Evolution and Impact of AI

IVR 2.0: The New Trend: From Customer Service Villain to Marketing Superhero

If you still think IVR (Interactive Voice Response) is just an annoying robot designed to keep customers on hold until an agent shows up, I have news for you: welcome to the future!

When used correctly, IVR can be the superhero of customer service, turning every interaction into a smooth, personalized, and even (believe it or not!) pleasant experience. But for that to happen, we need to stop treating it like a never-ending maze and start seeing it as a strategic marketing and relationship channel.

The problem starts with how they’re designed: many companies build their IVR flows with only operations in mind, without involving the marketing team. The result? A system disconnected from the brand identity, filled with endless menus, lacking personalization, and, worst of all, failing to solve the customer's needs.

Imagine calling a bank to purchase insurance. You go through authentication, listen to half a dozen irrelevant options, press buttons like you’re solving a puzzle, and… finally, you reach an agent who asks for EVERYTHING all over again. Sounds familiar? Exactly. This type of experience not only frustrates customers but also directly impacts brand perception.

Here’s where an interesting concept comes in: the Losada Ratio. According to this theory, for every negative experience, three positive interactions are needed to restore balance. Now think about it: how often does a customer leave an automated service frustrated? And more importantly, what is your company doing to turn this around?

The New Game: Smart and Contextual IVRs

Thanks to artificial intelligence and advancements in speech-to-text, voice biometrics, and cognitive assistants, it’s now possible to create an IVR that truly understands customers, anticipates their needs, and enhances the experience from start to finish. Here are some ways to turn your IVR into a competitive advantage:

Using CRM data and interaction history, IVR can tailor the customer journey. If someone frequently calls to renegotiate payments, why not offer this option right away instead of making them navigate an extensive menu?

Example:
[CRM] → [Smart IVR] → [Satisfied Customer]

Forget “Press 1 for this, 2 for that.” With AI-powered speech recognition, customers can simply say what they need and be directed correctly—no buttons required.

Example:
Customer: "I want to talk about my plan."
IVR: "Got it! You’re looking for information about your current plan, right?"

If a customer starts an inquiry via WhatsApp and later calls the service center, IVR should recognize this and continue from where they left off. Nobody likes repeating information!

Example:
[WhatsApp] → [IVR] → [Human Agent]
(without repeating anything!)

Instead of just playing the same old hold music, IVR can suggest relevant products or even allow customers to request a callback when an agent is available.

Example:
[Waiting for an agent…]
IVR: "Would you like a call back as soon as an agent is available? Press 1!"

Forget endless security questions. With voice biometrics, customer identity can be instantly verified, speeding up service.

Example:
IVR: "Your voice has been verified! You’re securely authenticated."

IVR: The New Influencer in Customer Experience

When well-designed, IVR can be a powerful tool for customer loyalty and conversion. Companies already investing in smart automation are seeing significant improvements in customer experience, reduced service time, and, of course, increased sales.

Want an example? Imagine calling an online store and the IVR already knows your purchase history, suggesting a complementary product. Or calling a bank, where the system detects you’ve just received a payment and suggests investment options tailored to your profile.

This isn’t science fiction. This is possible TODAY.

Conclusion: From Annoying IVR to Smart IVR

The future of customer service is no longer about replacing humans but empowering interactions. Modern IVR should be fast, proactive, and personalized, integrating with other channels to deliver a seamless experience.

If your IVR still makes customers feel like they’re in a “Black Mirror” episode, maybe it’s time to rethink your strategy.

So, are you ready for IVR 2.0?

FAQ

IVR 2.0 uses AI, voice biometrics, and real-time data to offer personalized, efficient, and frictionless customer interactions.

By recognizing past interactions, offering contextual responses, and eliminating unnecessary steps, IVR 2.0 prevents repetitive processes and long waiting times.

An intelligent IVR can recommend relevant products, personalize offers, and enhance engagement, turning every call into an opportunity for conversion.

Dialogi AI

Find out how our solutions with empat. AI can revolutionize customer service in your business.

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How Generation Z Is Impacting CX

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20 20 February 2025

How Do Large Language Models (LLMs) Work

How Do Large Language Models (LLMs) Work?

Continuing the topics related to Artificial Intelligence, we will now talk about Large Language Models or LLMs (Large Language Models).

An LLM is created with a very large amount of data and words (billions), so it can predict what the user wants to say and complete sentences, as well as learn in an evolutionary way.

These models are trained with large volumes of data from the internet and learn patterns of how words and phrases are commonly used together. When a new text input is provided, an LLM will try to predict or generate the most likely continuation of that text based on what it learned during training. These models are trained with large amounts of text data to learn patterns and relationships between entities in the language.

They can understand complex textual data, identify entities and relationships between them, and generate new text that is coherent and grammatically accurate.

Although the mathematical principles behind these LLMs may be complex systems, a basic understanding of the architecture used to implement them can help you better grasp the concepts of how they work.

Large-scale models are based on transformer architecture, which builds upon and expands on some techniques that have proven successful in modeling vocabularies to support NLP tasks and, particularly, language generation.

Transformer models are trained with large volumes of text, allowing them to represent semantic relationships between words and use these relationships to determine likely sequences of text that make sense.

An encoder block that creates semantic representations of the training vocabulary.
A decoder block that generates new language sequences.

In practice, specific implementations of the architecture vary: for example, the BERT (Bidirectional Encoder Representation from Transformers) model developed by Google for the search engine support only uses encoder blocks, whereas the GPT (Generative Pre-trained Transformer) model developed by OpenAI uses only the decoder block.

Next, we present some of the most relevant large language models today. These models perform natural language processing and influence the architecture of future models.

According to the ranking of Chatbot Arena as of today, these are the TOP LLMs:

Focuses on constitutional AI, which shapes the outcomes of AI guided by a set of principles that help the AI assistant to be helpful, harmless, and precise. Claude was created by the company Anthropic.

A "generative" model designed to predict the next word in a sequence of text based on the previous sequence. GPT uses a unidirectional approach for training, processing text from left to right or sequentially and learning to generate text based on what it has seen before.

Designed as a "comprehension" model of language, BERT processes text bidirectionally, meaning it learns the context of a word based on all the other words in a sentence. It uses a technique called MLM or Masked Language Model, where some words from the input are hidden or masked. In this environment, the model tries to predict these words based on the context provided by the non-hidden words.

Gemini models are multimodal, meaning they can handle images, audio, and video, in addition to text. Gemini is also integrated into many Google applications and products. The model replaced Palm in the chatbot feed, which was renamed Bard to Gemini after the model change.

It is a family of open-source language models from Google that were trained with the same resources as Gemini. The Gemma models can be run locally on a personal computer.

Large Language Model Meta AI (Llama) is the LLM from Meta released in 2023. Llama was initially launched for approved researchers and developers, but it is now open source.

Although LLMs are a recent phenomenon, their precursors date back decades. Find out how the recent precursor Seq2Seq and the distant precursor ELIZA set the stage for modern LLMs.

What is the future of LLMs?

The introduction of large language models like ChatGPT, Claude, and Llama, which can answer questions and generate text, points to interesting possibilities in the future. Slowly but surely, LLMs are achieving human-like performance. The immediate success of these LLMs shows great interest in robotic-type LLMs that emulate and, in some contexts, outperform the human brain. Below are some thoughts about the future of LLMs:

Greater Capabilities
Audiovisual Training
Workplace Transformation
Conversational A

FAQ

LLMs are trained on massive amounts of text data to predict and generate meaningful sequences. They use transformer architecture, which helps them understand and generate text by learning semantic relationships.

BERT is bidirectional and designed for language comprehension, while GPT is unidirectional and generative, focused on predicting the next word in a sequence.

Popular LLMs include Claude (Anthropic), GPT (OpenAI), BERT (Google), Gemini (Google), and LLAMA (Meta), which are used for tasks like text generation, comprehension, and multimodal processing.

Dialogi AI

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Exploring More About the Types of Artificial Intelligence and How They Work

UX Design: How to Turn a FRUSTRATED User into a FAN

The Importance of Segmentation in Marketing Communication

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19 19 February 2025

Exploring More About the Types of Artificial Intelligence and How They Work

Exploring More About the Types of Artificial Intelligence and How They Work

In this article, we will explore more about the types of Artificial Intelligence (AI) and some real-life examples of their use. To understand the scope and complexity of each of these technological advancements, it’s necessary to use systems that allow them to be grouped according to classification.

I invite you to continue reading to learn more about the types of artificial intelligence and their different classifications.

1. Rule-Based AI

Based on a set of predefined logical rules and formal logic to make decisions and perform specific tasks.

Solve mathematical calculations or logical instructions.
Analyze large sets of fast-moving data to identify patterns and potential fraud.

Examples:
Imagine a chatbot as a digital librarian, which methodically organizes and answers inquiries based on a pre-established script. They operate through a defined set of rules and algorithms and respond to user inputs that match certain triggers or keywords. These chatbots are programmed to handle a variety of expected scenarios. For example, if a customer asks about store hours, the chatbot responds with pre-loaded information. However, if the question deviates from the script, the chatbot may fail to understand or respond accurately.

Benefits

Since the rules are clearly defined, these systems are transparent and easy to understand. This makes it easier for programmers to adjust the system and for users to understand the logic behind specific actions.

They work quickly and effectively since they don’t require a lot of data or complex algorithms to function.

They can be highly accurate as long as the rules are well written.

They can be easily updated with new rules, adapting to new situations.

Disadvantages

Limited learning capacity

These systems cannot learn from mistakes or adapt to new situations.

Difficulty with uncertainty

Lack of clear information can cause errors, as precise data is needed for a correct decision.

High maintenance costs

They require continuous maintenance to keep the rules updated.

Difficulty with complex interactions

They can be limited when dealing with interactions that involve multiple rules or inputs.

2. Machine Learning (ML)

In the previous article, I discussed machine learning. To read it again, visit the link:

3. Deep Learning

This type of learning uses artificial neural networks with multiple layers to learn hierarchical representations of data. It is responsible for driving major advancements in areas like image recognition, natural language processing, and voice recognition.

Examples:
Virtual assistants like Alexa or Siri use deep learning to understand human speech and the language used by people when interacting with them.

Benefits

They handle unstructured data well without requiring extensive data preparation.

Neural networks enable deep learning models to understand complex data relationships.

These models are scalable, able to evolve to handle various tasks.

Disadvantages

High computational cost

Training models requires powerful resources such as GPUs, which can be expensive and time-consuming.

Privacy and data security concerns

The use of large data sets raises privacy concerns.

Limited to training data

They can only make predictions based on the data they were trained on.

Overfitting

When a model specializes too much on training data, leading to poor performance on new data.

4. Natural Language Processing (NLP)

According to IBM’s definition, NLP is the field of computer science that enables computers to understand spoken and written language as humans do.

Morphological: Identifying different words.
Syntactic: Separating sentences and analyzing their structure.
Semantic: Understanding the meaning of individual words and sentences.
Pragmatic: Analyzing the intent behind the text based on its context.

Examples:
Sentiment analysis on social media: NLP systems can identify sentiment in comments about products or services, providing useful information for businesses.

Benefits

Flexibility: Can process a wide variety of languages.
Accuracy: Can achieve high accuracy in tasks like text classification and automatic translation.
Scalability: Can handle large volumes of data.

Disadvantages

Data requirements

Needs large amounts of data for training.

Bias

They may reflect biases present in the training data.

5. Generative AI

Generative AI models are designed to create new content, such as text, audio, images, or videos.
These models work based on machine learning principles, enabling machines to learn from data and then generate new data instances that mimic the properties of the original data.

Generative AI is used to generate design options for graphics and architects, helping them create unique floor plans based on relevant training data.

Benefits

Creativity and innovation: Can create new and original content.

Automation: Can automate content creation, saving time and resources.

Personalization: Can create personalized content based on individual preferences.

Disadvantages

Quality control

The quality of generated content can vary.

Ethical and legal issues

Generative AI can be used to create deepfakes or harmful content.

Dependence

Overreliance on generative AI could lead to a loss of human content creation skills.

AI has the potential to transform organizations. Although AI cannot solve all your organization’s problems, it has the potential to completely change the way business is done, impacting sectors from manufacturing to finance with unprecedented efficiency gains.

FAQ

Rule-Based AI uses predefined logic and rules to make decisions and solve tasks. It is fast, efficient, and easy to understand but lacks learning capabilities.

Deep Learning uses artificial neural networks to process complex data, like speech and images, enabling advancements in virtual assistants and speech recognition.

Generative AI creates new content like text, audio, or images. It offers creativity, automation, and personalization, but may raise ethical concerns.

Dialogi AI

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19 19 February 2025

Multi-Agent Systems: The Future of Collaboration in Artificial Intelligence

Multi-Agent Systems: The Future of Collaboration in Artificial Intelligence

Imagine a soccer team where each player is a specialist: a quick goalkeeper, experienced defenders, and strikers who know exactly how to score goals.

Now, imagine we’re solving complex problems with artificial intelligence instead of playing soccer.

This is how Multi-Agent Systems (MAS) work — each AI agent has a unique specialty, all working together toward a common goal.

Unlike a single, all-knowing AI, MAS is like assembling a “dream team” of specialized agents, each with skills and unique ways of understanding problems. They’re not lone wolves; they’re adaptive teams solving challenges more efficiently and resiliently.

Why Is a Team of Specialists Better Than a Superhero?

Think of a complex programming problem. Is it better to have a generalist AI try to solve everything or a team of specialists focused on different aspects? The answer is clear: specialists solve faster and more effectively. This is the essence of MAS: divide and conquer.

Benefits of Multi-Agent Systems

Specialists working together are faster than one generalist doing it all.

New tasks? Just add a new agent or adjust the existing ones, like a team recruiting players as needed.

If one agent fails, others keep the system stable. It’s like a soccer game where the team continues even if one player is injured.

Each agent has a clear role, so contributions are trackable for better decision-making.

Working with multiple agents allows tackling larger and more complex problems.

MAS in Real World: Where Are They Active?

Multi-Agent Systems are already used across industries:

AI agents optimize energy demand, ensuring stable supply even with renewables.

Autonomous robots with specialized skills work together in rescue missions.

Companies use multiple chatbots to efficiently respond to customer inquiries, directing each to the right “specialist.”

Managing traffic in complex networks, like railways or big cities, with agents communicating to optimize flow.

How Multi-Agent Systems Works

Each agent in a MAS has:

Knowledge Base: Internal data relevant to its task.
Capabilities: Actions related to its specialty.

Agents operate in an environment where they interact to perform tasks. For effective collaboration, they need to:

Interact: Like in a team, communication is essential for coordination.
Organizational Structures: Can be centralized, decentralized, or hierarchical, depending on the best way to organize tasks:
Centralized: A leader agent coordinates others, efficient but vulnerable if the leader fails.
Decentralized: Agents communicate among themselves, creating a resilient but harder-to-coordinate network.
Hierarchical: Roles are defined with a clear command flow.

MAS and Nature

This concept has always existed in the world. Many MAS systems draw inspiration from nature:

Like birds flying in formation, agents synchronize for a common goal.

Agents follow simple rules, creating complex behavior, like ants in a colony.

The Future of Multi-Agent Systems

With Large Language Models (LLMs) advancing, we can expect more sophisticated AI agents capable of tasks we can barely imagine today. The future of MAS lies in optimizing supply chains, personalizing medical treatments, and maybe even uncovering the universe’s mysteries.

Challenges and Considerations

Despite their potential, MAS also pose challenges:

Agent Failures: A malfunctioning agent can affect the whole system, requiring safety mechanisms.
Coordination Complexity: With many agents, keeping them coordinated is challenging.
Unpredictable Behavior: Decentralized networks can lead to conflicts and unpredictable behavior.

Lessons We Can Learn from MAS

Multi-Agent Systems are reshaping AI and teaching us about complex systems in general. Understanding how AI agents collaborate provides valuable lessons in communication, structure, and conflict resolution — applicable in both digital and human realms.
Did you enjoy learning about Multi-Agent Systems?

Leave a comment with your thoughts or experiences! Let’s explore the future of artificial intelligence together.”

FAQ

MAS are AI systems where each agent has a unique specialty, working together towards a common goal, similar to a team of experts collaborating to solve complex problems.

MAS provide efficiency, flexibility, resilience, transparency, and scalability, allowing specialized agents to tackle large and complex problems more effectively.

MAS are applied in smart grids, disaster rescue operations, customer service, and transportation to optimize tasks and improve coordination.

Dialogi AI

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Fighting Against the Clock: How to Win with Technology

UX Design: How to Turn a FRUSTRATED User into a FAN

The Importance of Segmentation in Marketing Communication

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18 18 February 2025

The Evolution of AI, from Your Grandparents’ Time to Today

The Evolution of AI, from Your Grandparents' Time to Today

Did you know that AI (Artificial Intelligence) already existed during your grandmother’s time?
🎙️ Yes, it’s true. Let me tell you more…

In 1942, amidst World War II, the father of computing, Alan Turing, designed the Bombe machine—an electromechanical device capable of decoding Enigma cipher messages faster than any person. The Enigma machine was responsible for sending encrypted messages from the German Air Force.

In a 1950 article, Alan Turing asked: “Can machines think?” His contribution questioned whether something could possess artificial intelligence. This question became the foundation for developing the Turing Test, one way of measuring a machine’s intelligence. However, even today, many experts consider the concept incomplete.

🎬 The movie “The Imitation Game” dives deeper into his story. It's available on Netflix Brazil.

Where Did the Concept of AI Originate?

A man named John McCarthy challenged himself to develop a computer that could pass the Turing Test. In 1956, during the Dartmouth Conference, he used the term “Artificial Intelligence” for the first time, defining it as: “The science and engineering of making intelligent machines.”

McCarthy also created the LISP programming language (List Processing) and contributed to the creation of the Internet. He worked on the book and movie “2001: A Space Odyssey” and inspired the concept behind the movie “Jurassic Park.”
{I told you, he was the guy!}

The concept of Machine Learning (ML) emerged in 1997, marking the second wave of AI. ML is a subset of AI that enables machines to learn from existing data, enriching it for decision-making or predictions.

Practical Example:
Instagram uses behavioral analysis to suggest relevant content for each user, based on likes or visited pages.

There are generally four main types of Machine Learning:

Human intervention teaches the machine using a dataset.

Example: spam detectors.

The machine learns independently, identifying patterns.

Example: anomaly detection.

We have several use cases:

Facial recognition
Sentiment analysis in texts
Medical diagnostics using images
Automated translation
Document classification

Based on trial and error, seeking to maximize rewards.

Example: stock market strategies.

Baseado em tentativa e erro, buscando maximizar recompensas.

Exemplo: estratégias no mercado de ações.

Since 2006, Deep Learning has been a technique within ML that uses deep neural networks and vast amounts of data to create patterns and make predictions. Examples include Siri, Alexa, and Google Assistant.

Finally, we reach Generative AI, capable of creating texts, images, and other content. Examples include ChatGPT (launched in 2022).

Check out Sam Altman (ChatGPT's creator) in an interview with Lex Fridman from MIT, discussing GPT-5 and other topics.

Are There Other Platforms? MANY.

We’ll explore this in another article, but here are links to try them out:

FAQ

The concept of AI began to take shape in 1956, with John McCarthy, who defined it as "the science and ingenuity of creating intelligent machines."

Machine Learning involves teaching machines with data, while Deep Learning uses neural networks and large volumes of data to create patterns and make predictions.

Generative AI can create text, images, and other content, such as ChatGPT, launched in 2022, which can generate intelligent responses in natural language.

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