Like mobile phones, chatbots and virtual assistants entered our lives with little resistance. And just like gadgets, virtual assistants evolve, delivering more value and convenience into our daily interactions and activities.
This is why 86% of PwC study respondents admitted making AI their mainstream direction, with nearly 61% of leaders using AI technology for improving customer experience and operations. It's a natural outcome of the COVID-19 pandemic that left organizations grasping for ways to lower costs, improve customer experience, and leverage innovation. From that point, computer-vision AI significantly transformed contact centers and customer care, enriched workflow with data, and optimized costs and employee time.
In this blog post, we'll outline the concept of conversational AI and see why it's becoming a game-changer for many industries.
Before we elaborate on the specifics of conversational AI, let’s get one thing out of the way—conversational AI and chatbots aren’t the same thing.
Сonversational AI is a program designed to interact with human users in a more natural way, identify their service-related issues, and assist with them—either by executing a previously human-management operation or connecting clients with correspondent business representatives.
Conversational AI technology allows for creating improved AI-powered chatbots with expanded functionality—which explains why people use “conversational AI” and “chatbots” interchangeably.
However, aside from chat interfaces, there are AI-based voice-activated assistants and interactive voice assistants. This versatility makes them able to guide their clients across every platform they interact with—from the company’s website to the company's app.
Meanwhile, traditional chatbots are rule-based and can’t handle tasks outside their scripted scope. Similarly, they can’t deviate from their chat interface and menu-based structure, so they don’t provide customers with replies to specific questions and requests. Instead, traditional chatbots offer generic scripted suggestions or directions that aren’t always helpful for clients who expect a more personalized approach.
Relies on script code and replies to specific keywords only. Can't be trained to respond to different variables and requires constant updates.
Uses deep learning, ML data insights, and NLP. Understands queries with spelling mistakes or short forms.
Requires the client to choose from several options to collect enough data for proceeding to the next stage.
Identifies the sentiment and intent of the client and can instantly proceed to resolve their problem.
Used to manage simple client queries, such as redirecting clients to payment pages or placing orders online.
Can handle and process complex customer requests that previously required human management.
Requires manual and time-consuming maintenance and is challenging to scale.
Can be easily updated along with the company’s website and database.
While conversational AI is a proactive helper that learns, understands and provides back-and-forth interaction with users, a traditional chatbot is mostly a shortcut to a booking reservation page, payment page, or any other part of the website relevant to the user's inquiry.
It's easier to understand the advantages of conversational AI when looking at them in the context of a certain industry and its pain points. However, let's begin with the benefits applicable to any sector.
Compared to assembling and maintaining a human-managed contact center, conversational AI requires lower operating costs while offering a higher return on investment (ROI). Capable of instantly processing information, finding and verifying data, conversational assistants can work with a considerably larger volume of clients within a shorter time, which leads to decreased setbacks and increased revenue.
Conversational AI enhances productivity within the company by providing relevant data and liberating employees from monotonous tasks. It allows businesses to reimagine their approach to workflow organization, provide employees with new growth opportunities, and make more impactful decisions.
Human-managed interactions don't always result in more personalized customer experiences. If staff gets overwhelmed by requests and callers, unique customer insights and data may get lost in the chaos.
Meanwhile, conversational assistants keep track of every interaction, enabling more accurate customer behavior analysis. As a result, the company is more informed about the needs of every segment of its target audience and can personalize its client interactions.
It’s mistakenly assumed that conversational assistants are used only for customer support. Conversational AI’s potential allows businesses to cover a wide range of directions, from customer interactions to sales and marketing activities. Whether automating cross-selling and upselling campaigns or managing clients' accounts, conversational assistants help monitor performance and enrich the company with exclusive data.
Contrary to popular belief, AI can’t take over someone’s job. Even though conversational AI is designed to inject humanity into interactions, it does so as an employee's assistant, not their replacement. It exists to maximize the efficiency of the person's work by taking care of repetitive processes and letting experts focus on more complex and rewarding tasks.
Like chatbots, conversational AI platforms have found a wide application across all industries involving human interactions.
When it comes to conversation AI adoption leaders, financial organizations are certainly among the top users. The demand for conversational AI chatbots and assistants across the BFSI sector isn't surprising, given the numerous areas for improvement that can be covered by AI-powered technology.
As the number of digital customers using financial services increased after the pandemic, so did their demands. Organizations had to do more than handle the bulk of client requests. They were expected to provide clear communication and instant information delivery without compromising security. Above all, they were expected to give their customers autonomy, letting them decide when and how they want to interact with employees. With digital banking expected to grow by 73% in 2024, yet only 34% of banks have fully adopted digital self-service options, the BFSI sector found itself searching for effective digital solutions.
The shift to remote has also affected financial services, with only 23% of employers reporting their readiness to continue working from an office in 2020. This and the necessity to increase the number of employee well-being checks made financial organizations accelerate their digital transformation, adopting their workflow to the new reality. Naturally, technology that would help HR managers keep employees in the loop as well as gather feedback on their mental and physical state regardless of their location was a must-have element of the new digital workplace.
With financial services being frequently targeted by cybercriminals, organizations across the BFSI made it their priority to prevent cyberattacks and data breaches. However, a truly efficient threat prevention strategy isn’t built on technology and antimalware. It must also involve a cybersecurity culture that motivates teams to refresh guidelines on identifying potential threats and protecting company data. To nurture that mentality, security awareness testing became a must for financial institutions. But with employees shifting to working from home, financial services companies needed a better and more accurate test monitoring system for evaluating employee results and handling individual questions and requests.
Challenges like these prompted major players like Wells Fargo and Fidelity Investments to switch from massive call centers to a more automated approach. With other financial companies following their example, conversational AI played a major role in the transformation across the entire sector.
Albeit applicable to companies, conversational AI chatbots became HR managers' helpers, handling employee requests and turning repetitive tasks such as onboarding, ticket generation, and data updates into a self-service.
With more time on their hands, HR managers can concentrate on improving employee satisfaction rates and gathering more feedback from every worker. The latter is necessary for making impactful changes and keeping up with ever-growing employee expectations.
Conversational AI platforms designed specifically for monitoring security awareness tests ensure better security guidelines compliance as they can address each employee inquiry and deliver detailed information about their scores. In addition to simply providing information and monitoring test results, conversational AI chatbots can provide relevant advice, notify employees about the recent changes in cybersecurity guidelines, and keep them up-to-date with the latest safety measures.
Aside from security testing, conversational AI chatbots also apply to employee education, creating a more structured and personalized experience for every participant. Conversational AI can monitor employee scores, keep track of their overall course progress, and generate reports pointing out their performance—but that's not all. In some cases, conversational AI can manage online lessons for employees, test their knowledge, and engage in automated conversations.
The most prominent example of such an AI is, of course, the DuoLingo bot that evaluates each user's skill level and provides exercises of matching complexity. The same approach is used when developing conversational AI chatbots for intracompany employee training to increase their qualification.
As digital technologies get more dynamic and versatile, FAQ sections and pages get more redundant. While they used to address most common service-related questions, they're not enough nowadays. First, FAQ sections usually offer generalized answers that don't provide a detailed response, so if clients need more specifics, they have to spend more time searching and consulting. Second, all data gets outdated over time—and FAQ sections aren't an exception.
However, monitoring the changes and adding them to pages is another repetitive task that wastes the team's time. This is where conversational AI comes in handy, replacing static and generic information with useful and smart assistants that engage in conversations, answer questions, and provide detailed advice on demand. Such conversational AI chatbots can be trained by feeding them new data and variables, which allows them to accurately identify and address customer requests.
Since 2020, banks have been racing to embrace and implement disruptive technologies to keep their competitive advantage and be better prepared for future challenges. Their search led them to dip further into fintech and discover the potential of AI technology to address their top-of-mind concerns.
Digital banking is here to stay, and banks that deliver the best online experience get to lead the competition. Currently, around 41% of US bank customers are digital only, which is a 15% increase compared to 2020. This is why banks like JP Morgan Chase are getting rid of massive call centers in favor of more streamlined and efficient automated options. Following their example, banking organizations search for and implement solutions to eliminate long waiting times and cumbersome request processing and provide clients with instant online support.
By the end of 2022, cutting costs became one of the banks' top priorities. Faced with large technical debts, banks needed to shift to a more modern and tech-savvy approach, which involved a large system overhaul. At the same time, they had to maintain their CapEx vs. OpEx balance while keeping up with their competitors, who offered agile, transparent, and intuitive banking services to their target audience. Some banks made the wrong call by shifting away from massive changes favoring small-scale solutions or putting their digital transformation on halt. However, the key to cutting costs is not rejecting technology but choosing the right one.
Fraudsters' tendency to exploit human error made human-managed call centers a convenient target for their activities. By manipulating overworked agents and using anonymous numbers, fraudsters were able to commit a number of attacks between 2020 and 2022, costing US banks around $4.00 for each $1.00. These incidents made banks more attentive to their fraud prevention measures, shifting their focus to technology that would effectively handle Knowledge-Based Authentication Questions (KBAs) and provide additional customer verification steps.
Aside from these challenges, banks needed to improve data accessibility and adapt their employee management to hybrid work. Conversational AI was able to facilitate the process and help banks build a better, more pleasant digital experience for their teams and clients.
The most prominent example of conversational AI in banking is Amy from HSBC, who accompanies clients at every step of their customer journey, onboards them, and resolves their problems related to managing their banking accounts.
Such conversational AI platforms can assist customers with a wide range of requests—from changing their pin code and checking account balance to handling lost card reports or processing a payment.
Implementing conversational AI helpers enables banks to avoid putting customers on hold due to a lack of available call center operators and facilitates client experience.
Whatever questions they might have, there is a useful and knowledgeable assistant that is accessible 24/7.
Conversational AI chatbots keep their virtual eye on every access and login attempt, including failed ones. They ensure that every client is aware of their security by notifying them of suspicious activity. After alerting the client, a conversational AI platform instructs them on the steps to protect their money (password change, card lock, bank statement check) or redirects them to a bank representative who already has all data on the incident.
But that’s not the only way for conversational AI to prevent fraud. Conversational assistants provide a more effective and reliable alternative to frustrating and time-consuming KBAs via voice recognition. The voice-based conversational AI is based on a robust ID system trained to recognize not just the sound of a client's voice, but all of the 100 unique identifiers it contains. Due to this, voice-based conversational AI can differentiate between a forged client's voice and a genuine one, instantly identifying criminals and protecting client data from vishing.
Many modern consumers are hesitant to contact a financial or banking institution because they anticipate receiving an aggressive promotion of products, services, and packages instead of relevant information. The painful navigation through the phone menu and being put on hold don't improve their experience.
Issues like that happen due to poor CRM and lack of thorough agent selection—and there are two ways for banks to improve themselves.
The first option is to be more thorough in agent selection and qualification, nurturing diligent and empathetic employees.
Another option is to entrust a smart digital agent with engaging website visitors, handling inquiries, and sending the data they submit to marketing and sales departments for further nurturing. Although both options are viable, the former takes more time and resources than banks can afford. Meanwhile, conversational AI bots are easily integrated into the system and appeal to potential customers by educating them on banking services without pressuring them into joining.
During the pandemic, telehealth has peaked, becoming the most reliable way for medics to increase accessibility to patients and provide medical assistance to as many people as possible (without breaking quarantine restrictions). However, surprisingly, it wasn’t the healthcare workers who became the most proactive telehealth advocates. By February 2021, the use of telehealth options was reported to be 38 times higher than before the pandemic, with nearly 40% of patients expressing their readiness to continue using virtual health services.
Although physicians fear that their work would be overshadowed by telehealthcare service providers, leveraging the elements of virtual health is detrimental to overcoming post-pandemic challenges.
One of the most glaring issues in the healthcare industry is the lack of qualified health professionals. For instance, by 2023, the USA is expected to run short of up to 121,900 physicians. The statistics for nurses and other medical practitioners aren’t looking any better —the total deficit of healthcare experts worldwide is estimated to be around 6.4 million. Therefore, existing physicians and nurses found themselves dealing with an increased workload. In the long-term perspective, this situation can result in a spike in negligence cases, human error, and employee burnout.
Given that the number of consumers and families in need of long-term care has increased since 2019, healthcare organizations are facing a growing rate of upcoming medical visits, health checkups, and patients to manage and monitor. With governments and administrations still having to enforce COVID-related regulations from time to time, physicians need to manage the bulk of new post-acute/long-term care patients and stay compliant with the restrictions. Paired with the shortage of nurses, medical assistants, and doctors mentioned above, this task becomes even more stressful.
2020 has forever changed consumers’ approach to choosing service providers. Healthcare organizations found themselves scrutinized by patients who expected change and service improvements. Naturally, their expectations included self-service options which would preserve their autonomy and give them all the tools for resolving their minor health-related concerns.
Patients also expect to spend less time handling matters such as booking appointments, checking their insurance, or managing medical documents. Meeting those needs requires medical institutions to either expand their number of professionals or use advanced technology capable of injecting personalization into customer interactions.
With all those three challenges outlined above, it's clearly seen how they overlap and, therefore, can't be resolved separately. Addressing them requires adopting a solution that would help untangle these issues, both from the employers' and the clients' side.
What makes a conversational AI such a solution?
Automation is a go-to option for any industry facing a shortage of human resources. For that reason, conversational AI chatbots found themselves at home at various healthcare institutions where workers needed swift access to patient records, status monitoring, request processing, or appointment data.
Additionally, conversational AI assistants granted the very self-service opportunities patients sought by providing onboarding and appointment-booking options. Conversational AI for healthcare also serves as a FAQ hub, responding to patients' questions regarding the facility, their health plan, insurance status, or the specifics of any medical service.
Since physicians find themselves under immense workload, they need to optimize their time as much as possible. This means they must swiftly identify emergencies, prioritize patients, and ensure that the right expert is assigned to the right case. Such an approach is possible with max data insights, transparency, and instant communication. Conversational AI hits all these boxes by connecting professionals and patients.
With the help of a conversational AI assistant, patients can report their condition and symptoms when making an emergency call—the AI assistant would then use their data to alert a relevant medical professional and provide full information about the patient and their emergency. Also, conversational AI chatbots can handle minor tasks like monitoring symptoms or health tracking, enabling healthcare workers to monitor patients 24/7.
The worst part of operating in overworked conditions is losing precious insights due to managing huge amounts of customers and paperwork. Even the most diligent and dedicated employees can get exhausted and miss out on important information that can positively impact the facility.
Conversational AI never gets tired. Instead, it meticulously documents every aspect of patient behavior, letting the healthcare administration see the bigger picture.
Viewing the analytical data gathered from conversational AI chatbots, healthcare providers can see what kind of experts they should hire more, which equipment they need to buy, and what procedures have the highest demand. With that great knowledge comes more accurate decision-making, helping providers improve the experience for doctors and patients.
In general, the process of developing a conversational AI can be broken down into five stages.
At this stage, the delivery manager meets with the AI architect and business analyst to discuss the potential conversational AI product. The development team's priority here is to determine what the client needs by discussing the company’s goals, pain points, and potential use cases for the future conversational assistant.
After the team establishes main goals and priorities, they can develop an outline of the future conversational AI assistant, its feature set, and the platform it will be based on. The end goal of the discovery phase is to create a detailed vision of the project, complete with a price estimate and KPIs for tracking progress.
Communication with stakeholders is a vital part of the entire conversational AI development process—the more transparent, regular, and detailed it is, the more realistic the stakeholders' expectations of the end result. In some cases, depending on the project's scale and specifics, the development team can conclude the discovery phase with a simple product demo to illustrate how the future conversational AI would work and interact with users.
Like any ML-based model, conversational AI is data-hungry. So, developing a smart virtual helper capable of replacing call center operators means teaching it everything a call center operator must know.
Due to this, once the vision and priorities are established, AI trainers step in. Their job is to feed the conversational AI large volumes of necessary data and as many variations of potential queries and requests as possible. This step is essential for designing a conversational assistant that can recognize intent, identify the sentiment behind the request, and respond in a human-like manner.
Meanwhile, developers integrate the AI into the company's system and configure how it reacts to relevant triggers (payment processing, transactions, failed login attempts). The end goal is to ensure that conversational AI provides a seamless user experience and interacts with the company's system without friction.
The team runs several tests, evaluating the conversational assistant's performance, how much time it needs to respond to a query or process a request, and how it reacts to various wording.
This testing goes hand-in-hand with user experience testing, where the team ensures the conversational assistant is intuitive and easily accessible for end-users as well as well-integrated with the website and messengers.
During phase 3, communication with stakeholders is essential as they help look at the conversational AI from the end user's perspective and (in case the product was meant for employee interactions) providing insights from the employees working with the AI.
This is the pre-launch stage, where stakeholders and end users get to interact with the MVP. They run the product through different scenarios to test its capabilities and evaluate how it responds to their questions and requests. If there is feedback from stakeholders (questions and variables missing), the team works on implementing stakeholders' suggestions and polishing the product. If the product meets expectations and they're satisfied with the results, the project is approved for deployment.
The UAT stage is necessary for releasing a product that delivers a flawless user experience from the get-go. Hence, it’s important to pay attention to details and make your feedback as informative as possible.
After the conversational AI assistant is deployed, the development team monitors its performance and provides technical support to stakeholders. Some companies overlook the importance of AI trainers and developers being present for the long haul, but the truth is that a conversational AI doesn’t learn new data on its own. Instead, it's vulnerable to data and concept drifts, affecting its accuracy and limiting its ability to assist clients or employees.
The post-production support helps to avoid this, with AI trainers identifying potential data drift risks and supplying the conversational AI chatbots with new data or adjusting them to respond to disruptive situations. With a team ready to decipher new experiences to a conversational AI platform, stakeholders can rest assured that their workflow, clients, and employees remain resilient to potential changes.
Depending on the complexity of the AI project, conversational AI development can take from several weeks to several months. The time brackets are usually outlined during the discovery phase once the team knows the volume of work and the end goal.
So, if you have ideated a conversational assistant to shoulder your employees' tasks and facilitate your work processes, let’s chat and set this journey in motion.
Our result-driven business analysts and AI architects will provide a detailed development roadmap explaining all the whats, hows, and whens of bringing your project to life. Working with our team, you can rest assured that your personalized AI-based solution hits the spot for end users and your decision-making group.