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The global healthcare chatbots market accounted for $116.9 million in 2018 and is expected to reach a whopping $345.3 million by 2026, registering a CAGR of 14.5% from 2019 to 2026.

Take a moment. Let that sink in.

Over the last couple of years, especially since the onset of the COVID-19 pandemic, the demand for chatbots in healthcare has grown exponentially.

And why not.

A couple of years back, no one could have even fathomed the extent to which chatbots could be leveraged. Such applications would be across industries, not just healthcare.

But, once the pandemic hit, the healthcare industry was utterly chaotic. As if the massive spike in patient intake and overworked health practitioners were not enough, healthcare professionals were battling with yet another critical aspect. Patient anxiety.

Patient anxiety automatically translated into a need to provide instantaneous and accurate information to patients and intelligent chatbots played a key role in managing patient queries, providing timely information, and keeping panicked patients at bay. Soon enough, organizations like WHO and CDC started adopting conversational AI-powered chatbots to provide curated information to a wide audience with ease.

It is safe to say that as we seem to reach the end of the tunnel with the COVID-19 pandemic, chatbots are here to stay, and they play an essential role when envisioning the future of healthcare.

So then this brings us to the question. How exactly are chatbots being leveraged in the healthcare industry today? What are the applications and use cases of chatbots in the healthcare industry?

Let’s dive right in.

Understanding the use cases of chatbots in the healthcare industry

1. Enhance patient engagement

Patient engagement is a tricky concept.

For the uninitiated, patient engagement simply means that the healthcare system enables patients to take basic healthcare into their own hands. It involves a constant flow of information from the practitioner’s side and from the patient’s side, it involves timely check-ins and incorporating healthy habits.

It is evident that patient engagement thrives on two-way communication.

Earlier, this involved folks calling hospitals and clinics, which was fine. But, ever since the pandemic hit, a larger number of people now understand the importance of such practices and this means that healthcare institutions are now dealing with higher call volumes than ever before.

This is precisely where chatbots come in. Healthcare practices can equip their chatbots to take care of basic queries, collect patient information, and provide health-related information whenever needed.

Here’s an example

Livi, a conversational AI-powered chatbot implemented by UCHealth, has been helping patients pay better attention to their health. This involves timely interventions from their healthcare provider. The oday Livi is a key tool for patient engagement at UCHealth. Livi can provide patients with information specific to them, help them find their test results. use case for Livi started with something as simple as answering simple questions. T interact with their doctors through messages. She is an integral part of the patient journey at UCHealth, with a sharp focus on enabling a smooth and seamless patient experience. It could also help patients

2. Symptom assessment before in-person appointments

You are a healthcare provider. Every day, you have thousands of patients walking in with different symptoms. Your doctors are exhausted, patients are tired of waiting, and you are at the end of your tether trying to find a solution.

Now, let’s reimagine the situation with a healthcare chatbot in place.

You discover that you can implement and train a chatbot so that once a patient enters all of his symptoms. The bot can analyze them against certain parameters and provide a diagnosis and information on what to do next.

This reduces the burden on hospitals and clinics since it brings down the number of patients that come in with symptoms that are not urgent and allows practitioners to focus on patients that are in need of critical care.

Another advantage is that the chatbot has already collected all required data and symptoms before the patient’s visit. equipping doctors to go through their appointments quicker and more efficiently. Not only does this help health practitioners, but it also alerts patients in case of serious medical conditions.

Here’s an example

Symptomate is a multi-language chatbot that can assess symptoms and instruct patients about the next steps. The workflow is quite simple. You need to enter your symptoms, followed by answering some simple questions. This completes your assessment. You will receive a detailed report, complete with possible causes, options for the next steps, and suggested lab tests.

3. Scheduling appointments with ease

One of the most prevalent uses of chatbots in healthcare is to book and schedule appointments.

Implementing a chatbot for appointment scheduling removes the monotony of filling out dozens of forms and eases the entire process of bookings. They can provide information on aspects like doctor availability and booking slots and match patients with the right physicians and specialists.

In addition, using chatbots for appointment scheduling reduces the need for healthcare staff to attend to these trivial tasks. By automating the entire process of booking, healthcare practices can save time and have their staff focus on more complex tasks.

4. Maintaining patient records and enabling online consultations.

AI chatbots in the healthcare sector can be leveraged to collect, store, and maintain patient data. This can be recalled whenever necessary to help healthcare practitioners keep track of patient health, and understand a patient’s medical history, prescriptions, tests ordered, and so much more.

This increases the efficiency of doctors and diagnosticians and allows them to offer high-quality care at all times.

Case in point, Navia Life Care uses an AI-enabled voice assistant for its doctors. It is HIPAA compliant and can collect and maintain patient medical records with utmost privacy and security. Doctors simply have to pull up these records with a few clicks, and they have the entire patient history mapped out in front of them.

The chatbot can collect patients’ phone numbers and even enable patients to get video consultations in cases where they cannot travel to their nearest healthcare provider. Both practitioners as well as patients, can highly benefit from this implementation.

5. Appointment reminders and other critical notifications

Chatbots can be trained to send out appointment reminders and notifications, such as medicine alerts. Advanced chatbots can also track various health parameters and alert patients in case immediate medical intervention is required. This is, again, another critical use case for chatbots in healthcare.

Here’s an example

Take Florence, a “virtual” nurse, as an example. She can remind patients to take their medicines on time, a feature that is quite useful to the elderly. She can also track your body weight, mood, and other indicators to ensure you are healthy and fit. Florence can continually learn new things and is quite helpful in telling more about a disease. It can also simply locate the nearest pharmacy or doctor.

Advantages of Healthcare Chatbots

Healthcare chatbots are transforming modern medicine as we know it, from round-the-clock availability to bridging the gap between doctors and patients regardless of patient volumes.

Here are some detailed advantages of healthcare chatbots:

1. Continuous availability

Since chatbots are programs, they can be accessible to patients around the clock. Patients might need help to identify symptoms, schedule critical appointments, and so on.

No matter the task, medical chatbots can help patients with the help they need.

2. Instant access to critical information

Time is an essential factor in any medical emergency or healthcare situation. This is where chatbots can provide instant information when every second counts. When a patient checks into a hospital with a time-sensitive ailment, the chatbot can offer information about the relevant doctor, the medical condition and history, and so on.

3. Data collection through patient engagement

As medical chatbots interact with patients regularly on websites or applications it can pick up a significant amount of user preferences. Such patient preferences can help the chatbot and in turn, the hospital staff personalize patient interactions. Through patient preferences, the hospital staff can engage their patients with empathy and build a rapport that will help in the long run.

4. Handling high patient volumes with ease

Chatbots in healthcare are not bound by patient volumes and can attend to multiple patients simultaneously without compromising efficiency or interaction quality. Such scalability is a must for large hospitals and medical institutions.

Identifying healthcare services

Several healthcare practices, such as clinics and diagnostic laboratories, have incorporated chatbots into their patient journey touchpoints. Such chatbots provide information about the nearest health checkup centers, health screening packages and their guidelines. There’s also an interpretation of test results and so much more.

It allows information to be disseminated quickly, effectively, and at reduced costs.

Minmed, a multifaceted healthcare group, uses a chatbot on its website that offers comprehensive information on several health screening packages, COVID-19 detection tests, clinic locations, operating hours, and so much more.

The chatbot offers website visitors several options with clear guidelines on preparing for tests such as non-fasting and fasting health checkups, how to prepare for them, what to expect with results, and more.

And what’s even more interesting is that the chatbot has extensive information on fitness classes and virtual workouts offered by Minmed. You can even book your workouts through the chatbot!

Advantages of Healthcare Chatbots

Healthcare chatbots are transforming modern medicine as we know it, from round-the-clock availability to bridging the gap between doctors and patients regardless of patient volumes.

Here are some detailed advantages of healthcare chatbots:

1. Continuous availability

Since chatbots are programs, they can be accessible to patients around the clock. Patients might need help to identify symptoms, schedule critical appointments and so on.

No matter the task, medical chatbots can help patients with the help they need.

2. Instant access to critical information

Time is an essential factor in any medical emergency or healthcare situation. This is where chatbots can provide instant information when every second counts. When a patient checks into a hospital with a time-sensitive ailment the chatbot can offer information about the relevant doctor, the medical condition and history and so on.

3. Data collection through patient engagement

As medical chatbots interact with patients regularly on websites or applications it can pick up a significant amount of user preferences. Such patient preferences can help the chatbot and in turn, the hospital staff personalize patient interactions. Through patient preferences, the hospital staff can engage their patients with empathy and build a rapport that will help in the long run.

4. Handling high patient volumes with ease

Chatbots in healthcare are not bound by patient volumes and can attend to multiple patients simultaneously without compromising efficiency or interaction quality. Such scalability is a must for large hospitals and medical institutions.

Conclusion

Clearly, there are several use cases for chatbots in healthcare. When envisioning the future, automation, and conversational AI-powered chatbots definitely pave the way for seamless healthcare assistance.

But, despite the many benefits of chatbots in healthcare, several organizations are still hesitant to incorporate bots. This attitude is present towards automation as well. This situation arises because chatbots are prone to errors and can sometimes be difficult to implement. This is especially true for non-developers who need to gain the skill or knowledge to code to their requirements.

However, today’s state-of-the-art technology enables us to overcome these challenges. For instance, Kommunicate builds healthcare chatbots that can automate 80% of patient interactions. Not only can these chatbots manage appointments, send out reminders, and offer around-the-clock support, but they pay close attention to the safety, security, and privacy of their users.

If you’d like to know more about our healthcare chatbots and how we can enhance your patient experience, simply get in touch with our customer experience experts here.

For more on chatbots

If you are interested in knowing how chatbots work, read our articles on What are Chatbot, How to make chatbot and natural language processing.

Originally published at https://www.kommunicate.io on April 8, 2022.

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Top 5 Healthcare Chatbot Uses Cases & Examples 2023 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Until recently, much before ChatGPT could stir the industry, people didn’t have much frenzy around LLMs or generative AI. It is ChatGPT that potentially is evoking a new kind of ambition in leaders to try out something new with this superpower deep learning-based chatbot offering and drive maximum business outcomes.

In the preview of the business complexity that the enterprise encompasses, AI-powered chatbot solutions are critical assets for them to alleviate those complexities and meet user expectations by resolving specific queries. Maybe this is what chatbots dwell on top of the IT budget plan for enterprise leaders.

As per the Gartner survey report conducted in 2022, 54 respondents agreed to use some form of conversational AI platform or chatbots for customer-facing issues.

Also, as predicted by Gartner, the AI market will reach $134.8 billion by 2025, wherein a large part of this market encompasses chatbot technology.

As enterprise service delivery becomes more chatbot-driven, the question is 一 can ChatGPT fulfill conversational AI needs for enterprises?

What is ChatGPT?

ChatGPT, an acronym for Chat Generative Pre-trained Transformer, is a chat interface built on top of large language models or LLMs (trained with a massive database with 117 Million parameters of 4.6gigabytes of data) that can answer pretty much any questions using natural language processing.

Brainstormed by OpenAI, ChatGPT can engage in human-like conversations and perform many exciting creative tasks for users by receiving prompts.

Its amazing ability lies in summarizing content, suggesting fixes for codes or some programming languages, generating images, scripting marketing emails, and much more.

Surprisingly, it can be used to create new lyrics and storytelling for movies 一 all from scratch and in an innovative way that no one has ever produced.

The potential of ChatGPT is such that it is expected that the industry will produce 30% of outbound marketing messaging synthetically by 2025.

Can enterprises unleash conversational AI potentials through ChatGPT?

With the ability to read future words and detect the intent of future sequences using a deep learning model, ChatGPT can produce output for the prompt, which it is trained on 一 basically text-based output.

ChatGPT can generate output when it receives text-based prompts in a human-like conversation.

But, it is rare that ChatGPT can provide any solution to its end-users in real time. It means ChatGPT is unlikely to help build a conversational chatbot or virtual assistant that can interact with humans and offer a solution.

On the contrary, conversational AI platforms, which are AI-powered components with features such as natural language process, natural learning understanding, intent detection, and context extractions, can easily understand human queries and offer a solution.

If the objective is to detect user queries, offer suggestions in an enterprise context, and help provide a solution, ChatGPT can lack that human experience.

Highlight the fundamental definition of ChatGPT and conversational AI technology in the context of enterprise needs (this can be more or less similar to that of Kore.ai)

How ChatGPT differs from Enterprise Conversational AI?

There are a number of ways in which ChatGPT differs from enterprise conversational AI.

How does ChatGPT lack as an enterprise conversational AI platform? (a detailed elaboration)

Furthering from the above table of differences between ChatGPT and conversational AI platforms, let’s have a detailed explanation of the topic and understand what is needed to unleash the best of ChatGPT in an enterprise context.

No integration available for enterprise systems

ChatGPT does not offer API access to integrate with the internal enterprise systems. The inability simply discourages customers or internal users from using a conversational mechanism. As a result, the pre-trained model like that of ChatGPT falls short of enough knowledge needed to communicate with the backend systems like CRM, ERP, ITSM platforms, Enterprise Service Desk, and others to help proceed with real-time response and feedback.

This certainly prevents ChatGPT from absorbing the capability of conversational AI models and scales up enterprise workflows that can help resolve problems in real-time or help with a number of tasks such as,

• Placing orders and fetching order history
• Helping in transactions
• Following up with future or prospective customers
• Closing a deal or retrieving product information
• Revealing chat history or transferring a call to a live agent for IT support teams
• Reporting fraud and resolving issues

With that said, every department can leverage conversational AI platforms, from marketing to IT and operations to finance, to drive meaningful business results through human-like interactions.

It is true that ChatGPT Plus comes with API. But, this feature is only used to offer product recommendations and suggestions that aim to improve the shopping experience for shoppers on e-commerce platforms. It is more of working on the personalization experience side. Shopify and Instacart are early adopters of this API access.

On the other hand, Azure OpenAI service makes it easy for enterprise integration with ChatGPT combining Azure Cognitive Search. But, this process involves high computing costs, with developers supervising the training session strictly to enable constant API connection with the external database.

With no-code conversational AI platforms, the enterprise does not need to take all these tough iterations. Enterprise deployment and design are easy and fast.

Averse to customizability

Enterprise operations need current data to help teams respond and move business processes at scale. But with ChatGPT as an enterprise chat interface, accessing the pre-trained core language model and tweaking its existing database can be challenging.

So, developers are unable to customize its data model to enable it to perform and manage enterprise-wide tasks, such as 一

• Printer issues
• Account Unlock
• Password resets
• Device or apps provisioning
• Accounts payable and receivable

Also, it is evident that ChatGPT has 2021 cutoff knowledge and is not connected to the internet. The lack of current data poses risks to the enterprise’s operational efficiency.

The conversational AI platform allows ease of customization for virtual assistants by communicating with its underlying language model to allow for functionalities as per existing or future task patterns.

Open to prompt-based attacks

The ChatGPT interface examines and produces answers based on prompts across its large database. So, it is easy to manipulate its capacity and ask it to generate that is unethical and biased. Or use the platform to divulge significantly important information about internal work processes. This can be damaging to the organizational reputation.

With a little cautious step as you tailor instructions for the ChatGPT through MLChat meta-data, it is possible to define prompts more clearly and avoid the chances of mitigating the chances of prompt attacks.

Although the standard ChatGPT misses this capability, it is fast to append prompts using ChatML available through a higher level of API access.

The whole process isn’t just complicated but needs robust control over prompts. On the other hand, conversational AI platforms eliminate the probability of ambiguity across the workplace or business environment.

Security loopholes with LLMs or NLP models

LLMs like ChatGPT have their own database and can store data that is being shared. But, while using the chat engine to generate and automate content production for emails or meeting minutes, it is likely to get overwhelmed and expose company data, personal identification information (PII), or other company data to OpenAI’s external database.

The challenge is that once the data is shared, it is impossible to retrieve or delete it. The recent Samsung incident highlighted major data security lapses in the ChatGPT model.

An appropriate safety guardrail must be in place to prevent security threats while continuous robust supervision is necessary.

Conversational AI platforms are designed to maintain robust security for enterprise data and prevent any kind of security vulnerabilities.

Lack of explainability and accuracy

ChatGPT is a ‘black box’ solution. It means an LLMs-powered AI chat solution like ChatGPT produces output that lacks visibility. It is because there is a lack of data transparency and accountability, which makes it difficult for humans to understand how different parameters and algorithms combine to generate those outputs.

ChatGPT will likely make mistakes, and users who possess little to no domain expertise to interact with ChatGPT-like interfaces will produce misinformation without any traces of references or citations that could verify the truthfulness of the data.

One major reason why ChatGPT produces ‘black box’ responses is that it can’t think and judge from real-world experiences. So, the response it produces cannot be altered.

On the contrary, Conversational AI platforms can improve search results by allowing internal and external users to provide appropriate search terms and continue the conversation flow with the interface, such as self-service chatbots.

Simultaneously, enterprise leaders can easily fetch chatbot analytics data to find where it goes wrong, which specific issue was tough to handle, and many other issues to find a proper remedy to mitigate and easily handle future cases.

High cost for enterprise deployment

The standard ChatGPT is not as huge as the commercial tier features regarding context capacity. In order to harness the enterprise-level benefits of ChatGPT(mainly GPT 3.5 turbo), CIOs or CTOs need to shore up large budgets to spend over huge computing infrastructure such as hardware or software costs, including the budget to run Azure in the backend.

It is also important to note that dedicated capacity for a longer context limit does not ensure that GPT 3.5 turbo will mitigate the chances of toxicity. It means you need to have someone to supervise the content generation and ensure its veracity.

Another concern is the additional operational costs. This is because enterprises must look at ways to keep the ChatGPT 3.5 model running and operational at all times, which further necessitates hiring a team of expert engineers and developers for configuration, installation, and ongoing maintenance.

If conversational capabilities are a key priority to improve enterprise-wide operations, AI-powered virtual assistants are better at solving the current problems for CTOs or CIOs as they look to minimize operational costs in the high time of recession. They are no-code cloud-native platforms that can easily integrate with the on-prem infrastructure through API or work as an independent model for your enterprise needs.

How can Workativ use ChatGPT to improve conversational AI needs for Enterprises?

ChatGPT’s huge potential to aid in auto-generated content and automation of repetitive content production tasks is an ideal way to improve the user experience with virtual assistants or self-serve chatbots in the enterprise ecosystem.

Workativ ensures its chatbot builder harnesses the potential of ChatGPT and facilitates users to automate tasks at scale while improving search results for knowledge articles or dialog management.

Knowledge AI search

Workativ chatbot builder harnesses the power of LLMs or generative AI, the underlying technology in ChatGPT making, to expedite knowledge article search.

With the chatbot builder, it is easy and fast to upload your knowledge articles no matter what. You can choose to upload knowledge articles for IT support, HR support, and Marketing to the folder to the database of the chatbot interface.

ChatGPT helps eliminate the need to train the chatbot model. Instead, it accelerates user search performance using Knowledge AI capability and retrieves the right specific knowledge article to solve real-time issues.

Intent AI extraction

It is evident that creating dialog for a pre-trained chatbot takes a lot of time for copywriters, which also needs document approval before releasing the conversations to the live environment.

By integrating ChatGPT-like interfaces or LLMs into the chatbot builder, Workativ makes it possible to automatically generate conversations with intent extraction for various use cases like,

• Account unlock
• Password resets
• Device provisioning, etc

As a result, Workativ expedites faster time to market for enterprise-wide use cases and alleviates the challenges faced by customers or internal users in the IT or HR department.

Generating grammatically correct responses

One more effective use case of the ChatGPT-powered chatbot builder is that Workativ enables developers to draft messages for various use cases easily. With the power of sensing the next sequence of search queries, it can generate email messages rapidly and allow for publication instantly. This makes the send-and-receive messages effective and useful for end-users as it requires minimal reviews for edits and fixes.

Conclusion

ChatGPT unleashes a huge potential in making the user experience more enriching. Although enterprise-wide use cases are limited when it comes to fully leveraging its capabilities for conversational AI needs, ChatGPT enhances the chat experience with proactive knowledge AI search, content intent extraction, and rapid dialog creation. These features can be useful to bring the next level of enterprise operational efficiency with a combination of conversational AI platforms like Workativ.

As you look to drive improved employee experience as well as customer experience through end-to-end IT delivery, Workativ virtual assistants are a true companion in your enterprise infrastructure. With that, it is worth mentioning that app workflow automation with third-party app integrations improve business results while also enabling you to harness the benefits of ChatGPT.

Disclaimer: This article was originally published here.

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Why Is ChatGPT Not The Answer To
Your Enterprise Conversational AI Needs? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

In today’s fast-paced world, mastering the art of time management is essential for personal and professional success. Effective time management allows us to accomplish more, reduce stress, and create a fulfilling balance in our lives.

In this blog post, we will explore the concept of time management, and its importance, and provide you with easy tips for success to optimise your productivity and achieve your goals. Whether you’re a student, professional, or entrepreneur, implementing these strategies will help you make the most of your time and lead a more productive and fulfilling life.

Benefits of Effective Time Management:

Implementing effective time management strategies brings numerous benefits. Firstly, it enhances productivity, allowing you to accomplish more in less time. By allocating time for important tasks and avoiding distractions, you can stay focused and achieve better results.

Additionally, time management helps you meet deadlines, reduce stress levels, and maintain a healthy work-life balance. It allows you to identify and prioritise your activities, make informed decisions about how to allocate your time, and avoid wasting time on non-essential tasks.

Easy Tips for Successful Time Management:

1. Set Clear Goals

Start by setting clear and achievable goals. Clearly defining what you want to accomplish helps you stay focused and prioritise your tasks effectively.

2. Plan Ahead

Plan your day or week in advance. Create a to-do list, prioritise tasks, and allocate time slots for each activity. This helps you stay organised and ensures that important tasks are completed on time.

3. Prioritise Tasks

Identify the most important and urgent tasks and tackle them first. Prioritising tasks helps you allocate your time and energy efficiently.

4. Avoid Multitasking

Contrary to popular belief, multitasking can actually decrease productivity. Focus on one task at a time, complete it, and then move on to the next. This allows you to give your full attention to each task and produce better results.

5. Delegate and Outsource

Learn to delegate tasks that can be handled by others. Delegating frees up your time for more critical and high-priority tasks. Additionally, consider outsourcing certain tasks or seeking help when needed.

6. Manage Distractions

Minimise distractions as much as possible. Put your phone on silent mode, close unnecessary tabs on your computer, and create a conducive work environment that promotes focus.

7. Take Regular Breaks

Taking short breaks throughout the day helps refresh your mind and maintain productivity. Use break times to stretch, meditate, or engage in activities that relax and rejuvenate you.

8. Learn to Say No

Be assertive and learn to say no when necessary. Protect your time by declining tasks or commitments that don’t align with your priorities or overload your schedule.

9. Time Blocking

Allocate specific time blocks for different tasks or types of activities. This technique helps you stay focused and prevents tasks from overlapping or taking longer than necessary.

10. Learn from Mistakes

Reflect on your time management practices and identify areas for improvement. Learn from any mistakes or inefficiencies and adjust your approach accordingly.

Conclusion:

Mastering time management is a skill that can transform your life by optimising your productivity, reducing stress, and enabling you to achieve your goals effectively.

By implementing the easy tips mentioned in this blog post, along with leveraging tools like Nikabot, you can take control of your time, unlock your full potential, and create a harmonious balance between work and personal life.

Embrace the power of effective time management, and witness the positive impact it has on every aspect of your life. Remember, time is a precious resource, and how you choose to manage it can shape your future.

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Mastering Time Management: Easy Tips for Success was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Think of a superhero movie without special effects. It would be a very different experience. Wouldn’t it? The characters would be less believable. The action would be less exciting, and the movie’s overall impact would be diminished.

For enterprise software, AI and ML are like special effects. They can be used to automate tasks, improve decisions, and personalize user experiences.

By examining how AI and ML in enterprise software can drive business success, we aim to highlight these technologies’ transformational potential and underscore their importance in today’s competitive business environment. Organizations can gain a significant advantage and position themselves for long-term success by understanding and leveraging the benefits of AI and ML in enterprise software.

Role of AI and ML in enhancing enterprise software capabilities

The tasks of Artificial Intelligence and Machine Learning in enhancing the capabilities of enterprise software are multi-faceted. It includes automating, making intelligent decisions, advanced analysis, personalization, natural language, prediction, managing risk, fraud detection, security, and continuous learning. By taking advantage of these technologies, organizations can unlock new efficiency, intelligence, and innovation levels in their operations, ultimately gaining a competitive advantage and driving business success.

Here are essential ways AI and ML enhance enterprise software capabilities:

• Drive automation and efficiency: Leveraging AI and ML in enterprise software development enables the automation of repetitive and manual tasks in enterprise software, which frees up valuable staff time and resources. This automation increases productivity and cost-effectiveness by streamlining processes, improving operational efficiency, and reducing the risk of human error.
• Make intelligent decisions: These technologies help analyze massive amounts of data, identify patterns, and predict with precision. By integrating these capabilities into enterprise software, organizations can access valuable insights and make data-driven decisions in various areas, such as supply chain management, sales forecasting, financial analysis, and predicting customer behavior.
• Enhanced analytics: Enterprise software can perform advanced analytics on large, complex data sets using AI and ML techniques. This enables organizations to gain deeper insights into how they operate, how customers behave, how markets evolve, and the competitive landscape. Enterprise software can generate actionable insights for strategic planning and informed decision-making by uncovering hidden patterns and correlations.
• Customer Experience Personalization: AI and ML algorithms allow enterprise software to personalize customer interactions and experiences. The software can deliver targeted recommendations, personalized marketing messages, and customized user interfaces by analyzing customer data, preferences, and behaviors. This level of personalization leads to increased customer satisfaction, engagement, and loyalty.
• Natural language processing (NLP): Enterprise software can understand and process human language, both written and spoken, through AI and ML techniques, particularly NLP. AI and ML techniques, particularly NLP, allow enterprise software to understand and process written and spoken human language. These capabilities improve communication channels, allowing chatbots, virtual assistants, and speech recognition systems to provide efficient and personalized customer support, automate queries, and facilitate natural language user interfaces.
• Preventive maintenance and risk management: AI and ML algorithms can predict maintenance needs, equipment failures, and potential risks by analyzing sensor data and historical patterns. With this functionality, enterprise software can optimize maintenance schedules, decrease downtime, and improve overall operational reliability and effectiveness.
• Fraud Detection and Security in Software: AI and ML in enterprise software can enhance security measures in enterprise software. These technologies can be used to identify anomalies, detect patterns of fraudulent activity, and flag potential security threats in real time. Organizations can strengthen their defenses, protect sensitive data, and mitigate cybersecurity risks by integrating AI and ML into security systems.
• Continuously learning and improving: ML algorithms are designed to learn from the data they process and improve their performance over time. Organizations can leverage continuous learning to improve accuracy, adapt to changing conditions, and deliver precise results by building ML capabilities into enterprise software. This iterative learning process allows the software to continue to evolve and improve as it learns.

Real-world examples of AI and ML in enterprise software

Across industries and business functions, the following examples show how integrating AI and ML into enterprise software can increase efficiency, improve decision-making, enhance customer experiences, optimize operations, and strengthen security measures.

• Customer Service: Tasks such as answering FAQs and resolving simple issues are automated by AI and ML. This frees human agents to focus on more complex issues.
• Fraud detection: AI and ML are being used to detect fraudulent transactions. This can help companies to protect themselves from financial losses in the future.
• Risk management: AI and ML are used to assess the risk of a transaction. This can help companies make better decisions about whether to lend or invest.
• Product development: The development of new products is possible with the help of AI and ML. Leveraging AI consulting or maybe ML, companies create products more likely to succeed in the marketplace.
• Marketing: To personalize marketing campaigns, AI and ML will be used. This can help businesses reach their target audience more efficiently.
• Pricing: AI and ML will be part of the pricing process. This can help companies maximize their profits.
• Chatbots: AI-powered chatbots are being used to service and support customers. These chatbots can answer questions, troubleshoot problems, and even sell products.
• Recommendation engines: Products, content, and services are recommended to users by AI recommendation engines. These engines can learn from user behavior and preferences to provide more personalized recommendations.
• Fraud detection: AI fraud detection systems are used to identify fraudulent transactions. These systems can identify patterns indicative of fraud by analyzing large amounts of data.
• Risk assessment: AI risk scoring systems assess the risk of defaulting on a loan or churning a customer. The likelihood of these events occurring can be predicted using data.

Conclusion

To summarize, the emerging trends and advances in AI and ML in enterprise software open up business opportunities. The rapid evolution of these technologies is creating transformative capabilities. They have the potential to reshape industries and revolutionize the way organizations operate.

Artificial intelligence and machine learning are already delivering benefits to industries. This is not a time to delay; this is a time to act. Harness the technologies today and wait for the unprecedented success they will bring your business.

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AI & ML in Enterprise Software: Navigating Business Success was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Telegram is a popular messaging app that offers a wide range of features to its users. One such feature is the ability to create your own Telegram bot.

Telegram bots are automated programs that can perform various tasks, such as providing information, sending notifications, or even playing games.

Creating a Telegram bot without coding may seem like a daunting task, but it is actually quite simple.

Telegram Bots

Telegram bots are AI-driven programs that can interact with users and perform tasks based on predefined commands.

These bots can be integrated into Telegram groups or used in one-on-one conversations. They offer a wide range of functionalities, making Telegram a versatile platform for various purposes.

Benefits of Creating a Telegram Bot

Creating a Telegram bot without coding can be highly beneficial for individuals and businesses alike. Some key benefits include:

• Automation: Bots can automate tasks, saving time and effort.
• Customer Support: Bots can provide instant responses and assistance to users.
• Information Delivery: Bots can deliver real-time information and updates.
• Engagement: Bots can engage users through interactive games, quizzes, or polls.
• Marketing: Bots can be used for promotional activities and lead generation.

Getting Started with BotFather

BotFather is a special bot provided by Telegram that allows users to create and manage their own bots. To begin, you need to have the Telegram app installed on your device.

Creating a New Bot

• To create a new bot, follow these steps:
• Open the Telegram app and search for “BotFather” in the search bar.
• Click on the BotFather’s profile and start a chat.
• Type “/newbot” and follow the on-screen instructions to set a name and username for your bot.
• Once your bot is created, BotFather will provide you with an API token. Save this token as it will be used to interact with your bot.

Configuring the Bot

After creating the bot, you can configure its settings. Some important configurations include:

• Profile Picture: Upload an image that represents your bot.
• Description: Provide a brief description of your bot and its functionalities.
• Privacy Mode: Choose whether your bot should respond to messages from all users or only those who have started a conversation with it.

Adding Functionality to the Bot

To add functionality to your bot, you can use pre-built modules or integrate APIs. There are several third-party libraries and services available that allow you to create complex bots without coding. Some popular options include:

• BotPenguin
• Botpress
• Chatfuel
• ManyChat

Testing and Deploying the Bot

Once you have added the desired functionality to your bot, it’s time to test it. You can test your bot by interacting with it through the Telegram app. Ensure that all the features are working as expected and make any necessary adjustments.

To deploy your bot, you can use hosting platforms such as Heroku or AWS. These platforms provide easy deployment options and ensure that your bot is accessible to users at all times.

Interacting with the Bot

To interact with your bot, users can search for its username in the Telegram app and start a conversation. You can configure the bot to respond to specific commands and provide the desired information or perform actions accordingly.

Enhancing the Bot’s Capabilities

To make your bot more engaging and useful, you can consider implementing the following features:

• Natural Language Processing: Use NLP libraries to enable your bot to understand and respond to user queries in a more human-like manner.
• Multimedia Support: Allow your bot to send images, videos, and documents to users.
• User Authentication: Implement user authentication to provide personalized experiences and secure sensitive information.

Best Practices for Telegram Bots

To ensure the success of your Telegram bot, consider the following best practices:

• Clear Purpose: Define a clear purpose for your bot and align its functionalities accordingly.
• User-Friendly Commands: Use simple and intuitive commands to make it easier for users to interact with your bot.
• Error Handling: Implement proper error handling to provide informative responses when the bot encounters errors.
• Regular Updates: Keep your bot up to date with the latest features and improvements to provide a better user experience.

Conclusion

Creating a Telegram bot without coding is an accessible way to leverage the power of automation and enhance user interactions.

With the help of BotFather and various third-party tools, you can bring your bot to life and provide valuable services to your users.

Start exploring the possibilities and create your own Telegram bot today.

Build your own Telegram Chatbots using BotPenguin, it also offers chatbot creation for social platforms, websites, WordPress:

• WhatsApp Chatbot
• Telegram Chatbot
• Facebook Chatbot
• Website Chatbot
• WordPress Chatbot

Frequently Asked Questions

1. Can I create a Telegram bot without coding skills?

Yes, you can create a Telegram bot without coding skills using tools like Botpress, Chatfuel, or ManyChat.

2. Is it necessary to host my bot on a platform?

Yes, hosting your bot on a platform ensures that it is accessible to users at all times.

3. Can I add multimedia support to my Telegram bot?

Yes, you can allow your bot to send images, videos, and documents to users for a more interactive experience.

4. How can I make my bot understand user queries better?

You can use Natural Language Processing (NLP) libraries to enable your bot to understand and respond to user queries more effectively.

5. What are some best practices for creating a successful Telegram bot?

Some best practices include defining a clear purpose, using user-friendly commands, implementing proper error handling, and regularly updating your bot.

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Configuring the BotHow to create a Telegram Bot without coding? Find out was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

What is a Chatbot API?

A chatbot API is a set of protocols that allow developers to access the functionalities of a chatbot. A chatbot API enables seamless integration into various applications, systems or platforms by standardizing the way you send, receive and extract messages via the chatbot.

Chatbot APIs connect your messaging software (SMS, Social media messengers, WhatsApp) with chatbot software and features.

How does a Chatbot API work?

We are going to explain how a Chatbot API works in Four simple steps:

Step 1: Message Processing:

This is the step where the Chatbot API receives a message from the developer. It then processes the message using NLU techniques, which helps the chatbot API understand user intent. NLU also helps the chatbot API extract relevant information and determine the appropriate response.

Step 2: Intent recognition:

This is the step where the chatbot API uses machine learning algorithms and NLU to make sense of what the user is trying to say. This step involves advanced computing, where the chatbot API has to analyze the speech or text input and identify keywords which it then maps to predefined intents.

Step 3: Generating response:

Based on the underlying logic and data, the chatbot API then generates a response once it recognizes the input. This response can come from an existing knowledge base, or perform external API calls.

Step 4: Deliver response:

Through the API’s response endpoints, the chatbot API sends the generated response back to the developer’s application. The response can be of different formats such as JSON, XML or plain text depending on the API design.

The chatbot API uses session management techniques to maintain context in multi-turn conversations. Keeping track of the conversation history, the chatbot maintains context and gives meaningful responses to queries.

What are the features you should look for in a chatbot API?

Here are some of the features you must consider when you are considering investing in a chatbot API.

1. NLP Capabilities: Make sure the chatbot NLP you choose offers advanced NLP Capabilities. A good NLP engine means the chatbot can better understand, and respond to user queries.
2. Intent recognition: This is a subset of the NLP capability, a chatbot API that can clearly understand the intent behind a user query should be a top feature that you must look for.
3. Ability to understand context: A good chatbot API should maintain the context through a conversation, remembering previous user inputs and providing coherent responses.
4. Numerous integration capabilities: When considering investing in a chatbot API, look for one that supports integration with various systems, platforms, and messaging apps.
5. Analytics and reporting: A good chatbot API should provide analytics and reporting capabilities so that you can track user interactions and see how effective the chatbot is.

It provides a method for sending and receiving messages while managing users and conversations.

It provides a method for understanding user queries, generating responses, and handling different types of conversations.

Now that we know the basics of Chatbot APIs, let us look at 8 of the best Chatbot APIs available in the market today. This is by no means a comprehensive list, and it can be expanded to include a lot of other players.

8 Chatbot APIs you should watch for in 2023

Let us now take a look at each of them in detail:

1. Kommunicate

We are beginning this list with a Chatbot API platform that we are the most familiar with, since we have been building and perfecting it over the past few years — Kommunicate. With Kommunicate, you can be up and running building a chatbot in less than 10 minutes.

Test your bot in parallel as you are building it, and see the changes that you make reflected in real time. Flow designer, ChatGPT integration, Multilingual bots, all come bundled in a neat package with Kommunicate.

With Kommunicate, you have:

Price:

1. Lite: The Lite plan is priced at $100/month, with 2 teammates included and 500 Monthly Tracked Users (MTU).
2. Advanced: The Advanced plan is priced at $200/month, with 5 teammates included and 5000 MTU.
3. Business: Talk to Kommunicate sales.

2. Drift:

First on our list is a company that claims to be the masters of Conversational AI, providing tools for marketing, sales, and support. Drift is known for its “Drift conversation cloud” where chatbots, email, and live chat come together.

Drift has one of the best chatbot API libraries available in the market today, and you can leverage their Users, Contacts, Playbooks, Drift SDK, and App Admin. With Drift, you can:

Price:

For small businesses, Drift has a premium plan that starts at $2500/ month. For larger plans, you will have to contact Drift Sales.

Did you know?

There are 4 major components of a smart conversational AI, which you can learn in this blog.

3. Intercom Fin

Fin is Intercom’s way of Supercharging an OpenAI chatbot. Intercom claims that it is the most advanced AI chatbot available in the market and provides safer, more accurate answers.

Fin is aimed specifically at the customer support teams. The chatbot has built-in safeguards that ensure that it doesn’t give any misleading answers. The chatbot builder has also built in a “Custom” section, where customers can input their most important questions and Fin prioritizes them over its AI answers.

Price:

Fin usage is measured in Resolutions. It is currently priced at $0.99 per Resolution. To get this pricing, you need to be subscribed to an active Intercom plan.

4. Chatbot API

Chatbot uses NLU to help end users create intelligent chatbots that integrate with your messaging applications. The intelligent chatbots that you create can handle requests, interact with rich messages and images and carry on conversations.

The chatbots API also helps you make bots and track their performance across a wide variety of metrics. Chatbot also has an easy drag-and-drop builder, using which you can create intelligent chatbots from scratch.

Other features of Chatbot API are:

1. Integrations with FB Messenger, Slack, and various CMS systems.
2. Advanced chatbot analytics.

Price:

The entire chatbot package has been split into 4 plans

5. Slack API

Slack is an internal communications tool, kind of like Discord, but more professional. It is a preferred mode of communication for startups and small businesses around the world. Slack comes bundled with a Slack bot API that allows you to build chatbots. These chatbots in turn help you communicate with customers and also handle small tasks.

Slack has 4 different types of APIs, which includes “Real Time Messaging API,” “Events API,” “Web API,” and “Conversations API.” The RTM API allows you to receive “events” from Slack in real time and send messages as users.

With Slack API you can:

1. Give your bot a name or even a profile in your directory.
2. Program your bot to automatically post messages and reminders.
3. Add interactive components like buttons and polls to your messages.

Price

Slack is free to use for a limited number of features, and the Pro plan generally costs $8.75/per user per month.

6. Wit.AI

Wit is an API platform that makes it easy for developers to build apps and devices that you can then talk to or text to. It uses AI to train its chatbots to identify the intent and implied meaning of the user inputs.

What makes Wit.ai special is that it can understand user emotions and respond accordingly. This makes it the perfect Chatbot API to build complex bots where user input may not always be clear. Other features of Wit.AI include:

Price:

Wit.AI is free to use, including for commercial purposes.

7. Zendesk API AI

Zendesk provides what is known as Zendesk AI API as part of its broad range of offerings. There are pre-chat surveys, team collaboration and ticket routing. Zendesk also lets you build no- code bots that improve agent collaboration, through an agent-to-agent chat feature.

Zendesk has an impressive list of more than 700 integrations, including Atlassian, Freshbooks, etc. There are also reports and dashboards that you can use for analytics.

Price:

Zendesk offers a free trial, followed by 4 plans which include:

8. IBM Watson Assistant

IBM Watson Assistant is also an open-source chat API, which uses AI to provide a chat widget for your website and communication channels. It is a cloud based system, and uses NLU capabilities to understand and respond to user queries.

What makes this API solution a bit different from others on this list is that it is a bit complicated to implement. Users need to know the basics of programming to build bots using IBM Watson. Other features of this platform include:

Price:

IBM Watson has a free plan and a Plus plan that starts at $140/month. The Enterprise plan details are available on request.

3 of the best ChatAPIs in the market

1. Facebook Messenger API:

Facebook Messenger API allows businesses to automate conversations with their customers through Facebook Messenger. Companies can now respond to customer queries quickly and efficiently, with minimal manual effort or intervention.

Facebook’s Messenger API helps businesses create automated messages, which can be sent to customers based on specific triggers. For example, if a customer purchases a new item, then you can have the Messenger API send that customer a specific message.

Other features of Facebook Messenger API include:

Price:

Facebook Messenger API is currently free for developers, if you do not exceed the rate limits.

Twilio ChatAPI

Does your business need chat-based communications? Twilio offers a convenient, all-in-one API for scalable, multi-party conversations across channels. The system allows developers to add real-time messaging and chat functionality to their applications.

Users can send and receive messages, share images and videos, and engage in conversations via the Twilio ChatAPI. There are cloud-based archives, advanced opt-out and opt-ins, chat client SDKs, and webhooks and scoped webhooks, among other features, that make this API attractive.

Twilio provides access to an extensive resource library. Its pay per-user pricing makes sure you are only paying for the functionalities you are using. This makes Twilio chatAPI the perfect solution for small businesses that are on a budget, and also for larger businesses that can scale at the pace that they want.

Price:

Like we said, Twilio offers a pay-per-user model of pricing. Currently, the model is priced at $0.05 per active user per month. There is also a price for the stored data per month, which starts at $0.25 GB per month.

3. SendBird

SendBird claims to be the world’s largest private in-apps conversation platform. It offers a myriad of features such as group chat and one-on-one chat, typing indicators, messaging history, etc. It is also an easy-to-use API and comes bundled with push notifications.

SendBird API has a reliable infrastructure that ensures that the application can handle millions of concurrent users. It also ensures that there is smooth communication across mobile, web and desktop platforms.

If you are building a social networking app, a customer support platform or a collaborative workspace, SendBird offers a robust set of APIs. This empowers developers to create engaging chat experiences. SendBird offers pre-built UI for major platforms, and flexible messages format like .json and .xml.

Price:

A subscription for SendBird is priced at $4788 for 12 months, and additional usage cost if your usage exceeds your contract.

There you have it. 8 of the top chatbot APIs that you must know in 2023, along with 3 of the best ChatAPIs. Some of the chatbot APIs mentioned here are open source. For others, you may need to pay to access the advanced features. It all depends on the type of chatbots you want to build and the kind of conversations your customers are going to have. So, choose wisely.

Originally published at https://www.kommunicate.io on June 22, 2023.

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8 Chatbot APIs To Watch Out For In 2023 + 3 Chat APIs was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

I was among the first to talk in detail about the new craft of prompt engineering and even prompt hacking in my newsletter back in February, a few months after the launch of ChatGPT.

Since then, I’ve observed its transformation from a niche interest to a widely embraced field and a mainstream profession, with salaries purportedly as high as New York’s Empire State Building. In its essence, prompt engineering is the science of instructing AI models. It’s about crafting that “perfect” question or command that lets AI generate meaningful responses — like a key turning in a lock, unlocking the vast potential of AI.

But a few days ago, the company that started it all — OpenAI — released a new version of DALL-E, which it claimed would be the death knell for prompt engineering, much in line with OpenAI’s CEO Sam Altman’s claims that prompt engineering was a temporary phase in the gen-AI journey. Given that the person who began it all is making this prediction, we should take it seriously and look at the state of prompt engineering more closely.

DALL-E 3, claims its designers, understands significantly more nuance and detail than its earlier siblings. Which means it translates ideas into highly accurate images, as compared to before.

So we have an “intelligently superior” version of DALL-E. And soon, we may also have a sophisticated version of the rest of the gen-AI tools.

Personally, I tend to agree to a degree with Sam. Here’s why:

Let me give you my own example: I use gen-AI tools every day—from text to image to video generators—for which, of course, I need to input my instructions to the machine. But very rarely, maybe just 1%, have I used a templated prompt for this.

I get the fact that the more nuanced and “in context” the prompt, the better the output. And so I often use 2–3 commands to get what I want. Almost every time, the output is fairly decent. And things only keep getting better as the “machine” “understands” me over time.

Despite the growing interest in generative AI, most people like me haven’t even created a single professional prompt. But if giving instructions to the machine is also one definition of prompt engineering, then, of course, we all have done it.

One of the tools I use, and I must say I am extremely satisfied with, is Microsoft Designer. In one of its iterations, it introduced a feature where the AI itself suggests a “professional” prompt based on your initial inputs.

Two things here:

a) The machine is self-writing a prompt

b) The outputs based on my initial instructions and from the professional prompt are not vastly different

I don’t want to bore my readers with the technical stuff, but prompts are nothing but instructions given in human language to an AI. Unlike computerization and other things digital, it does not require complex, code-based input every time you want an output. That’s thanks to natural language processing (NLP). Which means human talk is translated into computer lingo by the AI, saving the human the time, energy and effort to learn “code”.

I often wonder why prompt engineering became such a “big deal” for the mainstream. I mean, at the very least, the very idea of using gen-AI is to have an assistant, an ally, or even a “smarter” colleague to help you in your pursuit of creative and professional work. So giving instructions to a machine should be as easy as talking to a fellow human being, right? At least, that’s what the theoretical idea is. Of course, for now, the communication between Man and Machine is nowhere close to that between humans, but we seem to be getting there.

When we use computing devices as laypersons, we are not expected to use any form of code to communicate. Most of us, even today, do not know how to use HTML, C/C++ to C#, Java, or whatever. So why should there be any form of input engineering for AI, which is a far more sophisticated piece of technology than anything we have ever had?

In light of all I’ve said above, it is but natural to ask the question: Is Prompt Engineering teetering on the brink of obsolescence?

For me, the time is still not here where the answer can be a simple Yes or No.

Prompt engineering, for now, remains an integral part of AI’s functionality. It’s the compass guiding the neural networks through the vast seas of human language, helping the model generate coherent, contextually accurate responses. But as we sail further into the future, rapid advances in technology do suggest that the tide could turn.

One such advancement is the move towards autonomous learning systems. These AI models are designed to learn independently, without explicit instructions or prompts. They mimic the human brain’s ability to absorb, process, and react to information, reducing the need for human intervention. If these models eventually become a reality, it just could be that prompt engineering as a science would lose its relevance.

No matter how sophisticated the current crop of machines get, they will always lack the intuition and creativity inherent to humans. Which will then ensure some form of prompting remains. Till Artificial General Intelligence (AGI) is born. So, while gen-AI might learn to operate independently, the nuanced understanding of language, context, and culture — a feat achieved through prompting — may still prove elusive. Therefore, it’s likely that prompt engineering will evolve rather than become irrelevant. It might transform from crafting explicit instructions to instilling an understanding of implicit cues in AI models.

Using precise prompts, we teach AI to grasp context, deduce meaning, and produce coherent, pertinent responses. However, there may come a time when just a simple sentence will suffice for the machine to comprehend your intentions completely.

Moreover, the idea of complete autonomy in AI raises ethical and safety concerns. As machines grow more independent, the risk of misuse or unintended consequences increases. Prompt engineering, thus, could serve as a regulatory mechanism, ensuring the responsible use of AI technology. In this regard, the role of prompt engineers might shift towards safeguarding the ethical boundaries of AI applications.

Should Scientists Focus More on Problem Formulation Than on Prompt Engineering?

Oguz A. Oguz, Chair in Marketing at King’s Business School introduced an interesting angle to this debate. Writing in the Harvard Business Review, he asks, “Should scientists invest more energy in problem formulation than in prompt engineering?”

Problem formulation, in essence, is the art of defining the questions that AI should answer or solve. It’s about identifying the gaps, defining the boundaries, and setting the course for our AI-driven solutions. In contrast to prompt engineering, which is more about instructing AI on how to respond, problem formulation focuses on what problems AI should tackle in the first place.

When we view AI through the lens of problem formulation, we shift our perspective from instruction to inquiry. We ask, “What issues can AI help us solve?” rather than “How do we make AI respond appropriately?” This shift requires a deep understanding of both AI capabilities and human needs. It demands an interdisciplinary approach, blending technology with sociology, psychology, economics, and more.

Indeed, some argue that this holistic, problem-focused approach could drive more impactful advancements in AI. Rather than focusing narrowly on refining the prompts we feed into AI systems, we might achieve more by broadening our vision and addressing larger, more complex societal problems. The potential for AI to revolutionize healthcare, education, environmental conservation, and myriad other areas is immense. Of course, to realize this potential, we must first define the right problems for AI to solve. But that’s a different story altogether.

(A confession: Some help was taken from a machine to write/re-write bits and portions of this newsletter.)

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Will Prompt Engineering Die Soon? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Chatbots are cropping up and gaining popularity everywhere. It’s also commonplace for chatbots to appear on websites, instead of a user attempting to search your website to find what they need, they can just engage in directions conversation with your chatbot.

In this post, we will learn how to add a Dialogflow bot to WordPress websites with the help of Kommunicate. If you don’t have Kommunicate a/c, please signup here for free.

One needs to follow two steps to add a Dialogflow bot to WordPress websites. We will go through them in detail below. We will use Kommunicate’s chat widget and Dialogflow bot in this article. Both these tools are free to try.

If you don’t have a Dialogflow chatbot, please read this step-by-step guide and build your own chatbot.

Bonus: Want to build your chatbot without any coding?

Stage 1: Integrate Dialogflow bot with Kommunicate

Here is the step-by-step video for integrating Dialogflow bot with Kommunicate. I have also mentioned the steps below.

Login to your Kommunicate and click on the Bot section. If you do not have an account, you can create one here. Locate the Dialogflow section and click on it.

Get your Dialogflow API Credentials

After clicking, a setting popup box will open. You will be asked for Dialogflow credentials. By logging into your Dialogflow console, you can obtain these.

Click on the Settings icon (gear icon on the left panel) and choose V2 API as the preferred API version. Save your settings, and follow the setup instructions.

1. Open Dialogflow agent settings (gear icon on the left panel)
2. Click on the Google Cloudlink which is mentioned in the Google Project bar
3. In Google cloud page navigate to the Credentials section by clicking on API’s & services
4. Under the Credentials section find the service account and click on edit for Dialogflow integrations
5. At the bottom of the screen click on ADD key button and create key option, JSON key will be downloaded
6. Now upload the key file.

Integrate Dialogflow Bot into Kommunicate

Go back to Dialogflow settings screen of Kommunicate, enter your credentials, then click on next to save and progress. You’ll be able to give your bot a name in the user profile section that follows. Your customers will see that name whenever the bot interacts with them.

To integrate Dialogflow CX into Kommunicate follow the same instructions to download the JSON key and mention the Agent Id in the kommunicate dashboard

To get the Agent Id: Go to Dialogflow CX console >> Select the Project >> In the Agent you have created ‘Copy the agent name to the clipboard will be in the format mentioned below, where the Agent Id is 54b0c663-2c01-4a09-a482-277ff39c0b05

Save, and move to the next steps to complete the setup.

You can check your newly created bot here in the Dashboard →Bot Integration → Manage Bots section.

Suggested Read: Connect Dialogflow With Facebook Messenger

Suggested Read: WordPress Chatbot: How to Create Without Any Coding

Stage 2: Integrate Kommunicate with WordPress

Log in to your WordPress dashboard and click on Plugins from the left navigation panel. Then click on Add New button.

Search for “Kommunicate Live Chat” plugin. On the search results page, you’ll see many options. Locate the “Kommunicate Live Chat” plug-in and click the Install Now button. Please make sure you Activate the plugin to make use of it.

Once you’ve installed and activated your plugin, you can then navigate to it in the left sidebar menu or through the Plugins page under Kommunicate settings.

Here you need to add your APP_ID to enable chat-based support in your product.

Add the Kommunicate App ID

You will get the same in the Kommunicate dashboard -> Settings -> Install. Insert your App ID > Copy it and paste it in the WordPress plugin > Save the changes.

And that’s it. You have completed the integration. Open the website, check out the chat widget, and play with your bot. You will find it in the bottom-right corner. Now your website visitors can chat effortlessly with you. You can manage the conversations and appearance from the Kommunicate dashboard.

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How to Add Dialogflow Bot to WordPress Website was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Whether you are a small business or big enterprise, customers need prompt support. The first thing that comes to our mind is live chat. Given that the world is going mobile-first, it is important to have live chat implemented properly on your mobile website and apps as well. In this simple tutorial, we will learn how to add live chat in Android apps.

We will be using Kommunicate Android Live Chat SDK, which is free to get started with. I have created a sample in Github for your reference as well.

Steps by Step Guide to Add Live Chat in Android Apps

Basic Setup

Adding Gradle Dependency

First thing first, let’s add Gradle dependency in your project and rebuild it.

dependencies {
implementation 'io.kommunicate.sdk:kommunicateui: 2.1.6'
}

Setup Your SDK

You need to initialize the SDK with the Kommunicate Application ID (APP_ID). You can get the Kommunicate APP_ID by signing up for Kommuncate and navigating to the Install section.

More details can be found here as well. Add below code in onCreate method in your activity where you want to add Live Chat.

Kommunicate.init(context, APP_ID );

Launch Live Chat

Floating buttons are widely used in Live Chat, I am using email action in my sample here. You can use your custom buttons as well. Now, we are ready to launch our live chat. Add below code into ‘click listener’ of the live chat button from where you want to launch chat.

new KmConversationBuilder(activityContext)
                   .launchConversation(new KmCallback() {
                        @Override
                        public void onSuccess(Object message) {
                            Log.d("Conversation", "Success : " + message);
                        }

                        @Override
                        public void onFailure(Object error) {
                            Log.d("Conversation", "Failure : " + error);
                        }
                    });

This how a conversation list and conversation look inside your app.

Customizations

Here are a few customizations you can do to your Android live chat. Note that, these are optional.

🚀 Suggested Read: Add Joomla Live Chat Plugin to Your Website

🚀 Suggested Read: How to Add Live Chat Widget to Your GoDaddy Website

Email collection before starting a chat

Sometimes we need to collect the email before users start the actual live chat. This is normally termed as ‘Lead collection’. To collect lead’s email, you just need to add one more setting in the above method while launching live chat.

new KmConversationBuilder(activityContext)
                 .setWithPreChat(true)
                 .launchConversation(new KmCallback() {
                        @Override
                        public void onSuccess(Object message) {
                            Log.d("Conversation", "Success : " + message);
                        }

                        @Override
                        public void onFailure(Object error) {
                            Log.d("Conversation", "Failure : " + error);
                        }
                    });

You can get more information about pre-chat lead collection here.

Change colors, themes, and other customizations

You can change the colors, fonts, themes and add ton os other customization to your newly added live chat. Here is the detailed instruction to customize the live chat SDK.

Notification Setup

If you want to enable notification for incoming messages you can follow the steps here.

Wrapping Up

In a few simple steps, you can get started with new-age sales and customer service by adding live chat to your Android apps.

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How to Add Live Chat in Android Apps was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.