How to Use LLMs with Your NLU: The Power of a Hybrid Approach

How to Use LLMs with Your NLU: The Power of a Hybrid Approach

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LLMs have been leaving a notable impact and what was once just a small presence in conversational AI, is now gaining immense attention.

With LLMs capable of engaging convincingly on a wide range of topics, many are questioning the necessity of NLUs. Why invest time and resources in refining intents, training data, and entities when an LLM can chat effortlessly for hours without such specifications? Moreover, weren’t NLU-based bots too limiting, confined to predetermined paths and unable to assist users with unanticipated needs?

The reality isn’t a binary choice between the two and it’s crucial to explore the potential synergy between them. Each approach has its own strengths and weaknesses, and by integrating both, many longstanding challenges in the conversational AI industry can be addressed.

Here are three strategic ways to leverage LLMs alongside your NLU.

#1: Utilizing an LLM-powered bot for better semantic understanding

Unlike NLUs, which require meticulous training to categorize user inputs into specific intents, LLMs leverage their training on extensive datasets to predict language behavior. This allows them to interpret diverse user queries, from straightforward ones like “what’s my balance” to more colloquial expressions such as “have I any dough,” without the need for predefined rules or examples.

The potential of LLMs as front-end assistants in conversational AI is substantial. They excel at analyzing user input to discern their underlying needs and accurately route them to the appropriate intent. Cathal showcased a demo involving an embassy inquiry about visa applications. The LLM correctly identified semantically similar phrases like “how much does it cost for a visa” and “how much does it cost to apply for a visa” as inquiries about pricing. In contrast, the NLU, weighted towards certain keywords, misinterpreted the latter query as an inquiry about the application process instead of focusing on cost.

While NLUs could be updated to accommodate new utterances and refine intent matching, Cathal highlights the dual benefits of employing LLMs. Firstly, they grasp meaning inherently, alleviating the need to explicitly instruct the bot on semantic nuances. Secondly, they minimize reliance on training data; instead, clear language defining intents suffices, with the LLM intuitively triggering relevant actions.

Although creating intents remains essential for guiding user interactions, integrating LLMs in this manner can reduce the necessity for extensive training data, as Cathal suggests.

#2: Establishing guardrails for an LLM with a pre-designed flow

Flowcharts are conventional tools utilized in crafting conversational AI assistants. Essentially, they provide a roadmap for the beginning, middle, and end of a conversation. Initially, you outline the parameters of the interaction (the bot’s identity and capabilities), then the middle phase involves the exchange or collection of crucial information by the bot, and finally, the various outcomes represent resolutions to different user inquiries.

Traditionally, flowcharts dictated the potential conversation paths, with NLUs ensuring functionality during live interactions by capturing user inputs and directing them based on training. An alternative approach is utilizing a flowchart to define the interaction while bypassing the NLU. Instead, user inputs are processed by ChatGPT to generate responses.

This approach incorporates design guardrails to restrict the LLM’s responses, addressing concerns like potential exploitation or “jailbreaking” of LLMs by malicious entities seeking unauthorized information disclosure.

This underscores the shift in mindset required when employing LLMs in conversational AI. Rather than meticulously designing every aspect of the bot’s responses, the focus shifts to providing a comprehensive information base and instructing the bot on what to exclude from its replies.

Despite the potential benefits, such as multilingual response generation, this method entails foregoing NLU training in favor of defining constraints for the LLM. While it may seem time-saving initially, ongoing updates to the guardrails around the LLM are necessary as new issues emerge, raising questions about long-term efficiency gains.

#3: Using LLMs for bot testing and training

NLUs are in a perpetual state of refinement, as they require substantial amounts of data to function effectively, typically hundreds or thousands of utterances per intent. However, as more data is added to address interpretation issues, the risk of model confusion with false positives and negatives increases.

Continuous refinement of NLU training is standard practice but can be labor-intensive. Identifying confusion, augmenting data to address it, training, testing, and analyzing outcomes are iterative tasks prone to unintended consequences. LLMs offer a potential solution by serving as vast repositories of varied language usage. They can assist in testing NLUs with semantically similar utterances to gauge their performance and augmenting training data where weaknesses are identified.

Automating NLU testing and data generation using LLMs could streamline management significantly. As interactions with the bot increase, NLU training data should expand, reflecting observed user interactions. However, managing this growth becomes increasingly complex over time. Leveraging LLMs in this capacity can help maintain oversight of the intricate relationships between intents and training data, ensuring ongoing NLU effectiveness.


Decades of accumulated expertise in NLU design and maintenance highlight its efficacy in addressing user needs when their intentions and communication patterns are well understood. A proficiently trained NLU is generally robust enough to cater to the majority of user requirements. Hence, discarding a functional system prematurely seems unnecessary.

Despite the extended tenure of some individuals in the LLM domain, the technology remains somewhat enigmatic. As exemplified by Cathal, there exist numerous innovative approaches to integrate LLMs alongside NLUs to harness the advantages of both. LLMs can be particularly valuable in assisting users with unconventional needs or expressions, occurrences that are commonplace in interactions with most bots.

Why limit oneself to a singular option? Combining NLUs and LLMs expands the scope of assistance, accommodating a broader spectrum of users and their diverse requirements. Ultimately, the objective is to serve users optimally. Therefore, it’s important to weigh the benefits of both technologies and consider how they collectively serve the varied needs of users.

Find out how you can leverage Born Digital's Generative and Conversational AI solutions to drive business results.

Small Language Models (SLMs): Definition and Benefits

What are Small Language Models (SLMs)?

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Definition of Small Language Models

Small Language Models (SLMs) are a distinct segment in the domain of artificial intelligence, particularly in Natural Language Processing (NLP). They stand out for their concise design and reduced computational requirements.

SLMs are tailored to carry out text-related tasks efficiently and with a focused approach, setting them apart from their Large Language Model (LLM) equivalents.

Small vs Large Language Models

Large Language Models (LLMs) like GPT-4 are revolutionizing enterprises by automating intricate tasks such as customer service, providing swift, human-like responses that enrich user interactions. However, their extensive training on varied internet datasets may result in a lack of tailoring to specific enterprise requirements. This broad approach might lead to challenges in handling industry-specific terms and subtleties, potentially reducing response effectiveness.

Conversely, Small Language Models (SLMs) are trained on more targeted datasets customized to individual enterprise needs. This strategy reduces inaccuracies and the risk of generating irrelevant or erroneous information, known as “hallucinations,” thereby improving output relevance and accuracy.

Despite the advanced capabilities of LLMs, they present challenges such as potential biases, generation of factually incorrect outputs, and substantial infrastructure costs. In contrast, SLMs offer advantages like cost-effectiveness and simpler management, providing benefits such as reduced latency and adaptability crucial for real-time applications like chatbots.

Security is another distinguishing factor between SLMs and open-source LLMs. Enterprises utilizing LLMs may face the risk of exposing sensitive data through APIs, whereas SLMs, typically not open source, pose a lower risk of data leakage.

Customizing SLMs necessitates expertise in data science, employing techniques like fine-tuning and Retrieval-Augmented Generation (RAG) to enhance model performance. These methods not only improve relevance and accuracy but also ensure alignment with specific enterprise objectives.

The Technology of Small Language Models

Small Language Models (SLMs) distinguish themselves by strategically balancing fewer parameters, typically ranging from tens to hundreds of millions, unlike their larger counterparts, which may have billions. This intentional design choice enhances computational efficiency and task-specific performance while preserving linguistic comprehension and generation capabilities.

Key techniques such as model compression, knowledge distillation, and transfer learning play a crucial role in optimizing SLMs. These methods allow SLMs to distill the broad understanding capabilities of larger models into a more focused, domain-specific toolkit. This optimization enables precise and effective applications while maintaining high levels of performance.

The operational efficiency of SLMs stands out as one of their most significant advantages. Their streamlined architecture results in reduced computational requirements, making them suitable for deployment in environments with limited hardware capabilities or lower cloud resource allocations. This is particularly valuable for real-time response applications or settings with strict resource constraints.

Furthermore, the agility provided by SLMs facilitates rapid development cycles, empowering data scientists to iterate improvements swiftly and adapt to new data trends or organizational requirements. This responsiveness is complemented by enhanced model interpretability and debugging, facilitated by the simplified decision pathways and reduced parameter space inherent to SLMs.

Benefits of Small Language Models

Better Precision and Efficiency

In contrast to their larger counterparts, SLMs are specifically crafted to address more focused, often specialized, needs within an enterprise. This specialization enables them to achieve a level of precision and efficiency that general-purpose LLMs struggle to attain. For example, a domain-specific SLM tailored for the legal industry can navigate complex legal terminology and concepts with greater proficiency than a generic LLM, thereby delivering more precise and relevant outputs for legal professionals.

Lower Costs

The smaller scale of SLMs directly translates into reduced computational and financial expenditures. From training data to deployment and maintenance, SLMs require significantly fewer resources, rendering them a feasible choice for smaller enterprises or specific departments within larger organizations. Despite their cost efficiency, SLMs can match or even exceed the performance of larger models within their designated domains.

More Security and Privacy

An essential advantage of Small Language Models lies in their potential for heightened security and privacy. Due to their smaller size and greater controllability, they can be deployed in on-premises environments or private cloud settings, thereby minimizing the risk of data breaches and ensuring that sensitive information remains under the organization’s control. This aspect makes small models particularly attractive for industries handling highly confidential data, such as finance and healthcare.

Adaptability and Lower Latency

Small Language Models offer a level of adaptability and responsiveness crucial for real-time applications. Their reduced size allows for lower latency in processing requests, making them well-suited for tasks like customer service chatbots and real-time data analysis, where speed is paramount. Additionally, their adaptability facilitates easier and swifter updates to model training, ensuring the continued effectiveness of the SLM over time.

Limitations of Small Language Models

Limited Generalization

The specialized focus of SLMs provides a significant advantage but also introduces limitations. These models may excel within their specific training domain but struggle outside of it, lacking the broad knowledge base that enables LLMs to generate relevant content across diverse topics. Consequently, organizations may need to deploy multiple SLMs to cover various areas of need, potentially complicating their AI infrastructure.

Technical Challenges

The landscape of Language Models is evolving swiftly, with new models and methodologies emerging rapidly. This ongoing innovation, while exciting, presents challenges in staying abreast of the latest developments and ensuring deployed models remain cutting-edge. Moreover, customizing and fine-tuning SLMs to fit specific enterprise requirements may demand specialized knowledge and expertise in data science and machine learning, resources not universally accessible to organizations.

Evaluation Difficulties

As interest in SLMs grows, the market becomes inundated with a plethora of models, each claiming superiority in certain aspects. However, evaluating LLMs and selecting the appropriate SLM for a particular application can be daunting. Performance metrics can be misleading, and without a comprehensive understanding of underlying technology and model size, businesses may struggle to identify the most suitable model for their needs.


In summary, contrasting Small Language Models (SLMs), specifically domain-specific LLMs, with their generic counterparts highlights the critical need for customizing AI models to suit specific industries. As enterprises integrate AI-driven solutions like AI Customer Care or Conversational AI platforms into their specialized workflows, prioritizing the development of domain-specific models becomes imperative. These bespoke models not only promise enhanced accuracy and relevance but also offer opportunities to augment human expertise in ways that generic models cannot replicate.

With these advanced, tailored AI tools, industries spanning from healthcare to finance are poised to achieve unprecedented levels of efficiency and innovation. Experience the transformative potential of custom AI solutions tailored to your enterprise’s unique requirements—explore a custom AI demo and consider Born Digital today!

Find out how you can leverage Born Digital's Generative and Conversational AI solutions to drive business results.

How do you calculate ROI for an AI chatbot and email bot?

How do you calculate ROI for an AI chatbot and voice bot?

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How to determine if your business would benefit from conversational AI

Considering the implementation of an AI chatbot or voice bot for customer service, sales and marketing, or HR? Evaluating the return on investment (ROI) for chatbots is more straightforward than you might imagine.

Businesses are increasingly deploying AI chatbots to complement human agents, a move that can enhance customer satisfaction while managing workforce growth amid rising demand. AI-driven chatbots address specific and measurable issues such as reducing resolution time and enhancing key performance indicators (KPIs) in customer service. Post-integration of a chatbot or virtual assistant, companies have reported a notable decrease of up to 70% in calls, chats, or emails necessitating human agent intervention, resulting in potential savings of up to 30% in customer service expenses. This efficiency stems from AI-powered chatbots autonomously handling up to 80% of routine inquiries, such as order status queries and refund requests for retailers, early check-ins and flight updates for travel agencies, and troubleshooting and account updates for streaming platforms.

Wondering if the investment in building AI chatbots and voice bots is worthwhile?

Factors that drive customer service costs

Before delving into the calculation of chatbot ROI, it’s crucial to understand the reasons behind the high costs associated with customer service. Annually, an estimated 265 billion customer support requests are handled, amounting to a staggering $1.3 trillion in expenses. According to the Help Desk Institute, the average cost per minute for live chat support is $1.05, with the average cost per chat session standing at $16.80. Several key factors contribute to the elevated costs of customer service:

1. Agent Salaries: While the adoption of bots might not heavily impact rationalizing company headcount, it does assist in curbing additional workforce expansion as ticket volume rises. The average hourly wage for customer service agents is $21, and considering employee benefits, optimizing salary expenditures can lead to significant savings, potentially amounting to hundreds of thousands of dollars, contingent upon the size of the agent team.

2. Day-to-Day Expenses: These encompass various operational costs such as licensing fees for human agent desk platforms, overhead expenses, hardware maintenance, paid time off, sick leave, and more.

3. Recruitment and Training: Customer service roles frequently experience high turnover rates, averaging at 45% annually. The expenses associated with recruiting, onboarding, and training new employees can reach approximately $4,000.00 per agent.

Utilize our new calculator to assess Your Chatbot ROI

In order to calculate your chatbot’s return on investment (ROI), you’ll only need a few key pieces of information:

1. Number of agents

2. Agent salaries

3. Customer service ticket volume (chats and calls)

4. Average resolution time

Let’s illustrate this process with an example. Consider a business that has 10 support agents whose salary is $2,900 a month. $1,300(support requests)/10 agents = 130 requests per agent.

If a chatbot takes on 260 requests per month then this is the equivalent of two agents with a total cost of $5,800 ($2900*2)

That’s $5,800 spent on questions a chatbot could take over in a month. Our AI solution costs just 10-20% of that.

How to maximize AI chatbot ROI

Companies aiming to optimize their return on investment (ROI) through a chatbot platform can employ five key strategies:

1. Address the Right Challenges: Utilize AI to automate high-volume, expensive tickets that can be fully resolved by AI. Instead of guessing, identify the most suitable use cases for automation by analyzing historical tickets and data.

2. Enhance Training: If leveraging a modern AI platform incorporating deep reinforcement learning, expect improvement over time. Monitor conversations to reinforce positive outcomes and provide additional training if the chatbot misinterprets user intent.

3. Choose Appropriate Channels: Avoid the common pitfall of launching a chatbot on the wrong channel. Chatbots can operate across various mediums including email, social media, messaging platforms, and even voice interfaces. Select channels with high volume and resolution times for optimal deployment.

4. Scale Across Channels: After successful deployment on a high-volume channel and subsequent improvement from real interactions, expand the chatbot’s presence to other channels. 

5. Integrate with Backend Systems: Empower the AI chatbot to fully resolve tickets and deflect queries from other channels by integrating with backend systems such as CRM, order management, and e-commerce platforms. Enable the chatbot to access personal data to address issues on a personalized level.

Find out what your ROI will be if you build an AI chatbot or voice bot

Our team at Born Digital understands the unique needs of every business. Our flexible pricing structure is designed to align with your specific goals, ensuring you get results without the one-size-fits-all approach.

Get in touch with our team of experts dedicated to mapping the fastest path to a strong ROI. Ready to transform your conversational AI journey? Fill out this form and we’ll be in touch as soon as possible.

Find out how you can leverage Born Digital's Generative and Conversational AI solutions to drive business results.

Conversational AI in eCommerce

Conversational AI in eCommerce: How Online Retailers Can Benefit from Digital Assistants

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The introduction of online stores to the retail industry permanently shifted the way consumers think of shopping. Instead of needing to travel to a physical store, you can easily shop online for anything you may need. While improving convenience for customers, e-commerce also provides opportunities for retailers to maximize their sales, collecting income from both in-store and online purchases.

Equipped with machine learning and natural language processing capabilities, conversational AI is the next step in advancing the online shopping experience. As more companies pour money into their online shops, retailers must implement the best available technologies to set themselves apart from their competition and offer the most pleasant user experience.

Top Challenges Faced by Online Retailers

Common issues in the e-commerce space include high return rates, frequent cart abandonment, and support agents with too many responsibilities to handle. 

It is estimated that online retailers experience return rates of up to 30%, whereas their brick-and-mortar counterparts face a rate of around 9%. While return rates can be different depending on which industry you look at, the difference between online purchase and in-store purchase returns is an issue that impacts e-commerce as a whole. These high rates of return can be due to changing customer preferences, inaccurate product listings, or even a lack of information during the purchasing process, all of which can be aided by the implementation of AI technologies. Conversational AI can also help reduce the rate of cart abandonment for online retailers, who often experience customers losing interest in their shopping experience before completing an order. 

The average cart abandonment rate across all industries is 70%

Due to the recent increase of online shopping, human representatives are often overwhelmed by the amount of incoming customer requests. Employing a team of human agents can be costly and time-consuming for businesses. With the development of conversational AI technologies, retailers are now able to delegate routine customer inquiries to chat and voice bots to reduce expenses and streamline the customer experience. 

Benefits of Conversational AI in Retail

Conversational AI is useful in the retail industry, as it is a data-driven and cost-effective method of improving customer experience and maximizing company profits. Using data derived from past experiences with customers, chatbots are able to naturally communicate with shoppers and update them with personalized recommendations and assistance. Offering prompt and accurate answers to questions, chatbots can make the shopping experience much easier, which increases the likelihood of a completed sale.

But, conversational AI doesn’t stop there! 

After customers have made purchases, AI technology can gather feedback from shoppers to understand sentiment towards the shopping experience and make suggestions for retailers. It can also help to proactively reduce the frequency of returns by being involved in each step of the shopping process, providing all pertinent information to the customer. 

Use Cases of Conversational AI in eCommerce

There are countless ways conversational AI and chatbots can be used to benefit the online retail experience. From basic help for customers to providing analytical information back to retailers, AI technology is setting new expectations for the overall e-commerce experience. 

Below are 4 use cases for conversational AI in e-commerce:

Offering a Personalized Shopping Experience

Using data from past customer behavior, including product interactions and purchases, conversational AI can provide informed product recommendations and promotional information. Being based on individual preferences, this raises the bar for the overall customer experience. Should customers have difficulty making decisions, conversational AI offers a solution by providing an informed comparison between products and highlighting the different benefits of each. When it comes time to check out, customers feel more confident in their purchasing decisions after working with a built-in chatbot, which decreases the likelihood of cart abandonment, which is an issue faced by many online retailers.

Offering a personalized shopping experience can increase satisfaction levels of employees, and can even increase consumer spending by up to 40%.

Strengthening Customer Loyalty Programs

Customer Loyalty Programs are seen by many as a ploy to increase sales, and many assume they exist for the benefit of the company, rather than that of the customer. However, having a strong customer loyalty program can mean increased revenue for a given retailer. In fact, according to a 2020 study by the Harvard Business Review, a strong customer loyalty program can help generate 2.5x more revenue for retailers, compared to those with a weak loyalty program. Chatbots can be assigned to communication pathways, sending material out to loyal customers after they take certain actions, like making a purchase or adding an item to their cart. Using shopping data like this to personalize the customer experience can increase customer loyalty and drive future engagement with retail sites. 

Automating Notifications

Online retailers have a reputation for sending out unnecessary amounts of advertising material and flooding customer inboxes with mostly irrelevant information. When customers want to receive a specific notification from an online retailer regarding something like an item coming back in stock or a price change, they may be deterred from doing so if they need to input an email address. Fear of an overcrowded inbox and email communications becoming less and less relevant as the years go on combine, causing customers to abandon their goals. With AI chatbots, these notifications can be sent through a multitude of different messaging platforms like Facebook, SMS, or online shopping platforms. The ability to integrate these different systems sets conversational chatbots apart from other automated systems and increases the customer experience.

Market Research and Feedback

With the emergence of conversational AI, collecting customer feedback and turning it into useful recommendations has never been easier. Oftentimes, after using an online store, customers can expect to be met with a survey that asks them all about their shopping experience. These surveys are often ignored as they can be seen as intimidating or unnecessary to customers. With conversational AI and natural speech capabilities, collecting feedback can be done engagingly and conveniently. Personalized questions make customers more willing to respond, since they easily know their answers, while natural language processing ensures comfortable and familiar communication. With conversational AI, online retailers can gain meaningful insights into their customer bases and refine their offerings and processes based on collected data. 

The Future of Conversational AI in eCommerce

The online retail industry has been booming since its beginning and shows no sign of slowing down. With large e-commerce websites like Amazon and eBay taking control of a large part of the consumer goods industry, many customers have become accustomed to relying on online shopping for many of their wants and needs. Now, with the implementation of conversational AI and chat and voice bots in e-commerce, this process will be both popular and personalized, increasing customer loyalty and raising expectations for how online shopping should be. Using chatbots to make the online shopping process easier will soon be a standard in e-commerce; in fact, “by 2024, the global consumer retail spend via chatbots is predicted to reach $142 billion, up from a mere $2.8 billion in 2019.”

If online retailers want to be ahead of the curve and set standards, instead of meeting them, adopting conversational AI technologies is the right decision.

Why Born Digital?

In conclusion, a user-friendly return process is pivotal for e-commerce success, attracting new customers and fostering loyalty. At Born Digital, our leading conversational AI solutions can streamline your return experience. Available in any language and any channel whether it be voice, social media, or web, we help you intelligently automate your e-commerce processes and promptly assist your customers wherever they already are.

Key features of Born Digital AI include:

  1. Advanced Integration Capabilities: Seamless integration with any website, as well as CRM, makes Born Digital simple to implement for any online retailer. Requiring no coding on the company’s end, Born Digital’s conversational AI software is convenient for businesses to implement into their pre-existing e-commerce systems.
  2. Multilingual Capabilities: Serving companies all over the world, Born Digital bots interact in almost all languages, making businesses accessible to a diverse clientele. This can help retailers that operate in one country broaden their horizons and increase their international customer base. 
  3. Sophisticated AI Conversations: Born Digital voice and chatbots engage in natural, dynamic conversations, replicating human interaction for clients to feel heard and understood, enhancing their experience; this is critical in e-commerce because customers need to express their desires and be confident they will be understood, and they will get a helpful response. Without clear and natural discourse, it can be difficult for customers to get what they need from online retailers, which can lead to issues like cart abandonment and low levels of satisfaction.

Find out how you can leverage Born Digital's Generative and Conversational AI solutions to drive business results.

Conversational AI in HR | Benefits and Use Cases

Conversational AI for HR | Benefits and Use Cases

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HR personnel spend a lot of time dealing with repetitive tasks like onboarding, payroll, leave management, employee relations, and legal affairs. With this wide array of responsibilities, it can be difficult for HR staff and managers to deal with everything they need to in a timely manner. Increased responsibilities can lead to issues like delayed response times and inaccurate reporting, which can cause more problems down the line and impact areas like employee satisfaction and administrative efficiency. 

“76% of HR Leaders say their managers are overwhelmed by the growth of their job responsibilities” – Gartner

Using Conversational AI technologies to offload some of their responsibilities reduces the workload for overworked managers, and decreases costs for companies, who no longer need to overstaff their HR departments to reach goals. Conversational AI and AI bots provide solutions to common HR-related issues, like workplace management, compensation issues, and benefits management, which increases productivity and ensures internal satisfaction within a business. Implementing conversational AI in Human Resources departments establishes a bridge between management and employees, reinforcing the connection between the two and allowing for a more effective form of communication.

Challenges Faced by HR Departments

Human Resources is quickly becoming more than just an administrative necessity for businesses. With a hand in each department, HR is a strategic partner for businesses in planning and executing their goals. With increased responsibilities, however, comes an increased amount of challenges.

One issue faced by HR departments, especially in the post-COVID-19 world, is employee engagement. Many employees struggle to see their positions as important in the grand scheme of their companies, which decreases their desire to perform. Additionally, there is a rising sentiment among employees about when, and where, they should be permitted to work. Since the pandemic, many prefer working remotely, while many managers would prefer to have a full office. To understand their employees and the work environment they desire, managers should strive to increase communication between themselves and their teams. With conversational AI technologies, this goal can be met, and there can be an increased understanding between managers and employees, creating a more efficient and pleasant work environment.

As HR becomes more relevant in all aspects of a business, more tasks are put on the plates of HR employees. Dealing with tasks like recruitment, candidate screening, onboarding, off-boarding, and leave management, HR staff can easily find themselves buried under an overwhelming amount of work. Conversational AI offers a solution to this problem, with the ability to handle many of these tasks, taking the responsibility away from HR employees. 

Benefits of Conversational AI

Using conversational AI to deal with HR processes can help businesses tackle difficult tasks, and redistribute resources to focus on other, non-automated processes. HR representatives, who deal with issues of understanding employee’s perspectives, can use AI technologies to send out surveys and gather employee feedback about topics like workplace environment, preferences for remote or in-person work, and individual purpose in the company. This increases employee engagement and develops a better sense of understanding for managers to know what their employees want.

With the abilities of chat and voice bots at their fingertips, companies can simultaneously deal with tasks like employee acquisition and routine processes in a cost effective manner. Allowing AI to take over the more routine processes of an HR department gives human agents more time to deal with more complex issues; conversational AI is also able to evaluate certain situations and determine if they require the intervention of a human agent.

Use Cases of Conversational AI in HR

Using conversational AI to maximize efficiency in HR will benefit businesses in many ways. Below are 5 use cases to illustrate how conversational AI can be used in HR:

Employee Recruitment

Conversational AI completely changes the recruitment landscape for managers, taking over many roles at once and reducing the amount of pre-selection work for hiring managers. AI technologies are able to gather information from candidates and then analyze it to make suggestions to agents about the best suited candidates. AI can benefit the recruitment process by making applicants feel more valued in the process, asking questions about job and compensation expectations, and career aspirations. With it’s ability to tackle multiple tasks at once, conversational AI is able to analyze individual applicants, while handling scheduling duties to ensure an efficient recruitment timeline. 

Promoting Equity in the Workplace:

Conversational AI’s ability to handle administrative duties like these are impressive and beneficial for employers, but the use of AI can also benefit companies in terms of offering an equal-opportunity recruitment process. Implementing AI technologies for the hiring process can prevent unconscious biases from playing a role in recruitment, ensuring a more equitable workplace.

Onboarding Assistance

The onboarding process can be tedious for new employees and managers alike. This process is also a formative time for a new employee to form an opinion about their company and employer, so it is important that managers offer a good onboarding experience. Therefore, implementing AI technologies to streamline this process can be cost-saving and efficient for businesses. Conversational bots can offer information to new hires that will help them in routine onboarding processes. If a new employee faces difficulties in any administrative tasks like filling out forms or setting up accounts, AI can provide assistance to set them back on track, involving a human manager when it is deemed necessary. 

Internal Engagement

Born Digital conversational AI can be programmed to routinely send out forms to employees to gather information about the workplace attitude towards certain things. Regularly checking in with employees helps managers gather feedback on workplace experience and any individual issues/ concerns employees might have. Using AI technology to automate the collection of employee opinions can help managers to deal with any issues expressed in a timely manner, getting ahead of a larger issue before it occurs. Using AI to connect with employees can also help foster a sense of community in the workplace, creating a sense of understanding between employees and their managers. 

HR Processes

Just as they can help with the onboarding process, AI bots can help with managing basic, day-to-day HR processes. Conversational AI can handle tracking employee attendance, progress, and engagement. They can also process routine paperwork like leave requests and overtime payments. In fact, conversational AI can assist with many payroll related inquiries, giving answers and explanations when prompted. When employees need clarification on standard processes for certain tasks, AI can easily find this information in a company database and provide the information in a timely manner. 

Promoting Diversity and Inclusion in the Workplace

A benefit of using AI for businesses is that they eliminate all conscious and unconscious bias from the tasks they handle. In the onboarding process, AI bots only take into consideration information that is relevant to the requirements of the position. Therefore, factors like race, sexual orientation, and disability can be ignored by the AI bots, offering a more level playing field for all applicants. As businesses become more focussed on highlighting diversity in the workplace, they can utilize AI technologies to send out newsletters and articles to employees to increase their understanding of social issues that affect their line of work, encouraging employees to be more aware of their position in the world, as well as that of fellow employees.  

The Future of Conversational AI in HR

As conversational AI technologies continue to develop, it will become standard for businesses to use the technology to improve their HR processes. Tasks like processing day-to-day forms, gaining employee feedback, and answering basic inquiries can become burdensome, especially at larger firms. Therefore it is more cost and time-efficient to pass these duties off to an AI bot. 

“47% of employees believe generative AI will make their jobs easier and improve efficiency” – BetterWorks 

AI is also becoming more effective in providing analytics to HR departments, as they can analyze employee-manager interactions, employee performance, and even look at long-term employee retention for the company. Analytics are then provided to managers, along with suggestions from the AI bots for how to improve performance, increase efficiency, and decrease costs. There are also developments in the realm of Digital Humans, which combine the human-like communication offered by voice bots and a realistic physical appearance, which enhances the user experience and makes interactions feel more natural. Conversational AI technologies are also being developed to offer enhanced personalization for users. HR departments will be able to use AI to offer services like personalized training plans for new employees, personalized schedules for offices, and a personalized experience for individual applicants looking for open positions. 

Why Born Digital?

Implementing conversational AI into HR departments will prove to be effective for businesses, as it can make many standard processes much easier to complete while opening up time for managers and HR representatives to deal with more complex tasks, which require more attention. As these technologies get more popular in the industry, applicants and employees will start to expect to deal with a chat or voice bot, as they are the easiest and most reliable option for dealing with inquiries at any time. Using Born Digital AI solutions allow for businesses to get ahead of their competitors and streamline their HR processes, increasing efficiency and overall satisfaction.

Key features of Born Digital AI include:

  1. Advanced Integration Capabilities: Seamless integration with any website, as well as CRM, makes Born Digital simple to implement for any HR department. Requiring no coding on the company’s end, Born Digital’s conversational AI software is convenient for businesses to implement into their pre-existing HR systems.
  2. Multilingual Capabilities: Serving companies all over the world, Born Digital bots interact in almost all languages, making businesses accessible to a diverse clientele. This also allows for all candidates to have an equal opportunity in the hiring process.
  3. Sophisticated AI Conversations: Born Digital voice and chatbots engage in natural, dynamic conversations, replicating human interaction for clients to feel heard and understood, enhancing their experience; this is critical in HR because of the constant communication between employees and management. Without clear and natural discourse, it can be difficult for employees to get what they need from HR departments, and vice versa. 

Find out how you can leverage Born Digital's Generative and Conversational AI solutions to drive business results.

What is LLM Fine-Tuning?

What is LLM Fine-Tuning?

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Neural networks known as Large Language Models (LLMs) undergo training on extensive internet datasets to acquire a comprehensive “world model” based on statistical correlations. These models exhibit remarkable generative capabilities across various tasks such as answering questions, summarizing documents, writing software code, and translating human language.

Nevertheless, employing LLMs within the enterprise setting necessitates harnessing their inherent power and refining their abilities to cater to specific customer requirements.

Tailoring LLMs for Specific Use Cases

To achieve this, it is essential to grasp the specific use case requiring attention and determine the most effective method for aligning the model’s responses with reliable business expectations. Several approaches exist for contextualizing a general-purpose generative model, with fine-tuning and RAG (Retrieval Augmented Generation) emerging as two widely recognized methods.

Understanding Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) involves enhancing system prompts (instructions given to the model) with external knowledge sources, such as an organizational document library, commonly known as a Knowledge Base. This approach is optimal for producing accurate, well-informed factual responses, minimizing instances of the model generating inaccurate information.

RAG operates by combining a retriever and a generator, allowing each component to be optimized independently. The retriever indexes the data corpus within the Knowledge Base, pinpointing relevant passages concerning a user’s query. Meanwhile, the generator utilizes this context, along with the original query, to create the final output. This modular design enhances transparency and scalability.

When is Fine-Tuning Necessary?

On the other hand, Fine-Tuning offers additional customization by incorporating new knowledge directly into the model, enabling it to learn or adapt its acquired knowledge for specific tasks. This process involves supervised learning based on labeled datasets, updating the model’s weights. Typically, the demonstration datasets consist of prompt-response pairs that specify the refined knowledge required for a particular task.

Several crucial considerations should be taken into account before determining how to tailor a generic model to meet specific business requirements. Fine-tuning becomes relevant when attempts to direct the model to execute a particular task prove ineffective or fail to consistently produce the desired outputs. The initial step in comprehending the problem or task involves experimenting with prompts and establishing a baseline for the model’s performance.

Addressing Business Needs Through Fine-Tuning

Fine-tuning becomes particularly advantageous when dealing with proprietary data, providing a heightened level of control and privacy. Instances involving sensitive data, edge cases, or scenarios where establishing a specific tone is essential may justify allowing the model to learn and adapt in an unstructured manner, rather than relying on intricately crafted prompts.

Fine-Tuning for Domain-Specific LLMs

When an out-of-the-box model lacks familiarity with domain or organization-specific terminology, opting for a custom fine-tuned model, also known as a domain-specific model, becomes a viable solution for executing standard tasks within that domain or micro-domain.

Fine-tuning proves effective when there is a need to reduce costs or latency during inference. A fine-tuned model can yield high-quality results in specific tasks with concise instruction prompts. However, it’s essential to acknowledge that interpreting or debugging predictions from a fine-tuned model is not a straightforward process. Various factors, including data quality, data ordering, and the model’s hyperparameters, may impact its performance.

The success of fine-tuning relies heavily on the availability of accurate, targeted datasets. Before embarking on the fine-tuning process, it is crucial to ensure that there is a sufficient amount of representative data to prevent the model from overfitting to limited information. Overfitting refers to a model’s restricted ability to generalize to new data.

Automating Dataset Preparation and Workflow

The preparation of datasets is a resource-intensive endeavor, and introducing automation into segments of this process is a crucial step toward establishing a scalable solution for fine-tuning Large Language Models (LLMs) in enterprise use cases.

Consider this scenario: Suppose the goal is to tailor a model to generate social media posts aligning with the company’s marketing strategy and tone. If the organization already possesses a substantial collection of such posts, serving as golden outputs, these outputs can construct a Knowledge Base. Employing Retrieval Augmented Generation (RAG), key content points can be generated from this Knowledge Base. Combining these generated content points with their corresponding outputs forms the dataset essential for fine-tuning the model to excel in this new skill.

It’s essential to note that fine-tuning and RAG are not mutually exclusive; in fact, a hybrid approach combining both could enhance the model’s accuracy, warranting further investigation. A recent Microsoft study demonstrated that capturing geographically specific knowledge in an agricultural dataset, generated through RAG, significantly increased the accuracy of the model fine-tuned on that dataset.

To make fine-tuning LLMs more transparent and accessible to enterprises, simplifying each step in the workflow is crucial. The high-level workflow involves the following steps:

1. Experimenting with different LLM prompts and selecting a baseline model that aligns with the specific needs.

2. Clearly defining the precise use case for which a fine-tuned model is required.

3. Applying automation techniques to streamline the data preparation process.

4. Training a model, preferably with default values for the model’s hyperparameters.

5. Evaluating and comparing different fine-tuned models using various metrics.

6. Customizing the model’s hyperparameter values based on feedback from the evaluation step.

7. Testing the adapted model before determining its suitability for use in actual applications.

Find out how you can leverage Born Digital's Generative and Conversational AI solutions to drive business results.

How to Handle Angry Customers: 15 Tips​

How to Handle Angry Customers: 15 Tips

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Dealing with irate customers is a universal challenge that businesses of all sizes and industries encounter. Whether you run a bustling restaurant, manage an e-commerce platform, or oversee customer service for a tech company, the reality is clear: angry customers are an inevitable part of the business landscape. 

In this article, we’ll explore the art of handling disgruntled customers with finesse and efficiency. With 10 practical tips, we’ll navigate the complexities of diffusing tension, identifying root causes, and transforming potentially negative encounters into opportunities for customer satisfaction.

Common reasons customers get angry

Ensuring a clear presentation of your products and services is essential to align promises made before purchase with post-purchase expectations seamlessly. Dissatisfaction among customers often arises due to various reasons:

1. Product or service usability: A discrepancy between promised features and actual performance can lead to customer annoyance. Emphasizing transparency in the sales process is vital to minimize challenges after purchase, preventing an influx of frustrated customer calls to your inbound contact center.

2. Impact of updates on customer experience: Regular updates to products and services, including price changes, are necessary to stay competitive. Having a crisis communication plan for impending upgrades is crucial to avoid issues like potential service disruption and unexpected performance lags that customers might face after a significant overhaul.

3. Accessibility of customer support: The ease with which customers can reach your customer service is pivotal. Even minor issues can escalate into frustration if the path to resolution is cumbersome. Monitoring key contact center metrics, such as average hold time and call abandonment rate, is essential for maintaining customer satisfaction.

4. Poor service experience: Frustration often sets in when customers struggle to find a satisfactory resolution. While meeting every customer request may not be practical, navigating these service scenarios determines whether a customer departs with a sense of reasonable satisfaction or vows never to engage with your business again.

5. Personal problems: Customers dealing with personal difficulties may experience heightened emotions when these issues intersect with inconveniences caused by your product or service. Although beyond your control, this scenario highlights the critical role of call center agent training in effectively serving sensitive customers with empathy and active listening.

10 practical tips to handle angry customers

First and foremost, it’s imperative for businesses to acknowledge that customers typically reach out when they face unresolved difficulties, despite having explored other channels such as the website or helpdesk software. Simultaneously, frontline agents must understand that the tone directed at them is not personal; rather, it is an expression of inconvenience faced by a paying customer who trusts the products or services. This section outlines 19 proven tips on handling interactions with angry customers.

1. Introduce Yourself:

Initiate the conversation by introducing yourself and your role in the company, signaling that the issue is taken seriously by the relevant employees. Address the customer by name, demonstrating a personalized approach.

2. Maintain Your Composure:

Stay composed, even if the customer blames you for poor service. Losing your cool can negatively impact the situation and hinder effective resolution.

3. Listen Carefully:

Allow the customer to express their concerns actively, whether on call, in person, or through digital channels. Actively listening helps understand the root cause of the issue.

4. Acknowledge and Apologize

Regardless of the complaint’s validity, acknowledging the customer’s situation and offering an apology can de-escalate the situation. Taking responsibility builds trust and showcases commitment to resolution.

5. Identify the Real Issue

Train agents on effective communication to avoid negative perceptions. Encourage the use of the right questions to understand the root cause and expedite resolutions.

6. Summarize the Problem

After listening to the customer’s concerns, summarize and reiterate the issues. This ensures alignment on concerns and facilitates navigation toward an optimal solution.

7. Employ Historical Data

Retrieve customer and issue-related historical data from the contact center CRM to provide informed suggestions and adopt a data-driven approach for effective problem-solving.

8. Identify the Plan of Action

Establish an action plan, communicate a clear resolution timeline, and prioritize issues based on customer loyalty, SLA breaches, or potential customer churn.

9. Develop Critical Thinking

Collaboratively examine each stage of the process, seeking comprehensive answers and considering temporary solutions, such as service extensions or prompt refunds.

10. Avoid Putting Them on Hold for Too Long

Minimize hold times using a unified agent console equipped with AI, providing real-time information and reducing the need for extended waiting.

11. Apprise Your Manager if Needed

If necessary, escalate the situation to a senior manager, demonstrating commitment to customer sentiments and accessible upper management.

12. Know Your Limits

Avoid overpromising and underdelivering; stand firm against unfair demands to maintain a balanced and fair interaction.

13. Be Grateful

Express gratitude for the customer’s trust and outreach, building rapport and leaving a positive impression.

14. Follow Up

After resolution, follow up with the customer to ensure satisfaction, demonstrating ongoing commitment to their experience.

15. Deploy Call Center Technology:

Leverage call center technology and AI to streamline issue resolution, utilizing chatbots and virtual agents for immediate responses, especially advantageous for prompt solutions with angry customers.

Turn angry customers into happy ones

As per a survey, 66% of participants express extreme frustration when compelled to repeat the same information across various employees or channels. To address this prevalent challenge, consider utilizing the Born Digital platform. It equips your agents with AI-driven tools tailored for managing upset customers effectively. These include:

1. Analytics Software:
Analyze customer intent and sentiment in real-time. Evaluate individual agent performance using AI-scoring parameters like opening/closing quality, introduction, active listening, empathy, and more.

2. Conversational AI platform:
Easily build, train, and deploy AI bots using our no-code platform across all languages and channels.

For a comprehensive exploration of Born Digital, sign up for a demo with our experts today.

Find out how you can leverage Born Digital's Generative and Conversational AI solutions to drive business results.

Guide to creating a knowledge base for customer service

Guide to Creating a Knowledge Base for Customer Service

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Nowadays, customers prefer taking matters into their own hands. Research indicates a strong inclination towards resolving issues independently rather than engaging with customer support reps. A noteworthy 91% of survey participants expressed a willingness to utilize an online knowledge base, provided it aligns with their specific needs. Consequently, it’s commonplace for your competitors to have established knowledge bases.

However, the challenge lies in making your knowledge base stand out in delivering a superior customer experience. This guide comprehensively explores the essential elements required to construct a customer service knowledge base that surpasses the expectations of today’s consumers.

What is a knowledge base?

A knowledge base serves as a repository of information that empowers users to access details regarding a particular subject, product, or service.

A typical knowledge base encompasses diverse content formats, including articles, videos, and FAQs. This setup ensures that when a customer faces challenges like changing their password, they aren’t dependent solely on support. Instead, they can refer to an article or watch a video that guides them through the process.

Knowledge bases can be classified as either internal or external. While the conceptual framework remains consistent for both, they diverge in terms of accessibility and content.

Types of knowledge bases

Knowledge bases can be classified as either internal or external. While the conceptual framework remains consistent for both, they diverge in terms of accessibility and content.

Internal knowledge base

Accessibility: Restricted to internal use within an organization.

Content Focus: Primarily geared towards employees, containing information relevant to company processes, policies, and internal procedures.

Purpose: Aids employees in finding and utilizing organizational information efficiently, fostering better internal collaboration and problem-solving.

External knowledge base

Accessibility: Open to external users, typically customers, partners, or leads.

Content Focus: Tailored to address customer queries, providing information on products, services, troubleshooting guides, and frequently asked questions (FAQs).

Purpose: Enhances customer self-service by offering a readily available resource for finding solutions and information independently, reducing the need for direct support interactions.

How to create a knowledge base for customer service

Certain companies prefer a gradual approach, generating one resource at a time. However, for those prioritizing speed, the option exists to develop all knowledge base resources simultaneously. While this method may initially seem daunting, requiring your team to determine content coverage, generate textual and video content, design the knowledge base, and organize resources, employing a well-structured process and suitable software can simplify the undertaking. Now, let’s navigate through the systematic steps involved in crafting a customer service knowledge base.

Step 1: Define your audience

The target audience for an external knowledge base comprises your customer base. It necessitates the incorporation of details about your product, encompassing tutorials, troubleshooting guides, company policies, and frequently asked questions (FAQs).

In contrast, internal knowledge bases are constructed to serve employees. In addition to encompassing all the information found in the external knowledge base, an internal knowledge base includes supplementary resources such as training modules, compliance information, and onboarding materials. When developing an internal knowledge base for customer service, the primary objective should be to streamline the process of accessing and conveying information for support agents and AI-powered automation tools.

Step 2: Research and prioritize your subject matter

Thorough research plays a crucial role in prioritizing information within your knowledge base. While you may already have a comprehensive understanding of your customers, delving even deeper through meticulous research is recommended to ensure the provision of relevant information.

Neglecting this step could lead to a lack of clarity on what information to prioritize, resulting in potential consequences such as diminished customer loyalty and suboptimal return on investment for your knowledge base.

To discover content topics for your knowledge base and ensure the sourcing of genuinely valuable information for customers, we suggest the following ideas:

1. Examine support tickets: Identify prevalent customer issues by reviewing interactions in support tickets. Incorporate solutions to these common problems into your knowledge base.

2. Analyze use cases: Review your product’s various use cases and pinpoint situations where customers might require assistance. For instance, a user utilizing a task management SaaS for setting up reminders may need guidance on configuring recurring reminders and notification channels.

3. Solicit feedback: Gather insights from customer feedback to understand common pain points. Address these issues by providing solutions in your knowledge base. If customers frequently express frustration about refund processes, create an article outlining the steps and link to relevant sections of your refund policy.

Step 3: Organize your content

After investing time in research, the next step is organizing the gathered information. Crafting an effective information architecture demands careful consideration. Here are some guidelines to help structure your content:

1. Establish clear categories and subcategories to logically organize the content. Ensure that categories do not overlap, as this prevents confusion for both humans and AI when searching for information. Logical categorization reduces the likelihood of users and AI seeking information in the wrong category, thus minimizing time wasted on irrelevant content.

2. Use relevant and descriptive signposts to guide users effectively. Ensure that the information under each signpost is highly pertinent, facilitating seamless navigation for users and AI. Placing verbs closer to the beginning of the signpost aids users in relating signposts to their objectives. Whenever possible, avoid referencing complex concepts or terms, as they tend to confuse both users and language models (LLMs).

3. Employ proper HTML structure when creating signposts to ensure that AI comprehends the content’s formatting. While text decoration may visually resemble a heading, AI might struggle to recognize it. Utilize H1, H2, H3 tags, and beyond to facilitate easy readability for AI and assistive technology tools.

4. Implement effective navigation mechanisms. Intuitive menu design and robust search functionality are essential for delivering a superior customer experience. Customers should easily access category menus to locate desired information and utilize search functionality to find content using specific keywords. Additionally, organizing information from the broadest to the most specific aids in simplifying navigation.

Seeking inspiration? Take a look at HubSpot’s knowledge base. The journey begins with a user-friendly search bar, offering a straightforward option for users. Beneath it, prominent articles are showcased. In cases where the desired information remains elusive, users can refine their search by choosing a category based on user needs. The experience culminates with a comprehensive list of self-help topics.

Step 4: Use a knowledge base software

There are over 170 knowledge base software options on G2. While evaluating and choosing the right solution can be challenging, we can provide you with a checklist to simplify the decision-making process. Look for knowledge base software that is user-friendly, scalable, boasts an extensive feature set, and seamlessly integrates with the applications in your tech stack. Here’s a brief overview of key features to consider in knowledge base software:

1. Integrations: Integrations streamline workflows and save time for both you and your customers. For instance, integration with conversational AI customer service automation, such as an AI agent, accelerates customer query resolution and enhances the overall experience.

2. Customizable UI: A knowledge base software with a customizable user interface facilitates the creation of an easily navigable platform. Ensure the software allows customization of CSS and HTML for optimizing the user experience with a clean and intuitive design.

3. Collaboration and Versioning: Enable authorized employees from various departments to contribute information to the knowledge base. Versioning becomes crucial when multiple contributors are involved, helping identify and rectify errors promptly.

4. Analytics and Reporting: Knowledge base software can be a rich source of data to enhance both customer service and product development. Opt for software with built-in analytics and reporting features to convert collected data into insightful reports for informed decision-making.

If your software lacks these features, consider integration with another tool, such as an AI Analytics platform, that provides analytics and reporting capabilities.

Step 5: Assign contributors

Elevating your customer service team’s capabilities becomes a pivotal focus during the transition to an AI-centric customer service organization. In an ideal scenario, dedicated individuals with technical writing proficiency and robust collaboration skills would manage your knowledge base.

Regardless, it is imperative to assign someone capable of producing authoritative content and staying abreast of company changes, product launches, and policy updates. While involving multiple stakeholders in the knowledge base is beneficial, having at least one person to consolidate and ensure the currency of this information is crucial. This maintenance ensures that your AI draws from reliable sources, fostering a mutually beneficial relationship — improved AI performance and the utilization of AI insights for continuous knowledge base enhancement.

In the absence of a dedicated overseer for knowledge base management, select contributors from each department and designate them to manage specific sections of the knowledge base. Consider the following criteria:

1. Domain Expertise: Contributors must possess extensive knowledge in their respective domains to craft authoritative content. Seek individuals skilled in information architecture and technical writing to transform domain expert information into easily digestible content.

2. Communication Skills: Contributors should excel in communication, effectively delivering their ideas with clarity and conciseness.

3. Commitment: Contributors committed to constructing a comprehensive, high-quality knowledge base are more likely to assist in its maintenance. A contributor’s dedication to building the knowledge base correlates with their motivation to consistently enhance the quality of information.

Future of customer service: AI-powered knowledge bases

While the majority of your rivals already possess a knowledge base, infusing AI into yours can set it apart. The fusion of knowledge base software with an AI Agent not only provides a competitive advantage but also delivers the sought-after experience your customers desire. Instead of investing in recruiting and training additional support agents like your competitors, take a decisive step towards constructing an AI-powered knowledge base with Born Digital. Our mission is to revolutionize customer experiences through the application of AI.

Find out how you can leverage Born Digital's Generative and Conversational AI solutions to drive business results.

Best Communication Channels for AI Chatbots

Best Communication Channels for AI Chatbots

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AI chatbots help you chat with customers wherever they hang out – whether it’s on social media, your website, or some online app. So, where do these chatbots party the most? Well, from what we’ve seen, they’re popular on Facebook, Skype, WhatsApp, and good old web chat.

In this article, we’re going to dive into how businesses are making use of AI chatbots to make customers happy, save some money, and make talking smoother on those apps and sites.

What is an AI chatbot?

A chatbot is like a computer buddy that’s programmed to help you out with questions or stuff you need. But here’s the cool part – not all chatbots are the same. Some just follow simple rules (we call these rule-based chatbots), while others are like chat ninjas using fancy AI tricks to chat almost like a real person (we call these conversational chatbots).

Advantages of AI Chatbots on Facebook Messenger

Every day, over 375,000 people from more than 200 countries interact with Messenger bots. These bots follow specific rules, responding in a linear way. Despite this, numerous businesses have begun incorporating Facebook Messenger AI chatbots, and here are the benefits they bring:

1. Time Savings: Messenger AI chatbots quickly understand what the user wants, minimizing time wastage. They function like personal assistants for your team, handling tasks such as scheduling appointments, collecting feedback as support agents, and even suggesting additional features to customers.

2. Sales Automation: For e-commerce stores, AI chatbots can take care of communication throughout the entire customer journey, from the initial purchase to assisting with returns.

3. Sales Boost: AI chatbots enhance business efficiency and drive sales by providing updates, engaging customers through Natural Language Understanding, qualifying leads, and increasing conversions.

4. Lead Qualification: Chatbots are adept at qualifying leads and guiding them through the sales process with a series of follow-up questions, making the entire process efficient and user-friendly.

5. Conversion Boost: Messenger chatbots contribute to increased sales by offering special deals, sending exclusive invitations, personalizing checkouts, suggesting upsells, and gathering valuable customer feedback.

What industries can benefit the most?

Healthcare: AI chatbots help chiropractors, doctors, massage therapists, physical therapy clinics, hospitals, and more with administrative tasks, including scheduling appointments, follow-ups, and giving specific information about a patient.

Travel: AI chatbots can help travel agencies and airports by automating updated itineraries, account information, travel information, complaints, and FAQs.

Banking: Automating basic tasks and communications via AI chatbots can lead to a 22% reduction in operating costs, freeing up resources for enhanced service and growth.

Advantages of AI Chatbots on Skype

Skype for Business AI chatbots play a vital role in enhancing communication within organizations and for B2B client interactions. Here are various ways in which Skype AI chatbots can positively impact business operations and enhance customer experiences:

1. Enhanced Customer Engagement: Businesses using Skype for Business can experience a 25% increase in booking rates by employing AI assistant chatbots that deliver prompt and immediate responses, meeting customer expectations.

2. Streamlined Customer Onboarding: Chatbots make customer onboarding hassle-free by swiftly addressing queries related to services, platforms, or mobile apps, ensuring a smooth and seamless experience.

3. Simplified Ordering and Refunding: Skype for Business chatbots efficiently handle the automated collection of information for orders and refunds, simplifying processes for both businesses and customers.

4. Effective Lead Generation: AI chatbots engage in natural conversations, generating leads and providing personalized suggestions based on user preferences. This approach is more user-friendly compared to traditional pop-ups

The integration of AI chatbots has revolutionized both B2B and B2C communications, and Skype for Business stands out as a prime example. AI bots on Skype for Business offer innovative and efficient solutions to a variety of business challenges.

What industries can benefit the most?

AI chatbots have the potential to bring benefits to a wide range of industries. However, given that a significant number of businesses still using Skype for Business operate in the IT sector, it’s likely that these companies will be among the early adopters of AI in their communication strategies:

1. Information Technology Companies: Chatbots play a dual role by externally resolving customer troubleshooting issues and internally assisting employees with various inquiries.

2. Computer Software Companies: Chatbots serve as onboarding specialists, addressing queries from new customers and freeing up the support team’s time for other crucial tasks.

3. Marketing & Advertising: In this sector, chatbots prove valuable for lead generation and ensuring 24/7 customer engagement, promptly addressing inquiries about the services offered.


Advantages of AI Chatbots on WhatsApp

WhatsApp has really spread its wings worldwide and has become one of the top messaging apps in more than 60 countries, which is roughly around 30% of the whole world, as of 2022. Because it can speak in over 60 languages, this messaging app can be super useful for businesses too. Here are some cool things about investing in WhatsApp AI chatbots:

1. Always Around: A study by Zendesk found that 76% of customers expect a quick response when they reach out to a business. The faster you reply, the happier your customers will be with your brand. People nowadays want quick answers from companies whenever they have a free moment in their busy lives, and this isn’t likely to change anytime soon.

2. Happy Customers: WhatsApp chatbots can be trained to handle lots of different questions just like a real human would. They can suggest products, offer special deals, and deal with complaints, passing the problem on to a human when needed.

3. Boosting Sales Teams: Chatbots can deal with the simpler questions, freeing up your sales team to focus on the big sales and more complicated stuff where their skills are really needed. Plus, your sales team can check out the data from the chatbots to understand what customers are really interested in.

With WhatsApp being the go-to messaging app in more than 60 countries, there’s a big chance for smart business owners to shake things up in how they talk to their customers.

Advantages of AI Chatbots on Website

Webchat is the go-to method for people interacting with chatbots, usually showing up as chat windows on websites. Investing in webchat AI bots can be a game-changer for businesses aiming to improve customer service. These bots handle common questions, freeing up skilled staff for more complex tasks.

Gartner reports a 70% reduction in inquiries after implementing chatbots. Webchat AI bots offer benefits like:

1. Cost Reduction: By dealing with common queries, chatbots cut call center staffing and training costs, allowing skilled staff to focus on exceeding customer satisfaction.

2. Client Satisfaction: Customers often contact support only when facing issues, expecting long waits. A quick and efficient chatbot can significantly enhance their brand experience.

3. Managing Complaints: Chatbots follow set protocols for recording and addressing customer complaints, involving a representative when needed.

4. Processing Payments: Chatbots respond to billing and account balance inquiries and can support digital payment processes.

5. Availability: In a fast-paced world, customers demand 24/7 support. A live webchat AI bot meets or exceeds these expectations.

Find out how you can leverage Born Digital's Generative and Conversational AI solutions to drive business results.

The Importance of Conversational AI in HR

How Conversational AI can empower your HR team

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Due to the widespread interest in Generative AI, CEOs are turning their gaze toward HR leaders, recognizing AI’s potential to revolutionize HR functions. The expectations placed on HR departments have expanded significantly, necessitating an improvement in data quality and a comprehensive understanding of the current and future workforce.

“Half the time HR personnel spend solving mundane and repetitive queries such as payroll, HRMS systems, leave and attendance, and employee exits.” –  Forbes 

Consequently, HR professionals are now compelled to adopt data-driven approaches for decision-making and to optimize workforce management. The impact of AI on the composition of the workforce has created a new sense of urgency for HR leaders. With nearly three-quarters of employees expressing a likelihood of quitting their jobs in the next 12 months, HR departments urgently need to prioritize technology that enhances their ability to enhance the employee experience, thereby retaining valuable staff, reducing costs, and driving business outcomes.

This article explores ten ways in which AI supports Human Resource departments in delivering unprecedented value.

What are the different processes in HR?

HR processes encompass a spectrum of strategic endeavors undertaken by HR leaders to secure and retain the right talent crucial for accomplishing organizational objectives. These processes span various facets of your business and are designed to fulfill specific objectives.

Recruitment: Recruitment involves attracting qualified candidates, while selection is the process of identifying the right candidate for a role.

OnboardingOnboarding is the crucial first impression on new hires, aligning them with company goals and setting them up for success; successful onboarding includes automation of document sharing, engagement with chatbots, short application processes, remote-friendly welcome kits, scheduled introductory calls, and assigning work buddies.

Workforce Planning: This process analyzes, forecasts, and plans workforce supply and demand, ensuring the right skills are in the right places at the right time; this involves understanding organizational goals, tracking attrition, conducting demand analysis, analyzing gaps, and iterating solutions to meet strategic objectives.

Leave Management: This handles time-off requests while ensuring business workflow efficiency; best practices include outlining leave policies clearly, ensuring compliance with labor laws, utilizing leave management dashboards, deploying chatbots for on-the-go leave applications, and measuring policy effectiveness with goals and KPIs.

Performance ManagementPerformance management ensures employees perform to the best of their abilities; key elements involve goal setting, performance reviews, and improvement, with effective practices including clear goal-setting, measured feedback, empathetic communication, and periodic employee feedback.

Learning and Development: L&D systematically encourages employee professional growth through training courses, seminars, and certifications; essential elements include skill gap analysis, personal analysis, designing learning paths, analyzing program impact on individual and team performance, and engaging enterprise chatbots for an interactive approach.

Compliance Management: Compliance ensures workplace policies align with labor laws and industry regulations; effective practices involve knowing industry-specific regulatory requirements, building a document repository, conducting compliance training, regular audits, tracking violations, and reviewing compliance programs for vulnerabilities.

Compensation and Benefits: This encompass salary, monetary, and non-monetary perks to boost employee morale and motivation; components include fixed salary, variable salary based on performance, and equity pay in the form of company shares.

What are HR's main challenges?

1. Repitition

HR professionals often dedicate a significant amount of time to manual tasks, such as addressing basic employee inquiries, creating time-log spreadsheets, and managing payroll calculations.

2. Unoptimized processes

Current processes are not optimized to cope with the dynamic work environment, resulting in increased costs and reduced revenue for businesses.

3. Human errors

Manual data entry not only proves inefficient for managing organizational data but also introduces the possibility of human errors, lowering the overall quality of the system.

4. Outdated workflows

The “one size fits all” approach is no longer viable when designing learning and development tracks or managing employee rewards and incentives. Employees have unique needs and aspirations that evolve over time, and HR professionals must adapt their perspectives accordingly.

How conversational AI comes into play

Automating HR tasks involves streamlining time-consuming and repetitive activities, enabling the HR team to concentrate on more intricate responsibilities such as planning and decision-making.

The adoption of HR automation significantly influences a company’s financial performance by enhancing overall business productivity, expanding operational scalability, minimizing turnaround times, and reducing error rates. Worldwide, organizations are increasingly integrating HR automation to optimize the effectiveness of their endeavors. In fact, as reported by Personnel Today, 38% of enterprises are currently utilizing AI in their workplaces, with 62% anticipating its implementation as early as this year.

1. Corporate FAQs

Often, your colleague or HR has likely responded to the same inquiries multiple times. Developing a dedicated application to address frequently asked questions about company policies and processes can be costly. However, by employing HR bots with AI, machine learning, and natural language processing (NLP), your company can deploy an assistant that provides real-time responses to your queries.

2. Performance review

Conversational AI can enhance various aspects of performance reviews, including self-assessment processes. Instead of relying solely on traditional self-assessment forms, an AI-powered interactive assistant can provide employees with a conversational experience. HR bots can efficiently remind employees of goal-setting tasks or prompt them to fill out appraisal forms.

3. Leave requests

Imagine an employee needing to check their leave balance while on the go. The traditional methods involve sending emails or logging into an application, which are both time-consuming and tedious. By integrating Conversational AI into your HR system, the employee can simply ask the bot through text or voice command, instantly accessing their leave balance. Similar to a human HR professional, the bot can grasp urgency through its sentiment skills, addressing queries promptly without prolonged wait times or, in case of misunderstood intent, seamlessly handing off to a real HR professional with the complete history and context.

4. Automating compliance

The HR team must address new regulations to ensure the business operates safely and legally from the outset. How this information is communicated and enforced is crucial.

5. Streamline onboarding

An AI-powered interactive assistant can optimize the end-to-end onboarding process, making it efficient and engaging. This assistant can assist the HR department in gathering necessary information, offering new recruits a quick start guide to help them understand the organization, its products, clients, and overall culture.

What to look for in HR automation software

Employee Information Management: Typically structured as a database housing personal and professional details of employees, this functionality encompasses resumes, contact details, addresses, positions, compensation, vacations, and more. The automated software ensures prompt updates to any alterations in the recorded information, maintaining systematic and current records.

Document Management for the Company: This system or database is utilized for capturing, tracking, and storing electronic documents related to employees, including legal documents, agreements, manuals, safety guides, induction materials, templates, and others.

Applicant Tracking System: Facilitating the electronic management of recruitment and hiring processes, this feature stores information on multiple applicants, job openings, ongoing applications, and interviews for specific positions. Additionally, it often integrates with third-party solutions for candidate screening and assessment.

Payroll: The American Payroll Association (APA) estimates that automation can reduce payroll processing costs by up to 80%, primarily by minimizing errors in invoices and paychecks. It gathers employee data, such as tax withholdings, benefits contributions, and total hours worked, and performs calculations automatically.

Absence Management: Automated absence management streamlines the tracking and reporting of leave requests, overtime, balances, and accruals. This feature meticulously monitors all types of time-off and automatically recalculates salaries for each employee.

Talent Management: Automation extends to career path planning and creating mentorship opportunities to retain high-performing employees. HR teams can leverage this feature to offer personalized career guidance based on employees’ inherent capabilities, potential, and professional aspirations.

Find out how Born Digital's Generative and Conversational AI solutions can help you automate your HR processes

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