This two-part series of articles provides a practical guide to anyone who wants to learn how to create and deploy engaging conversational AI applications.
AI is revolutionizing every aspect of our existence. Conversational AI, as a highly transformative branch of AI, facilitates natural and engaging human-machine interactions, enabling the creation of applications that yield valuable business insights, personalized assistance, and captivating experiences.
Embracing conversational AI offers a myriad of advantages to businesses and organizations, encompassing enhanced customer service, amplified sales, heightened productivity, and substantial cost reduction, and acts as a catalyst for driving innovation. Building conversational AI applications requires expertise, research, and continuous improvement. However, the resulting benefits are profound, allowing organizations to deliver personalized omnichannel experiences that create substantial value for both employees and customers.
This two-part series of articles is written to provide a practical practitioner’s guide to anyone who wants to learn how to create effectively and deploy engaging conversational AI applications.
Part one discussed the key components of conversational AI and how they work, as well as the best practices for designing Conversational AI assistants. Part two answers the following questions:
Developing conversational AI applications is not a one-time project but a continuous process that requires constant monitoring, maintenance, and improvement. Along the way, you may encounter various challenges and pitfalls that can affect the quality and effectiveness of your solution.
Here are some of the common challenges and pitfalls of conversational AI development and how to avoid them:
Data is the fuel for conversational AI platforms. Without enough data, the system may not be able to learn or perform well. However, collecting and labelling data can be costly and time-consuming. Moreover, the data may be noisy, incomplete, or biased, which can affect the system's accuracy and reliability.
To avoid this pitfall, you can use various techniques and strategies to acquire more data or improve data quality, such as using transfer learning or pre-trained models to leverage existing data or models or by using active learning or human-in-the-loop methods to involve human experts or users in data collection or labelling.
Users are diverse and variable in their backgrounds, preferences, behaviours, and expectations. They may use different languages, dialects, accents, or styles of communication. They may have different goals, needs, or preferences. They may also behave differently in different situations or contexts.
To help create an exceptional user experience for everyone, you can use various techniques and strategies to accommodate user diversity and variability, such as using multilingual or cross-lingual models or techniques to support different languages or dialects or using speech recognition or text normalization techniques to handle different accents or styles of communication.
Systems are complex and scalable in their architecture, functionality, and performance. They may involve multiple components, layers, models, or services that need to work together seamlessly. They may also need to handle multiple tasks, domains, or scenarios that require different skills or knowledge. They may also need to cope with increasing demands, traffic, or expectations that require more resources or capabilities.
Organizations can use a variety of techniques and strategies to manage system complexity and scalability, such as using modular or microservice architectures to divide the system into smaller and independent units that can be developed, deployed, or updated separately; using cloud computing or distributed computing techniques to leverage the power and flexibility of cloud-based or network-based resources or services.
Building conversational business applications can be challenging if you don’t have the right people, tools and platforms, which is why technical and people behavioural skills are important.
A conversational AI team should include a project sponsor, a solutions architect, a business analyst, a process owner, a tester, a conversational flow designer, quality assurance and developers who understand the technology. As the better conversational AI platforms are now low-code/no-code, there is often limited or no requirement for specialist computer software engineers to code a conversational AI system.
Conversational AI applications can be applied to various domains and industries. Often teams start small and then scale up their conversational AI as they develop confidence in designing exceptional conversational experiences. Here are some examples of successful conversational AI applications to take advantage of:
Banca Transilvania is one of the largest banks in Eastern Europe. It uses a conversational AI digital assistant named David to drive internal efficiency and external customer satisfaction. For example, the bank uses conversational AI to automate help desk support to increase employee activity. It also uses conversational AI to automate closing clients’ accounts and products faster to reduce customer wait times. Banks are constantly looking to boost sales, reduce costs and enhance customer retention, and conversational AI technology allows them to do just that.
Regina Maria is a healthcare company that uses a conversational AI-powered assistant named ANA to take on tasks, communicate decisions and operate in internal systems while also connecting three of Regina Maria’s most important departments: medical, sales, and customer service. ANA has increased the speed and efficiency with which managers can dispense with routine yet time-consuming tasks such as leave requests, expenses and budget approvals, contracts, and salaries. By automating tasks and minimizing errors, artificial intelligence can take over mundane and repetitive tasks across the business. This liberates employees to focus on problem-solving and more creative and exciting assignments.
EVA is a modern conversational AI assistant that helps registered landlords to interact with residents and employees 24/7. Eva is designed to significantly improve communications with more first-time resolutions, reduce inbound calls to contact centres and deliver a step change in the resident experience that results in higher operational efficiency whilst lowering costs.
Top CEE courier services market leader, FAN Courier, uses conversational AI virtual assistant technology to bring significant digital enhancements to its customer interaction processes. For example, conversational AI enables parcel tracking, as well as the importing and tracking of multiple AWBs in complex cargo shipment scenarios. In addition, it also helps automate customer service FAQs. The adoption of conversational interfaces allows consumers to seamlessly interact with the business, leading to an extraordinarily positive impact on customer satisfaction levels and, subsequently, loyalty.
Profi, a major East European chain of supermarkets and convenience stores, uses DRUID’s conversational AI to automate manual and repetitive HR tasks. It used conversational AI to help the heads of almost 1,400 stores and logistics centres automate time-consuming administrative tasks relating to employment contracts and staff onboarding.
A virtual AI assistant automatically collects data from an identity card through Optical Character Recognition (OCR) technology and then works with a candidate to fill in employment documents. Ultimately, the conversational AI assistant sends a finalized contract to the HR team to be registered and filed. Concurrently, a new electronic file is automatically created for each new employee, which will subsequently contain all that person’s information, certificates, and records. Using conversational AI technology to digitize HR processes can increase efficiency and offer a better experience for internal and external audiences, clearly impacting the bottom line.
DRUID’s EMA conversational AI assistant enables Provident’s agents to seamlessly review their monthly sales objectives, closed contracts, upcoming bonuses, and even their standing compared to other colleagues for an extra energy boost. In addition, these valuable insights and reports act as real-time support for faster and better business decisions across the entire company.
EMA also uses AI to simplify and automate HR processes, providing a self-service platform where employees can submit leave requests, automatically generate certificates, check payslips and extra-salary benefits, or request files from the electronic HR folder providing a self-service platform where employees can submit leave requests, automatically generate certificates, check payslips and extra-salary benefits, or request files from the electronic HR folder.
Conversational AI will work in most businesses and contexts, bringing exceptional value to those who invest in its capabilities. It can improve businesses’ productivity, customer and employee experience, cost base and profit when it is implemented successfully.
McKinsey, for example, reports that companies that have deployed conversational AI effectively saw a “twofold increase in customer experience, reduced cost to serve by 15-20%, improved churn, upsell and acquisition by 10-20% as well as a fourfold increase in employee productivity”.
Companies must measure the benefits of conversational AI on their business outcomes. To do so, they must first align their conversational AI program with business goals, e.g., improve customer experience by 8% to reduce churn by 3% by enabling a 24/7 conversational experience; or reduce agent-handled calls by 34% to reduce agent churn by 17% to save $200,000 in recruitment and rework costs over the next 9 months. Investing in a platform which provides deep analytical insight makes it possible to obtain excellent ROI from your investment in a conversational AI platform.
Conversational AI is a transformative technology that can enable natural and engaging interactions between humans and machines 24/7. It can provide huge benefits for businesses, such as improving customer service, increasing sales, enhancing employee productivity, reducing costs, and driving innovation.
In this series, we explored the key components of conversational AI, best practices for designing applications, common challenges and effective mitigation strategies, relevant tools and platforms, and real-world examples from various domains and industries.
Now, the power is yours!
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