The customer is pivotal to business success, with leaders increasingly recognising the valuable impact of CX strategies on their business outcomes. AI is set to enhance these investments by supercharging CX technologies and strategies. In this article, we explore how AI can drive business success.
Author: David Paulding, Vice President of International at Nextiva
AI is having a dramatic effect on workplace productivity by automating tasks, providing personalised solutions, and offering data-driven insights across industries like healthcare, retail, and finance. It boosts efficiency, strengthens customer loyalty, and supports compliance while driving innovation and brand management, which is why leaders are changing their attitude to this game-changing technology.
However, technology alone is not the answer; to truly capitalise on the CX opportunity, businesses must focus on deploying AI models that automatically perform key business functions and work hand-in-hand with human agents to deliver personalised interactions.
A workplace revolution
Typically, personalisation is more often associated with consumers than enterprises. But in the workplace, tools and capabilities that respond to unique needs and business contexts are powerful enablers of productivity – demonstrating a key AI business use case.
Online shoppers are well-used to systems which point them towards products they may be interested in purchasing. McKinsey has said that Amazon’s recommendation engine generates up to 35% of its sales – proving the power of personalisation. By closely analysing customer data, financial context and behavioural patterns in real-time, companies can recommend relevant products, services, or solutions or offer proactive guidance to automatically help them solve challenges – sometimes before they are even aware of any problems.
In the workplace, AI can have a similar effect on productivity, by quickly surfacing relevant data and offering intelligent assistance tailored to specific user needs. A next-best-action recommendation engine, comparable to Amazon’s, can provide personalised suggestions for customer-facing agents based on their unique profiles and previous interactions. This makes it easy for human representatives to deliver tailored guidance for a diverse range of customer scenarios to ensure consistency and quality of support interactions.
From fragmented feedback to actionable insights
AI is already transforming the world of work as CX becomes a recognised driver of business success. Â As investment accelerates, a wide range of chatbots are now dealing with customer interaction through multiple channels, including email, apps, texts, phone calls, social media and more. All the data generated during those interactions can be collected to deliver insights which can improve productivity and customer satisfaction.
Take feedback, for example. A big part of the problem in unlocking data from feedback for deep testing and analysis is that it comes from multiple platforms and is difficult to consolidate, interpret, and act upon effectively. Until organisations can effectively consolidate that data, the extraction and delivery of key insights to guide more effective strategic decision-making will be a challenge.
Yet if companies get the data architecture and processes right, that information can be brought to life with AI-powered tools that turn feedback into actionable insights. Natural Language Processing (NLP) can analyse text to identify key points, recurring themes and even customer sentiments, enabling leaders to prioritise and address issues effectively. Sentiment analysis provides deeper insights into how customers feel about their requests, highlighting the urgency and potential impact.
By creating a feedback loop built around collecting, analysing, applying, and following up on feedback, companies can continuously improve their offerings based on customer input. As AI advances, automating more of this loop will make it easier for businesses to act on feedback or other internal data points. With the integration of chatbots and automated systems, organisations are increasingly capable of tapping into a treasure trove of customer insights, leading to improved satisfaction and responsiveness.
When the same techniques are applied to staff feedback data, leaders have a powerful way to gain visibility of potential issues and take proactive steps to mitigate challenges.
Transformative and panoramic AI tech
AI offers a wide range of workplace performance enhancements. Dynamic scripting lets models adapt their communications to the flow of conversation, giving customer agents access to real-time, contextually appropriate responses which lighten the cognitive load and allow staff to focus on solving problems whilst AI tells them what to say to the humans on the other end of the line.
Knowledge management can also fetch and rank articles from a secure database, presenting the most relevant information quickly and easily. Searches can be constrained so that queries can only access an organisation’s secure knowledge base, reducing the risk of inaccuracies and speeding up information retrieval.
Speech-to-text transcription is another important case that involves translating spoken words into text. Audio or video recordings of every meeting, customer call, or other business interaction can now be turned into text quickly and automatically. These recordings can then be processed using data loss prevention (DLP), redaction, classification, speech adaptation, summarisation, and scorecards to give decision-makers panoramic visibility of their team’s work. Supervisors can easily review transcripts, search for specific words or phrases, mine insights, or automatically produce summaries. It’s a step change in teamwork.
Delivering better customer experiences
To turn CX into a revenue generator, the emphasis should be on simplifying operations, consolidating technologies, and creating seamless, personalised customer experiences. A crucial step forward also involves establishing trust in AI and building confidence in the ability of systems to deliver results without compromising data, governance, or security. This includes delivering technical components such as robust code, secure architectures, proactive monitoring, and the human elements of transparency, accountability, and trustworthiness. Together, we call this the Trust Stack.
Zero trust architectures are emerging as a critical foundation for the Trust Stack, ensuring data and systems are protected by verifying every interaction and eliminating blind spots. Additionally, proactive measures, such as visibility into system activities and unified security layers, are essential for mitigating vulnerabilities.
Earning trust is not a one-time achievement but a continuous process. Every interaction from that initial demo up to full-scale deployment is an opportunity to demonstrate excellence and reinforce confidence. It’s also a chance to lose customers’ confidence – which means leaders must tread carefully.
In 2025, many organisations are only just beginning their AI journey and understanding why it is a passport to improved productivity. But they will need to be quick because new winners and losers are already being decided. There’s everything to play for, with major rewards for companies that make the right decisions during this early stage of the game.
For enquiries, please contact vaishnavi.nashte@31media.co.uk
