Conversational AI, often referred to as chatbots or virtual assistants, has promised many industries that the exorbitant costs of human-led support would decrease significantly through the automation of customer interactions. In the first generation of Natural Language applications, while trying to deliver on that promise, this industry took the problem it was trying to solve and reinserted it right back into the solution: Human support is expensive and not scalable. Building these semi-competent virtual assistants required so many data scientists, engineers, and designers that a return on investment was hardly realized, if ever. Considered one of the top-tier virtual assistants in the financial services market, Bank of America leveraged a team of over 100 technologists and spent two years before they initially launched Erica in 2018. [Tearsheet 2017] The Bank of America Erica team has since grown to 200 people and is considered one of the rare success stories in this industry — the return on their investment is not public knowledge but it’s understandable why other companies have apprehension that investing hundreds of millions of dollars to build a virtual assistant will net them a profitable return.

Before large language models (LLMs), the methods of training these virtual assistants were extremely data-intensive and far too cumbersome to scale the way needed for customer interactions. It’s not an accident that most chatbots in the market have buttons restricting you to their ‘happy paths’ instead of starting the conversation with “How can I help you.” LLMs have effectively changed how this market will function for the foreseeable future. Not only has the paradigm shifted in how we build these virtual assistants, but consumers’ expectations have only risen. If you are an enterprise, it will not be long before your customers start to wonder why they can use ChatGPT to plan their vacations, create recipes based on ingredients in their fridge, or write their high schooler’s term paper. However, they still have to call their utility company to update their address.

Most enterprise IT teams are enamored with the idea of building virtual assistants themselves, more recently demonstrated by those leveraging foundational models from Open AI or Anthropic. Time will tell how that turns out, but the data strongly suggests that buying software rather than building in-house is usually the safe bet. According to Mendix, 54% of projects exceed their original budgets by almost 200%, and organizations cancel 31% of projects. Additionally, 3Pillar Global reported that the failure rate of building is two in every three projects. As we enter May, ChatGPT has now been out for 1.5 years, and very minimal progress has been made in releasing experiences for direct-to-consumer self-service. The ones that have released experiences are grabbing headlines for the wrong reasons (i.e. Air Canada). As a result, many enterprise executives are timid to rush forward with this uncertainty in the market.

The latest trend in the enterprise world is leveraging Generative AI as a tool to assist agents. It seems ironic that the industry has collectively decided that since we cannot trust these models to be direct-to-customer, the next best option is to let our agents filter out the harmful and misleading information these models can produce. It feels like the “lazy” solution to the real problem, which is that we know these models are not 100% accurate. A solid argument against this approach is that it simply minimizes the financial impact the solution can have. If you could engage in a quick thought exercise to understand how agents will use this tool, it will confirm this perspective. Agents can ask this virtual assistant any questions they might have while interacting with customers. Unfortunately, any time the agent is unsure of the answer, they are required to verify that the output generated by the assistant is correct. For clarification, the agent only doesn’t have to verify the answer if they are absolutely certain the output is correct, in which case they wouldn’t need to ask the question in the first place. So if the agent only uses this virtual assistant when they are not certain of the answer, and then they are required to verify that answer from the source of truth every time, then how much time is the agent really saving? To substantiate these claims, scholars from the Stanford Digital Economy Lab within Stanford HAI and the Massachusetts Institute of Technology studied the impact of generative AI deployed at scale in the customer service sector at a call center. They found that access to AI assistance only increased agent productivity by 14%, with the most significant impact on less experienced workers. [Stanford HAI 2023]

If there’s anything I want you to take away from this blog is that waiting around for a model to get “good enough,” where the hallucinations are so infrequent enterprises will finally feel comfortable putting them in front of customers, is not a sound strategy. The companies creating these foundational models continue to release new versions, but the progress made toward lowering the number of hallucinations has been negligible, at best. And in reality, we need these models to be near perfect before they can be put in front of customers. The reason for that is how we as a society are wired. We are absolutely fine with humans getting into cars and causing accidents every day, but everyone is in uproar as soon as an autonomous vehicle causes one accident. Although the stakes are different, the fact that people are not okay with computers or machines making mistakes remains true. So if waiting for a foundational model to get “good enough” is not a sound strategy, what is the best path forward for these enterprises?

If you look at what the top-tier research papers tell us, LLMs need more reasoning in their outputs. To further prove that assertion, I will use computer vision as an example of how these models lack reasoning. Computer vision and LLMs have more in common than most people realize: both deep neural networks trained on billions of parameters of data that essentially allow the models to understand real-life patterns at scale. The issue is these models behave much more like a “knee-jerk” reaction to an input rather than developing a coherent, reasoned output. In computer vision, there are studies around how these models are able to subitize compared to humans [CVPR’15, Zhang et al.]. Subitization is a technique that is used to help elementary students understand numbers. It essentially is the ability to look at a small set of objects and instantly know how many there are without counting them. For example, if I showed you a die with two dots on it, you would instantly say there are two dots on there. You would not need to count the dots one by one, or use reasoning in any way to conclude that there are two dots. However, if I were to give you a die with nine dots on it, there is a better than not chance you wouldn’t be able to immediately recognize there are nine dots without using some type of reasoning. You may do it very quickly, but you will more than likely group the dots into sections (e.g. four and five) and then use reasoning to add those numbers together. What researchers initially noticed is that computer vision performs pretty terribly at this exercise compared to humans. Humans can essentially count to infinite numbers if they are not bound by time. However, what was later realized is that if you add a “layer of logic” to the model, it was able to perform orders of magnitude better [CVPR’23, Xu et al.]. This “layer of logic” can be thought of as human-created reasoning that allows the model to perform at a much higher level than without it. This idea of adding human-created reasoning to these models has recently taken form across numerous research papers in the NLP space [EMNLP’23, HK Poly., Feng et al]. The only issue is this reasoning or logic looks very different for every use case so it’s not something that can be generalized to these foundational models through the likes of prompt engineering.

Since foundational models are not (alone) going to solve direct-to-customer automation safely, the opportunity for a new category leader has presented itself. This leader must have the capabilities of a research lab, similar to universities or the Big Tech companies, to uniquely harness the power of these LLMs, but provide the control and explainability brands need for customer experience quality and accuracy. Uniquely wielding these capabilities into automation ease is not enough; however, the right leader will help orchestrate automation seamlessly into journeys and touchpoints for effortless customer experiences. This rare combination of dedication to research and focus on the customer experience will unlock an entirely different paradigm of how we build, manage, and scale direct-to-customer automation. If designed correctly, this paradigm will transform our traditional views of deploying virtual assistants with less technical reliance and instead allow business teams to act as supervisors of ‘AI building AI.’

While Big Tech has previously developed Conversational AI platforms for the enterprise, the majority of their efforts are moving towards building foundational models rather than applications. [Stanford HAI 2024] Most CXM application providers are integrating Generative AI to complement their core capabilities but not reimagining the approach to how Conversational AI solutions are built. This creates an excellent opportunity for purpose-built solutions that can deliver on the original promise of reducing the need for human-led support and offer a unique design with control and ease for business operators (i.e., those currently training and managing live agents). Knowbl’s Autonomous AI Platform allows these business operators to build, design, manage, and control automated direct-to-customer experiences for the Fortune 500 Enterprise market leveraging the latest research and development in the NLP domain. Our platform leverages LLMs in innovative ways that remove the difficulty and friction of creating virtual assistants that customers will find usable and enjoyable for the first time in this industry.

Guest post, written by: Matt Taylor, Co-Founder and Chief Product Officer of Knowbl

Knowbl is leading the transformation of Enterprise Conversational AI with a modern approach to leveraging LLMs for unsurpassed Speed, Ease, and Scalability.

To learn more about “Enterprise Conversational AI” go to: www.knowbl.com

The post Why Enterprise Conversational AI is Ripe for a New Leader appeared first on Execs In The Know.

In this blog post, we will delve into the topic of Conversational AI, exploring its various applications and highlighting the benefits it offers in terms of future-proofing businesses, enhancing customer satisfaction, driving sales, and bolstering brand image and credibility all without the fear of hallucinations. Join us as we uncover the immense potential of Conversational AI in transforming businesses for unlimited growth.

Future-Proof Your Business with BrandGPT

In today’s fast-paced and ever-changing business landscape, it has become imperative for organizations to stay ahead of the curve and adapt to emerging technologies. Today’s technology in focus is Conversational AI. With its ability to enhance customer experience and boost efficiency, Conversational AI solutions offer immense value to businesses, enabling them to future-proof their operations.

Conversational AI refers to technology that simulates human-like conversations through voice and text interfaces, allowing businesses to communicate with their customers in a more natural and personalized manner. By leveraging advanced natural language processing capabilities, Conversational AI solutions can understand and interpret customer queries, enabling companies to provide real-time assistance and resolve issues promptly.

The adoption of these solutions offers numerous benefits for businesses. It enhances the customer experience by providing instant and accurate responses, reducing customer frustration and increasing satisfaction. Conversational AI streamlines processes and saves time by automating routine tasks, such as answering frequently asked questions or processing simple requests. This not only improves operational efficiency but also frees up employees’ time, allowing them to focus on more complex and value-added tasks.

So, why wait? Start exploring and implementing Conversational AI solutions to future-proof your business today.

Harnessing the Power of Conversational AI for Unparalleled Customer Satisfaction

Conversational AI has skyrocketed in popularity as businesses seek to provide exceptional customer experiences. The American Customer Satisfaction Index made a post in which you can see the shocking levels to which customer satisfaction has declined over the past 15+ years. In 2022, the trendline finally began to recover. Businesses must be proactive in the pursuit to keep this trend on an upward rise.

The key to customer satisfaction lies in personalized conversations that make customers feel valued and understood. Conversational AI allows businesses to achieve this by leveraging advanced technologies such as natural language processing and machine learning. By analyzing customer data and behavior, organizations can create tailored conversations that resonate with each individual, providing personalized recommendations, assistance, and support. This level of personalization not only enhances the customer experience but also boosts customer loyalty and retention.

Unlock New Revenue Streams through AI-driven Conversational Sales

Another hot topic that is currently trending is the ability of AI-driven conversational sales to unlock new revenue streams for businesses. It is important to stay informed and embrace the potential value and opportunities that conversational AI can offer.

You can see where Conversational AI empowers businesses to engage with customers, drive sales, and generate additional revenue. By leveraging AI-powered chatbots and virtual assistants, companies can now provide instant and efficient customer service, offer product recommendations, and even facilitate seamless transactions.

The true value of AI-driven conversational sales lies in its ability to provide recommendations and offers based on the customer’s preferences and behavioral data. With AI algorithms crunching vast amounts of customer data, businesses can tailor their sales pitches and promotional messages to meet the individual needs and interests of each customer. This level of customization not only increases the chances of making a successful sale but also ensures long-term customer loyalty and repeat purchases.

By embracing AI-driven conversational sales, businesses can tap into new revenue streams by boosting their sales conversion rates and also uncovering upselling and cross-selling opportunities. Leveraging conversational AI can also result in cost savings by reducing the need for human customer support representatives, as well as streamlining sales processes and improving operational efficiencies.

Enhance Brand Image and Credibility with Conversational AI

 In today’s rapidly evolving digital landscape, businesses are constantly looking for innovative ways to enhance their brand image and credibility. By harnessing the power of artificial intelligence and natural language processing, this technology allows brands to elevate their brand image and credibility.

One of the key benefits of Conversational AI is its ability to provide a seamless and personalized customer experience. Through chatbots, virtual assistants, and voice-enabled devices, brands can engage with their customers in real-time, resolving queries and providing assistance round the clock. This not only improves customer satisfaction but also portrays the brand as reliable and customer-centric.

Conversational AI can also help brands gain a competitive edge by delivering highly targeted and relevant marketing campaigns. By analyzing customer interactions and preferences, AI-powered chatbots and virtual assistants can personalize recommendations and promotions, leading to better customer engagement and brand perception. This level of personalization enhances the perceived credibility of the brand, as customers feel understood and valued.

Join the Conversation: Harness the Power of Conversational AI at CRS Nashville

As the corporate landscape continues to evolve  and customer expectations grow exponentially, it has become increasingly crucial for businesses to adapt to new technologies that drive customer engagement and deliver exceptional experiences. In the world of customer experience (CX), one trend that has been gaining significant momentum is Conversational AI, a technology that has the potential to revolutionize businesses and unlock unprecedented success. If you are a Corporate Executive, don’t miss the opportunity to stay ahead of the curve and explore the immense possibilities of Conversational AI at CRS Nashville. Join us and learn how to harness the power of Conversational AI to take your business to new heights, optimize operations, and deliver unparalleled customer experiences. Don’t wait, register now and be a part of this game-changing event.

Guest post written by Abby Nichols, Digital Marketing, at Knowbl.

Knowbl provides a patented AI virtual concierge platform, optimized to be a fast and efficient solution for answering inquiries and customer transactions. Our business user-friendly platform is simple and intuitive, enabling departments and businesses to give accurate support more effectively.

The post Revolutionizing Businesses: Unleashing the Potential of Conversational AI for Unrivaled Success appeared first on Execs In The Know.