Conversational AI vs Generative AI: Which is Best for CX?

By analyzing vast amounts of customer data and generating customized solutions, businesses can provide more personalized and tailored experiences, resulting in increased customer satisfaction and loyalty. In this era of personalization and engagement, generative AI enables brands to analyze extensive customer data and behavior, unlocking profound insights into individual preferences and purchase patterns. A digital transformation has emerged as a critical strategy for brands across all industries.

Business leaders need to ensure they have the right security strategies in place to protect sensitive data. By analyzing previous discussions and real-time sentiment or intent, conversational AI can help ensure every customer gets a bespoke experience with generative ai for cx your contact center. They can also operate across multiple channels, accompanying your contact center IVR system, chat apps, social media service strategies, and more. Plus, they can learn from interactions over time, becoming more effective and advanced.

Additionally, these firms report an average ROI of 250%, underscoring the substantial value of the technology. What’s more, the insights acquired through the implementation of generative AI use cases in contact centers will contribute to addressing challenges Chat GPT in various business domains. That includes deploying generative AI to query large customer research datasets, encompassing everything from vendor-led studies, organizations’ studies, surveys and customer feedback across all channels and modalities.

Generative AI models can quickly analyze vast customer data sets, both historical and real time, and combine human prompts to deliver outputs (recommendations, content, and so on) tailored to suit individual preferences and requirements. The case studies explored clearly demonstrate the potential of Generative AI in customer experience. As this technology matures, we anticipate a future where interactions are increasingly seamless, personalized, and even anticipatory. Companies that embrace conversational applications early on will position themselves for long-term success. They will create the kind of frictionless and responsive digital journey that consumers crave and reward with their loyalty.

Everyone is using it to learn, write, suggest, invest, organize, and even create art and music. You can foun additiona information about ai customer service and artificial intelligence and NLP. The company is further exploring creating podcast summaries and audio ads by leveraging generative AI. It transforms the buying journey from a search-focused task to a personalized, conversational experience.

As AI has more information to work with, it can generate more relevant responses. According to a 2023 MIT Technology Review report, most CIOs were prioritizing automation and other technology over AI in 2022. In 2023, however, CIOs are bullish on AI—balancing the promise of efficiency with measured trepidation.

Using these tools, businesses can effectively measure, understand, and enhance their customer experience strategies, ensuring they meet their customers’ needs and exceed their expectations. A positive customer experience not only enhances brand loyalty and fosters positive word-of-mouth but also increases the likelihood of repeat business. For example, CX has a direct influence on impulse purchasing, with 49% of buyers making spontaneous purchases after receiving a personalized experience. Moreover, 64 percent of customers are willing to spend more with a company that resolves their issues in their preferred channel.

Writes summaries of customer feedback in real-time

For instance, predicting the next customer order and generating a personalized marketing email. Moreover, the assistant continuously learns from user feedback, ensuring it can always provide reliable support. Helvetia also prioritizes transparency and security, addressing the potential for AI-generated errors. This positions the company as a leader in both customer service and the responsible use of Generative AI within the insurance industry. Technology leaders have an incredible opportunity to be the customer champion in their organizations, working alongside their business partners to deliver connected, differentiated, revenue-driving customer experiences.

It fills gaps based on learned patterns, applies knowledge from content snapshots, and works across various digital mediums. Third-party risks arise from leveraging pre-trained models, leading to biases and challenges in explaining AI actions to customers. The unpredictability and potential unreliability of GenAI outputs underscore the need for a human-in-the-loop approach.

JPMorgan is taking a strategic leap forward with IndexGPT, a potential ChatGPT-based service. The retailer introduces a new dimension to the industry with the beta release of its AI-powered assistant. The brand sees Generative AI-inspired fashion as a path to a more customized, engaging shopping experience.

Communication, consent, and adopting key privacy principles contribute to responsible and ethical GenAI use. GenAI facilitates inclusive content creation, enhances customer service through agent support, and propels firms towards the next best customer experience paradigm. For all their expectations from chatbots, customers still want humans; 4 in 5 consumers expect human agents to assist them with service, sales and support in chat-based interactions. Second, AI will be used to offer the best, most personalized product offer for every customer.

While customer service is a single aspect of the interaction focused on resolving problems, CX includes this and every other interaction that leads to a holistic view of the customer’s feelings about the brand. Effective CX management means thinking beyond problem-solving to how every element of the business operation affects the customer, aiming to optimize these interactions to create a seamless, positive experience overall. Conversely, a negative customer experience can lead to increased churn and significantly damage a company’s reputation.

Technology Magazine focuses on technology news, key technology interviews, technology videos, the ‘Technology Podcast’ series along with an ever-expanding range of focused technology white papers and webinars. In June, AWS expanded its AI business with the commitment to invest $100 million to create a center to help companies use generative AI. Eventually, as AI models are trained on additional data like email and chat conversations, they can augment prewritten replies with suggested customizations, including modifications according to each communication channel. AI can be trained to proactively suggest replies, resources and next-best steps when building journeys and workflows. Teams can vet and tweak them and then pass them to end users, eliminating time spent searching through help articles or manuals. A 2012 McKinsey study estimated that one-fifth of a support professional’s workweek was spent searching for information to help customers.

Similarly, Carbon Health reduced patient wait times and clinic answer rates by 40%. This is a crucial step for adapting the model to your specific processes, language and context. It involves adjusting the model’s parameters and then monitoring different metrics to ensure it is accurate. AI training is usually performed by engineers as it is a complex task and requires a fair amount of fine-tuning. Data can come from CRM systems, chat logs, surveys and social media, among other sources.

By analyzing customer data and behavior patterns, AI algorithms can identify complementary products or services that are likely to appeal to existing customers, thereby increasing the average transaction value. Additionally, AI-powered recommendation engines can personalize product suggestions based on individual customer preferences, further enhancing the likelihood of conversion. By leveraging generative AI for targeted cross-selling and up-selling initiatives, CX management companies can maximize revenue from their existing customer base. Like conversational AI, generative AI tools can have a huge impact on customer service. They can understand the input shared by customers in real time and use their knowledge and data to help agents deliver more personalized, intuitive experiences. For brands, generative AI can go a long way in delivering more personalised customer experiences, essential in maintaining a loyal customer base and boosting revenue, Arlia explains.

Top Customer Experience Challenges

“By analysing customer data, such as purchase history, browsing behaviour, and social media interactions, AI algorithms can provide tailored recommendations and messages that meet customers’ needs and preferences,” she describes. The customer experience (CX) management has emerged as a critical differentiator for companies seeking to retain customers and drive revenue growth. With the advent of generative artificial intelligence (AI), businesses in the CX management industry have a powerful tool at their disposal to enhance customer interactions, optimize processes, and ultimately increase revenue. This article delves into how generative AI can be harnessed to unlock new revenue streams and drive profitability within the CX management sector. With minimal human intervention, generative AI helps create personalized content across various categories, including text, images, and videos. Generative AI can fuel product innovation within the CX management industry by facilitating the development of new solutions and capabilities.

It’s the strategic partnership with our customers that will ensure these AI solutions remain customer-centric, responsibly driving value. A new generation of automation and intelligence for the contact center is our continued mission to simplify AI for our customers and innovate with products uniquely designed to deliver against the outcomes that matter most. We kept pushing boundaries by adding generative AI for customer support to drive crucial outcomes. All through potent no-code tools, such as Talkdesk AI Trainer™, placing the reins of AI control directly into the hands of our customers, without the need for expensive data scientists.

One major use case for generative AI in the contact center is the ability to automate repetitive tasks, improving workplace efficiency. Generative AI bots can transcribe and translate conversations like their conversational alternatives and even summarize discussions. Plus, since they’re reliant on collecting and processing customer data, there’s always a risk to the privacy and security of your contact center.

By now we’ve all heard of the power of OpenAI’s ChatGPT, but it is not the only one of this powerful new class of systems also known as large language models (LLMs). With commercial use cases emerging rapidly, executives will need to consider where generative AI can enrich customer journeys; how it might be integrated and what the potential implications are for employees. Ultimately however, it is the customers who will benefit the most from this technology as their voice will be much easier to “hear” through the organization. The implication of this is that analytics is about to become a lot more accessible to audiences outside of the data analytics and BI functions. GenAI can mine and synthesize feedback at an unprecedented scale for customer insight, offering a nuanced understanding of consumer behavior. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

Creating informative, quality content is one of the most effective ways for brands to attract and retain customers. LLMs can be great at helping you craft the outline of an article on your chosen topic, but be weary of churning out content without taking time to proof it yourself, as generic content will not get much enthusiasm from customers. Michelle Martinez, formerly the head of post-order CX strategy at online furniture retailer Wayfair, explains how LLMs benefit interactions with contact center staff.

To help our clients deliver innovative, transformational customer experience faster and at scale, we leverage our Digital Customer Experience Foundry which is a collaborative and dynamic environment for ideation and innovation. Fostering collaboration with our clients and partners, it operates as a global delivery incubation hub for addressing the current and future business needs of our clients worldwide, in all industries. There are a lot of unknowns, but what we do know is that through the power of Generative AI, organizations can enhance their relationships with their customers through greater personalization. To effectively gauge the impact of customer experience enhancements and ensure strategic goals are being met, businesses need to establish robust metrics and utilize advanced tools for monitoring and analysis. Navigating the complexities of customer experience (CX) is vital for sustaining customer loyalty and trust. Effectively addressing common pitfalls and understanding the elements of poor CX are critical steps in optimizing interactions and ensuring positive outcomes.

Generative AI can streamline CX management operations by automating repetitive tasks and optimizing workflows. AI-powered chatbots, for example, can handle routine customer inquiries and support requests, freeing up human agents to focus on more complex issues or high-value interactions. Moreover, AI-driven analytics tools can analyze customer feedback and sentiment data in real-time, enabling businesses to identify areas for improvement and implement targeted interventions to enhance the overall customer experience. By automating processes and leveraging AI-driven insights, CX management companies can operate more efficiently and effectively, leading to cost savings and revenue gains. Generative AI enables CX management platforms to deliver highly personalized customer interactions at scale.

Agents expressed feeling under-trained on how to use AI tools, especially generative AI-based tools. They’re also unclear on how such tools will change their roles and are unaware of generative AI guidelines that CX leaders say exist. But Zendesk found a disconnect between CX leaders and agents when it came to generative AI. “And so what we’re going to see is the value being brought to customers by using AI is probably more likely in the back office or in the middle office versus actually being front [and] center with customers,” Mango said. Plus, since generative AI creates unique “original” content, it’s subject to AI hallucinations, which means not all of the answers it gives will be correct.

Merchat AI streamlines the process while uncovering items customers might never have found on their own. Overall, such an integration makes secondhand shopping more accessible and appealing. According to NewVoiceMedia’s report, it translates to a loss exceeding $75 billion annually.

Hear from our product and engineering team about the new innovations in CX product portfolio to drive organizational transformation. His unwavering commitment to innovation and profound understanding of the data landscape have redefined industry standards, empowering businesses to make data-informed decisions with unparalleled precision. Under Sir Winston’s leadership, Datahuit™ stands as a global juggernaut, lauded by industry peers and experts worldwide, poised to conquer new frontiers and redefine the future of data-driven success. More granularly, with sentimental data training generative AI on customer conversations, it can identify specific pain points, understand satisfaction drivers, and strategically enhance the overall CX. Such data breaks down human emotions and pinpoints areas of improvement that customers feel, providing real-time instruction to agents for elevating CX to unprecedented levels. Generative AI allows companies to gain valuable insights into customer behavior, preferences, and needs, enabling them to create more seamless and engaging experiences that meet the individual needs of their customers.

Brands must remain agile enough to stay relevant to consumers, and generative AI is no exception. Rather than simply predicting an output of information or classifying what already exists, generative AI produces new data utilizing machine learning. VoiceOwl equips you with the tools to deliver personalized, meaningful interactions that build loyalty and drive growth.

Therefore, customer service leaders need to have a keen understanding of their verticals and their specific customer base. Like conversational AI, generative AI is becoming a more common component of the contact center. CCaaS vendors offer companies access to generative AI-powered bots that can provide real-time coaching and assistance to agents or enhance the customer service experience. Oracle AI for CX is a collection of traditional and generative AI capabilities that help marketing, sales, and service teams enhance operational efficiency and revolutionize how they connect with their customers. Optimize your engagement strategies, anticipate customer needs, and deliver personalized support while allowing technology to perform low-value tasks—freeing your teams to focus on growing your business and delighting your customers. AI can deliver benefits that save time and money, enhance customer experience, and improve efficiency.

Unfortunately weeks after it launched the chatbot exposed sensitive information, and even used abusive and sexually explicit comments in interactions with customers. This privacy breach resulted in a US$93,000 fine by the South Korean government. Earlier this year eBay launched a plug-in that uses gen AI to automatically create text for items that sellers want to list, helping to cut down on the time and effort it takes to make a listing. It can also leverage insights from the 20 billion images it has stored in its index, helping sellers to create better product images.

“Generative AI” was the buzz phrase of the past 12 months, but this game-changing technology still needs to truly make the leap from early adopters to the mainstream in 2024. Part of this will be making it useful in everyday workflows and bringing AI to where people are. In the bigger picture, there is always the risk that keeping up with the rest of the market on generative AI technology does not create any differentiation whatsoever in the near term. Organizations’ governance structure must enable long-term investment in generative AI to yield a sustainable advantage. CX leaders can use generative AI to develop recommendations by feeding it prompts, such as resource or budget constraints.

Artificial Intelligence in CX

TallierLTM™ capitalizes on Gen AI to create a unique “behavioral bar code” for each customer. The model analyzes transaction history, developing a deep understanding of individual spending patterns. As a result, the system can quickly spot anomalies that could signal fraudulent activity. Shane Schick tells stories that help people innovate, and to manage the change innovation brings.

Predicting the future may seem like a fool’s errand to some, but modeling the year ahead is part of planning that every global company has to do. As CX leaders coordinate CX strategy across an organization, they need to account for the potential risks that come with deploying generative AI. Prioritizing CX investments, especially technology investments, can be a complicated https://chat.openai.com/ task, depending on the constraints within the business and how organizations measure CX progress. In the near term, CX leaders should work with CX business partners to plan and deploy small pilots of new AI capabilities. From there, they can identify the benefits from the pilot results and budget for and launch larger-scale incorporation of generative AI into VoC programs.

Businesses globally have seen significant impacts—the ability to send proactive alerts, more upsell and cross-sell opportunities, and an unprecedented level of personalization—of generative AI across the customer experience arena. Chandni Bhatt, senior UK manager, member happiness at Beauty Pie, explains that to maintain a human element while deploying a chatbot for customer queries, her team wrote out all the responses to ensure consistency with branding and language. And rather than issue instant responses, queries are answered within five minutes, so the bot seems as human as possible. Read more about brands like Coca-Cola and Expedia using AI in 7 ways companies are using generative AI in customer experience. To do this, assess things like customer satisfaction and response times to measure the impact on your CX processes. Once up and running, continuously collect feedback from customers and agents to find areas for improvement, tweaking the AI where needed.

Marketing, sales, customer experience (CX), customer service, and support are on the front lines of customer interactions and are therefore high-risk, high-reward users of generative AI. Most of these teams have started to implement GenAI, with more than half expecting to implement eight of the top 10 use cases in the next 12 months. Genesys empowers more than 8,000 organizations in over 100 countries to improve loyalty and business outcomes by creating the best experiences for customers and employees. Through Genesys Cloud, the #1 AI-powered experience orchestration platform, Genesys delivers the future of CX to organizations of all sizes so they can provide empathetic, personalized experience at scale. Artificial intelligence (AI) is enabling organizations to organize and optimize their resources — people, knowledge and data — to increase productivity and uncover insights. And within the contact center, AI is being leveraged to predict the next step in a customer’s journey, increase agent efficiency and optimize customer journeys in real time.

It’s bound to be an invigorating year for everyone in this increasingly important space. In 2024, the race will focus on getting AI into the platforms people use rather than creating individual tools that people are expected to tinker with. And a cold shower is coming for generative AI tools that aren’t adequately meeting user demand or healing a user’s pain.

Generative-AI-powered tools can assist agents, dramatically reducing the time it takes for them to become proficient and decreasing their time helping customers. Many traditional AI capabilities, such as machine learning algorithms and natural language technology, have long been embedded in VoC solutions. This functionality is already available in VoC features like speech and text analytics to derive insights from customer conversations, email and chat interactions. In the context of customer experience, generative AI can completely transform brands’ CX with real-time decision making and action between support workflows and the customer-facing agent. With the power to reduce total customer interaction time by 55%, leading to increased accuracy and CSAT, generative AI can be an invaluable tool for elevating CX in consumer technology.

Generative AI possesses the capacity to profoundly enhance customer experience (CX) in various domains, leading to valuable outcomes beyond just productivity gains and cost reduction. Generative technologies provide strong foundational capabilities that can be applied across the customer lifecycle to enhance CX. Content plays a critical role in creating engaging and memorable experiences across digital touchpoints. Generative AI can help businesses create more personalized and relevant content at scale. In a market flooded with choices, a strong Customer Experience (CX) serves as a pivotal differentiator, setting businesses apart from their competitors.

CustomGPT.ai is reshaping customer experience (CX) processes, guiding businesses towards more profound and meaningful engagements. This technology not only meets but exceeds customer expectations through enhanced personalization, automated service, and improved engagement. By blending emotional intelligence with ethical AI, CustomGPT.ai deepens every customer interaction as part of a holistic engagement strategy. To elevate customer experience effectively, businesses must engage in concrete actions that systematically enhance interactions at every touchpoint.

Marketers will soon be able to skip the manual process of architecting customer journeys thanks to the new CustomerAI Generative Journeys tool developed by Twilio. It will enable users to describe campaign type, define their audience and which channels they want to use. Ensure you are complying with relevant data privacy regulations, such as the  General Data Protection Regulation (GDPR) in Europe, or the California Consumer Privacy Act (CCPA) in the US. Implement safeguards to protect customer data and be transparent with customers about data usage. To ensure the AI is seamlessly integrated with chatbots, virtual assistants, email responses or any other CX channels, engineers will write coding and perform testing to ensure there are no errors. There is some overlap between the two, as gen AI can also be used to power conversational agents by generating text-based responses in response to queries.

The initiative resulted in a 60% quiz completion rate, a 78% prize claim ratio, and 38% of clients opting for generated greetings. One innovation demonstrating a significant impact in this regard is unsurprisingly Generative AI. A study by IDC and Microsoft indicates an 18% increase in consumer satisfaction among AI companies.

Moreover, 67% of clients are “serial switchers,” readily abandoning brands after a negative incident. This should make it easier for brands to cut through the noise and find the best CX tools and implement them. In the contact center realm, this means laying strong foundations for AI integration. To successfully implement AI into CX stacks in 2024, enterprises will have to establish building blocks, such as high-quality transcription, and get their data house in order before they can take full advantage of the benefits of AI. Generative AI is already being incorporated into many CX technologies — from Voice-of-Customer and customer service platforms to CRMs — offering an immediate opportunity to enrich customer understanding. Whenever new technologies emerge, businesses learn to adapt and shift their business processes to fit in with the industry landscape.

This collaborative approach guarantees the solution continues to iterate alongside client preferences. This directly improves the customer experience for millennials and thin-file individuals. They can gain the resources they need without the hurdles of traditional underwriting. Overall, this tool boosts inclusivity and orchestrates smoother financial journeys for clients. Building and fine-tuning your company’s foundational model is crucial because poor data will generate incorrect outputs. Leaders must also test, curate and supervise AI interactions — not doing so could do harm.

• According to a recent Gartner poll, 38% of executives indicated the primary focus of Generative AI- investment is customer experience.
• You can, for instance, make use of tools such as VoiceOwl to simplify, and automate repetitive tasks and establish workflows that range from lead management to sales pipeline automation, and even custom marketing campaigns.
• Prioritizing CX investments, especially technology investments, can be a complicated task, depending on the constraints within the business and how organizations measure CX progress.
• Additionally, generative AI has the unique ability to “learn” as it gets exposed to new information.
• According to a 2023 MIT Technology Review report, most CIOs were prioritizing automation and other technology over AI in 2022.

The system also provides managers with valuable insights into communication quality. They identify areas for improvement and offer targeted coaching to contact center employees. In the call center space, the difference in performance between top-performing and low-performing call center agents is substantial, with a gap of approximately 3X. This contrast will catch customers’ attention when service is subpar, and a single experience can have a significant impact on the lifetime value of their relationship with the company.

They can provide immediate responses to customer enquiries, offering support, answering frequently asked questions, scheduling appointments, and handling routine customer service interactions. Conversational AI works with pre-loaded prompts to provide human-like conversation with users in natural language, for instance through chatbots and virtual assistants. Gen AI, meanwhile, is focused on creating content based on large amounts of input data.

Understanding how, where and when to deploy it strategically is the key to unlocking its true potential. “Companies should also refrain from using outdated data because these algorithms will only amplify past patterns and not design new ones for the future. For example, this was highlighted by the OpenAI Dall.E2 model, which, when asked to paint pictures of startup CEOs, all were male.

Despite the increasing prevalence of tech-driven interactions, research consistently emphasizes the indispensable role of humans in delivering seamless and effortless CX. The future of enterprise success lies in embracing transformative technologies like Generative AI and not getting lost in AI myths. By harnessing the power of natural language processing and voice-powered interactions, businesses can unlock a new era of efficiency, agility, and customer-centricity. Both those trends will catch the eye of the CEO and CFO at large companies, and it will result in renewed interest from the top down in the power of great customer service, to attract and retain customers. In turn, business leaders will allocate much larger investments in CX as a whole, opening up opportunities for customer service leaders to experiment and drive further innovation. Using AI-driven analysis, it identifies important moments within conversations, and detects and redacts sensitive customer data.

Generative AI in telecommunication also takes a leap forward with a new chatbot from SK Telecom. This tool fuses the conversational power of LLMs with the convenience of a “super app”. “A.” bot offers a customized, friendly experience that goes beyond simple question-answering. Clients can chat as if with a friend, receiving practical solutions to everyday challenges.

By analyzing vast amounts of customer data, including past interactions, purchase history, and preferences, AI algorithms can generate tailored responses and recommendations in real-time. This level of personalization not only enhances customer satisfaction but also increases the likelihood of upselling or cross-selling additional products or services. By leveraging generative AI for personalized interactions, CX management companies can drive higher conversion rates and ultimately boost revenue. Generative AI has emerged as a disruptive force in transforming customer-facing functions, including marketing, sales, commerce, and customer service, accelerating the shift toward personalized and intelligent customer experience (CX). This research byte covers how generative AI can transform CX by enhancing personalization, the potential of generative AI across the CX landscape, and the need to break down data silos to unlock the full potential of the technology. By harnessing AI, businesses can automate routine tasks such as handling basic customer inquiries or processing orders, freeing up human agents to focus on more complex and value-added activities.

In reality, AI strategies are created to enhance the capabilities of humans, and not replace jobs. The Workday survey found that 98% percent of CEOs support making use of Artificial Intelligence (AI) and Machine Learning (ML) in their businesses, increasing their team’s efficiency and creativity. They tend to be more inclined to embrace AI/ML technology to build an inclusive and creative workforce.

But for customer support, a thorough explanation might be exactly what they need. A quick patch was to have the CX Assistant always add “discount doesn’t apply to accessories”. We put guardrails in place to keep the AI focused on the specific knowledge our clients provide. We tap into the language skills and logic of the big models, but we don’t rely on their general knowledge. This first phase of the strategy, which will run until the end of 2024, is focused on constructing a fresh data platform using Snowflake technology. As Etam completes the first phase of the strategy, Gallay will look for fresh ways to help Etam make the most of its data, including generative AI.

• CX leaders should be exploring VoC and CX applications to leverage what is available in production environments and understand what is on the roadmap for the solutions they already use.
• To that end, generative AI can extract insights from big data much faster than a human agent, allowing it to deliver unique marketing promotions and relevant suggestions in real-time.
• As Etam completes the first phase of the strategy, Gallay will look for fresh ways to help Etam make the most of its data, including generative AI.
• Helvetia also prioritizes transparency and security, addressing the potential for AI-generated errors.
• One more example of Generative AI adoption in hospitality is “Jen AI” from a famous cruise line.

“It’s easy to get caught up in the capabilities of generative AI, but it’s important to stress that this technology isn’t a people replacer,” Arlia concludes. At the same time, AI tools like ChatGPT can’t thrive without being fed reliable and factual data sets from, you guessed it, humans. An efficient way to create a centralized data lake for AI to work with is through the use of a middle orchestration layer that plugs into legacy systems and partner platforms via APIs to pull customer data into one place. AI, while seemingly magical, is dependent on the quality and quantity of the data it’s fed. If data accessibility and integrity were essential to companies before, they’re even more imperative now when using AI to drive CX excellence. Unifying this data languishing in separate platforms is critical to capitalizing on AI’s potential.

To better understand their audience, companies need to collect customer data, but to effectively analyze it and take meaningful action requires resources that many organizations simply don’t have. British fashion retailer ASOS was the first company of its kind to sell products through Enki, its fashion bot available via Google Assistant. In 2020 the company went one step further and deployed a voice assistant to work alongside frontline advisors to tackle increasing customer care workloads. This move added 50 points to its net promoter score (NPS) and saw improvements in resolution rates and waiting times.