How AI Chatbots Have Recovered in eCommerce

9 months ago marked one of the biggest transitions in the way humans communicate that will become an irreversible change shortly in every industry. E-commerce is no exception, and the implications of Chat GPT and tools alike pose massive advantages for businesses that can leverage AI effectively.

Like anything new, there has to be the trial and error stage where businesses figure out how the tool fits into their processes. The first issue that immediately stands out with Chatbots is the generic and repetitive responses. If your site implements a Chatbot to manage customer support, how are you going to want the experience to be? For most business owners, the answer is a simple “Unlike anything they’ve ever seen before” which is great but we should also add “And can’t get anywhere else”.

What Happened With Chatbots in eCommerce at The Start (The Epic Fail)

In the early days of eCommerce Chatbots, rather than expediting processes, they ended up causing delays. Chatbots would struggle to locate information, resulting in sluggish responses that left customers feeling more frustrated than if they had waited for a human representative to assist them.

Even when Chatbots managed to provide fast responses, they frequently failed to address the specific questions customers posed. For instance, if a customer inquired about the precise location of their package, the chatbot might respond with a generic message like "Your package is in transit".

In their initial stages, Chatbots were limited in their ability to handle anything beyond basic requests. While they could handle queries like "How do I start a return?" they were incapable of handling more complex requests like "I'd like to check the status of an ongoing return."

The biggest downfall of these early-day Chatbots was their struggles to retain previously gathered information. If a customer was transferred to a human representative, that representative often had none of the information the chatbot had already collected.

Even today, Chatbots are not universally trusted. Under the Bot Disclosure Act implemented in California in July 2019, retailers are required to inform consumers when Chatbots are in use, with non-compliance resulting in fines of up to $2,500 per violation. 

What’s Changed?

We can’t come off talking about this Chatbot dystopia without telling you about the strides the technology has made in recent years. So with that said, here’s a look at what’s been going on: 

From a technological perspective, this is what’s gotten better: 

1. Natural Language Processing (NLP): NLP lets Chatbots understand and interpret human language, which makes interactions feel more natural and meaningful.

2. Machine Learning (ML): ML algorithms let Chatbots remember and learn from past interactions, which over time makes them more efficient. This is essential for personalization and handling any issues brought to light by customers.

3. Chatbot Architectures: The design and development of Chatbots have evolved to include components like user interfaces, NLP engines, and ML algorithms, which make the Chatbots more powerful and enhance their responsiveness.

4. Rule-Based vs. AI-Based Chatbots: Rule-based Chatbots use predefined rules to respond to queries, and AI-based Chatbots leverage NLP and ML to understand and respond to user queries. Match those up against each other, and AI Chatbots are the clear winner. 

5. Best Practices: Developers now follow best practices in chatbot design, focusing on clear purposes, and the user experience, and prioritizing ongoing testing and refinement.

With this part covered, let’s shift to what these technological advancements have translated into:

1. Human-Like Chatbots: Chatbots have become more human-like, thanks to the advancements in Natural Language Processing and machine learning algorithms. This makes interactions with Chatbots more relatable and user-friendly. Recall that in the past, Chatbots often provided generic and robotic responses.

2. Deep Customer Insights: Modern Chatbots are designed to use deep customer insights to inform their responses. This is a fancy way of saying; they can analyze user data and give those personalized/relevant responses that companies want their users to have.

3. Voice Bots: Voice bots (Siri, Alexa, etc.) have obviously become a massive deal since they also give a more natural and intuitive interface for users. Think about booking appointments, ordering food, or making reservations using voice commands. This was a massive improvement over text-only Chatbots.

4. Improved Customer Satisfaction: Chatbots are now designed to create a sense of connection between the customer and the company instead of simply being a means to automate support services. They provide quick, personalized experiences that improve customer satisfaction and loyalty. In the past, as we know, Chatbots often left customers feeling disconnected and dissatisfied.

What a Successful Chatbot Implementation Looks Like

A few good examples of companies leveraging Chatbots effectively include Rawbank, Starbucks, and Lyft. To break down what each of these companies is doing as straightforwardly as possible, we’ll say that effective Chatbots can be recognized under three pillars:

1. How it understands language

2. How it personalizes the experience

3. How it continues to get better

With Rawbank for example, it has over 50 different use cases which is what makes it so well regarded. With this amount of ground covered, it’d mean that there isn’t a whole lot users could throw at the system that it wouldn’t be able to handle. This brings me to the next point; how it personalizes.

We’ll use Starbucks for this one just because it’s super simple. The chatbot can access a customer's order history, it lets them customize things, it gives recommendations, and it’s a barista in your pocket. This is a system that’s going to set the standard for any local coffee shops now and guess what? The companies who leverage it better than others, will get more customers and retain them longer. 

Lastly, Lyft. They recognize that their market is heavily controlled by Uber - which puts a lot of pressure on them when it comes to the customer experience. At first glance, you can see that the Chatbot interface for Lyft closely resembles an iMessage chat which is certainly user-friendly, but how does it stand out? Well, Lyft beat Uber to market. Which has given them time to get some mileage on their Chatbot and optimize the user experience. 

The Takeaway

It’s no surprise that a lot of people’s knowledge and understanding of AI stops at Chat GPT. These Chatbot interfaces are setting a new standard for how people find and interact with information, which is now pouring over into the business world. Want to get behind the shift? Find out if your business is ready for AI today.

Written By Ben Brown

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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

Best Chat GPT Plug-ins For Software Development

We know about the prompts Chat GPT has to offer software developers, which are great starting points— but truly, they are nowhere near the full scope of leveraging streamlined workflows from AI. Since the dawn of its existence, coding has been about 3 things; creativity, problem-solving, and innovation. With artificial intelligence, the horizon for these aspects of coding expands exponentially.

Plug-ins elevate this further, especially when a programmer knows exactly which to use and when. What plug-ins do is leverage AI algorithms to cut down the development process by automating tasks and getting data-driven insights. A typical scenario for development with plug-ins might look like this:

Scenario of Leveraging Plug-ins

A programmer is working on developing a web application for a client. The project involves developing systems for user authentication and authorization, implementing complex business logic, integrating with external APIs, and load balancing. The deadline is also tight. 

To start, they use a plug-in that automates the process of setting up secure user authentication systems, including user registration, login, password encryption, and session management. Once that’s done, they go to an AI-driven tool that will give them 3 things; intelligent suggestions, code templates, and algorithms for handling not just super complex workflows, but also calculations. Still with me? We’re almost there.

As the project involves integration with external APIs, the programmer moves to an AI-based tool that simplifies the process of connecting to external services. The main purpose of this tool is 4 things: automatically generating code for authentication, making API requests, handling responses, and managing errors. By now, communication with external APIs is seamless and they move on to the last phase. 

They need load balancing for performance and scalability. For this reason, they use a tool that does 3 things: monitors server loads, distributes incoming traffic across servers, and optimizes resource allocation. With this, the workload can be distributed which ultimately lets the system handle user traffic without compromising stability. 

Throughout this entire process, the programmer is leveraging a tool that analyzes code and performance metrics. The Chat GPT plug-ins utilize these insights and data from the analysis to generate code snippets and insights. So if you thought plug-ins were limited to providing prompts, think again. With that said, here are some Chat GPT plug-ins that developers need to pay attention to:

Zapier

This is likely the most popular integration tool as it integrates over 5,000 apps with ChatGPT. You can connect apps like Google Sheets, Gmail, and Slack directly to ChatGPT. Users can leverage any of Zapier's 50,000 actions, such as search, update, and write to automate tasks. 

Code Interpreter

Although currently in a closed alpha phase, this plug-in is very promising for the future of software development. It enables the execution of Python code directly within a chat session with ChatGPT. The plugin allows users to upload and download files, make code adjustments, and even receive suggestions and modifications from ChatGPT. 

ChatWithGit

ChatWithGit is a plugin that enables users to search GitHub directly within the ChatGPT interface. It allows users to find relevant code snippets and provides a preview of the code along with a link to the corresponding repository. This feature saves developers time by leveraging existing code resources on GitHub without having to reinvent the wheel.

Visual ChatGPT Studio

Visual ChatGPT Studio is an extension from the Visual Studio Marketplace. It integrates with Visual Studio. Its features include method code autocompletion, adding unit tests, bug detection, method optimization, explanation writing, commenting, providing summaries for C# methods, and users can ask questions (code-related questions) and get answers within the editor.

Prompt Perfect

The Prompt Perfect plug-in helps users create effective prompts by optimizing and rewording them for better results. It’s also not exclusive to ChatGPT. All you do as a user is begin your prompt with “perfect” followed by your request. 

Wolfram Alpha

The Wolfram Alpha plugin addresses ChatGPT's limitations in math. It gives ChatGPT access to computation, math functionalities, curated knowledge, and real-time data through Wolfram Alpha and the Wolfram language. Using this plugin, ChatGPT can give you improved responses and visualizations for math-related queries.

Link Reader

Link Reader enables ChatGPT to extract and analyze information from various types of links, like PDFs, PPTs, Word docs, and all the fun stuff alike. Users then have ChatGPT translate, summarize, interpret, and even analyze the contents of the given link, to get insights far more in-depth than a typical search engine.

Moving Forward

Emad Mostaque's statement that "There are no developers in 5 years" is something companies in the tech space need to really think about. The amount that one person can achieve increases exponentially with AI-powered tools. And trust me, you won’t lose your job to a machine, but the person who knows how to leverage machines will blindside you.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

Introducing Computer Agents

Most people’s knowledge of AI stops at Chat GPT. The “GPT” part of which stands for “Generative Pretrained Transformer”. Didn’t know that? Good, let’s break it down:

Generative: The AI model's ability to generate new content rather than relying solely on pre-existing data

Pretrained: The system has been trained on a large dataset to learn patterns, structures, and relationships in language.

Transformer: The underlying neural network infrastructure that allows the model to process and generate text quickly and efficiently. 

To say the absolute least, OpenAI set a whole new bar and got an absurd head start once they introduced Chat GPT, which has placed OpenAI among the top 20 most visited websites in the world during the 6-month span the platform’s been out. But this success alone won’t sustain them forever, especially since it’s influenced rival companies and researchers who are working tirelessly to catch up by developing their own products.

The Other AI Systems on The Rise

When you give a tool to the public, it’s a certainty that they will do whatever they can to make it as beneficial to them as possible. This mentality has led to the creation of Computer Agents which essentially act as a computer’s personal assistant. In case you’ve never heard of a computer agent, we’ll break the concept down.

The entire premise for computer agents is to do one thing: automate the tasks required to reach goals. This advanced AI infrastructure goes beyond basic copywriting and idea generation. It’s more so tailored to cater to the needs of enterprise businesses.

There are 3 key types of agents: Deliberative, Hybrid, and Reactive. While all are unique in their processes and abilities, they share the common goal which is to complete tasks as quickly and efficiently as possible. But not in the same way that Chat GPT responds to your prompts, computer agents use APIs to work with applications and services to perform tasks on your behalf.

Here is a closer look at each one:

Deliberative Agents: Deliberative agents use advanced planning and decision-making algorithms to perform complex tasks. Ever heard someone say “Use your brain”? Well, these systems do just that. They take their databases of past experiences and use them to analyze problems and make informed choices based on the situation they’re presented with. While they may not have a brain, they generate a plan of action to achieve the intended goal. These helpers are great for tasks that require foresight and optimization.

Reactive Agents: These tools are the complete opposite of what we know about deliberative agents. As the name implies, the system responds to its environment, except without any clear understanding of its purpose. The agent's behaviour and the rules they follow are usually pre-determined and don’t depend on reasoning. It has no learning capacity and instead relies on inputs to trigger pre-programmed responses. For tasks that need quick responses to a predictive environment (like an assembly line), this tool can be a game changer.

Hybrid Agents: With hybrid agents, you get the best of both worlds. These are your self-driving cars, Siri on your iPhone, and even the robots used for manufacturing. These systems combine the strengths of both deliberative and reactive agents to form one process. They can reason and plan like deliberative agents but also react quickly to dynamic environments just like reactive agents. They’re that perfect balance between preplanned actions and on-the-fly adaptation, which makes them great for tasks that require flexibility. 

You might be wondering then, “What’s the point of deliberative and reactive when there’s a hybrid?” Each agent serves a unique purpose, and there are going to be times when a hybrid isn’t as suitable a choice. Here are 4 reasons why: 

1. Complexity: While hybrid agents can handle a wide variety of tasks with the conjoined capabilities of deliberative and reactive agents, the integration does add complexity to the system. In some cases, the simplicity and efficiency of reactive or deliberative agents are simply better for the task which as everyone in IT knows - tasks never need to be more complex. 

2. Application Requirements: Different applications have different requirements. Some tasks prioritize speed and real-time responsiveness, while others require careful planning and decision-making. Having that variety of agent types allows programmers to select the most suitable one based on the application’s specific needs.

3. Specialization: As we said before, reactive agents are best for applications where immediate reactions are crucial, such as emergency systems— especially cybersecurity. Deliberative agents, on the other hand, will cover the big-picture tasks. It’s just like a development team, members have their unique roles and abilities but all contribute to an end goal. 

4. Resource Constraints: Depending on the available computational resources, it may be more practical to use simple reactive or deliberative agents as opposed to a complex hybrid agent. This consideration becomes crucial when dealing with limited processing power or memory constraints in resource-constrained environments.

How it Helps Businesses

Artificial Intelligence, Machine Learning, Autonomous Agents, you name it— it’s cool, but how can people use it to help their business? These systems integrate to fit the custom requirements of your product or service. As the trends suggest, if a company is not trying to automate as much of their workflows as possible, they’re putting themselves at a major competitive disadvantage.

Why would a company not want to move toward automation? Among many reasons, here are the most common:

• A simple lack of awareness/underestimating new technology

• Resistance to change

• Security concerns

• The complexity of the process

• The investment

• Volatile business environment

Most of these concerns are to be expected since the process of digital transformation— especially with AI involved— can be a significant undertaking with no clear guarantees. However, this is what it takes for companies to survive in times of change.

If a business leverages AI, ML, and these autonomous agents with a clear strategy defined and aligns them with their unique goals, they can embrace automation and adapt to the evolving landscape. What we’re getting at here is that leveraging the capabilities of systems like deliberative, hybrid, and reactive agents, such as a GPT, can drive efficiency and innovation, especially in the long term.

Frameworks Behind Computer Agents

Integrating computer agents into a business’s internal system requires a structured approach. Because of this, there are several framework options:

1. Behavior Tree (BT) Framework: The Behavior Tree framework is used for designing reactive agents. It structures an agent's behaviour into a hierarchy of tasks and conditions. The agent then evaluates these tasks and conditions in real-time and makes decisions based on its immediate environment and the rules you’ve given it.

2. Belief-Desire-Intention (BDI) Model: The BDI model is a very popular framework used in designing deliberative agents. It focuses largely on the agent's beliefs about the world, its desires or goals, and its intentions to achieve those goals. This model puts a big emphasis on reasoning, decision-making, and planning capabilities.

3. Sense-Plan-Act (SPA) Architecture: SPA is a popular hybrid architecture used to create highly intelligent agents. Each step is part of a cycle that is sensing the environment, planning its actions, and executing those actions. This is considered the foundation of intelligence that every robot needs.

   The architecture enables agents to combine reactive behaviours with some higher-level deliberative processes. This makes responsiveness happen in real-time through the reactive component, while also providing the ability to reason, plan, and then make strategic decisions using the deliberative component. 

4. Multi-Agent Systems (MAS): Simply put, MAS frameworks involve multiple agents working together to achieve common goals. Using a MAS framework, agents can coordinate their actions and communicate to solve problems that would be impractical for a single agent to handle alone.

The Takeaway

With a solid infrastructure in place, businesses can get the most out of integrating computer agents into their workflows. Whether it's leveraging reactive agents for quick responses, deliberative agents for strategic decision-making, or hybrid agents for the best of both, businesses first need to think about how their goals align with their technology selection. 

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.