Guide to AI Chatbots for Marketers: Overview, Top Platforms, Use Cases, & Risks

It can create novel chemical compounds by analyzing biological data and molecular structures, expediting the identification of viable drug candidates. This technology also allows researchers to simulate how molecules interact and assess the possible effectiveness of new compounds, dramatically decreasing the time and expense of early-stage drug development. Hospitals and clinics can use generative AI to simplify many tasks that typically burden staff, like transcribing patient consultations and summarizing clinical notes. GenAI healthcare tools reduce the time clinicians spend on paperwork by pre-filling documentation and suggesting relevant updates based on patient data. They also optimize doctor-patient scheduling with personalized appointment reminders. One of the most tedious parts of software development is creating documentation, but it is required for long-term maintainability.

Chatbot abilities vary depending on the type of automation technology used to create each tool. Another use case that cuts across industries and business functions is the use of specific machine learning algorithms to optimize processes. Machine learning also enables companies to adjust the prices they charge for products and services in near real time based on changing market conditions, a practice known as dynamic pricing. It is a powerful, prolific technology that powers many of the services people encounter every day, from online product recommendations to customer service chatbots. When dealing with process data, the large amount of data and its real-time nature constantly provides new information to GenAI.

Innovative Uses of AI in Customer Experience

When used in knowledge bases, generative AI can retrieve accurate and relevant data rapidly, giving human agents the information they need, when they need it. This functionality is also useful in self-service portals, providing customers immediate access to guides, troubleshooting steps, and FAQs. Through natural language processing (NLP), generative AI understands the context of customer queries and delivers precise solutions. With the conversational chatbot handling a significant number of customer conversations, the call load on human agents was reduced by 60%. The chatbot also helped reduce wait times and provided quicker, more accurate responses, leading to higher customer satisfaction levels.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Autodesk is experimenting with Einstein for Sales and other AI tools to bring the same efficiencies and added revenue opportunities to its sales teams. Kota’s overall AI plan involves introducing tools in a very targeted manner to various “personas” and roles within the company, he says. At Dreamforce 2024, Salesforce is launching its next-generation AI platform dubbed Salesforce Agentforce that will build on Einstein with more sophisticated generative AI capabilities, enabling customers to build their own AI agents. Kota says the capabilities will offer new insights into the AI-built case summaries the company is already capitalizing on. TM Forum has identified seven families of use cases spanning the entire breadth of the CSP organization, two of which sit within what could be seen as a larger category that spans customer experience, sales and marketing. In its research with CSPs, TM Forum has identified this as the biggest overarching category where operators will focus much of their early investments over the next one to two years.

Human agents are provided with real-time guidance and advice on the best way to help out customers, and the AI learns to automatically direct tasks and inquiries to the best agent – human or machine – for the job. Interpreting a customer’s emotional state is one of the best capabilities of generative AI solutions. These tools can analyze the tone, language, and emotional cues within customer interactions to assess sentiment, so customer service teams can tailor their responses more effectively.

Extracting Insights from Customer Feedback

An issue that is “simple,” “boring,” and “repetitive” to a seasoned contact center employee may still feel confusing, high-stakes, and personal to a customer. Suppose the customer has to ask and answer an egregious amount of questions and exerts considerable effort only to receive an inadequate resolution. When identifying appropriate chatbot use cases, it is essential to listen to the voice of the customer. Due to poor past experiences with self-service platforms, only 17% of consumers are confident that they can solve their problems in chatbots. If brands do not address this lack of trust before forcing customers into self-service experiences, they will be uninterested in honoring customer preferences. Customers, moreover, will resist engaging with these bots – pursuing immediate (and inefficient) escalations to a live agent in the best case and switching to a competitor in the worst case.

Finally, insights gained from predictive analytics can inform broader business decisions, such as product development and marketing strategies. Understanding customer behavior and anticipating their needs can lead to more targeted and successful product enhancements and marketing campaigns. At Allianz Trade, we have always maintained that our focus is you – our valued customer.

Automating Post-Call Processing

By measuring the growth of selected metrics (engagement, followers, signups, leads), marketing teams can gauge its success. AHT measures the time agents take to handle calls, including hold time, talk time and any post-call tasks, such as recording call details in a CRM. An easy-to-search and comprehensive knowledge base lets agents find answers so they can quickly move on to the next customer in the queue. Google’s Search Generative Experience (SGE) is an AI-powered enhancement to Google’s traditional search, designed to offer more conversational and nuanced responses to user queries. It leverages genAI to gather information from multiple sources and present it in a detailed, human-like format, making search results more interactive.

• Generative AI presents an exciting opportunity to empower people without a technical background to harness the power of data – all through the simplicity of natural language interaction.
• The current role of AI is to make processes faster and more efficient, but as time goes on it will likely take a more autonomous role in managing CX.
• An AI agent can pull together a view of the customer from all relevant systems that customer support agents could query.
• While marketers still make the call to post, the agent serves as a valuable source of ideas.
• Major businesses have started to harness the power of AI in customer experience and are starting to see its ROI.
• Machine learning’s capacity to understand patterns, and instantly see anomalies that fall outside those patterns, makes this technology a valuable tool for detecting fraudulent activity.

As generative AI monitors customer intent, many vendors have built dashboards that track the primary reasons customers contact the business and categorize them. Knowing this, they can stay focused on what the customer is saying, not trying to remember what they said previously, which should improve their call handling. Sprinklr’s “call note automation” solution aims to overcome this issue by jotting down crucial information as the customer talks. However, even that can impede an agent’s ability to engage in active listening as they multi-task, resulting in increased resolution times. Indeed, GenAI applications – like Service GPT by Salesforce – can do this by first understanding the customer query and sieving through various knowledge sources looking for the answer. “This level of insight will enable you to make informed decisions on changes in your business which can reduce contact volumes being presented to your human workforce,” added Budding.

As we continue to explore the potential of AI agents in the enterprise, it’s essential to focus on well-defined use cases that deliver measurable results by enhancing efficiencies, reducing risk or improving time to revenue. By starting with these top five use cases—or similar ones that fit your organization—and expanding gradually, organizations can unlock the true power of AI agents to drive efficiency, compliance and revenue growth. Complex invoice reconciliation work has ChatGPT rules and standard operating procedures that can be turned into agent workflows. Manually scrutinizing a 30-page invoice and matching it against internal systems might take hours for a human, but agents can free up this time for more productive work. A KM strategy that integrates GenAI can help organizations offer the speedy yet personalized service customers demand. A KM strategy breaks down data siloes and stores knowledge in centralized and easily accessible repositories.

Tomi’s extensive knowledge spans operations, architecture, security management, product expertise, solution design and security offerings. In his current role as Lead Product Manager, he focuses on ensuring the value of customers’ security investments and protecting their organizations. Tomi is dedicated to delivering the best results from security initiatives and protecting organizations from potential threats. Think of GenAI as a tireless process assistant, always ready to dive deep into data to uncover hidden insights and optimize operations. From supply chain processes to IoT device data management and financial data analysis, GenAI has the potential to revolutionize a wide range of functions.

If agents are not truly empowered to handle the responsibilities of an AI-driven world, they will not enjoy any day-to-day benefits. The business, meanwhile, will fail to reap the rewards of greater agent satisfaction and productivity. To build trust in automated engagement options, focusing on the proper use cases is essential. A common mistake, however, involves defining these “simple issues” from an internal perspective.

ChatGPT is the chatbot that started the AI race with its public release on November 30, 2022, and by hitting the 1 million-user milestone five days later. It looks at the major players shaping the technology and discusses ways marketers can use the technology to engage audiences, customers, and prospects. For example, a cosmetics business might use a conversational AI application, such as Shopify Inbox, to help users find the best products that meet their needs. Without a doubt, one of the standout use cases for generative AI in business is in customer service and support. This use of machine learning brings increased efficiency and improved accuracy to documentation processing. “Machine learning and graph machine learning techniques specifically have been shown to dramatically improve those networks as a whole. They optimize operations while also increasing resiliency,” Gross said.

AR and VR extend beyond traditional support methods by providing visual and experiential means of assistance, which can be especially useful in complex or technical scenarios. Sentiment analysis can identify patterns and trends in customer feedback, enabling support teams to proactively address underlying issues. For example, if there’s a surge in negative sentiment regarding a specific product feature or service, the company can quickly investigate and address these concerns. In customer support, this is particularly valuable as it helps in understanding the customer’s experience and satisfaction levels. These innovations, once the hallmarks of businesses at the cutting edge of technology, are now setting new standards for personalized, efficient and insightful customer interactions within the customer service industry and beyond. Conversational AI refers to any communication technology that uses natural language processing (NLP), deep learning, and machine learning to understand human language.

Speeding up the communication with donors with smart chatbot for NGO

Automate troubleshooting and answering both simple and complex queries, as well as routing to human agents when needed. But this is changing, thanks to today’s powerful large language models and natural language chatbots. And while reports suggest that we still prefer to talk to a human when it comes to handling complex or sensitive inquiries, when it comes to more straightforward help, robots are increasingly capable. Integrating such technology with a robust CRM system ensures a seamless flow of information and maintains a comprehensive log of customer interactions, essential for continual service improvement and customization. Each time standards or expectations are not met, customer churn risk looms over insurers customer centers.

Far too many businesses focus on the inefficient, repetitive, or “boring” tasks that they hope today’s AI technology can sufficiently handle. That often goes hand-in-hand with Gen AI-boosted internal knowledge searches, which help customer service agents find the information they need, when they need it, reducing call times and increasing resolution rates. Generative AI is expected to bring about big changes in many business customer service use cases functions in the years to come, though the technology is already here and already delivering tangible results. While some use cases still are three to five years away still, customer service and sales desk operations are already being boosted by Generative AI. As Generative AI continues to mature, businesses in a wide array of sectors are taking advantage of powerful new tools that boost productivity in many areas.

With AI copilots that automate tasks like note-taking, wrap-up codes, and more, employees can focus on more critical onboarding topics. As a result, it’s not only easier to respond quickly to queries but also makes the process far less stressful, as people don’t have to spend time reading pages upon pages of company documents to find the right solution. Organizations can now expect that their customers will receive a consistent quality of service regardless of which agent the customer speaks with. Below, each industry expert shares their favorite agent-assist use case before highlighting several benefits of deploying the technology.

A successful AI-enabled customer service system relies on a robust data layer that encompasses both commercial and service data. A unified customer experience strategy enables telcos to address service issues promptly and efficiently. When customers see that their concerns are being heard and resolved, their satisfaction levels rise significantly. Throughout the hour-plus chat with a service agent, the customer asked to cancel their subscription a staggering 18 times before the company finally solved the issue. By scanning financial reports, news, and other relevant data sources, generative AI can spot trends, collect competitive intelligence, and produce insights for customer behaviors.

Companies should begin by assessing their capabilities and identifying areas for improvement. Focusing on ‘lighthouse’ use cases can demonstrate the value of an integrated customer experience system and guide further development. AI algorithms can analyze vast amounts of data in real-time, identifying trends and anomalies that humans might miss. For instance, machine learning can identify subtle signs of network congestion or impending outages, allowing telcos to take preemptive action. It includes machine learning models for various use cases, such as churn prevention and service issue prediction.

Embracing advanced technology is key, but Héléna reiterates that the core of our customer value proposition lies in nurturing client relationships and providing direct access to human expertise. Héléna underscores the power of machine learning-based tools in improving grading performance, increasing acceptance rates, accelerating response times, and enhancing coverage with more accurate grades. Importantly, the conversational intelligence solution is also able to provide ChatGPT App a constant temperature check, informing contact centers as to whether or not the intervention(s) had the desired impact. The only trouble is – without conversational intelligence – businesses can’t measure FCR accurately. Thanks to conversational intelligence engines, contact centers can draw insights from every conversation, automatically identifying how effective individuals are in dealing with different elements of an interaction across customer touchpoints.

While there are other methods for voice-to-text and translation, travel companies are beginning to include generative AI in the mix. MakeMyTrip is in the early stages of implementing voice-to-text translation, as well as hybrid interaction using voice and visual options, which they say is increasing conversion. Leveraging AI, particularly Large Language Models like GPT-4, can be a game-changer. These models can consume and comprehend the multifaceted customer complaints, dissect the insurance policies, and synthesize this information to generate a responsive summary and proposition. Predictive maintenance differs from preventive maintenance in that predictive maintenance can precisely identify what maintenance should be done at what time based on multiple factors.

When deployed mindfully, AI can proactively anticipate customer needs, offering relevant solutions before issues arise, which reassures customers of the AI’s reliability and competence. He is the former Editor in Chief of TechRepublic, where he hosted the Dynamic Developer podcast and Cracking Open, CNET’s popular online show. Bill is an award-winning journalist, who’s covered the tech industry for more than two decades. Prior to his career in the software industry and tech media, he was an IT professional in the social research and energy industries. For over two decades CMSWire, produced by Simpler Media Group, has been the world’s leading community of digital customer experience professionals. Additionally, these advanced capabilities reinforce Tesla’s reputation for innovation and reliability, as they continue to set a high standard in the automotive industry by integrating cutting-edge technology into their vehicles.

Finally, one of the key areas where AI excels in the contact center, is in processing data, and making insights more accessible to teams and business leaders. With the right AI tools, companies can collect valuable information about customer experiences, sentiment, and employee performance across every touchpoint and channel. Companies can create this layer gradually by improving processes and simplifying operations one step at a time, delivering tangible value to maintain momentum and ultimately delivering the customer experience we all want.

To build a robust data layer, telcos must break down data silos by integrating data from various sources, including customer interactions, network performance, and billing. Implementing a centralized data repository with strong governance ensures data accuracy and consistency. AI-driven personalization can recommend additional services or upgrades based on each customer’s usage patterns and preferences. This can result in upselling opportunities and higher average revenue per user (ARPU).