Natural Language Processing

03
Nov
Featured Articles  ·  Main Posts
Applying Retrieval Augmented Generation (RAG) in Enterprise Search: First-Hand Insights

In the fast-paced world of business operations, having swift access to accurate and relevant data is a pivotal advantage. As such, enterprise search solutions have emerged as indispensable tools that facilitate the streamlined retrieval of data from diverse sources, whether internal databases or externally facing applications and websites. Today, the advancement of generative AI, especially large language models (LLMs) and Retrieval Augmented Generation (RAG), is revolutionizing the enterprise search paradigm. This article delves deep into the transformative roles of RAG and LLMs in enhancing enterprise search solutions, sharing first-hand experience and insights from our own building journey.

What is an enterprise search solution?

An enterprise search solution is designed to create an intuitive interface for users across roles and teams to conduct advanced, intelligent searches, thus enabling businesses to operate more efficiently and effectively. These solutions enable users to collect and update information from varied data sources, types, and formats. They also facilitate data indexing or archiving and offer intelligent search options such as autocomplete, ‘find similar’, and ‘rank by relevance’. Users can refine their searches using advanced filters for more targeted results. Crucially, these solutions also consider data governance and security, defining different user permissions for information access.

How RAG transforms the landscape of enterprise search

What is Retrieval Augmented Generation (RAG)？

Retrieval Augmented Generation or RAG, refers to a dual-component framework: an information retrieval component, and a large language model (LLM) capable of generating answers in response to user queries.

Introduced by Meta AI researchers, this framework addresses two inherent challenges associated with LLMs: the potential for inaccurate answers, and provision of outdated information. By providing LLMs with credible sources, RAG effectively curbs the hallucination issues of LLMs, enhancing accuracy and trustworthiness of the generated content. It also empowers LLMs to tap into the most recent information via information retrieval, compensating for the static nature of LLMs’ parametric knowledge.

How does Retrieval Augmented Generation work in enterprise search?

RAG takes a user inquiry and retrieves a set of relevant documents from a given source (i.e. company data stored in a vector database). The searched documents, being considered as context, are sent to the LLM together with the original query prompt. The LLM will then generate the answer to address the user inquiry, based on information extracted from the searched documents. 

Enhancing enterprise search: benefits of leveraging LLMs under the RAG framework

Thanks to the advent of the Retrieval Augmented Generation framework, the cutting-edge technology of LLMs can now be harnessed in the realm of enterprise search. By integrating LLMs within the RAG framework, users can interact with their enterprise search tools as they would with a colleague. They get the tailor-made answers specially addressing their questions. 

Precision is paramount in enterprise search solutions. With RAG, we can guide LLMs to retrieve content from the database first, taking both searched information and users’ queries into consideration, and subsequently generate the answers. This effectively prevents LLMs from fabricating plausible-sounding responses and improves AI accuracy. 

Another key benefit is the enhancement of semantic search. It delivers more relevant results than traditional keyword-based searches. Leveraging natural language processing and machine learning techniques, semantic search can comprehend user intent, content, and the context of the search. This makes the search results more meaningful to users.

RAG also expedites knowledge discovery. Rather than presenting searched documents directly to users, LLMs summarize and extract critical information from these documents, creating tailor-made answers to address users’ questions in a matter of seconds. This allows users to quickly access crucial knowledge and insights, enhancing productivity.

Behind the scenes: the creation of WIZ.AI’s enterprise search solution

WIZ.AI is building RAG-powered enterprise search solutions for different clients. The solution we are building is a versatile enterprise copilot, assisting both enterprises’ employees and their customers to retrieve information they need.

Our approach is twofold. Firstly, we develop a comprehensive vector database stocked with the most recent and relevant knowledge from targeted enterprises. Secondly, we fine-tune pre-trained Large Language Models (LLMs), using specific, enterprise-focused knowledge and workflows. This enhances the LLMs’ domain comprehension and cultural alignment within individual organizations.

Throughout this journey of exploration, our product and R&D experts have gained invaluable insights and lessons, enriching our first-hand experience in the field.

The art of data pre-processing

There is diverse and valuable data to be incorporated into the database for LLMs’ information retrieval. This includes public data such as a company’s FAQs on their website, as well as internal data like customer service recordings, documentation, meeting notes, emails, and more.

To enhance LLMs’ response accuracy and user experience, we preprocess available data before incorporating them into the database. One strategy that is showing remarkable effectiveness is to divide lengthy documents into multiple question-answer pairs. These Q&A pairs are not randomly generated, rather they are tailor-made for specific business scenarios. For queries that require precise responses, we label the answers and guide LLMs to adhere to standard answers without generating their own interpretations.

### Long Q&A Example:

#### Question: 
What should I consider when choosing a savings account at a bank?

#### Answer: 
When selecting a savings account, it's important to consider several factors to ensure it meets your financial needs. Firstly, look at the interest rate offered, as this determines how much your savings will grow over time. A higher interest rate will yield more returns on your deposits. Secondly, be aware of any fees that may be associated with the account, such as monthly maintenance fees, minimum balance fees, or transaction fees, as these can significantly reduce your savings. Thirdly, check the accessibility of your funds. Some savings accounts offer ATM cards, online banking, and mobile banking apps for convenient access, while others may have more restrictive withdrawal policies. Additionally, consider the minimum balance requirements and whether you can maintain them to avoid fees. Lastly, ensure that the bank is insured by a reputable government agency, which in the United States would be the Federal Deposit Insurance Corporation (FDIC), providing protection for your funds up to a certain limit. By carefully evaluating these aspects, you can choose a savings account that best suits your saving goals and financial situation.

### Smaller Q&A Examples:

#### Q1: 
What is the importance of the interest rate on a savings account?

#### A1: 
The interest rate is crucial as it dictates the growth of your savings. A higher rate compounds over time, increasing your savings more significantly than a lower rate.

#### Q2: 
How do fees impact a savings account?

#### A2: 
Fees can diminish your savings, especially if they're frequent or large. Monthly maintenance, transaction, and minimum balance fees are common and should be considered when choosing an account.

#### Q3: 
What should I know about accessing my funds in a savings account?

#### A3: 
Accessibility varies; some accounts offer ATM cards and online banking for easy access, while others may limit withdrawals. Consider how often you'll need to access your funds before choosing an account.

#### Q4: 
Why is it important to consider minimum balance requirements for a savings account?

#### A4: 
Minimum balance requirements are important because failing to maintain them can result in fees or lower interest rates, which could negate the benefits of saving.

#### Q5: 
How does FDIC insurance affect my savings account?

#### A5: 
FDIC insurance protects your savings up to a certain limit if the bank fails, ensuring that your money is safe. It's a critical factor for the security of your funds in a savings account.

Fine-tuning LLMs: tailoring large language models for bespoke services

During the fine-tuning process, we equip LLMs with enterprise-specific knowledge and workflows. An essential part of this process involves a thorough examination of our collected data to discover any underlying workflows. For instance, our data analysis revealed that human agents from a Singapore government entity typically start their responses by verifying applicants’ ages. This stems from the entity’s distinct procedures for adults (aged 16 and older) and children (under 16 years of age). Unearthing such hidden knowledge and workflows during the fine-tuning process is vital. We ensure that LLMs are trained to adopt these necessary behaviors before they start generating responses.

Capability boundaries of LLMs

Additionally, we’ve made some noteworthy discoveries regarding limitations and constraints during our development of Retrieval Augmented Generation (RAG) applications in enterprise search. One such observation relates to the limited capacity of LLMs in understanding and executing complex prompts. For example, we’ve tested the below prompt:

# Global Instructions
...

# Context

## Context 1

### Question: What are the benefits of opening a savings account with a bank?
### Answer: Opening a savings account with a bank offers several benefits including earning interest on your deposits, which helps grow your savings over time. It also provides a safe place to keep your money, as savings accounts are typically insured by a government agency like the FDIC in the United States up to a certain amount. Additionally, having a savings account can encourage financial discipline through regular deposits and can offer conveniences like online banking, mobile deposits, and easy access to funds through ATMs or debit cards.
### Instruction: Please provide a detailed comparison of the interest rates, fees, and services associated with savings accounts from three different banks.

## Context 2
...

Here, the question, answer, and instruction related to Context 1 are all stored within the Vector Database(VDB). These elements are then retrieved and amalgamated into the prompt. It’s our expectation that the LLM applies the instructions of Context 1 in its inference process, when addressing issues related to this context.

However, disappointingly, only GPT-4 can consistently execute these instructions as anticipated. Other models, including GPT-3.5 and all variants of LLama 2, failed to do so consistently. Our analysis is that such prompts necessitate the LLM to possess advanced abstract capabilities. These capabilities should enable LLMs to translate complex prompt formats into actionable business logic and execute the derived logic effectively. Unfortunately, these abstract abilities are challenging to acquire through fine-tuning alone, especially on smaller scale models.

Nonetheless, our curiosity drives us to continuously explore new possibilities. One such avenue under preliminary consideration is formalization. This could involve storing instructions within the context using a VDB-based format to ensure these instructions are correctly applied in the prompt.

Additional resources

Article: Retrieval Augmented Generation(RAG)
Video: What is Retrieval-Augmented Generation (RAG)?
Article: How generative AI is transforming enterprise search solutions
Article: Enterprise Search powered by LLM

Wondering how Retrieval Augmented Generation and LLMs can supercharge your enterprise search? Speak to one of our specialists today.
Contact us

06
Oct
Featured Articles  ·  Main Posts  ·  Talkbot Basics  ·  Voice AI Technology
Voicebots Vs LLM agents: Navigating the Future of AI for Your Business

Artificial Intelligence (AI) is revolutionizing how businesses operate. For companies, the path is full of potential yet accompanied by complexities. In this article, we would like to guide you through this intricate AI landscape, by unraveling two key AI technologies, the established voicebot and the rising LLM agents.

What is a voicebot?

When you receive a phone call reminding you about an upcoming appointment or pending repayment, you could already be talking to a voicebot. Voicebots utilize Automatic Speech Recognition (ASR), Natural Language Processing (NLP) and Text to Speech (TTS) technologies. They can really understand how people talk and stimulate human-like interactions based on customer reactions.

A common use case for voicebots is 24/7 customer service. They can handle common inquiries from inbound calls at any time, so human agents can focus on more complex issues.

Voicebot also excels at high volume outbound calls for reminder, promotion and leads filtering. They are effective to help businesses to boost their customer and revenue growth. Voicebot can make customer outreach calls on a massive scale to help remind all relevant customers, or quickly filter and qualify prospects in the entire CRM contact list. With voicebots handling initial outreach, human agents can follow up on qualified, sales-ready leads.

What is an LLM agent

As we introduced in our previous insight article, an LLM agent is a combination of a large language model with three other key components for planning, memory and tool use. We can also refer to an LLM agent as a LLM powered autonomous agent system, in which the LLM functions as the brain.

LLMs have unveiled their powerful Natural Language Understanding (NLU) capabilities. But LLMs alone are like brains in vats, and they can not realize a wide array of real-world applications. To unlock the full potential of LLMs, we need to build systems that can acquire and apply knowledge to solve practical problems. That’s where LLM powered autonomous agent system or LLM agents comes in. LLM agents can perceive, reason and act like our copilot, beyond just conversation capabilities. In theory, when giving LLM agents any general objective and a robust enough agent system, they can plan for task completion step by step. They are also capable of calling available agents or tools for help when necessary.

Comparing voicebots and LLM agents

Voicebots adhere to rigid pre-determined dialogue flows. They only understand the expressions and words they are trained for. For instance, a seemingly simple use case like transferring a hospital patient’s call from the main line to different department extensions could require a matrix of dialogue flow. This could take weeks for conversation designers to develop.

In contrast, LLM agents possess a new edge in their potent Natural Language Understanding (NLU) capabilities. They can understand diverse human expressions, comprehend in context during real-time conversations. If your query includes something LLM agents don’t understand, they can continue the dialogue until they achieved clarity. This closely resembles how a human would ask follow-up questions to understand better.

Furthermore, while voicebots can perform what they are trained for efficiently, they lack autonomous planning or decision-making capabilities. Nor can they adapt to dynamic external environments.

In comparison, LLM agents are capable of self-learning new knowledge across languages and knowledge domains. They possess both short-term memory from in-context learning, as well as long-term memory from search and retrieval. They also can perform reasoning and planning, breaking down large and complex tasks into smaller, manageable steps.

How would a refund be processed differently

Let’s consider a customer refund request scenario to understand how a voicebot and an LLM agent would act differently.

A voicebot can understand all expressions and keywords meaning refund if trained for them. It can read out the refund process from a FAQ list when a refund is requested. However, a voicebot won’t be able to handle queries beyond the FAQ list.

On the other hand, an LLM agent can call different tools and leverage industry best practices to resolve the issues. Moreover, it is capable of continuous learning and optimization, acquiring new knowledge and best practices overtime. When a client requests a refund, a LLM agent can ask for the purchase ID, then call relevant agents or tools (i.e. logistics tracking sheet) to check delivery status. They can even soothe the client, suggesting waiting for a couple of days before processing the refund. Meanwhile, LLM agents would analyze order time, and assess whether the no-fault refund period has expired, or check the customer’s credibility status to decide if a refund is eligible.

Beyond customer engagement

Looking ahead, we expect LLM agents to go far beyond inbound and outbound customer engagement. They will fully integrate with the enterprise software system, and become the core command center of the entire system. By then, LLMs will be industry experts and decision makers. They would understand domain-specific enterprise knowledge and dynamically call different tools to automate task completions. The agents or tools being called by LLMs can vary, including common office systems such as CRM, ERP, OA and PMS etc. We envision LLM agents to be versatile copilots, managing workflows alongside employees and assisting customers.


Choosing the right AI solution for you

Despite the promising future of LLM powered autonomous agent systems or LLM agents, their development is still in early stages. LLM agents can alleviate the burden of training and setting up intricate dialogue flows. They have the capability to learn and optimize with minimal human intervention. However, the training of such LLM agents demands significant computational resources and GPU capacity. Therefore, LLM agents might not always be the most cost-effective solution for your business.

Voicebots, conversely, serve as robust business copilots, and are capable of driving impressive results. They enhance customer satisfaction by providing round-the-clock services, augment payment rates through automated reminder calls at scale, and efficiently qualify leads for human agents to follow up, among other tasks.

Consequently, we recommend businesses leverage voicebots for relatively straightforward applications like sending reminder calls, executing preliminary lead filtering, or addressing routine inquiries. Businesses should consider tapping into the power of LLM agents when they necessitate complex interactions, dynamic reasoning, industry-specific knowledge, or integration with a variety of enterprise systems.

The world of artificial intelligence offers a broad spectrum of solutions for businesses. Whether it’s a voicebot or an LLM agent, the choice depends on your specific needs and resources. The key to success lies in understanding these AI systems, and choosing the right AI assistant to navigate your business towards a prosperous future.


Reference

Zhiheng Xi et al. (2023). The Rise and Potential of Large Language Model Based Agents: A Survey. Retrieved from: https://github.com/WooooDyy/LLM-Agent-Paper-List


28
Sep
Featured Articles  ·  Main Posts  ·  Voice AI Technology
How LLM Agents are Unlocking New Possibilities

Recent advances in large language models (LLMs) are renovating the development of intelligent AI agents with powerful natural language understanding capabilities. This has sparked rising interest in LLM powered autonomous agent systems, or LLM agents.

According to Insignia Ventures Partners, the market for autonomous AI agents is estimated to grow at a CAGR of 43% (from USD 5 billion in 2023 to USD 29 billion by 2028), catalyzed by the democratization of LLMs. In this article, let’s explore what new possibilities LLM agents have unlocked and what are some feasible applications we can envision in the future.

*NOTE: For the simplicity of the article, we treat “LLM powered autonomous agent systems” and “LLM agents” as two interchangeable concepts. They belong to a bigger concept called AI agents. AI agents refer to Artificial Intelligence systems that can perceive environments and take autonomous actions to achieve goals.

What is LLM powered autonomous agent system

To answer the question, we can imagine LLM is the brain, and it can call different agents or tools, which are its hands and feet, to automatically perform a diverse range of tasks.

As Open AI’s Lilian Weng describes, a LLM powered autonomous agent system is comprised of an LLM functioning as the brain, and three other crucial components for planning, memory and tool use.

Planning
LLM agents can mimic human thinking patterns and proactively plan for task execution. During planning, LLM agents can break down large and complex tasks into smaller, manageable steps. They are also capable of self-reflecting and learning from past actions and mistakes, so as to optimize for future steps and improve final results.

Memory
This encompasses both short-term memory from in-context learning, as well as long-term memory from search and retrieval. Memory helps LLM agents to learn between context in real-time and recall information over extended timeframes.

Tool use
LLM agents can proactively call external APIs or vector stores for additional information, based on dynamic decision-making. By calling different tools and using semantic search and vector databases, LLMs agents can provide precise answers according to search results. This also avoids common LLM issues such as inaccuracy and hallucinations.

LLM agents: combining conversation capabilities with actions

LLMs have brought a new edge through their Natural Language Understanding (NLU) capabilities. This makes real human machine interaction a reality, and in natural languages! With LLMs, we can now communicate with machines just as we would with another human.

But LLMs alone can not realize a wide array of real-world applications. To unlock the full potential of LLMs, we need to build systems that can acquire and apply knowledge to solve practical problems. That’s where LLM powered autonomous agent system comes in. Without agents or tools, LLMs are like brains in vats-impressive but isolated from the real world.

Practical applications of LLM agents

Integrating LLMs into autonomous agent systems unlocks greater possibilities. Let’s look at two examples of LLM agents in action:

  1. Changing a client’s flight booking

Here the LLM agent must first understand what information is needed, like the airline’s change policy and available alternative flights (planning). It can then call tools like documentation APIs and flight databases to gather the necessary details (tool use).

  1. Transferring a hospital patient’s call

The LLM agent needs to comprehend what department the patient requested (short-term memory: in-context understanding). It can then check office hours, on-call numbers, and other relevant information (planning and tool use). This allows dynamically connecting the patient to the right destination, i.e. department extension during office hours, or the doctor on call during non-office hours.

The road ahead: from Software 2.0 to Enterprise Software 2.0

Intelligent LLM agents are taking a huge leap towards an era known as “Software 2.0”. This is a concept proposed by former Tesla director of AI, Andrej Karpathy. In Software 1.0, human engineers write codes and programs to complete tasks. These codes and programs are like individual dots with some desirable behavior.

While Software 2.0 refers to a new generation of software that leverages machine learning algorithms and neural networks to build intelligent, self-learning systems. Software 2.0 can analyze data, identify patterns, and constantly optimize its own code without human intervention.

Applying the above concept into today’s enterprise software system, we see AI capabilities are currently serving as supporting tools to perform isolated tasks. These AI capabilities can include recommendation algorithms, Natural Language Processing (NLP), Text to Speech (TTS), Automatic Speech Recognition (ASR) and so on.

Looking ahead, we expect to enter an era we call “Enterprise Software 2.0”. In the era of Enterprise Software 2.0, LLM agents will become the core command center of the entire enterprise software system. In the system, LLMs are industry experts and decision makers. They would understand domain-specific enterprise knowledge and dynamically call different tools to automate task completions. The ecosystem is with close-loop communication and operation. The agents or tools being called by LLMs can vary, including common office systems such as CRM, ERP, OA and PMS etc.

By then, LLM agents will have the ability to assist in solving intricate issues across diverse industries, and self-learn from their own experience. We envision LLM agents to be versatile copilots, managing workflows alongside employees and assisting customers. AI accessibility will also be further democratized by LLM powered autonomous agent system, enabling anyone to copilot with a multitude of LLM agents to manage as many tasks as possible, and amplify productivity.

The road ahead remains long, but the disruptive potential is immense. LLM powered autonomous agent system has displayed transformative impact in dynamic, unstructured environments. Although limitations exist, steady progress in machine learning algorithms and design patterns will empower LLM agents to tackle ever more complex challenges. The future of AI has never looked more promising!


Reference


Lilian Weng. (2023). LLM Powered Autonomous Agents. Retrieved from: https://lilianweng.github.io/posts/2023-06-23-agent/
Wechat official accout. (2023). AI Agents大爆发：软件2.0雏形初现，OpenAI的下一步. (The rise of AI agents: first glimps of software 2.0 and what’s next for OpenAI). Retrieved from: https://mp.weixin.qq.com/s/Jb8HBbaKYXXxTSQOBsP5Wg
Andrej Karpathy. (2017). Software 2.0. Retrieved from: https://karpathy.medium.com/software-2-0-a64152b37c35

 

At WIZ.AI, we are committed to democratize AI access for everyone. This belief guides our daily operations as we build inclusive AI solutions that benefit diverse demographics. A recent example is our groundbreaking launch of LLM for Bahasa Indonesia, which marks a major milestone in encouraging the international AI community to develop more LLMs for the ASEAN region.
Follow our LinkedIn page to stay informed of our latest news and product updates!

23
Dec
Talkbot Basics
Driving Viral Growth with Andrew Chen
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

 

Did you ever wish you could bottle the magic that helped companies such as Uber, Tinder and Dropbox transform into the Unicorns they are today?

 
Last Wednesday (15th December 2021), in celebration of Andrew Chen’s book launch in Asia, we were honoured to host him during our webinar on “Driving Viral Growth” as our guest speaker. Over a thousand registrants signed up within the first week and we ever very quickly oversubscribed.
 
Andrew Chen is the legendary Silicon Valley Growth Hacker, and General Partner at Andreessen Horowitz. Through his methodology, he has managed to grow companies the likes of Uber, Tinder and Dropbox to Unicorn status.
 
During the webinar, Andrew had an in-depth discussion on growth methodology with Jennifer Zhang, CEO and Co-founder of WIZ.AI. They discussed the importance of not only a Minimum Viable Product (MVP) but also the necessity of having a Minimum Viable Community (MVC), because even the most brilliant product in the world is useless without users and a strong community.
 
Most importantly, Andrew shared the real-world use cases of how Tinder, Slack and Clubhouse employed the Atomic Network leverage their community of users to grow at scale and create incredible retention rates past even the D +30 mark.
 

On this, Jennifer drew parallels to her experience on how WIZ.AI’s Talkbot solutions are helping our clients scale up customer engagements to help companies overcome the cold start problem while still ensuring cost efficiency. Part of Wiz’s vision is to grow with growing companies and we assist hundreds of companies

Jennifer noted that customers were growing numb to in-app notifications and other text-based engagement methods, providing diminishing returns and that traditional voice engagements provide the best Customer Satisfaction Score CSAT however were costly to set up and maintain.
By combining the cost efficiency of human-like conversational voice AI to provide the voice engagements that customers respond to, together with an omnichannel approach to customer engagement. WIZ.AI were able to help companies from regional banks to fast growing Unicorn start-ups to grow their network at scale.
 
WIZ.AI is committed and will continue to engage with the Growth Hacking ecosystem in SEA and contribute to the regional digital transformation.
 
If you have missed the session, fret not! A short summary of the key takeaways from Andrew was prepared to help you to scale the network effect.
  • A common trait of many successful Silicon Valley companies is that they are very good at connecting people together, to create communities of users that in turn invite more users to join.
  • Viral growth is not just a campaign, it is a journey of engagement and community that invites users to share their experiences with other users.
  • User cases such as Google Docs, Dropbox, Uber, and Tinder demonstrated network effect is equally valuable in both B2B and B2C industries. However, B2B is more about targeted engagements Company by Company and the growing communities within the Company, while B2C usually grows city by city.
  • One of the key approaches to tackling the Cold Start Problem is getting enough users to use the product at the same time. You will need to build not just the MVP (Minimum Viable Product) but the MVC (Minimum Viable Community) for your start-up to take off.
  • When a certain size of network is built, there are couples of metrics to monitor the ongoing viral growth, including yearly & monthly growth rate, retention rate and the number of connections, etc.
  • Growth hackers should be able to not only look at the micro but also the macro view on their strategy.

22
Nov
Featured Articles  ·  Main Posts
Tokio Marine Partners WIZ.AI to Automate Customer Service Via “Conversational AI Talkbot”

JAKARTA, Indonesia, Nov. 18, 2021 /PRNewswire/ — In its recent regional digitalisation transformation initiative, PT Asuransi Tokio Marine Indonesia (“TMI”) partnered with WIZ.AI, the ASEAN Voice AI leader, to launch its conversational voice AI Talkbot.

As one of the largest general insurers in Indonesia, TMI has a strong commitment to put customer satisfaction as main priority, guided by its core mission “To be a Good Company”. Combining the efficiency of self-serve solutions and the warmth of human engagements, the Talkbots have improved TMI’s Customer Satisfaction Score across the board.

Mr. Sancoyo Setiabudi, President Director of TMI, commented: “The digital age arrives with a set of big communication challenges for conventional communication strategies. It is important for us to find innovative, efficient and interactive ways to handle mass communication with our customers and partners. I believe this implementation will help us to provide more services to existing customers and proactively approach potential customers.”

The Talkbot is indistinguishable from a human call centre agent, with over 95% of callers not being able to tell the difference. It incorporates AI techniques, such as pauses, intent recognition and varying pitch and tone to interact with the customer in a natural human-like manner. It is also backed by their proprietary Natural Language Processing and Natural Language Understanding technologies, which enable the Talkbot to understand and speak several different ASEAN languages.

WIZ.AI Talkbots work closely with TMI’s human agents and only calls that have been identified to require more of a human touch will be channelled to a human agent. This cultivates a more agile contact centre, which boost TMI’s service capability and labour efficiency.

“I believe customer service should be accessible, human-like, warm and most importantly hyper-personalized. By working closely with TMI, we have pushed the boundaries of human-AI collaboration to provide a better customer experience,” said Jennifer Zhang, CEO and Founder of WIZ.AI.

About Tokio Marine Group

Tokio Marine was established in the year 1879 as the first insurance company in Japan and has grown over the decades, now offering an extensive selection of General and Life insurance products and solutions in 46 countries and regions worldwide.

About WIZ.AI

WIZ.AI is a fast-growing start-up specializing in providing humanistic AI conversational solutions to transform customer service. The company is headquartered in Singapore and with market presence in Indonesia, Philippines and China.

Source: Tokio Marine Asia


11
Nov
Pengenalan Talkbot
Pentingnya Revolusi Kecerdasan Buatan Bagi Bisnis
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Inventory Management Talkbot

Kecerdasan Buatan atau secara global dikenal dengan nama Artificial Intelligence (AI) adalah teknologi utama dibalik revolusi industri 4.0 yang telah membawa perubahan besar. AI biasanya didefinisikan sebagai studi tentang sistem pintar yang dapat melakukan aktivitas atau menyelesaikan tugas yang membutuhkan tingkat kepintaran layaknya manusia. Sama seperti revolusi industri sebelumnya, AI juga mempunyai dampak yang sangat signifikan pada produktivitas industry. 

Revolusi AI telah mengubah cara pengumpulan dan pemrosesan data secara fundamental, selain juga mentransformasi aspek bisnis pada berbagai industri. Secara umum, sistem AI didukung oleh 3 hal penting yakni; Domain Knowledge, Penghasilan Data, dan Pembelajaran Mesin (Machine Learning). Domain Knowledge mengacu pada pemahaman dan keahlian untuk mengetahui mengapa dan bagaimana kita harus melakukan suatu pekerjaan. Aspek data mengarah kepada prose persiapan database yang dibutuhkan yang akan diintegrasikan dengan algoritma pembelajaran. Machine learning dibutuhkan untuk mendeteksi pola dalam data, kemudian memprediksi tugas yang harus dilakukan dan melakukan tugas tersebut tanpa dilakukan pemrograman manual atau dengan kata lain dilakukan secara otomatis. 

Tiga Aspek Utama Teknologi AI
Kemampuan Pengambilan Keputusan Yang Cerdas

Simulasi kecerdasan manusia yang dilakukan oleh mesin dapat memberikan solusi cepat bagi kendala-kendala yang dihadapi manusia. AI dilengkapi dengan teknologi machine learning dan sistem analisis data yang canggih, yang artinya AI dapat belajar dan mendapatkan pengetahuan mendalam selama sistem diberikan data baru. Dengan input yang tepat, AI dapat membuat keputusan yang akurat dalam waktu singkat. Selain itu, aspek kepintaran AI meningkatkan produktivitas sistem dan mengurangi ketergantungan kepada bantuan manusia, yang membuat AI menjadi alat yang sangat berguna untuk dimiliki. 

 

Intensionalitas

Intensionalitas seringkali dianggap sebagai aspek teknikal dan ontologis dari program komputer yang dihasilkan dari algoritma dan pengetahuan teknis. Aspek ini dapat diinterpretasikan sebagai kemampuan AI untuk menghasilkan pengetahuan mendalam dari informasi yang didapatkan secara real time dan memberikan respons yang sama dengan respons kreator dan pengguna sistem AI tersebut ketika berinteraksi dengan informasi yang dimaksud. Respons yang diberikan biasanya merefleksikan konteks sosial yang dimiliki oleh kreator dan pengguna sistem. Selain itu, dengan perkembangan pengisian data, kapasitas penyimpanan, kecepatan pemrosesan, dan teknik analisis, AI menjadi lebih mahir merespon isu-isu dengan kecanggihan yang terus meningkat. Hal inilah yang membedakan AI dengan fungsi fundamental AI yang hanya berguna untuk tugas-tugas rutin yang sudah ditetapkan sebelumnya. 

 

Adaptabilitas dan Prediksi 

Teknologi Machine Learning memfasilitasi AI untuk menemukan pola dalam data yang sebelumnya telah diprogram, yang memperbolehkan AI untuk membuat perubahan secara otomatis sesuai dengan situasi dan kondisi. Aspek adaptabilitas secara mendalam memperkuat kemampuan AI untuk melakukan prediksi dan mengambil keputusan.  Salah satu contoh yang sering ditemukan adalah pada fitur penulisan pintar pada Gmail, yang memberikan saran kata atau kalimat yang dipersonalisasi saat pengguna menulis sebuah kalimat. Hal ini menggambarkan bagaimana AI beradaptasi dengan dengan pola penulisan seseorang dan memberikan saran yang sesuai. 

 

Penggunaan AI Dalam Bisnis

Tidak bisa dipungkiri lagi, revolusi kecerdasan buatan telah memberikan dampak besar dalam operasional bisnis. Praktik paling umum yang sering ditemui adalah otomasi dari pekerjaan repetitif yang membutuhkan sedikit input dari manusia. Tapi, dengan peningkatan algoritma yang konsisten, Teknologi AI tidak lagi hanya terbatas untuk meningkatkan produktivitas,  tapi juga menjadi alat untuk berinteraksi dengan pelanggan, memberikan pelayanan terbaik, hingga menjadi katalis inovasi-inovasi baru. Berikut beberapa contoh skenario yang mendemonstrasikan bagaimana AI mentransformasi aktivitas bisnis. 

 

Contact Centers

Contact Center telah mengalami perubahan signifikan seiring berjalannya waktu dan telah menjadi lebih canggih berkat otomasi berbasis AI. Kita dapat melihat kemajuan teknologi contact center dalam Chatbot dan Talkbot yang memperbolehkan perusahaan untuk siap selama 24 jam dan memberikan respon cepat dalam interaksi pelanggan yang dapat dilakukan dalam skala luas. Perubahan strategi interaksi pelanggan berbasis AI dapat dengan signifikan meningkatkan kapasitas layanan dan mengurangi kegagalan layanan yang biasanya terjadi karena kelalaian agen atau emotional labour. Agen contact center membutuhkan pelatihan pelayanan pelanggan secara terus-menerus untuk menjaga kualitas layanan, tapi Talkbot berbasis AI dapat belajar dari setiap interaksi pelanggan dan terus memperbaiki sistemnya untuk memberikan pelayanan terbaik seiring berjalannya waktu. Hal ini juga mengurangi biaya operasional yang berhubungan dengan evaluasi pekerjaan dan pelatihan contact center. 

Lebih dari itu, sistem AI di contact center seperti Talkbot memiliki kelebihan yaitu dapat disesuaikan untuk memberikan pengalaman pelanggan yang lebih personal melalui dialog dengan tujuan spesifik yang berdasarkan data pelanggan dan target bisnis. Dengan kata lain, Talkbot dapat dengan mudah melakukan up selling maupun cross selling ketika diberikan informasi pelanggan yang cukup, dan perencanaan bisnis yang matang. Talkbot juga mampu melakukan analisis sentimen dari percakapan untuk mengetahui informasi pelanggan yang lebih dalam melalui panggilan telepon, dan hal ini dapat dicapai Talkbot tanpa melakukan pelatihan pelayanan pelanggan. Selain itu, dibandingkan dengan contact center tradisional, sistem AI menunjukan kemampuan yang lebih superior dalam hal pengumpulan informasi yang setelah itu digunakan untuk membuat laporan dengan cara yang lebih pintar dan dengan informasi mendalam yang lebih baik. 

 

 E-commerce

Di masa sekarang pasar e-commerce dipenuhi oleh berbagai pemain dan sangat kompetitif. Perusahaan E-commerce terbaik harus bergantung pada teknologi AI untuk lebih baik memahami pelanggan mereka dan memberikan pelayanan terbaik agar mereka dapat tetap kompetitif dan dapat tetap meraup keuntungan. Fitur rekomendasi produk merupakan salah satu aplikasi penggunaan AI yang umum ditemukan pada industri e-commerce. Fitur ini merupakan aplikasi algoritma AI yang digunakan untuk memetakan preferensi pelanggan berdasarkan transaksi yang dilakukan, pencarian, dan kebiasaan konsumsi. Informasi yang dikumpulkan memperbolehkan perusahaan e-commerce untuk melakukan personalisasi rekomendasi produk untuk setiap pelanggan. Di satu sisi hal ini dapat memperkuat pengalaman belanja dan bahkan meningkatkan penjualan. Tapi, jika digunakan terlalu sering bersamaan dengan strategi marketing yang agresif efek sebaliknya mungkin akan terjadi. Selain fungsi rekomendasi produk, bisnis e-commerce juga sebaiknya menggunakan teknologi AI untuk kegiatan pelayanan pelanggan yang dapat dilakukan melalui chatbot atau talkbot untuk berinteraksi dengan pelanggan, melakukan manajemen stok lewat perkiraan permintaan, atau promosi produk. 

 

Logistics and supply chain

Penggunaan kecerdasan buatan atau AI dan machine learning telah secara fundamental mengubah manajemen supply chain dan menghadirkan optimasi yang berkaitan dengan manajemen yang akurat, produktivitas tinggi, biaya operasional yang rendah, dan pengiriman cepat. Sebagai contoh, dengan kemampuan untuk mengolah big data, teknologi AI dapat digunakan untuk otomasi alur kerja manajemen stok. Barang dapat dibungkus dan disortir dengan rapi dalam jumlah banyak, yang dapat secara signifikan mengurangi waktu pemrosesan dan meminimalisasi kesalahan manusia atau human error. Selain itu, Sistem AI juga dapat memperkirakan permintaan pasar berdasarkan sejarah pasar dan pembelian, yang informasinya dapat digunakan untuk memprediksi penjualan di masa mendatang, dan dapat membantu alokasi sumber daya. Hebatnya lagi, algoritma AI sekarang juga digunakan untuk mengoptimasi rute pengiriman barang, dimana beberapa sistem yang paling terdepan bahkan mampu memperhitungkan kondisi lampu lalu-lintas di rute yang akan dilewati. 

Secara keseluruhan, di era informasi dan data ini, potensi penggunaan AI dalam bisnis menjadi salah satu hal yang sangat penting. Otomasi aspek bisnis dapat mengurangi beban produktivitas dan ketergantungan pada tenaga manusia. Di saat bersamaan otomasi sistem bisnis juga mampu meningkatkan efisiensi biaya operasional. Adanya teknologi Machine Learning juga memperbolehkan perusahaan untuk mulai menggunakan pendekatan yang lebih cerdas dan secara berkelanjutan membawa perubahan bagi aktivitas bisnis. Bisnis harus mempersiapkan diri menghadapi gelombang revolusi AI, sehingga mereka dapat mencapai aktivitas operasional bisnis yang optimum. 


10
Nov
Talkbot Basics
What Is The Artificial Intelligence Revolution And Why Does It Matter To Your Business?
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Inventory Management Talkbot

Artificial Intelligence or AI is the technology behind the fourth industrial revolution that has brought great changes all around the world. It is usually defined as the study of intelligent systems that could execute tasks and activities that would require human level intelligence. Similar to the past three industrial revolutions, AI is leaving an incredible impact on productivity. 

Artificial Intelligence (AI) Revolution

The AI revolution has fundamentally changed the ways people collect and process data as well as transformed business operations across different industries. In general, AI systems are supported by three major aspects which are:  domain knowledge, data generation, and machine learning. Domain knowledge denotes the understanding and expertise of the real life scenario on why and how we need to engineer a task. The data aspect refers to the process of preparing databases required to feed on to the learning algorithms. Lastly, machine learning detects the patterns from the training data, predicts and performs tasks without being manually or explicitly programmed.  

Three key attributes of AI technology.
Intelligent Decision Making 

The simulation of human intelligence by machines can infer a fast solution for the problems that are often faced by humanity. AI is backed by advanced data analytics and machine learning, which means AI can learn and gain new insights as it keeps feeding on new data. With proper input, AI could come up with prompt and accurate decisions. In addition to that, the intelligence attribute of AI promotes productivity and reduces dependency on human support which makes AI highly autonomous and a convenient tool to have. 

Intentionality

Intentionality is often deemed as the technical and ontological attributes of computer programs that derived from the algorithms and knowledge engineering. This attribute can be interpreted as AI’s capability of delivering insights from the real time information and reacting in the way similar to its creators’ and users’. The responses usually strongly reflect the social context that creator and users are in. Additionally, with development of data ingestion, storage capacity, processing speed and analytic techniques, AI gets more capable of responding to the issues with increasing sophistication. This very much differentiates AI with the fundamental function of machines that merely carry out predetermined routines.

Adaptability and prediction

Machine learning facilitates AI to discover the pattern of the data that were previously programmed, which enables AI’s capability of making its own change as circumstances change. The attribute of adaptability profoundly enhances AI’s prediction and decision making. One of the commonly seen examples is Gmail’s Smart Compose feature, which offers the use of  personalised suggestions as users typing a sentence. It illustrates how AI adapts to one’s personal writing pattern and delivers appropriate suggestions.

AI in the Business 

Undoubtedly, the artificial intelligence revolutions had profoundly impacted the way businesses operate. The most common practises are the automation of repetitive tasks that require less human input. However, with the consistent improvement of algorithms, AI technology is no longer only limited to the capability of expanding productivity, but also becomes a necessary tool in engaging customers, providing service excellence, and driving innovation. Here are several industrial scenarios to demonstrate how AI transformed the nature and scope of business activities.

 

Contact Center

The contact center has evolved significantly over the years and has become more sophisticated thanks to the use of AI Automation.  We can see technological advancement of contact centers in the form of Chatbots and Talkbots that enables 24/7 availability and instant response for consumer engagement at scale. Changing the strategies to engage customers with AI based automation vastly boost service capability and reduce service failures that are usually caused by under-performing agents or emotional labour. While human agents require frequent and regular customer service training to maintain the service quality, AI Talkbot learns from every customer interaction and keeps improving to provide excellent service over time. This very much reduces labour cost associated with performance evaluation and contact center training. 

Furthermore, AI systems in contact centers such as Talkbot have the capability to be customized to deliver a more personal experience through goal driven dialogues based on the customer data and business metric. In other words, Talkbots can easily do upselling and cross selling if they are given sufficient information about the customers and the business plan. Even without any customer care training, Talkbots can conduct sentiment analysis from the conversation and unlock the hidden customer data in customer voice calls. This, in turn, provides great insights for future planning. Also, compared to the traditional contact center, AI systems show stronger capabilities in collecting information from each call which are used to generate the report in a more intelligent manner and with better insights. 

 

 E-commerce

Nowadays the e-commerce market is highly saturated and competitive. Top e- commerce companies heavily rely on AI technology to better understand their customers and to give their customers better service in order to remain competitive and profitable. Intelligent product recommendation is one of the typical applications of AI in the ecommerce industry. This is a real-time application of an AI algorithm that attempts to figure out customers’ preference based on their previous purchases, researches, and consumption habits. The collected insights enable e-commerce companies to personalize product recommendations for different online shoppers. To a certain extent, it enhances the shopping experience and potentially boosts sales. However, if the e-commerces overuses intelligent product recommendation and adopts an aggressive marketing strategy, the reverse effect might happen. Beyond the function of personalization, e-commerce businesses also leverage AI technology to support customer service through chatbots and talkbots to assist them with customer care, inventory management via demand forecasting, or product promotion. 

 

 Logistics and Supply Chain

 The use of artificial intelligence and machine learning has fundamentally transformed supply chain management and delivered strong optimization of capabilities associated with accurate management, high productivity, low operating cost and quick delivery.  For example, with the capability of handling big data, the AI technology could be used to automates the workflow of inventory management. Parcels could be packed and sorted in a seamless process at large scale, which would largely reduce processing time and minimize human error. Also, the AI system can forecast market demand from the market and purchase histories, facilitating the prediction of the future sales and providing information to support resource allocation. Moreover, AI algorithms are  also being used to optimize the shipping and delivery route, with some of the most advanced ones even involving the prediction and management of traffic lights.

Overall, in the information and data driven era, the potential of AI is tremendous. Business process automation could reduce stress on internal productivity and decrease reliance on human support while at the same time increase operational cost efficiency. Machine learning enables the company to delve into a more intelligent approach and continually drives the evolution business model. Companies should prepare themselves for the AI revolution wave, so they can leverage on the technology to achieve the optimal operational excellence.


02
Nov
Pengenalan Talkbot
Fintech: Pemanfaatan AI dalam Industri peer-to-peer lending
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

pinjol 

Peer-to-peer lending atau biasa juga dikenal dengan p2p, pinjaman online, atau pinjol sedang marak negara-negara Asia Tenggara. Kawasan yang disebut ASEAN ini merupakan rumah bagi lebih dari 650 juta jiwa yang sebagian besar tidak memiliki akses perbankan maupun layanan finansial, yang membuat pinjol menjadi alternatif finansial terutama di negara-negara seperti Indonesia, Malaysia, Vietnam dan Filipina. Mengingat hal ini, dapat dipastikan pertumbuhan industri p2p lending akan lebih besar lagi dalam waktu dekat. Tapi, pertumbuhan besar industri p2p lending juga berarti lebih banyak hambatan dan tantangan yang harus dilewati oleh perusahaan pinjol seperti peraturan pemerintah, peningkatan biaya operasional, peningkatan kebutuhan layanan pelanggan, hingga masalah kecil seperti lead generation atau aktifitas penawaran produk yang terdengar mudah tapi membutuhkan sumber daya perusahaan seperti waktu, biaya, dan usaha dalam jumlah besar. 

Memahami hambatan dan tantangan yang akan dihadapi hanya merupakan langkah awal bagi perusahaan pinjol untuk mempersiapkan diri menghadapi pertumbuhan industri lending  lending di masa depan. Selain itu, perusahaan pinjol juga harus mempersiapkan solusi bagi masalah-masalah yang akan datang. Salah satu solusi yang bisa dimanfaatkan perusahaan pinjol adalah integrasi teknologi Kecerdasan Buatan atau AI (Artificial Intelligence) pada berbagai aspek bisnis. Berikut beberapa cara AI dapat digunakan perusahaan pinjol untuk menghadapi pertumbuhan pesat industri p2p lending:

1. Manajemen Big Data

Big Data telah merevolusioner pertambahan nilai untuk berbagai industri termasuk p2p lending. Dengan banyaknya data yang dihasilkan, perusahaan yang tidak memiliki akses ke sistem AI untuk memproses data akan kewalahan menghadapi pesatnya pertumbuhan pasar dan industri. Dengan menggunakan AI untuk memproses data, perusahaan dapat dengan mudah mengumpulkan dan menganalisa informasi pelanggan seperti kebiasaan pelanggan, hal yang disukai dan tidak disukai, aktivitas pelanggan, hingga preferensi pelanggan. Informasi pelanggan merupakan instrumen yang sangat berguna untuk membantu perusahaan p2p meningkatkan aspek bisnis seperti; penjaminan emisi yang lebih akurat, penilaian calon penerima pinjaman untuk memberikan kredit yang lebih akurat dan wajar, serta pengambilan keputusan lebih cepat dan akurat yang berdasarkan data. Selain itu, dengan teknologi machine learning, sistem AI akan terus berkembang dan menjadi lebih baik dengan berjalanya waktu, yang artinya sistem ini akan terus mampu mengimbangi pertumbuhan pesat industri p2p lending. 

2. Otomasi Bisnis

Salah satu kesulitan terbesar dalam bisnis adalah sumber daya dan usaha yang dibutuhkan untuk melakukan pekerjaan yang bersifat repetitif dan monoton. Bagi perusahaan pinjol, hal ini menjadi semakin buruk karena perusahaan pinjol memiliki banyak sekali prosedur dan proses verifikasi. Untungnya, otomasi sistem berbasis AI mampu dengan mudah melakukan pekerjaan-pekerjaan repetitif, yang dilakukan secara efisien dan efektif. Lebih dari itu, hasil pekerjaan sistem AI akan selalu memiliki output terbaik karena mampu beroperasi dengan best practice secara konsisten. 

Dengan melakukan otomasi pekerjaan, prosedur, dan verifikasi perusahaan dapat menghemat sumber daya perusahaan yang nantinya dapat digunakan untuk aspek-aspek bisnis yang lebih strategis. Contohnya, dengan menggunakan Talkbot WIZ AI untuk melakukan penagihan dan pengingat, staf collection dapat melakukan pekerjaan lanjutan seperti melakukan retensi pelanggan, memberikan support dalam proses pelunasan, hingga melakukan analisa data pelanggan secara mendalam untuk keperluan penagihan. 

3. Lead Generation & Penjangkauan Pelanggan

Teknologi AI tidak hanya berguna untuk mengotomasi pekerjaan manual. Sistem AI yang canggih bahkan dapat menjadi alat untuk menjangkau dan berkomunikasi dengan pelanggan. Dengan teknologi seperti Kecerdasan Buatan Berbasis Suara Untuk Percakapan (Conversational Voice AI), perusahaan pinjol dapat mengotomasi call center mereka untuk menjangkau pelanggan dalam skala luas dan dengan biaya murah. Dengan mengotomasi aktivitas lead generation dan penjangkauan pelanggan melalui sistem AI seperti Talkbot WIZ AI, perusahaan dapat mengalihkan usaha dan sumber daya call center untuk pekerjaan yang lebih penting seperti melakukan penjualan atau menangani pelanggan bernilai tinggi daripada melakukan panggilan telepon hanya untuk mengetahui informasi pelanggan, atau menjawab pertanyaan yang semuanya dapat dengan mudah jika dilakukan secara otomatis. 

Keuntungan lain dari mengintegrasikan Voice AI pada call center adalah konsistensi. Dalam aktivitas lead generation, ini artinya perusahaan dapat secara konsisten menghasilkan lead berkualitas dalam jumlah banyak. Untuk penjangkauan pelanggan, Talkbot Voice AI dapat membantu perusahaan menjangkau pelanggan dan secara konsisten memberikan mereka pengalaman dan layanan pelanggan terbaik, sehingga dapat perusahaan dapat menjaga loyalitas pelanggan dan potensi bisnis di masa mendatang. 

4. Mitigasi Risiko

Seperti yang sudah disebutkan sebelumnya, Dengan adanya teknologi AI yang melakukan pengolahan data secara cepat perusahaan dapat melakukan perencanaan strategis, termasuk bagaimana caranya untuk menangani potensi risiko. Tidak hanya itu, dengan otomasi berbasis AI perusahaan juga memiliki instrumen untuk mengatasi masalah yang dihadapi. 

Dalam industri p2p, pemberi dan penerima pinjaman membutuhkan kanal yang dapat diandalkan untuk berinteraksi dengan perusahaan pinjol untuk berbagai kebutuhan seperti mendapatkan informasi mendalam, atau mengajukan komplain yang biasanya dilakukan melalui panggilan telepon. Sewaktu-waktu, call center perusahaan mungkin saja dibanjiri panggilan telepon yang membuat perusahaan tidak memiliki pilihan selain meningkatkan kapasitas call center. Sayangnya, merekrut dan melatih agen call center membutuhkan banyak waktu. Parahnya lagi, setelah kondisi krisis tersebut lewat, perusahaan harus menangani kelebihan tenaga kerja yang menguras pengeluaran perusahaan untuk periode waktu tertentu. Disisi lain, dengan sistem otomatis berbasis AI seperti Talkbot WIZ.AI, perusahaan dapat dengan fleksibel menambahkan dan mengurangi kapasitas call center sesuai dengan kebutuhan. 

5. Meningkatkan Efisiensi dan Mengurangi Biaya Operasional

Di seluruh industri ekspektasi yang dimiliki perusahaan terhadap integrasi teknologi AI dalam bisnis tidak terlepas dari efisiensi dan pertumbuhan, dan yang terpenting penghematan biaya. Untungnya, seluruh poin yang telah disebutkan sebelumnya menuju kepada efisiensi bisnis dan penghematan biaya. Dengan mengotomasi sebagian besar aspek bisnis, perusahaan dapat merampingkan operasional tanpa harus mengorbankan produktivitas. Selain itu, dengan sistem AI, perusahaan memiliki data yang lebih akurat yang dapat diakses secara real-time, yang dapat membantu perusahaan untuk melakukan perkiraan bisnis, anggaran, perencanaan strategis yang dapat meningkatkan efisiensi seluruh aspek bisnis.

Keuntungan dari mengintegrasikan teknologi AI dalam bisnis tidak lagi dapat dipungkiri. Hanya masalah waktu hingga otomasi berbasis AI diintegrasikan pada seluruh industri untuk berbagai aspek bisnis. Peer-to-peer lending merupakan salah satu industri yang beruntung karena memiliki kesempatan untuk mengintegrasikan teknologi AI untuk bisnis mereka sejak dini. Dengan begitu, ketika sistem AI menjadi lebih maju, perusahaan early adopter akan lebih siap dan lebih kompetitif karena sudah mengenal dan memahami teknologi AI, serta ke mana dan bagaimana teknologi AI akan berkembang sehingga perusahaan dapat memanfaatkan teknologi tersebut untuk kepentingan bisnis mereka.


02
Nov
Talkbot Basics
Fintech: Utilizing AI to assist the growth of Peer-to-peer Lending
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

pinjol

Fintech peer-to-peer lending (also known as p2p or p2pl) is all the hype in South East Asia right now.  The region is populated by more than 650 million people who mostly do not have access to proper banking or financial service, making peer-to-peer lending a welcomed financial safety net especially in countries such as; Indonesia, Singapore, Malaysia, Vietnam, and the Philippines . With that in mind, it could be assured that the market growth of peer-to-peer lending will just grow bigger from here. However, those rapid growth will be faced by similarly rapid increase of challenges such as regulation from the governments, increased costs to handle huge amount of customers, increased needs of proper customer engagements, and even small challenges such generating and engaging leads on daily basis which sounds simple but requires a lot of company resources in term of time, costs, and pure efforts. 

Understanding the challenges might be the first step to prepare for the incoming problems in the future. However, companies should also prepare the solution for these future problems. One solution that is available now is the utilization of AI for multiple purposes in the business process. Here are several ways AI could be useful in keeping up with the rapid growth of peer-to-peer lending industry:

1. Big Data Management

Big data has revolutionised value generation for many industries including peer-to-peer lending. With the amount of data that is generated right now, companies without access to AI systems to process data will struggle to keep up with rapid development in the market and the industry. By using AI to process data, companies now could easily collect and map out consumer information such as their habits, likes and dislikes, activities, and personal preferences in a short period of time. This information is a disruptive tool that allows p2p companies to elevate their business processes such as supporting more accurate underwriting decisions, better assessment of prospective borrowers to provide accurate and more fair credit pricing, and also faster and more accurate decision making process based on data. In addition to that, with Machine Learning Technology the AI will improve overtime, which means it would be able to keep up with the rapid growth of the p2p industry.  

2. Business Automation

One of the pain points of business is the resource and effort it takes to finish repetitive and mundane tasks. For p2p companies it is even worse since they have to deal with endless procedures and verification processes. Fortunately, AI automation is more than capable of dealing with all of these repetitive tasks with ease, efficiently and effectively. Even better, they are often resulting in great output since AI systems are capable of consistently operating with best practice . 

With most tasks, procedures, and verification being automated it frees up company’s resources to be focused on strategically valuable tasks. For example, by using WIZ AI Talkbots to automate collection and collection reminders, collectors could redirect their efforts to  focus more on follow-up tasks and customer retention. 

3. Lead Generation & Customers Outreach

AI is not only useful for manual tasks automation. Advanced AI systems could even be used as a tool to reach and engage customers. Through technology such as Conversational Voice AI, p2p companies could automate their call centers to engage customers at scale and with lower costs. By automating lead generation and customer outreach with AI powered systems such as WIZ.AI Talkbots, companies could also redirect the efforts of their call center agents to deal with more important tasks such as closing deals or taking care of high valued customers instead of making calls to collect customers’ information, or answering FAQ which could easily be done automatically. 

Another benefit of integrating Voice AI systems in call centers is the consistency that comes with it. For lead generation activity it means the company could consistently generate a huge amount of high quality leads. As for Customer Outreach, it means that Voice AI Talkbots will help companies to engage more customers and consistently give them better customer service and customer experience, thus ensuring customer loyalty and the future business potential.

4. Risk Mitigation

As mentioned before, with AI taking care of data processing it helps companies to better plan the next strategic moves, including how to deal with potential risks. Not only that, with AI automation companies would have a tool to deal with unexpected situations. 

In the p2p industry, lenders and borrowers always need a reliable way to contact the companies either to find information or file complaints which are usually done through phone calls. In unexpected circumstances, the call center might be flooded with calls and the company will not have any choice but to bolster up the call center capacity. However, training new call center agents takes too much time. Even worse, most of the time after the crisis has passed, companies still have to take care of additional manpower which would be costly for an extended period of time. On the other hand, with AI based automated systems such as WIZ.AI Talkbots, companies could flexibly increase and decrease the number of bots according to the current needs. 

5. Increase Efficiency and Reduce Cost

All in all, in any Industry the expectation of integrating AI to the business process would be to increase efficiency and growth, as well as reducing cost. Luckily, based on all the points that were mentioned before, all of them would lead to business efficiency and cost reduction. By automating most business processes, companies could streamline many operations without sacrificing productivity. Also with AI systems companies would have access to more accurate data that is available real-time, which help companies to make more accurate forecasts, budgeting, and strategic planning to increase the efficiency in every aspect of the business. 

The benefits of integrating AI to business is undeniable. It is only a matter of time before AI automation is integrated in every industry for various business aspects. Peer-to-peer lending companies are privileged enough to be able integrate AI in their business early on. Therefore, as the system gets more advanced, early adopters will be more prepared and have sharper competitive edges in terms of familiarity and understanding of AI technology, also where and how the AI technology will develop and how to utilize the technology for the benefit of their business.


22
Oct
Talkbot Basics
10 ways WIZ Talkbots increase call center efficiency
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Call center’s efficiency is strongly tied with its capability and cost. Most enterprises struggle to stay flexible and be able to maintain a consistent high-quality service due to the volatile nature of contact center. Fortunately, WIZ AI offers the cutting-edge solution to break the industry bottlenecks and provide solutions to deal with changes in demand, high staff turnover, poor integration, and other problems that might occur. To better understand how WIZ.AI provides help to all those problems, here are 10 specific ways how WIZ Talkbots tackle the prominent challenges that are faced by traditional contact center and help them to achieve customer service excellence.

 

  1. AI Based Automation

When it comes to customer service, most customers prefer to talk to a real agent compared to an automated system. Talking to the bot can sometimes make them feel uneasy, or even unappreciated. That is why, automating the call center service could often place companies in an awkward position. On one hand, it could significantly cut the operational cost. On the other hand, the company has to sacrifice the service quality and might result in customers being unhappy. With this in mind, WIZ.AI develops Talkbots as a top-grade solution powered by Hyper-realistic Voice Dialogue technology, which enables the Talkbots to speak in a Natural Human-Like voice and support conversations with localized accent such as standard English, Singlish, Mandarin Chinese, Bahasa Indonesia, along with several other ASEAN language and dialect. However, what distinct WIZ.AI Talkbots with other automated systems is its capability to handle interruption, recognize intention, and follow that up with appropriate clarification and chasing statements, just human agents. WIZ.AI Talkbots sound so natural that 95% of users are not even aware that they are talking to a bot. In conclusion, automating call center with Talkbots could help companies lower the operational costs, while staying customer centric.

 

  1. Best Practice Preservation

Attempting to provide the highest standard of customer service, WIZ.AI Talkbots is trained based on successful conversations of the best call center agents. Then, the Talkbots will automatically streamline the dialogue and adopt the standardized workflow approach to meet the business objectives in different kinds of scenarios. 

Customer service training used to be one of the key measures for call center to minimize service inconsistencies and failures.  However, with the technologies developed by WIZ.AI’s, companies no longer need to rely on regular customer service training programs to maintain the best front line service quality. This also means the elimination of the training curve for new agents.

 

  1.     One Call Solution

Another competitive advantage of WIZ.AI’s dialogue design and management could be attributed to its powerful knowledge base. Unlike human agents that sometimes need to follow up on the unsolved problem in the previous calls, WIZ.AI’s Talkbots are capable of handling a wide range of frequently asked questions and routine tasks. If the Talkbot is unable to follow up on customer’s inquiries, or if the matter requires hands on human help, Talkbot will automatically redirect the call to the appropriate human agent. With this foolproof system, customers’ issue can be resolved efficiently and effectively in one call.

 

  1. Reduce “Dead Air” on Calls

Human agents are the spearhead of companies for customer reach out.  However, even though trained by the best customer service training companies, human agents cannot always deliver a flawless customer experience. Sometimes there are human factors that prevent agents from doing their best. One of the problems that mostly occur because of this, is Dead Air on calls. Dead air happens when neither the agents nor the customers have anything to say in the call. This long pause and silence during the call is the typical human factor that ends up lengthens call time, and worse it could give customers bad experience which in the end will reduce customer confidence. 

There are several reasons why Dead Air occurs, for example personal habits of the agents, fresh agents that have no experience, or even when customers asked about matters that agents are not familiar with. Whatever the reason behind it, customers tend to interpret the long pause or silence as agents’ incompetence in solving the problem they are facing. Consequently, customers were left with negative impressions and service experience. That is why, WIZ.AI Talkbots were developed with technologies that could guarantee immediate response to customers inquiries in 0.5 second. This could vastly reduce the potential dead air on calls and gives the customer more confidence in companies’ customer service.

 

  1. Shorter Wait Time

When experiencing an unexpectedly high volume surge, the company has no choice but to put the customers on hold for a long period of time. To solve this problem,  WIZ.AI’s Voice AI is the right solution due to our ability to increase call center capability in a short amount of time and at a low cost. Instead of sourcing a larger call center, installing more equipment, hiring more agents, and providing long and dragging agents training, the company can simply deploy more Voice Talkbots to cater a surge in call during critical situations. The operation can quickly be scaled from one Talkbots to a hundred Talkbots in an instant.  With WIZ.AI’s technology, the company will become more prepared when facing unforeseen large increases in calls; and the customers will not have to wait and be left unattended for a long period of time.

 

  1. Eliminate Repetitive Query

Beyond providing consistent customer service, WIZ.AI technology helps businesses to draw information through a comprehensive and user friendly dashboard. The system collects, transcribes, and analyses data from every single call engaged, providing deep insights about the company’s performance and customers’ preference. For instance, the most frequently asked questions potentially reveal that the relevant information about market response to a product that is offered to the customers, or the information about the most commonly required assistance potentially indicates an awkward existing current service’s function. With clear and concise data, the company then can act based on the data and improve the relevant aspects accordingly. After improvements are made the call center will no longer have to deal with those similar queries and instead could focus on more important tasks.

 
  1.     High Quality Lead Identification

Beside the ability to spot the service gap in real time, WIZ.AI’s intelligent customer data report is also useful in identifying the high value customer. Leveraging on the customers’ preference and behavior that are reflected on detailed call data, the company could tailor a detailed telemarketing training for the Talkbots and then automate the out-bound service calls. Armed with the effective lead identification tool and outbound automation, the call center further boosts its operational effectiveness and efficiency.

 

  1. Robust AI Evolution

In the traditional call center operation, management rely on regular and redundant training to maintain service quality; yet the benefits of customer service training do not reflect immediately. Compared to that, WIZ.AI’s Talkbots are a more preferable solution. Powered by the Automatic Speech Recognition function, the Talkbots conducts speech tests during every customer interaction; and its overall recognition accuracy improves as a result. Meanwhile, the human dialogue engineers, who play a similar role as the call center trainer, conduct ongoing evaluation and training to improve the Talkbots’s capability as well. If there is any new skill required to support the operation, the Talkbots can learn and deliver the mature & data-proven scenario experience immediately while simultaneously improving as the system gathers more data. 

9.     Omnichannel integration

Switching from one to another database or service platform is time consuming which could drag down and ruin the efficiency of business operation.  That is why Wiz AI technology is available for Omnichannel integration. For example, when supported by the integrated system, WIZ.AI’s Talkbots could access the customers’ information via the integrated CRM system to identify the high value customers and perform an outbound call for new product promotion. After the conversation with the customer, WIZ.AI’s Talkbots activated the integrated SMS and sent them the information in detail. By achieving a well integrated ecosystem of existing platforms, WIZ.AI’s Talkbots are able to deliver a quick response and service; at the same time create customers a sense of feeling that they’re in the right hands.

 

  1. Workforce optimization and cost reduction

With the Talkbots helps to handle the routine issues, the call center agents are able to spend more time to service the high value customer. In addition, without relying on human support at a massive scale, the contact center is able to cut costs in terms of hardware expense, recruitment, call center courses designing, contact center training, agents retaining, and so on. Most importantly, the customer service excellence that is backed by Talkbot helps the company reduce financial loss associated with service failure compensation and customer loss.


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