Category: Blog

Conversational AI: 2024 Market Outlook

Conversational AI: 2024 Market Outlook

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Introduction

The conversational AI sector has seen significant growth into 2024, prompting providers to rethink their approaches to meet client and consumer demands. Initially sparked by the global pandemic, this wave of innovation has now matured, delivering desired outcomes for businesses.

As technology advances, vendors must adapt their development and deployment strategies. Simple Q&A chatbots have evolved into sophisticated virtual agents capable of providing 24/7 support and handling complex transactions.

Improvements in conversational AI have revolutionized customer self-service, surpassing previous standards of efficiency and convenience. Consequently, both businesses and consumers now expect more tailored solutions rather than one-size-fits-all chatbots.

Four key market trends will continue to enhance the business value of conversational AI in the future:

1) AI agent deployment time will be significantly lower

The pandemic has underscored the importance of having robust customer service systems in place, as many businesses found themselves overwhelmed by sudden surges in inquiries. Those with virtual agents were better equipped to handle the increased volume, provided their AI solutions were up to the task. However, many others faced challenges, hastily deploying chatbots that were either incomplete or required significant time and effort to implement.

It’s now crucial for vendors to demonstrate that their solutions offer tangible returns on investment from the outset. When assessing a conversational AI vendor, consider:

1. Does the solution feature scalable Natural Language Understanding (NLU) capable of handling multiple user intents simultaneously?

2. Can self-learning AI help bypass the initial ‘cold-start’ phase and assist with ongoing virtual agent development and maintenance?

3. How quickly can an AI chatbot project move from development to launch? Is it a matter of weeks (preferred) or several months (less ideal)?

2) Data-driven chatbot design is more important than ever

As we look ahead, the landscape of software engineering roles is poised to undergo a significant transformation by 2025, with Gartner forecasting that half of all top positions will necessitate direct oversight of generative AI. The prevalence of conversational platforms among employees highlights the growing importance of AI chatbots in professional settings, a trend that will likely continue to evolve in the coming years.

To ensure that these interactions remain meaningful, conversational AI vendors must elevate their offerings beyond traditional design principles that have been relied upon for years. Merely providing cutting-edge technology will no longer suffice in demonstrating the utility of virtual agents as effective tools for customers.

Moving forward, employing evidence-based design principles will be crucial for virtual agent development, encompassing elements such as personality, avatar design, and website visibility. Solutions equipped with robust analytics tools and comprehensive resources, including best practices, will be essential for companies seeking to leverage conversational AI to its fullest potential.

3) Going 'chat-first' will bring the fastest ROI

According to Gartner, the future of self-service is heading towards customer-led automation. By 2030, Gartner analysts anticipate that a billion service tickets will be automatically generated by chatbots and virtual agents, or their upcoming counterparts.

This projection aligns with the growing trend of chat-based self-service, which offers a cost-effective and accessible means of automating customer interactions on a large scale. As consumers increasingly embrace this approach, businesses are poised to capitalize on its potential.

Embracing a ‘chat-first’ strategy, wherein all customer service traffic is directed through conversational AI solutions, allows businesses to leverage automation effectively. This approach can lead to reduced support costs and higher customer satisfaction scores as it plays to the strengths of automation.

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Generative AI Voice Bot: Outbound Surveys for E-Commerce​

Generative AI Voice Bot: Outbound Surveys for E-Commerce

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Apologies for pulling off the band-aid so quickly, but the truth is, it’s never been easier for your customers to switch to your competition if they’re dissatisfied with your services or the options your e-commerce platform provides. How do you effectively combat this competition without relying solely on a bigger marketing budget? Well, that’s certainly not a sustainable path forward. Nearly 90% of customers are willing to abandon your brand after just two bad experiences. Moreover, it’s five times more costly for your business to acquire a new customer than to retain an existing one. So, the challenge is crystal clear – how do you ensure you understand your customers’ needs, prevent churn, and bolster their loyalty while outshining your competitors?

One way to tackle this is by analyzing customer data to drive business outcomes. However, today we’re here to introduce you to the simplest, most efficient method of collecting customer feedback.

Get responses from 83 % of customers

Hand on heart, what percentage of the feedback do you collect when you send out after-purchase email surveys? Now, what if we told you that we can obtain responses from most of your customers without needing to hire additional team members, except for one: the AI voice bot.

By automating outbound calls with AI to collect customer feedback, your company gains the ability to obtain actionable insights quickly and at a lower cost. The AI digital agent can handle hundreds of concurrent calls, efficiently gathering verbal feedback and ratings on a specified scale. 

Effortlessly analyze collected feedback

For instance, Born Digital’s solution transcribes conversations, simplifying the process of collecting and evaluating feedback by categorizing customer responses based on topic or sentiment. As a leader in both conversational and GenAI applications, our analytics stay ahead of the curve, providing cutting-edge technology at an affordable price.

You can effortlessly filter feedback based on the worst or best sentiment, enabling you to identify the most critical customer pain points that demand swift action, e.g. your delivery dates. Similarly, you can capitalize on your strengths by showcasing and marketing to customers what sets you apart and adds value compared to your competition, such as the range of products. Without AI, this process would take considerably longer and would be inefficient in today’s competitive landscape.

How to implement the AI outbound voice bot today

Implementing outbound AI calls for gathering customer feedback is straightforward. You can schedule the outbound call to occur after a purchase is completed. Additionally, you have the flexibility to test different timing options—whether customers are more likely to respond immediately after the purchase, after a few hours, the next day, and so forth. Once the call is triggered within your CRM, all information is automatically transcribed and categorized for later analysis.

The implementation process itself typically takes only a few days. However, before initiating it, you need to consider the specific questions you want the bot to ask your customers. You can easily customize and modify these questions later within the platform without requiring any coding skills. Furthermore, you can choose the voice—whether female or male, what accent, etc.—to ensure it aligns with your brand’s tone of voice.

Ensure human-like interactions

Our platform harnesses the latest advancements in Generative AI, guaranteeing that interactions between the AI agent and your customers are seamless, natural, and that the bot responds to situations in a way that closely resembles human conversation. Say goodbye to robotic responses like “sorry, I don’t understand your question.”

Many customers do not answer the first call or ask for a later one. The voice assistant puts the call on hold or will call again later time defined by the customer. If the customer calls back, the bot answers the call, intorduces itself, thanks the customer for calling back and continues based on the survey flow you set up. 

Grow your business

Now, let’s delve into the tangible business benefits that implementing AI-powered outbound calls for customer feedback can bring:

Cost Efficiency: By automating outbound calls with AI, businesses can significantly lower operational costs. The reduction in manual labor and the need for additional team members translates to substantial savings in the long run.

Enhanced Customer Satisfaction: The seamless and natural interaction experience provided by AI-powered voice bots leads to increased customer satisfaction. Customers feel heard and valued when their feedback is promptly collected and acted upon.

Competitive Edge: Utilizing AI for customer feedback collection gives businesses a competitive edge. With the ability to swiftly respond to customer needs and insights, companies can stay ahead of the competition by offering tailored solutions and improving overall customer experience.

Data Accuracy and Reliability: AI-driven feedback collection ensures data accuracy and reliability. With automated transcription and categorization, businesses can trust the integrity of the data collected, leading to better-informed decision-making and strategic planning.

Time Savings: Implementing AI-powered outbound calls streamlines the feedback collection process, freeing up valuable time for employees to focus on other essential tasks. This increased efficiency allows businesses to allocate resources more effectively and drive productivity.

In summary, integrating AI-powered outbound calls for customer feedback not only streamlines operations and boosts customer satisfaction but also offers a competitive advantage, ensures data accuracy, and saves valuable time and resources. Don’t wait any longer—start today and implement your outbound AI voic ebot to revolutionize your customer feedback process!

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How to leverage customer insights to fuel growth and retention​

How to leverage customer insights to fuel growth and retention

Customer_Insights

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The imporatnce of customer insights analytics

According to Microsoft, organisations that use customer data to gain insights outperform their peers by 85% in terms of revenue growth. This can be a game-changer for your future results, as it is easier than ever to find alternatives when customers feel undervalued. And it is 5 times more expensive for your business to acquire a new customer than it is to retain an existing one.

Robust customer insights analytics software that collects, analyses and interprets customer needs, pain points and brand sentiment can truly differentiate your business. It not only helps you to strengthen your customer base, but also drives growth and prevents churn.

While the statistics show the potential, success depends heavily on the specific data you collect and the questions you seek to answer. Let’s look at some specific examples to understand this further.

What customer data can you analyze and why

To start, one of the crucial steps is to gather data across multiple touchpoints. Your customers engage with your brand across various platforms and devices, making it essential to analyze their behavior wherever they interact. Employing an omnichannel analytics platform sets the stage for success. With it, you can examine conversations with customers occurring in chat on your website, within your app, on community forums, social media, emails, and feedback forms. This enables you to understand, for instance, whether customers in your your e-commerce business prioritize resolving issues like delivery date changes via phone or email, gauge their sentiment towards your brand on social media, and assess their likelihood of future purchases based on survey responses.

Such insights can empower you to optimize or automate processes and implement any necessary changes to enhance customer satisfaction and drive sales. For instance, if you discover that a majority of customers are calling due to changes in contractual data and these interactions exhibit a frustrated sentiment (with callers entering and leaving the call frustrated), you may identify two potential courses of action.

Firstly, it could indicate that your agents require further coaching on this topic. By delving deeper into their performance data, you can determine if the assistance they provide to customers is sufficient. Alternatively, it may reveal a recurring issue that doesn’t necessitate human intervention. In this scenario, frustrated customers might be unable to resolve the operation alone, suggesting the need to automate this process using AI.

In either case, analytics provide invaluable insights that can transform your business, reduce costs significantly, and enhance the efficiency of your teams.

Questions you should be asking

The reality is that you’re likely to uncover areas for improvement that you wouldn’t have considered without the extensive analysis of data. Nevertheless, before implementing customer insight analytics software and deciding on which data to focus first, it’s essential to set goals and think about the KPIs and questions you need your team to address to progress. This groundwork is crucial. Furthermore, analytics enable you to assess whether the changes you’ve implemented have yielded the desired results, empowering you to refine processes and enhance customer experiences even further. Let’s look at some examples to think about:

Let’s examine some examples of questions to consider:

1. What factors contribute to fluctuations in demand for specific products or services?
2. What sentiments do customers associate with your brand, and how do they differ across different segments/ interaction channels?
3. What strategies can be implemented to improve the ROI of marketing efforts?
4. How can you leverage customer data to identify cross-selling or upselling opportunities?
5. What are the primary reasons customers reach out to customer support, and what trends or patterns can be identified in their inquiries?
6. What are the common pain points or challenges encountered by customers during their interactions with your brand, and how can these be mitigated?
7. What training or coaching opportunities exist within the agent team to improve performance and customer satisfaction?
8. How can you identify and address knowledge gaps or areas where agents may require additional support or resources?
9. How can you prioritize and act upon customer feedback to drive meaningful improvements and enhance loyalty?
10. What are the emerging trends or insights gleaned from recent customer feedback that could inform strategic decision-making and future initiatives?

Find your answers within dashboards

For instance, with the Born Digital platform, you can customize your dahsboards to your liking so they fit to the needs of all your teams – no matter if it’s the frontline workers or your C-level. But there are definitely some feauters that are applicable in general and can be measured/analyzed. Let’s have a look at few examples.

Customer Sentiment Transition: Track changes in customer sentiment over time, including sentiment analysis at the beginning and end of conversations. This feature allows you to understand shifts in customer perception and satisfaction levels from the outset to the conclusion of interactions, providing valuable insights into overall customer experience and sentiment trends.

Agent Performance Metrics:

  • Avg Handling Time: Measure the average time agents spend on each interaction, helping optimize efficiency.
  • Avg Speech Pace: Analyze the rate of speech to ensure agents communicate clearly and effectively.
  • Avg Gaps Ratio: Evaluate the duration between agent and customer speech to minimize pauses and improve engagement.
  • Avg Agent Volume: Monitor the workload of agents to ensure balanced distribution and prevent burnout.
  • Abandon Rate per Hour: Assess the rate at which customers abandon interactions, indicating potential issues with service quality or wait times.
  • And so much more.

Interaction Summaries: Provide concise summaries of customer interactions, highlighting key points and outcomes for quick review and analysis.

Dominant/Minor Topics with Sentiment Heatmap: Visualize the frequency and sentiment of dominant and minor conversation topics across different seasons, hours, or days using a heatmap, facilitating trend identification and analysis.

Cross-sell/Upsell Opportunities (Call Adherence): Identify opportunities for cross-selling or upselling during customer interactions, ensuring agents adhere to established strategies and maximizing revenue potential.

Comparison of Team Performance by Department or Country: Compare the performance of teams, departments, or countries to identify strengths, weaknesses, and opportunities for improvement, enabling targeted interventions and resource allocation.

Business results

Our customers clearly experience numerous enhancements, effectively translating insights into tangible business outcomes:

Data-based decision making: No more guesswork; instead, decisions are grounded in precise data insights, leading to more effective strategies and operations. Consequently, businesses can save significant costs by avoiding wasteful or ineffective initiatives.

Increased Revenue: By accurately identifying customer wants and needs through data analysis, sales and marketing teams can tailor their approaches, offering products with compelling messaging that resonates with customers, resulting in increased sales and revenue generation.

Operational Efficiency: Data analytics enables businesses to optimize operations by strategically scheduling team shifts, identifying peak activity periods, and automating processes where beneficial. Conversely, it also highlights areas where automation may not be optimal, leading to more efficient resource allocation and streamlined workflows.

Customer Loyalty: Utilizing data insights, businesses can provide personalized and timely interactions to customers, meeting their needs precisely when and where they arise. This availability and tailored service foster customer loyalty and satisfaction, driving repeat business and long-term relationships.

Enhanced Competitive Advantage: Leveraging data-driven insights allows businesses to gain a competitive edge by staying attuned to market trends, understanding consumer behaviors, and adapting strategies accordingly. This proactive approach enables businesses to differentiate themselves, attract new customers, and outperform competitors in the market.

How to implement a customer insights analytics software

In one of our articles, we explore key considerations for selecting an AI conversation analytics software. Choosing the right solution entails defining your use cases, developing a robust business case, understanding essential capabilities, and thoroughly assessing your options. At Born Digital, we’re here to support you throughout your AI transformation journey. Contact us today to learn more.

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Digital Humans: Why give AI a face?

Digital Humans: Why give AI a face?

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What are Digital Humans?

Imagine a customer service agent who can answer your questions, understand your frustration, and even crack a smile to reassure you. That’s the world of digital humans – AI chatbots that shed their disembodied voices and take on a realistic human form. These aren’t just fancy avatars in a video game; digital humans are pushing the boundaries of technology to create interactive beings indistinguishable from real people.

But why give AI a face? While AI-powered chatbots have become commonplace, there’s something undeniably powerful about human connection. In this article we explore the reasons behind giving AI a face and examining the potential impact it will have on our interactions with technology in the future.

  • Emotional expressions build rapport

Just like with real people, a digital human’s facial expressions can create a sense of trust and understanding.  Imagine a digital therapist who can offer a reassuring smile or a concerned frown during a conversation. These subtle cues can make a big difference in how comfortable users feel opening up about sensitive issues. Studies, like the one with Ellie the digital soldier counsellor, have shown that people are more likely to disclose problems to a system with a face compared to a faceless one. This suggests that a digital human’s ability to express emotions can build a crucial sense of rapport, especially when dealing with sensitive topics.

  • It can simplify complex information

A face adds a human touch to complex information. Digital humans can use visual cues and nonverbal communication alongside their voices to break down difficult concepts. Imagine a digital financial advisor who can not only explain investment options but also use charts and diagrams on screen while conveying confidence or caution through their facial expressions. This combination of visual and emotional engagement can make complex information easier to understand and navigate, improving the overall user experience.

  • Interactive experiences make your brand memorable

People crave personal connections with brands.  Think about a time a helpful store clerk or quick customer service made a shopping experience stand out.  Even small gestures, like a barista remembering your order, can leave a lasting impression.  Studies show that customer experience is just as important as the product itself.

Let’s take e-commerce as an example. Online shopping often lacks this personal touch and therefore most online interactions are forgettable transactions.  Digital humans offer a solution.  They combine the convenience of online shopping with the helpfulness of a store employee and this creates a more memorable experience, even if it can’t replace the special connection you might have with a local coffee shop owner.  Digital humans can still bring a human touch to the online world.

  • It provides non-judgemental interactions

A digital human can provide a safe space for users to ask questions and get support in areas they might feel uncomfortable with a human representative.  For example, someone struggling with financial literacy might hesitate to discuss debt with a bank employee. However, a digital financial advisor with a non-judgmental demeanor could create a space for open and honest conversation. This can be particularly valuable for sensitive topics like mental health or financial difficulties, where users may worry about judgment or stigma.

  • It improves accessibility for some users

Digital humans with a voice interface can be a welcoming alternative for users with visual impairments.  Imagine a digital assistant who can not only answer questions but also navigate users through complex menus and forms using voice commands. Additionally, the ability to customize the appearance of a digital human can make AI interaction less intimidating and more approachable for a wider audience.  For example, users could choose a digital assistant that reflects their age, ethnicity, or even gender identity. This level of customization can foster a sense of connection and comfort for a more diverse range of users.

What can Digital Humans be used for?

Digital Humans can exist anywhere, from a company website to a physical store kiosk, and they come in a variety of roles, including digital bankers who offer financial advice, insurance advisors who help with claims, and even digital recruiters who streamline the hiring process. To learn more, check out our article on the top 5 use cases of Digital Humans.

The benefits of digital humans are vast. They can provide personalized recommendations and support, answer frequently asked questions, and efficiently handle tasks like scheduling appointments or generating reports. This not only frees up human employees for more complex work, but it also creates a 24/7 customer service experience that caters to individual needs. Data collected by digital humans further enhances their capabilities, allowing them to provide increasingly accurate and helpful interactions.

How to find out more about Digital Humans?

Curious to learn more? Whether you’re just starting your research or want to see real-world examples, we’ve got you covered.  Subscribe to our newsletter below to receive the Digital Humans e-book soon, or get in touch with our team of experts for a non-binding consultation.

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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.

Summary

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.

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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.

Conclusion

In summary, contrasting Small Language Models (SLMs), specifically domain-specific SLMs, 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!

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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. Monthly number of inquiries (chats and calls)

4. Average resolution time per quiery

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.

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Conversational AI in eCommerce

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

Table of contents

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.

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Conversational AI in HR | Benefits and Use Cases

Conversational AI for HR | Benefits and Use Cases

Table of contents

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.

Talk to an Expert

What’s new in the Digital Studio: March 2024 release

What's new in the Digital Studio: March 2024 release

You can upload documents better than ever

Uploading documents to your Knowledge Base Index is now asynchronous, allowing you to monitor the progress of each file in real-time.

Application Status Bar Expansion

We’ve integrated the Indexer application into the app status bar. This addition allows you to view all active applications and their deployment versions, including the Indexer app.

Enhanced visibility in chatbot bubble

The document upload process within the chatbot bubble has been revamped for greater visibility. Stay tuned for additional updates.

Additional changes

Indexer Performance and Logic: The performance and underlying logic of the Indexer application have been enhanced for greater efficiency.

Frontend Improvements: Minor technical updates have been made to the Flow Editor, enhancing usability.

Elevated Customer Experience: The process of creating a new, blank project has been streamlined, focusing on user-friendliness.

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