Category: Machine Learning

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Revolutionizing Customer Interaction: Companies Leading the Charge with Large Language Models – Applying AI: Transforming Finance, Investing, and Entrepreneurship

The rapid advancements in Artificial Intelligence (AI) have significantly transformed how businesses interact with their customers. Large Language Models (LLMs), such as OpenAI’s GPT-4 and Google’s BERT, are at the forefront of this revolution, driving the development of intelligent chatbots that enhance customer service and engagement. This article delves into how leading companies are harnessing the power of LLMs to create sophisticated chatbots that redefine customer interactions.

OpenAI’s ChatGPT: The Conversational Maestro

OpenAI’s ChatGPT is perhaps the most well-known LLM-based chatbot, renowned for its ability to generate human-like text responses. Businesses across various sectors have integrated ChatGPT into their customer service operations to provide instant, accurate responses to customer queries. For example, companies like Shopify and Stripe utilize ChatGPT to handle customer support, manage inquiries, and automate routine tasks, thereby improving operational efficiency and customer satisfaction.

Google’s BERT and Dialogflow: Empowering Customer Support

Google’s BERT (Bidirectional Encoder Representations from Transformers) powers many chatbots through its natural language understanding capabilities. When integrated with Google Dialogflow, BERT enables chatbots to understand and process complex customer queries more effectively. Retail giants like H&M and tech companies like NVIDIA leverage this technology to deliver personalized customer service, ensuring that customer interactions are as seamless and efficient as possible.

IBM Watson: The Cognitive Computing Pioneer

IBM Watson’s AI and natural language processing capabilities have made it a popular choice for enterprises looking to deploy intelligent chatbots. Watson’s conversational AI is used by companies such as KPMG and Humana to enhance customer service by providing detailed, context-aware responses. Watson’s ability to integrate with various data sources allows it to offer precise answers and insights, making it an invaluable tool for customer support teams.

Salesforce Einstein: AI-Powered CRM

Salesforce’s Einstein AI integrates LLMs to enhance its customer relationship management (CRM) platform. By embedding intelligent chatbots within their CRM, Salesforce enables companies to automate customer interactions and provide real-time assistance. Companies like Adidas and T-Mobile use Einstein AI to streamline customer support, predict customer needs, and personalize marketing efforts, significantly enhancing customer experiences.

Anthropic’s Claude: Ethical and Safe AI

Claude, developed by Anthropic, focuses on ethical AI usage and safety. Although still emerging in the financial domain, Claude is designed to handle customer interactions with a strong emphasis on privacy and security. Its adoption by companies concerned with ethical AI practices highlights the importance of maintaining customer trust and ensuring safe AI applications in business operations.

Transformative Applications and Future Directions

The integration of LLMs in chatbots extends beyond simple query handling. These models enable:

1. Personalized Customer Interactions: By understanding customer preferences and history, chatbots can offer tailored recommendations and solutions.

2. 24/7 Support: AI-powered chatbots provide round-the-clock support, addressing customer needs anytime, anywhere.

3. Operational Efficiency: Automation of routine tasks allows human agents to focus on more complex and value-added activities.

4. Enhanced Decision-Making: Real-time data analysis and response generation aid in making informed business decisions.

Customer Satisfaction and Impact

Studies indicate that the adoption of chatbots significantly enhances customer satisfaction. For instance, a study found that chatbots using social-oriented communication styles can improve customer satisfaction by enhancing the perceived warmth of interactions (Xu et al., 2023) . Another research highlighted that during different decision-making stages, chatbots’ language styles (abstract vs. concrete) play crucial roles in influencing customer satisfaction by providing emotional or informational support (Huang & Gursoy, 2024)

|  Emerald Insight.

Furthermore, the economic benefits of chatbots are substantial. According to Tidio, businesses deploying chatbots save up to 30% on customer support costs, with an average ROI of 1,275% in support cost savings alone. The projected global retail consumer spending via chatbots is expected to reach $142 billion by 2024, underscoring their growing importance in customer engagement strategies .

Detailed Benefits of Chatbots

1. Efficiency and Cost Savings: Businesses have embraced chatbots for their ability to handle a large number of requests simultaneously. In 2022, chatbots saved businesses around $11 billion in customer support costs . This efficiency is particularly beneficial for small businesses that often have fewer resources and need to optimize their customer interaction processes.

2. Enhanced Customer Experience: The quality of interactions provided by chatbots plays a critical role in customer satisfaction. High usability, reliability, and adaptability of chatbots contribute significantly to positive customer experiences (Chung et al., 2020; Trivedi, 2019)

|  Emerald Insight. Chatbots that can quickly and accurately respond to customer inquiries help in creating a seamless customer journey.

3. Emotional and Informational Support: Research by Huang & Gursoy (2024) highlights that chatbots can enhance customer service by providing emotional support during the informational stage and informational support during the transactional stage

|  Emerald Insight. This dual capability ensures that customers feel supported throughout their decision-making process, leading to higher satisfaction levels.

4. Social and Task-Oriented Communication Styles: The communication style of chatbots also affects customer satisfaction. Studies show that social-oriented communication styles can boost satisfaction by enhancing the perceived warmth of the interaction, especially for customers with high attachment anxiety (Xu et al., 2023) . Conversely, task-oriented styles are more effective for straightforward informational tasks.

Challenges and Future Prospects

Despite the numerous benefits, challenges remain in fully realizing the potential of chatbots. Consumer skepticism and a preference for human interaction over chatbot-based conversations are significant hurdles (Van Pinxteren et al., 2020) . Addressing these concerns requires improving the human-likeness and reliability of chatbots, ensuring they can handle complex queries and provide accurate information.

The future of chatbots is promising, with ongoing advancements in AI and natural language processing expected to further enhance their capabilities. As businesses continue to integrate these technologies, the focus will be on balancing automation with the human touch, ensuring that customer interactions remain personal and engaging.

Conclusion

The use of LLMs in chatbots is revolutionizing customer service by making interactions more efficient, personalized, and accessible. As companies continue to explore the potential of AI, the focus remains on enhancing customer experiences while ensuring ethical and safe AI practices. The future of customer service is undoubtedly intertwined with the advancements in AI, promising a landscape where technology and human ingenuity converge to deliver superior customer experiences.

For more insights on the transformative impact of AI in customer service, visit our recent articles on Applying AI.

By leveraging the advancements in LLMs, businesses can not only meet but exceed customer expectations, setting new standards in customer service and engagement. Stay tuned to Applying AI for the latest updates and in-depth analyses on AI innovations and their implications across various industries.

Sources

1. Xu, Y., Zhang, J., & Deng, G. (2023). Enhancing customer satisfaction with chatbots: The influence of communication styles and consumer attachment anxiety. Frontiers in Psychology. Retrieved from Frontiers

2. Huang, Y., & Gursoy, D. (2024). Customers’ online service encounter satisfaction with chatbots: interaction effects of language style and decision-making journey stage. International Journal of Contemporary Hospitality Management. Retrieved from Emerald Insight

3. Tidio. (2024). 80+ Chatbot Statistics & Trends in 2024. Retrieved from Tidio

4. Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587-595. Retrieved from Journal of Business Research

5. Trivedi, J. (2019). Examining the customer experience of using banking chatbots and its impact on brand love: The moderating role of perceived risk. Journal of Internet Commerce, 18(1), 91-111. Retrieved from Journal of Internet Commerce

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Book Review – Hello World: Being Human in the Age of Algorithms – Part 1 – Applying AI

Introduction
I often think of AI as something separate from traditional computer programming, something transcendent. However, most of the advances in modern AI are not the result of revolutionary new concepts or fields of study but rather the application of previously developed algorithms to significantly more powerful hardware and massive datasets.

Hannah Fry’s take on the world of AI covers topics ranging from justice to autonomous vehicles, crime, art and even to medicine. While the author is an expert in the field, she does a great job distilling the topics down to a level understandable by a layperson, but also keeps it interesting for someone with more background in programming and AI.

My favourite quote from the first part of the book comes on page 8, where Hannah succinctly describes the essence of what an algorithm is in only one sentence:

An algorithm is simply a series of logical instructions that show, from start to finish, how to accomplish a task.

Fry, Hannah. Hello World: Being Human in the Age of Algorithms (p. 8). W. W. Norton & Company. Kindle Edition

Once you read it, it seems obvious, but trying to describe to a first-year computer science student what an algorithm is can be a challenging task. The author manages this well. Despite the complexity and depth of the subject matter, Fry is able to bring context and relevance to a broad array of topics. The remainder of my review will speak to some of the book’s many sections and how someone with a business-facing view into the topics sees them.

Data
This section covers some of the unknown giants in data-science including Peter Thiel’s Palantir. The section also touches on some very public examples where analytics has played a negative role – Cambridge Analytica’s use of private user data during the 2016 Presidential Elections.

The story here is about data brokers. Data brokers are companies who buy and collect user data and personal information and then resell it or share it for profit. A surprising fact is that some of these databases contain records of everything that you’ve ever done from religious affiliations to credit-card usage. These companies seem to know everything about just about everyone. It turns out that it is relatively simple to make inferences about a person based on their online habits.

The chapter converges to one of the major stories of 2018, the Cambridge Analytica scandal. But it begins by discussing the five personality traits that psychologists have used to quantify individuals’ personalities since the 1980s: openness to experience, conscientiousness, extraversion, agreeableness and neuroticism. By pulling data from users’ Facebook feeds, Cambridge Analytica was able to create detailed personality profiles to deliver emotionally charged and effective political messages.

Perhaps the most interesting fact though, is how small of an impact this type of manipulation actually has. The largest change reported was from 11 clicks in 1000 to 16 clicks in 1000 (less than 1 percent). But even this small effect, spread over a population of millions can cause dramatic changes to the outcome of, say, an election.

That’s the end of part 1 of this review. In Part 2, I’ll touch on some of the other sections of the book including Criminal Justice and Medicine.

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AI Everything – Applying AI

These days it seems like businesses are trying to use AI to do everything. At least for startups, that isn’t far off. Anywhere there is a dataset remotely large enough and an answer that is vaguely definable, companies are putting together a business model to use machine learning to solve the problem. With some incredible successes in areas like image classification and defeating humans at video games, its hard not to be impressed.

One of the best channels for following recent breakthroughs in AI is the 2 Minute Papers YouTube Channel, started by Károly Zsolnai-Fehér, a professor at the Vienna University of Technology in Austria. Károly’s videos combine interesting clips of the programs in action with well-delivered summaries of recent papers illustrating advances in artificial intelligence.

In one of his latest videos, he covers an AI that not only can copy the most successful actions that humans take in video games but can actually improve on those actions to be better than the best human players. So does that mean that AI will be displacing office workers once it learns how to do their jobs better than them? Probably, yes. But maybe not quite how you think it might.

As much of a ‘black-box‘ as AI has been in the past, modern systems are becoming better and better at explaining how they arrived at an answer. This gives human operators predictive capabilities that we didn’t have with systems of the past that could spit out an answer but gave us no indication of how that answer was formulated.

This Forbes article on Human-Centric AI provides some examples of how modern AI systems can be implemented to train employees to do their jobs better and even enjoy their jobs more while doing it! If that doesn’t sound incredible to you, you may be a machine who is only reading this page to improve your search algorithm.

So what does this all mean? A lot of research is showing that AI is actually creating many more jobs than it destroys. So, as long as you’re willing to try and understand the systems that will one day be our overlords, you should be able to upgrade your career and stay employed.

Whether you still want the job that remains is another question entirely.

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The True Cost of an MBA – Applying AI

Everything has an opportunity cost. An MBA, for example, costs about fifty to eighty thousand dollars, but that’s just the face value. It turns out, by taking two years off of work to go to school, you are also sacrificing the earnings you could have had from those two years, not to mention any promotions, raises or job experience that would have come along with it. If we’re thinking about lifetime earning potential, we can calculate the incremental earnings that you’d need from the MBA in order to break-even on the investment. Of course, all of these calculations should always be done ex-ante (prior to enrollment) because otherwise, we’re falling prey to the sunk-cost fallacy, and that will only make us regret a decision we’ve already made.

For example, let’s say that your MBA will cost $75,000 up front and that you are currently making $50,000 per year annually at your current job. What incremental salary increase would you need in order to account for the opportunity cost of the MBA?

First, we have to calculate an appropriate discount rate for our money. In this case, we can probably use r_m , the market’s rate of return because if we choose not to put the money towards an MBA, we could instead put it in an Index Fund or another similar investment vehicle, where it would grow at around the market interest rate.

Source: Market-Risk-Premia.com

Based on the July 2018 numbers, the market risk premium is about 5.38%. Notice that we didn’t just use the Implied Market Return of 7.69%, this is because we need to subtract the Risk-free rate r_f in order to account for the incremental risk.

Let’s round down to 5% for simplicity. Assuming we’re starting our MBA in January of 2019 and Finishing in December of 2020 (2 years) with a cash outflow of $37,500 in 2019 and 2020 and sacrificed earnings of $50,000 in each of those years. We can calculate the future value (FV) of that money in 2021 as follows:

Future Value of Annuity Formula
Future Value of an Annuity

Our periodic payment, P , is $87,500, our discount rate,r , is 5% and our number of periods, n , is 2. That leaves us with the following:

FV = \$87,500*[((1+0.05)^2-1)/0.05]  = \$179,375

Assuming we’re able to land a job on day 1 after graduation, how much more do we have to make in our careers to make up for the opportunity cost of the MBA? For that, we can use another annuity formula to calculate the periodic payment required over a given number of years to equal a certain present-value amount.

Annuity payment formula

Let’s say that we will have a 30-year career and that our market risk premium stays the same at 5% (the historical average for Canada is closer to 8%, however, let’s be conservative and stick with 5%). Substituting in these values to our formula with PV = $179,375 r = 5% and n = 30, we find that the payment, P, is:

P = {0.05*\$179,375}/{ 1 - (1+0.05)^{-30}} = \$11,670

So, we need to make an additional $12,000 per year every year for the rest of our careers, because of the MBA, in order to make up for the opportunity cost of the program.

If that seems realistic to you, maybe you should consider an MBA.

Of course, if we’re being really clever, we should probably also include a risk premium for our MBA. There is not a lot of data out there to suggest what the probability of completing an MBA is, but we can assign some probabilities to our equation for reference. Let’s say that there’s a 60% chance that the market will be strong when we complete the MBA and we’re able to find a job that pays $62,000 per year right out of the MBA program. There is also a 20% chance that we’ll make the same amount as we made before the MBA program $50,000 per year, a 10% chance that we’ll make $75,000 per year after the program and a 10% chance that the market for MBAs tanks and we’ll make below $40,000 per year when we graduate.

Expected Value = 0.6 * \$62,000 + 0.2 * \$50,000 + 0.1 * \$75,000 + 0.1 * \$40,000 = \$58,700

How do we make a decision with all these different possible outcomes? Simply multiply the probabilities by the annual salaries and add them together to find the probable result. If these numbers are correct we’re looking at an equivalent salary of $58,700 per year coming out of the MBA program. Of course, these numbers are completely made-up, but if we find numbers like these in our real-world evaluation, the logical decision from a financial perspective would be to reject doing an MBA because the cost is outweighed by the potential gains.

According to PayScale, the average salary in Calgary for an MBA with a finance specialization is $87,500 per year, but the average salary for someone with a bachelor of science degree is over $75,800 per year. Based on these numbers, it might not make sense for someone with a science degree to do an MBA.

Of course, there are other intangible factors that come into play including career preferences, lifestyle, and happiness. These are all important and should definitely be factored into your decision.

Graphs and iPads are an important part of any MBA

Yes, this is a very hard decision to make but can machine learning algorithms help make these decisions easier for us? It should be possible to use machine learning algorithms to predict future earnings potential and even take into account qualitative variables like career preferences and working style to give us a better idea of which choices might be right for us.

It is my goal to understand the capabilities of machine learning models to assist in these types of financial predictions. Hopefully, in the next few weeks, I’ll have an update for you on whether this type of predictive capability exists and if it does, how to access it.

For now, good luck with your decision making! I did an MBA and I don’t regret it at all because it was the right decision for me. My hope is that this article has given you the tools to decide whether the decision might be right for you.

Posted on

The True Cost of an MBA – Applying AI

Everything has an opportunity cost. An MBA, for example, costs about fifty to eighty thousand dollars, but that’s just the face value. It turns out, by taking two years off of work to go to school, you are also sacrificing the earnings you could have had from those two years, not to mention any promotions, raises or job experience that would have come along with it. If we’re thinking about lifetime earning potential, we can calculate the incremental earnings that you’d need from the MBA in order to break-even on the investment. Of course, all of these calculations should always be done ex-ante (prior to enrollment) because otherwise, we’re falling prey to the sunk-cost fallacy, and that will only make us regret a decision we’ve already made.

For example, let’s say that your MBA will cost $75,000 up front and that you are currently making $50,000 per year annually at your current job. What incremental salary increase would you need in order to account for the opportunity cost of the MBA?

First, we have to calculate an appropriate discount rate for our money. In this case, we can probably use r_m , the market’s rate of return because if we choose not to put the money towards an MBA, we could instead put it in an Index Fund or another similar investment vehicle, where it would grow at around the market interest rate.

Source: Market-Risk-Premia.com

Based on the July 2018 numbers, the market risk premium is about 5.38%. Notice that we didn’t just use the Implied Market Return of 7.69%, this is because we need to subtract the Risk-free rate r_f in order to account for the incremental risk.

Let’s round down to 5% for simplicity. Assuming we’re starting our MBA in January of 2019 and Finishing in December of 2020 (2 years) with a cash outflow of $37,500 in 2019 and 2020 and sacrificed earnings of $50,000 in each of those years. We can calculate the future value (FV) of that money in 2021 as follows:

Future Value of Annuity Formula
Future Value of an Annuity

Our periodic payment, P , is $87,500, our discount rate,r , is 5% and our number of periods, n , is 2. That leaves us with the following:

FV = \$87,500*[((1+0.05)^2-1)/0.05]  = \$179,375

Assuming we’re able to land a job on day 1 after graduation, how much more do we have to make in our careers to make up for the opportunity cost of the MBA? For that, we can use another annuity formula to calculate the periodic payment required over a given number of years to equal a certain present-value amount.

Annuity payment formula

Let’s say that we will have a 30-year career and that our market risk premium stays the same at 5% (the historical average for Canada is closer to 8%, however, let’s be conservative and stick with 5%). Substituting in these values to our formula with PV = $179,375 r = 5% and n = 30, we find that the payment, P, is:

P = {0.05*\$179,375}/{ 1 - (1+0.05)^{-30}} = \$11,670

So, we need to make an additional $12,000 per year every year for the rest of our careers, because of the MBA, in order to make up for the opportunity cost of the program.

If that seems realistic to you, maybe you should consider an MBA.

Of course, if we’re being really clever, we should probably also include a risk premium for our MBA. There is not a lot of data out there to suggest what the probability of completing an MBA is, but we can assign some probabilities to our equation for reference. Let’s say that there’s a 60% chance that the market will be strong when we complete the MBA and we’re able to find a job that pays $62,000 per year right out of the MBA program. There is also a 20% chance that we’ll make the same amount as we made before the MBA program $50,000 per year, a 10% chance that we’ll make $75,000 per year after the program and a 10% chance that the market for MBAs tanks and we’ll make below $40,000 per year when we graduate.

Expected Value = 0.6 * \$62,000 + 0.2 * \$50,000 + 0.1 * \$75,000 + 0.1 * \$40,000 = \$58,700

How do we make a decision with all these different possible outcomes? Simply multiply the probabilities by the annual salaries and add them together to find the probable result. If these numbers are correct we’re looking at an equivalent salary of $58,700 per year coming out of the MBA program. Of course, these numbers are completely made-up, but if we find numbers like these in our real-world evaluation, the logical decision from a financial perspective would be to reject doing an MBA because the cost is outweighed by the potential gains.

According to PayScale, the average salary in Calgary for an MBA with a finance specialization is $87,500 per year, but the average salary for someone with a bachelor of science degree is over $75,800 per year. Based on these numbers, it might not make sense for someone with a science degree to do an MBA.

Of course, there are other intangible factors that come into play including career preferences, lifestyle, and happiness. These are all important and should definitely be factored into your decision.

Graphs and iPads are an important part of any MBA

Yes, this is a very hard decision to make but can machine learning algorithms help make these decisions easier for us? It should be possible to use machine learning algorithms to predict future earnings potential and even take into account qualitative variables like career preferences and working style to give us a better idea of which choices might be right for us.

It is my goal to understand the capabilities of machine learning models to assist in these types of financial predictions. Hopefully, in the next few weeks, I’ll have an update for you on whether this type of predictive capability exists and if it does, how to access it.

For now, good luck with your decision making! I did an MBA and I don’t regret it at all because it was the right decision for me. My hope is that this article has given you the tools to decide whether the decision might be right for you.