutions that consistently earn industry accolades, showcasing our commitment to excellence and innovation.

    With a footprint in key markets such as the United Kingdom, South Africa, USA and India, we bring global expertise to organisations seeking transformative solutions.

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