machine learning Archives - BotCore

Top 5 Ways Intelligent Virtual Assistants Augment Customer Contact Centers in Europe

Roopesh Poojala — Mon, 12 Sep 2022 06:39:25 +0000

Top 5 Ways Intelligent Virtual Assistants Augment Customer Contact Centers in Europe

In today’s fast-paced, digital-first world, customer needs and expectations continue to rise, raising the demand for customer contact centers all over the globe.

In Europe, particularly, the change in customer demographics and the growing popularity of e-commerce has led to increasing pressure on contact centers for customer support. Here, companies, small, medium, and large, believe in focusing on their core operations; hence, Europe is one of the biggest markets that is outsourcing contact center operations.

The call center outsourcing market share in Europe is expected to increase by USD 3.73 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 3.53%. The retail, IT, and telecom sectors will be the most significant drivers of contact center outsourcing in Europe from 2022-2024.

High agent volume and the need for diversity, scalability, and multilingual capabilities are the most significant reasons why most European enterprises are turning to automation and AI to scale their contact center operations.

That’s where intelligent virtual assistants, including chat and voice bots, come into the picture. Virtual assistants, also known as virtual agents, use intelligent conversational interfaces powered by AI, machine learning, and natural language processing technologies to understand and respond to customer inquiries in a human-like manner.

Let’s know the five ways virtual assistants are well-placed to support European contact centers.

Five Ways Customer Support Chatbots Augment Contact Centers in Europe

1. Deliver exceptional customer support across geographies and time zones.

European companies prefer to focus on their core revenue-generating activities and prefer to outsource their contact center operations. However, they mostly outsource to nearby regions and time zones to account for language and cultural similarities.

This strategy suffers from a major drawback. Rapidly changing customer demographics have necessitated quick, 24X7 customer support. Thus, organizations may want to set up contact centers in distant countries to cover customer support beyond their usual business hours, demanding additional resources in terms of staffing and equipment.

AI-powered customer support chatbots transcend locational boundaries, especially in a continent like Europe that has multiple time zones, and provide instant answers to all customer queries at all times.

2. Make omnichannel chatbots the new gold standard in customer support in Europe.

Customers in different parts of Europe prefer different ways and channels of contacting contact center support. For example, the Netherlands has the highest number of non-telephone channel users, with email and web chat being the most popular.

On the flip side, people in Norway, Denmark, and Sweden prefer social media and web chat over email. The bottom line is that people prefer to communicate with brands on the channel of their choice and may even want to switch channels in the middle of an ongoing support case.

That’s where omnichannel chatbots play a crucial role. Virtual assistants can offer omnichannel experiences so customers can experience consistent and personalized brand engagement on their preferred channels. Moreover, customers can seamlessly transition from one channel to another without losing the context of the original conversation.

3. Address inquiries and grievances in customers’ native tongue in the land of many languages.

In a continent like Europe, where people speak multiple languages, it is challenging to personalize customer support for the entire customer base. After all, customers prefer being engaged in their native language. Moreover, it makes them feel comfortable, valued and heard.

Hiring contact center agents who speak multiple languages or training existing ones to speak in different tongues is costly in terms of time and money. That’s where virtual assistants play a crucial role.

AI virtual assistants support contact centers by detecting the user’s language and switching the conversation flow to the native tongue. What’s more, the customer support chatbot can remember the customer’s language preferences and then reach out to them in the same language in case of a proactive outbound engagement.

4. Providing scalable self-service options and data-enriched proactive engagement.

In recent times, Europe has seen steady growth in using self-service solutions like websites, social networking channels (Facebook Messenger, Instagram, Telegram, whatsapp etc.), and IVR. These are easily accessible and take a considerable load off contact centers. To work productively and provide seamless, more meaningful customer engagement, organizations in Europe are embedding customer support bots on these channels.

70% of contact center agents say there are fewer calls when chatbots are available. Customer support bots and virtual assistants can shorten waiting times and collect relevant customer data before the call is passed on to the human agent. In cases where the requests are simple and repetitive, virtual assistants can solve them faster than the contact center agent.

Voice bot platforms have paved the way for conversational IVR in contact centers. Voice bots embedded within traditional IVR systems leverage natural language processing and speech recognition technologies to respond to users’ verbal commands using voice or text, enabling faster, more accessible, and more interactive user support.

Moreover, predictive data analytics maps out the entire customer journey to personalize interactions, reduce mean time to resolve (MTTR), and increase overall customer satisfaction (CSAT).

5. Cost is always a significant factor!

Using virtual assistants — chat and voice bots, leads to major cost reductions for all enterprises.

For starters, outsourced customer service agents cost as much as €22,200 per agent annually in countries like Denmark, Sweden, and Norway. Even for employees hired in Central and Eastern Europe, where contact center jobs are amongst the better-paid ones, the cost of hiring a contact center agent can come out to be around €6,436 per year.

In short, hiring more contact center agents will always prove heavier on the organization’s pockets. On the other hand, intelligent virtual assistants can solve thousands of problems at a few pennies per transaction, making them highly viable, almost a mandate for countries in Europe where labor costs are very high.

How can Acuvate help?

Acuvate helps clients build and deploy virtual assistants – customer support & engagement chatbots and voice bots with its enterprise bot-building platform, BotCore.

As a Microsoft Gold Partner, we have the opportunity of leveraging the best of Microsoft’s AI, machine learning, and natural language processing (NLP) frameworks, including the Microsoft Bot Framework, Azure Cognitive Services, and LUIS.

Besides supporting languages like German, French, English, Latin, and many more (multilingual functionality), our bots work on popular enterprise channels (Teams, Slack, ProofHub, etc.) and social channels (Facebook Messenger, Instagram, Telegram, whatsapp etc.), thus helping organizations engage a geographically-dispersed workforce and customer base.

To know more about our bots and virtual assistants, please schedule a personalized consultation with our experts.

The post Top 5 Ways Intelligent Virtual Assistants Augment Customer Contact Centers in Europe appeared first on BotCore.

Generate 5X ROI on chatbots with a personalized engine

Cinu Clement — Mon, 07 Mar 2022 10:53:00 +0000

Generate 5X ROI on chatbots with a personalized engine

Way back in 1998, Jeff Bezos once remarked, “If we have 4.5 million customers, we shouldn’t have one store. We should have 4.5 million stores.”

More than two decades later, one can say for sure that truer words haven’t been spoken. In today’s digital era, personalized experiences form the heart and soul of meaningful customer engagement. Customers prefer brands that can predict their needs and understand what they want to buy.

As a new age customer, you must have experienced the rapid transformations that have been happening around you. Well, this blog will talk just about that. So, let’s deep dive into the following aspects —

Changing customer expectations in the post-pandemic world
Introducing AI in your customer experience strategy
The perils of not personalizing customer engagement
Use cases of AI chatbots in personalizing the customer journey
Generate 5X ROI on chatbots with a personalized engine
POND’S successful journey to personalizing CX
How can we help

Introduction

91% of consumers say they are more likely to shop with brands that provide offers and relevant recommendations. Additionally, 74% of people hate being shown irrelevant content. Generic, non-personalized messaging can upset customers and lead to churn.

Customer needs have gotten more dynamic and harder to fathom by the day. Yet, customers worldwide continue to demand personalized and seamless engagement. In a survey of 350 executives conducted by consulting firm Gartner, about two-thirds (63%) of digital marketing leaders said they continue to struggle with personalization. The survey also found that even as 84% of respondents believe AI and machine learning enhance the ability to deliver real-time, personalized experiences, only 17% of the executives are using it.

For one, an unprecedented amount of customer conversations are taking place online — tweets, memes, likes, dislikes – and customers expect brands to listen to all these conversations. Secondly, they expect brands to leverage all this information, modify their CX strategy accordingly, and deliver proactive, meaningful, and personalized customer engagement.

The Perils of not Personalizing!

Personalization has become the “holy grail” of marketing. Businesses are striving to garner the benefits of personalized customer experiences (CX), including improved retention and increased ROI.

Organizations that fail to adopt personalization as a vital CX strategy face issues on various fronts, some of which are listed below —

Negative customer experiences and increased churn
Low customer retention
Lost business value
Lack of customer insights
Inaccurate customer data
Loss of competitive advantage

Research by Gartner says brands risk losing 38 percent of customers because of poor marketing personalization efforts.

Artificial Intelligence in the CX World

To empower customers with tailored brand experiences, CX leaders are making vast investments in artificial intelligence-powered virtual agents, like chatbots, voice bots, and conversational IVR.

AI-powered chatbots, in particular, play a significant role in forming connected brand experiences across the entire customer journey. By analyzing customer conversations to gain real-time insights into their preferences, sentiment, and intent, bots can help brands create unparalleled personalization at every stage of the sales funnel, delighting customers and delivering significant ROI from AI.

Let’s deep delve further into how chatbots are helping brands personalize the customer experience and generate 5X ROI.

How are AI chatbots driving personalized CX?

AI-driven chatbots leverage advanced analytics, machine learning, natural language understanding, and cloud technology to collect, store, and analyze vast amounts of customer data, discover patterns in customer interactions, and predict customer needs to deliver proactive and personalized engagement.

In the post-pandemic world, customer experience practitioners are making vast investments in AI to meet their customers at all possible digital touchpoints (omnichannel engagement) and satisfy their desire for instant, flexible, and more personalized interactions.

Moreover, such bots drive higher value customer interactions by proactively recommending intelligent actions to help contact center agents resolve customer issues quickly.

So, how do AI chatbots help brands tailor customer journeys, build lasting loyalty, and produce exceptional ROI?

Use-cases of AI chatbots in personalizing the customer journey

1. Reach:

Reach out to new customers by pushing alerts about products, collecting insights on customer preferences, and sending personalized tips and advice.
Study customer preferences, create cohort profiles, and better match them to recommend accurate products.

2. Acquire:

Acquire customers by curating personalized marketing campaigns, assisting in online shopping, guiding customers on product use, and sending tailored product suggestions.
If the customer’s preferred product is unavailable, AI chatbots can apologize for the inconvenience, suggest alternative products, and provide a “buy now” option to enable a smooth transition.
Suggest frequently bought together products to invoke new thoughts and help customers make better buying decisions.

3. Retain:

Create buyer personas (customer profiles) to understand their priorities and area of focus and send personalized reminders to “buy again.”
Study valuable customer data to predict customer needs, proactively upsell, and develop products based on customer needs.

4. Support:

Progressively profile customers based on their interests and click-rates and proactively recommend articles and FAQs based on their primary concerns.
Answer product-related queries, track orders, help with product use, and offer technical support.
Leverage sentiment analysis capabilities to comprehend customer emotion and respond to customer needs accordingly.

5. Loyalty:

Ask for product ratings and feedback to recommend better products in the future.
Build tailored loyalty programs (freebies, discounts, reward coupons, etc.) to develop lasting customer relationships.

Enjoy 5X returns on chatbots with a personalized engine

Research has shown marketers who exceeded their revenue goals were using personalization techniques 83% of the time. Moreover, businesses that employ data-driven personalization deliver five to eight times the ROI on marketing spend.

Deloitte’s study “Connecting with meaning: Hyper-personalizing the customer experience using data, analytics, and AI” reveals well-executed hyper-personalization can deliver 8X the return on investment on marketing spend and lift sales by 10% or more.

Additionally, personalization can reduce acquisition costs by as much as 50% and increase the efficiency of marketing spend by 10% to 30%.

A recent survey of 200 marketing leaders by Forbes Insights and Arm Treasure Data reveals that where personalization is being applied in a robust way, enterprises see positive results. Two in five executives surveyed, 40%, report that their customer personalization efforts have had a direct impact on maximizing sales, basket size, and profits in direct-to-consumer channels, such as e-commerce, while another 37% point to increased sales and customer lifetime value through product or content recommendations. More than one-third of respondents have seen increases in their transaction frequency due to personalization strategies.

Let’s take a quick example of SAL, POND’s personalized skincare expert.

Women often find themselves googling for skincare products and hacks for fighting uninvited spots and pimples before an important event. Yet, most are still searching for the “right” skin care product for their skin type. POND’S recognized this gap in the market and developed its chatbot SAL to meet this consumer need.

SAL is the “go-to” place for all women spending hours on the internet searching for skincare products.

Rohit Bhasin, Global Brand Vice President, POND’s Unilever, has stated, “POND’s has always been known for providing consumers with expert skincare and making beauty more accessible. We recognize how confusing and overwhelming it can be for anyone to pick the right skincare products when there are so many choices.”

Customers can access POND’s SAL through Unilever’s flagship store on Shopee. SAL leverages powerful technologies like AI and augmented reality (AR) to act as a personalized skincare assistant for users.

With SAL, POND’s has newfound ability to interact three-dimensionally with all its customers and provide immersive shopping experiences to them.

When in need of skincare, all you have to do is open SAL’s chat interface and upload a selfie. Based on your headshot, SAL will identify significant skincare concerns in four critical areas: pimples, wrinkles, spots, and uneven skin tone, and suggest the best product for your needs.

Additionally, SAL will keep sending additional beauty tips to keep you engaged while it analyzes your skin.

SAL’s success can be established because 98% of users have cited positive ratings with the bot. Moreover, 95% of users have enjoyed the personalized shopping experience on SAL.

How can Acuvate help?

According to Gartner, “By 2023, more than 60% of all customer service engagements will be delivered via digital and web-service channels, up from 23% in 2019.”

At Acuvate, we help clients build AI chatbots to assist them in hyper-personalizing customer journeys and generating maximum ROI from investment in AI using our enterprise bot-building platform called BotCore.

A low-code, graphical interface that allows companies to build and deploy AI-enabled chatbots quickly
Bot administrators can create/update bot responses using the drag and drop feature and rich media elements like Buttons, Carousel, Images, Videos, etc.
Supports seamless integration with existing CRM software
Our bots can engage in both simple and highly complex conversations.
BotCore leverages Microsoft’s most renowned AI, machine learning (ML), and natural language understanding (NLU) technologies.
Our bots are deployable on popular enterprise messaging channels (Slack, Teams, etc.) and support multiple languages, including English, German, French, Italian, etc.

To know more about BotCore, please feel free to schedule a one-on-one consultation with our experts.

The post Generate 5X ROI on chatbots with a personalized engine appeared first on BotCore.

Using AI to Accelerate Competitive Advantage

Abhishek Shanbhag — Thu, 25 Feb 2021 08:48:00 +0000

Using AI to Accelerate Competitive Advantage

Today’s business landscape is significantly tech-driven, and AI is at the heart of this change. Indeed, for an organization to fully reap the benefits of artificial intelligence, it is no longer sufficient to experiment with it in bits and pieces. Instead, a full-blown AI-driven digital strategy has become a “must-have” for companies that wish to accelerate growth and retain a competitive advantage.

Mitra Azizirad, Corporate VP for Microsoft AI, was quoted as saying, “In the next five years, every successful company will become an AI-company. It is now the next level of competitive differentiation.” No wonder the global AI market is expected to reach $15.7 trillion by 2030. Those still caught in the loop of experimentation and risk assessment face the dangers of being left far behind than organizations taking practical measures to implement AI.

Indeed, with its ability to operate and analyze faster and more effectively than the human brain, AI provides unprecedented performance benefits. The purpose of this blog is to get an in-depth insight on how organizations can leverage AI to accelerate competitive advantage, look at a few real-life examples of companies using AI to solve business problems, and tips for leaders to implement AI at-scale.

Moving from exploration to true AI implementation

AI capabilities are advancing quickly, and companies are achieving tangible benefits from implementation. While it is true that many companies haven’t deployed AI technologies yet, and some are working towards scaling it, the early-mover advantage of adopting AI may fade away soon.

Research conducted by Deloitte has found, “AI adopters are investing significantly, with 53% of the respondents spending more than US$20 million over the past year on AI-related technology and talent. At the same time, 71% of adopters expect to increase their investment in the next fiscal year by an average of 26%.” Consequently, to retain a competitive edge, leaders may quickly need to move beyond leveraging AI to optimize and automate processes and start using AI to create new products and operation methods.

In such a scenario, starters, or businesses still dipping their toes into AI adoption, with no concrete plan for the future, are at significant risk of jeopardizing their operations.

To implement and scale AI, organizations must consider the technical aspects and the ethical and cultural ones too. Consequently, it is not only essential to strategize in terms of the data, technology, and skills required. Instead, leaders must determine the business ventures in which AI can achieve something productive and ensure the benefits are felt inclusively by everyone in the organization.

How AI accelerates competitive advantage: A peek into the use cases

1. Acting as a smart assistant

AI solutions can act as intelligent assistants of employees and streamline business operations by merely taking over most of the mundane, repetitive, low-involvement work, allowing employees time to focus on the more productive, valuable, and expertise-requiring tasks.

2. Boost marketing ROI

Companies have a significant amount of customer data at their disposal. However, it is only AI, with its ability to learn and provide insights on such data, that helps organizations adopt an objective and data-fuelled approach to meeting customer expectations. Advanced AI analytics enables companies to obtain a holistic, 360-degree view of their customers. Keeping track of customer behavior helps companies tailor product recommendations to the customer’s taste and generates brand loyalty.

3. Data security

When the business landscape is becoming increasingly digitized and many organizations are moving their resources to the cloud, data security becomes a significant matter of concern. AI solutions can track patterns and locate any deviations or anomalies. They can predict and prevent threats and other suspicious activity or outages (for example, a DDOS attack) that can bring the entire network and business operations to a standstill.

Case studies: How companies are using AI to stay ahead

A) Ask Wilbur: Centrica’s chatbot for support agents

Here’s a case study by Microsoft:

Centrica is an energy and services company serving in Ireland, the UK, and North America. Their biggest AI venture, ‘Ask Wilbur,’ leverages natural language processing (NLP) technology to support contact center agents. When a customer reaches out to support, the bot pops up and asks the agent what the customer wants. So, if the agent says, “the customer wants a brand new supply connection,” the bot comes up with a quick list of questions that the agent may ask further. In Centrica’s own words, net promoter score for customers serviced with Wilbur turned out to be higher than for those who weren’t.

Today’s customers expect fast, easy, personalized, and accurate support. Moreover, employees need quick access to information to carry out daily operations and address customer concerns in the shortest time-frame.

AI-enabled chatbots provide information faster and more accurately, thus delivering a fair, competitive advantage. Automated support is available 24/7, and chatbots can quickly provide the relevant service and handle requests at scale with increased efficiency and reduced costs.

B) M&S’s AI-enabled advanced analytics

Let’s look at how Microsoft helped one of the biggest retail chains implement AI:

M&S, one of the most prominent names in UK retail since 1884, holds an outstanding brand image for quality products and services. Within M&S, AI-driven predictive analytics has proven to be valuable in building an efficient supply chain, forecasting customer requirements, and deriving useful insights from complicated datasets to assist with more accurate product management.

With the abundance of data available at every organization’s behest, decision-making at any organizational level has become more data-centric. Such information is used for AI-driven predictive analytics.

Be it decisions related to inventory reporting, purchase dynamics, demand planning, supplier discounts, supply predictions, or even customer preferences and customer loyalty programs, AI-enabled analytics helps leaders make smart choices in every aspect of the business.

C) How ESNEFT hosted its RPA platform on Microsoft Cloud

East Suffolk & North Essex NHS Foundation Trust (ESNEFT), a healthcare organization, post implementing its first-ever RPA solution, did a pilot project for invoice processing in the finance team. By the 12th month, not only was the company able to avoid potential human errors, but it also increased efficiency by releasing about 4,500 hours a month.

Robotic Process Automation (RPA), or RPA, leverages AI and machine learning to perform various routine, repetitive tasks, including calculations, data entry, etc.

When the cognitive ability of chatbots is combined with the automation capacity of RPA, enterprises can automate tasks end-to-end. Moreover, an RPA-enabled bot can integrate with legacy enterprise systems to retrieve information and handle more complex tasks.

Tips for leaders to scale AI

By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.
~ Gartner

As seen above, merely exploring AI in individual pockets of the business will no longer suffice. Instead, embedding AI at an enterprise-wide level will unlock its maximum potential in transforming employees, customers, and business performance.

So, here are a few tips for leaders to implement AI at scale –

View AI as a business change program and not a mere IT project.
Do not force AI on the people; instead, take everyone on board by articulating the reason for the change and the benefits they can reap.
Understand the problem and then brainstorm how AI can solve it.
Make sure your data is in order and ready to be capitalized for data-driven decision-making.
Do not expect organic growth. Instead, build an organization-wide strategy for AI to scale.
Don’t make a single person in charge of an AI-led transformation. Rather, motivate each person to take personal responsibility for the change.

At Acuvate, we assist clients with implementing a host of AI technologies, including advanced analytics, RPA, and chatbots. Our robust AI solutions help businesses build an agile, resilient, and future-proof workplace capable of meeting the expectations of all stakeholders.

Please feel free to schedule a personalized consultation with our AI experts to know more about our service offerings.

The post Using AI to Accelerate Competitive Advantage appeared first on BotCore.

How is AI Transforming Enterprise Software Applications

Rakesh Reddy — Thu, 25 Jun 2020 10:10:00 +0000

How Is AI Transforming Enterprise Software Applications

A recent survey by Gartner predicts, “By 2021, 40% of new enterprise applications implemented by service providers will include AI technologies.”

The world of business is undergoing a massive change owing to the rapid emergence of artificial intelligence (AI) for enterprise applications. Indeed, artificial intelligence has the power to solve several organizational problems as it offers functionalities that humans cannot practically perform at the same rate and accuracy.

AI has quickly changed status from a “technology to experiment” to a “technology to deploy.” By 2025, most enterprises will be using AI-enabled apps to gain a competitive edge from streamline operations, more incredible product innovation, and improved customer satisfaction.

Drivers of this shift include an unprecedented growth of enterprise data, advances in machine learning (ML), natural language processing (NLP) capabilities and the need to accelerate digital transformation journey.

Below, we present you with the latest insights into how AI is transforming enterprise software apps.

A Glimpse at AI for Enterprise Applications

1. Embracing conversational AI to simplify data analytics consumption

While data analysis is critical, it is extremely time-consuming to sift through multiple business dashboards and reports and find relevant data. To overcome such limitations, AI-enabled virtual assistants are integrated with business intelligence apps.

AI-enabled virtual assistants, leverage the NLP technology to converse with users in natural language. By merely initiating a chat on the enterprise messaging app, and sending simple messages like “What is the sales of product A for 2017?”, employees and business leaders can procure in-depth insights in the most granular form of data, without switching between multiple tools and dashboards. Users need not manually filter data to analyze information and arrive at crucial decisions.

This, AI is transforming the consumption of business intelligence and analytics, especially for on-field employees (for example, sales agents) or CxOs, who need quick access to information without having to dig through heaps of data.

Learn More: Business Intelligence Chatbots

2. Securing Every Aspect of Enterprise IT through AI

With the rise of remote working across the globe and as IT decision-making becomes more democratic, enterprises cannot ignore the increased threat of cyber attacks.

To combat the threat and secure every aspect of the IT infrastructure, organizations are scrambling to deploy applications that integrate machine learning to detect possible threats and vulnerabilities in real-time.

These tools use ML techniques to spot anomalies in network traffic, emails and user activities. Hence, they can quickly identify a potential attack and take steps to mitigate it, even if the threat is unlike anything the organization has witnessed before.

3. Transforming IT through AIOps

AIOps, an emerging variation of DevOps, uses machine learning (ML) algorithms on IT operative data to derive insights that optimize and improve operations.

While DevOps automates and simplifies IT operations, AIOps goes a step further by extracting information that is useful in overseeing IT activities.

Sometimes, it can automatically take requisite action based on such insights, thus enabling IT personnel to supervise larger IT environments than otherwise possible.

4. Making Intranet Smarter with AI

AI can make your digital workplace more intelligent – right from content management and collaboration to information accessibility, employee communication, and social networking.

Modern intranets, equipped with AI technologies, serve the following purposes –

Cognitive enterprise search: Cognitive enterprise search is an AI-enabled smart search tool that connects all the internal and external enterprise systems, thus acting as a one-stop-search engine for enterprise-wide knowledge and information It understands natural language phrases and enables shows personalized search results based on the users’ roles, locations, interests and past search activities
Automated metadata management: Enterprises can use AI bots to automate metadata generation, data-tagging, classification, and organization and generate good-quality taxonomy recommendations. As a result, organizations can illuminate and maximize the value of unstructured data
Personalized employee experiences: AI empowers the intranet to break the clutter and deliver personalized content recommendations to the users, based on their interests, locations, and job profiles
Improved collaboration: AI analyzes the user’s persona to suggest the right subject-matter experts to connect with within the organization; thus fostering a collaborative workplace culture
AI-powered analytics: AI-powered analytics help in harnessing and analyzing intranet users’ data. This provides insights into how employees across departments are engaging with the intranet.

5. Combining AI with CRM

The benefits of integrating AI into your customer relationship management are manifold. Here are a few reasons to start contemplating an AI-driven CRM software –

Increased CRM adoption: AI-enabled chatbots boost the CRM adoption rates by

(i) Automating data entry

(ii) Simplifying the data updating process

(iii) Allowing users to access information faster

(iv) Sending personalized updates automatically.

Prediction of future customer behavior: Artificial intelligence can draw learnings from the customers’ past decisions and historical engagement to generate valuable sales. Moreover, AI can analyze customers’ sentiments, to predict their future behavior
Automated segmentation: AI segments customers automatically into groups with similar characteristics and ensures your messages reach the right audience at the right time
Price optimization: AI can analyze past client data to predict the ideal discount rate and pricing that is most likely to lock the sales deal

6. Optimizing Supply Chain Management Through AI

Several enterprises are investing in AI-powered supply chain management apps. Such applications help improve just-in-time deliveries, anticipate potential issues, reduce costs, and recover from supply disruptions.

By leveraging AI-powered analytics, businesses can generate valuable recommendations and forecasts to build a resilient supply chain. Some of these scenarios include – demand forecasting, stock visibility, detecting out-of-stock situations, and supplier risk analysis.

Speaking volumes about the utility of these apps, a study by Mckinsey has found, “among organizations that were using AI for supply chain management, 61% experienced cost decreases, and 63% saw revenue increases.”

Learn More: Creating A Digital Supply Chain For The New Normal

7. Simplifying Vendor Billing through AI

AI is capable of simplifying financial operations within the enterprise.

Moving a step ahead from traditional Optical Character Recognition (OCR) systems that extract data from templated documents, an AI-embedded invoice management software can look at any document and extract all the critical information.

For example, by just feeding the software with invoices from different vendors, AI can figure out who the invoice is from, the due date, the amount to be paid, etc., without any human intervention.

Get Started

AI is and will continue playing a significant role in transforming enterprise software applications. As seen above, one cannot deny the essential role of AI-enabled apps in driving improvements in quality, speed, and efficiency within organizations. While it can be overwhelming for organizations to transform their operations and systems overnight, beginning the AI journey with low-hanging fruits can be a good start.

If you’d like to learn more about this topic, please feel free to get in touch with one of our AI consultants for a personalized consultation.

Further Insights:

The post How is AI Transforming Enterprise Software Applications appeared first on BotCore.

9 Components Of A High-Performing Chatbot

Rakesh Reddy — Wed, 22 Apr 2020 06:31:00 +0000

9 Components Of A High-Performing Chatbot

Chatbot conversations will deliver $8 billion in cost savings by 2022
– Juniper Research

Chatbots are being widely used by businesses. But what does it take to build a truly advanced and enterprise-grade chatbot? Let’s explore!

Conversational UX

An excellent Conversational UX is key to drive adoption and helps users reach their goal in the shortest time with maximum end-user experience. It enables bots to conduct human-like conversations.

Machine Learning

Machine learning algorithms enable chatbots to learn from previous conversations, and deliver better responses in the future.

Natural Language Processing (NLP)

NLP is a technological process that allows chatbots to understand the meaning behind users’ natural language inputs and then deliver relevant responses.

Sentiment Analysis

The technology with which chatbots can analyze users’ input text or voice and understand their emotions and gauge their mood.

Multilingual

Multilingual chatbots are capable of understanding and conversing in different languages. This ability enables bots to cater to a wide range of audience across countries.

Analytics & Administration

Chatbot platforms need to have an administration module in which you can track the bot’s performance, do maintenance activities and train the bot.

By integrating with back-office RPA bots, chatbots can capture information from legacy systems that lack modern APIs and perform actions on behalf of users.

Voice Bots

The next-generation of enterprise chatbots are voice-based. Voice bots relieve users from having to use their keyboard or mouse to send messages.

Cognitive Abstraction

Using cognitive abstraction, chatbot platforms can leverage any AI service available today and will scale for future services.

Build A Modern Enterprise Chatbot!

Ready to get started with your conversational AI journey? Check out BotCore – an enterprise chatbot builder platform driven by AI! Learn how Fortune 500 companies are using chatbots to drive employee and customer experience. Visit www.botcore.ai

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Chatbots Glossary – 35 Terms You Need To Know

Rakesh Reddy — Wed, 15 May 2019 12:26:00 +0000

Chatbots Glossary – 35 Terms You Need To Know

Chatbots have gone so mainstream that most people know what they are and their various applications. However, not many people can talk fluidly about it without asking for explanations on certain concepts. Here are a few chatbot-related terminologies that can help change that. These terminologies will definitely help people to share chatbot-related ideas and information more effectively. Let’s begin!

1. Artificial Intelligence (AI)

AI is a branch of computer science that enables systems to perform tasks that typically require human intelligence. In regards to chatbots, AI helps them conduct more meaningful and intelligent conversations.

2. Aggregator Bot

An aggregator bot is a centralized chatbot which unifies multiple individual chatbots together and prevents the challenges that arise due to fragmentation of chatbots across functions.

3. AI agnostic bots

AI agnostic bots don’t leverage AI to deliver responses. They interact with users based on a set of predefined rules. They can’t answer any questions outside of these rules

4. Broadcast

Broadcast is a message that is sent to all the users interacting with any of the organization’s chatbots irrespective of the channels.

5. Channel

Channel is an authorized medium for the chatbot-user conversation. For e.g.: Skype, Facebook Messenger, SMS, web chat window, and email etc.

6. Chatbot building platforms

A chatbot builder platform is a toolset with which you can quickly and seamlessly build, deploy and manage custom AI chatbots for your enterprise.

7. Chatbot Framework

A bot framework is a toolset which helps you develop chatbots with predefined functions and classes. Frameworks are usually used by developers as they involve some programming or coding.

Learn More: Comparing The Top Bot Development Frameworks

8. Chat Log

Chat Log is the history of the entire recorded human-to-chatbot interaction.

9. Compulsory Input

Compulsory Input is a piece of information that the user has to provide before they can move on to the next stage of the conversation. They usually are Order Tracking Number, Employee ID, or Date of birth etc.

10. Context

Context is the chatbot’s understanding of the scenario provided by the user.

11. Conversational User Experience (CUX)

CUX is the quality of the interaction between humans and computers, which can be in the form of chat, voice, or any other media. An excellent conversational UX helps users reach their goal in the shortest time with maximum end-user experience.

12. Conversational User Interface (CUI)

CUI enables the user to interact with a computer in a more social manner via messages, and conversations. It is a major change from traditional interfaces which involve syntax-based commands or clicking elements.

13. Corpus

Corpus refers to all the stored information about a particular Intent.

14. Decision Tree

With Decision Trees, chatbots help users find what they’re looking for. Using a step-by-step process, they help identify the right answer to the user’s question in a conversational way. The initial question in the question acts as the “root” of the tree.

15. Deployment

Deployment is the process of putting a chatbot in a communication channel where it can start interacting with the user.

16. Entity

Entity is the data that can be extracted from the Utterance. Common entities include names, organizations, places, and quantities.

17. Fallback

Fallback is the case in which the chatbot doesn’t understand the user’s context. It can be handled with a default answer that admits failure, specifying that the chatbot is still learning.

18. Human Handoff

Handoff refers to the scenario in which the conversation is transferred from the chatbot to a human agent. This usually happens when the chatbot is unable to handle the complexity of the conversation, or due to the preference of the user.

Read More: Human Handoff In Service Desk Bots

19. Intent

Intent refers to the goal the user has in mind when asking a question or sending a comment. Identifying user intent is critical to a chatbot’s success.

20. Knowledge Base

Knowledge base is essentially the brains behind the chatbot. It equips a chatbot with the information it needs to deliver the right responses

21. Live-Ops

Live-ops refers to the live chat by a customer service agent. Chatbots can be configured to transfer the conversation to a live agent if the bot is unable to deliver a satisfactory response.

22. Machine Learning

Machine learning enables chatbots to learn from the past user conversations and deliver personalized and better responses in the future.

23. Maintenance

Maintenance means analyzing the new learnings and actions of the chatbot.

24. Multi-factor Authorization (MFA)

MFA refers to the use of more than one method to authenticate a user’s identity.

25. Multilingual Chatbot

A Multilingual Chatbot allows enterprises to converse with users speaking various languages. They are capable of conversing in multiple languages – not just translation.

Learn More: Multilingal Chatbots: Benefits And Key Implementations

26. Natural Language Processing (NLP)

NLP is a technological process that allows chatbots to derive meaning from user input, either text or voice-based, and then act on it.

27. One-time Authorization

One-time Authorization is the process of validating the user’s identity for only one session.

28. Optional Input

Optional Input is a piece of information the user gives to the chatbot, that is not crucial to the conversation.

29. Quick Replies

Quick Replies are the suggestions that appear after a chatbot’s response, which prompts the users to continue the conversation as per the predesigned pathway. They can be questions, answers, or assertions, depending on the conversation flow and voice tone.

30. RPA Bots

RPA bots refer to the integration of front office chatbots with back office RPA software. This enables end-to-end automation and chatbot integration with legacy systems.

Learn More: RPA Bots: Understanding The Chatbot And RPA Integration

31. Sentiment Analysis

Sentiment analysis helps a chatbot to understand the emotions and state of mind of the users by analyzing their input text or voice.

Learn More: Understanding The Role Of Sentiment Analysis In Chatbots

32. Session

Session is the time period of the interaction between the user and the chatbot.

33. Training Phrases

Training Phrases are the simplified and annotated sentences used to train the Machine Learning algorithm of chatbots. They are associated with an Intent and mapped through Entities, helping the algorithm learn and improve.

34. Utterance

Utterance is the input provided by the user during a conversation with the chatbot. They are different from Training Phrases since they are spontaneous.

35. Voice Assistants

Unlike chatbots which converse with users via text, voice assistants or voice bots interact with customers and employees via voice. Amazon Alexa, Microsot Cortana, Google Assistant are some examples of voice assistants.

Learn More: How Voice Assistants Are Transforming The Enterprise Workplace?

While these are some key chatbot terms you need to know, there are many others that are equally important.

If you’d like to learn more about this topic, please feel free to get in touch with one of our enterprise chatbot consultants for a personalized consultation.

The post Chatbots Glossary – 35 Terms You Need To Know appeared first on BotCore.

Understanding The Role Of Sentiment Analysis In Chatbots

Abhishek Shanbhag — Fri, 01 Mar 2019 10:00:00 +0000

Understanding The Role Of Sentiment Analysis In Chatbots

Chatbot technology has had an undeniable impact on digital transformation of organizations; customer experience management, in particular. When we talk about conversational AI solutions and other AI-based applications for augmenting customer and employee experience, chatbots emerge as a front-runner.

According to Gartner, 70% of white-collar workers will engage with conversational platforms on a regular basis in the next three years. The research firm’s 2019 CIO Survey revealed that chatbots are the main AI-based application used by the participating companies. Therefore, we can see increased investment in chatbot development and deployment.

“There has been a more than 160% increase in client interest around implementing chatbots and associated technologies in 2018 from previous years. This increase has been driven by customer service, knowledge management and user support,” shared Van Baker, VP Analyst at Gartner.

However, in the earlier stages, chatbots used to have limited capabilities and deliver standard responses. With the advancements in AI and machine learning, chatbots have become more powerful and incorporated new features that helped improve user experience. And one of these latest features that is taking user experience to the next level is sentiment analysis.

Sentiment analysis helps a chatbot to understand the emotions and state of mind of the users by analyzing their input text or voice. This analysis enables chatbots to better steer conversations and deliver the right responses. Sentiment analysis is also playing a key role in driving user adoption for enterprise chatbots.

Let’s deep dive!

understanding sentiment analysis

Sentiment analysis is a sub field of machine learning and natural language processing that deals with extracting thoughts, opinions, or sentiments from voice or textual data. It is currently widely used in marketing and customer service functions to analyse customer data from surveys, social media and reviews. This not only enables businesses to understand the impact of their products/services but also to tweak their strategies as per the end consumers’ opinions.

In the context of chatbots, sentiment analysis helps in developing the bot’s emotional intelligence.

While machine learning helps to personalize the chatbot’s performance by harnessing historical customer data, NLP helps to evaluate and interpret the information sent by the customer in real-time.

These two features collectively help chatbots to deliver relevant responses and conduct meaningful conversations. Sentiment analysis takes this a step further by enabling bots to understand human moods and emotions.

Let’s break down how sentiment analysis in chatbots works:

It first identifies sentiment types and gauges if the emotions displayed in the conversation are positive, negative, neutral or objective. The technology detects emotions like anger, happiness, disgust, fear, sadness, curiosity, positivity and other range of emotions.
NLP and AI work in tandem to measures the intensity of the emotions and assign a numerical score to each of the core emotions.
After detection and classification, sentiment analysis presents the final output that enables chatbot to steer the conversation in the right direction. For example, for a text with a high positive score (joy + happiness), the digital assistant can use that as an opportunity for product recommendation or sales conversion. And in the case of a high negative score (sad + anger), the chatbot can escalate the complaint and transfer the call to a live support agent.

how can it be beneficial for your business?

Be it banking, insurance, hospitality, healthcare, travel or eCommerce, all customer-facing industries can benefit from sophisticated new-age chatbots that are integrated with sentiment analysis.

Take, American cosmetics brand CoverGirl, for instance. The company developed an influencer chatbot enabled by sentiment analysis, which helped them to improve mobile commerce performance. 91% of the conversations via the chatbot earned positive sentiment, and on an average 17 messages were exchanged per conversation that reflects high engagement rate. In addition, 48% of those conversations led to coupon delivery and the coupons’ click through-rate was an impressive 51%.

The above example illustrates the effectiveness of sentiment analysis-powered chatbots in stimulating conversations, identifying customers’ intentions, providing relevant answers and delivering a meaningful customer experience.

Listed below are a few of the benefits of using a sentiment analysis enabled chatbot to augment customer experience.

Learn how customers feel about your brand

Emotions heavily influence a person’s decision making process. How a customer is feeling determines the length and the nature of the relationship with the brand. Make use of this technology to understand how customers are feeling about your brand and communicate effectively at any stage of the customer lifecycle.

Seamless agent handover

It is important to understand the impact of timely escalation of issues to human agents when it comes to customer service chatbots. In the absence of sentiment analysis, chatbots would not be able to sense the tone of the aggrieved customer. But a digital assistant with emotional intelligence will help businesses to deal with displeased customers in an efficient manner. If the customer sounds frustrated or angry, the bot can easily hand off the conversation to a human agent.

Learn more: Human Hand-off in Service Desk Bots

Memorable customer experiences

The basic intent of sentiment analysis is to personalize and modify a chatbot’s responses to match the customer’s mood. This will enable businesses to build engaging conversations with customers at a very early stage and create a delightful customer experience.

Keep track of performance

The biggest benefit of using sentiment analysis is that it provides unique and powerful consumer insights. The conversations of an emotionally intelligent chatbot can act as a treasure trove of accurate data, which can be used to measure effectiveness of products/service, design future strategies, segment the customer base and devise strong brand positioning.

Upselling and new user on-boarding

As exemplified by CoverGirl’s influencer bot, chatbots can assist companies in product discovery, recommendation and upselling their products & services to existing customers. It can also improve new customer acquisition metrics by retaining the interest of a new visitor by analyzing his/her sentiments.

As stated above, emotions influence decision-making. Sentiment analysis help chatbots to adapt to the users’ mood and respond accurately, effectively and in the right way. By deploying and investing in this technology, companies can not only improve customer experience but also allow human agents to focus on productive issues.

If you’d like to learn more about the role of sentiment analysis in chatbots, please feel free to get in touch with one of our AI chatbot consultants for a personalized consultation.

FREE EBOOK

a guide to choosing an enterprise bot builder platform

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7 quick tips for designing a chatbot personality

Abhishek Shanbhag — Wed, 20 Feb 2019 12:52:00 +0000

7 Quick Tips For Designing A Chatbot Personality

One of the key factors for creating better conversational user experiences (CUX) and driving chatbot user adoption is the chatbot personality. Having the right personality enables the chatbot to conduct human-like, rich, personalized and relatable conversations with users and establishes an emotional connection with the user.

If the chatbot is built for a customer-facing function, its personality should ideally mirror that of your company’s and should be tailored keeping the end-user in mind. This is crucial as your bots are a representation of what your brand stands for and the experiences you want to deliver to your customers.

Humans are usually hard to please but can be frustrated easily. Hence, your chatbot’s personality should be consistent at every stage of the conversation – right from customer greeting, query handling, providing information to conversation sign-off.

7 quick tips for designing a chatbot personality

understand the persona of your target user

When designing your chatbot personality, keep in mind the demographic, age group and other key personality traits of the end-user the chatbot interacts with. For instance, if the majority of your end-users/customers are between 25 – 40 years, giving the chatbot an adolescent-like persona isn’t the best fit. Understanding the personality of the audience, their oft-used colloquial/slang language, verbatim, habits, mannerisms, interests, etc. can help in tailoring the personality of the chatbot to the customer base.

purpose of the chatbot

It is very important to design the personality of the chatbot according to its purpose. If the chatbot is built to conduct serious conversations like handling customer complaints or helping customers with time-sensitive actions, the chatbot should be efficient and straightforward with questions and responses. Trying to be clever with witty responses is the last thing the chatbot should be doing in such a situation.

brand tone of voice

Brands often use a specific Tone-of-Voice in order to successfully communicate their personality to the consumer. Maintaining a consistent tone of voice across all platforms of communication such as social media, marketing brochures, websites, etc., helps establish how the consumer perceives the brand.

Similarly, when developing a personality for the chatbot, it is important to factor in the tone-of-voice since users tend to perceive the brand through the chatbot. Maintaining consistency between the brand tone of voice and the chatbot’s use of it will inculcate user trust.

design chatbot personality at a country level

One of the typical strategies used when rolling out chatbots in multiple countries and languages is building the chatbot personality at a global level. This is not only incorrect but also risky for a chatbot roll out.

Cultures differ with regions. Some conversations that are polite in one country aren’t deemed the same way in another country. The word “crazy” might sound funny in the UK but it’s offensive in the US. So, it’s very important for Conversational Architects to build a chatbot personality at a country level than at a global level.

This means having a single Conversational Architect for a multilingual chatbot wouldn’t be enough – even if he/she has an exceptional cross-cultural understanding. Having a cross-cultural team of Conversational Designers is a better bet. This will help to bring in the flair of language in conversations which might be very locale-specific at times.

Learn More: Multilingual Chatbots: Benefits And Key Implementation Considerations

the greeting/opening a conversation

The greeting or the first message (first interaction) the bot sends to the customer is a crucial element of conveying the bot’s personality. Ideally, the bot should not only introduce itself but also convey the different services it offers. Instead of greeting with open-ended questions like “How can I help you”, the bot should send specific messages like “I can help you with raising an HR ticket, answering common HR questions, or connecting with an HR agent”

There are several ways of opening a conversation – “Hello”, “Hi”, “Yo”, “Greetings” etc. and all these reflect different personalities.

handling unexpected and unknown questions

Even when a human asks the bot a random question or something that has nothing to do with your offerings, the chatbot must still be able to offer a reply, no matter how rudimentary the question is. This characteristic, in turn, helps the human form an emotional connection with the chatbot. The chatbot should also have multiple none-intent responses. Sending a standard “Sorry, I didn’t get that” response every time a user asks an unknown question leads to bad CUX.

humour

Just as in everyday social interactions, humor tends to have a positive effect on how humans perceive conversations. It helps engage the user, especially in interactions or processes that may be long and arduous. A chatbot that is capable of humorous parlance helps the human user get more involved in the communication and perceive the chatbot as an emotionally intelligent entity. With the help of machine learning and NLP, enterprise chatbots can be trained to recognise humorous expressions, assess the user’s mood and respond appropriately.

conclusion

Designing a personality for your chatbot seems like a lot of work, but a chatbot with a great personality ultimately enhances the user experience for your customers who interacts with it. This, in turn, promotes greater user adoption, customer experience, and sales.

If you’re interested to learn more about this topic, please feel free to get in touch with one of our chatbot consultants.

FREE EBOOK

a guide to choosing an enterprise bot builder platform

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Top 5 Myths About AI You should stop believing

Abhishek Shanbhag — Mon, 11 Feb 2019 19:46:00 +0000

Top 5 Myths About AI You Should Stop Believing

As much as AI and its abilities are being spoken about and speculated today, there are quite a few myths that are doing the rounds as well. While some believe that it to be a deus ex machina, a tool that can resolve seemingly unsolvable problems, there are others who see it as just hype that will eventually die down. The reality about AI however, lies somewhere in between.

With the growing need for technology to make processes more efficient and provide companies with a competitive advantage, it is crucial to understand how AI can create value in your business and what its limitations are.

Alexander Linden, a Vice President Analyst at Gartner says “Business leaders are often confused about what AI can do for their enterprise. This is understandable, as there are many definitions and variants of AI that are present in the general discourse”. Misconceptions and a lack of understanding about AI is an impediment for IT leaders who are trying to incorporate AI in their organizations.

In this article, we bust the 5 myths that are often associated with AI and make a case for why you should stop believing them NOW.

The top 5 AI MYTHS

1. There Is No Need For AI Strategy

As the abilities of AI are evolving, organizations should consider tapping into its capabilities and understand the potential impact this technology may have in addressing the organization’s business needs.

Deliberately refraining from using AI is akin to forgoing the next phase of automation and could put your organization at a competitive disadvantage.

Even if you choose to abstain from adopting an AI strategy for your company, this decision should be made on pragmatic grounds supported by research and robust data. The need for AI in your organization should also be regularly evaluated and modified to fit the organization’s evolving needs.

2. AI Is All About Automation

While it is true that AI can automate several manual tasks, its range of capabilities are not always synonymous with automation itself. Automation is a subset of AI.

According to an extensive survey by Deloitte Research of AI applications in various industries, AI applications fall into three categories: product, process, and insight.

Product applications use AI to provide a better experience for the end user, either by enabling “intelligent” behavior or by automating tasks that a human user often performs.
AI is used in Process applications to enhance, scale up, or automate business processes.
Insight applications on the other hand use AI, machine learning and computer vision to analyze data and glean insights in order to drive better business decisions.

In only some of the cases is AI really used to automate human work. More often, AI is used to perform tasks that are unattainable by human cognitive abilities. While automation refers to the completion of a task without human interference, AI is used to power machines, making them capable of thinking or at least making intelligent decisions based on a series of predefined models and algorithms.

3. AI, Machine Learning And Deep Learning Are The Same

AI, Machine Learning and Deep learning are often interchangeably used or misunderstood to be the same, despite being fundamentally different.

Artificial Intelligence is the human-like intelligence exhibited by machines that encompasses various human cognitive abilities.
Machine Learning is a set of algorithms that allows a program to generate results accurately without the results being fed into the program explicitly. ML is in fact one of the tasks that AI performs by continually learning from the data fed into it and can be referred to as a sub-discipline of Artificial Intelligence.
Deep Learning, however, refers to the algorithms used to solve problems based on neural networks designed to mimic the neurons in the human brain. DL is one of the specialisations of ML and in turn, one of the aspects of AI.

4. AI Will Replace Humans

A common AI myth is that it can outsmart and replace humans at some point. This misconception is also one of the major barriers to AI adoption.

While it is true that AI makes machines extremely intelligent, we need to understand that machines cannot acquire such a potential all by themselves. The capabilities of a machine is limited to the data that is fed to it by a human being and the actions that they have programmed the machine to carry out.

It is important to identify the obvious benefits AI and ML add when it comes to automatically identifying patterns from an expansive amount of data with little to no human intervention. However, the algorithms and models that make this feat possible, have to be built by humans. So essentially, AI only can get as smart as a human mind can make it.

Another crucial feature that sets human intelligence well above AI is that humans are capable of recognizing when there is a problem or redundancy with a certain approach they are taking. AI models on the other hand, tend to pursue the best possible answer out of nearly infinite possibilities, even if it leads to them never exiting the process.

5. AI Is A Cost-intensive Undertaking

AI is looked at with apprehensive often, based on the assumption that it is going to be a costly investment for the company. However, high cost is not the case for AI in general. There are many AI tools that are available for businesses that do not demand an exorbitant investment to implement AI solutions. Some important AI solutions which are cost effective and also yield a huge ROI and several business benefits include chatbots, RPA, modern intranets with AI capabilities and advanced analytics.

On the other hand however, AI development does require expertise in programming languages and development practices. While hiring data scientists for this job may rack up costs, organisations can consider training the existing resources to effectively implement the algorithms themselves.

conclusion

It is necessary that companies take concerted initiatives to develop comprehensive strategies that accommodate AI and prepare themselves for futuristic environments that are compatible with AI powered technologies. It is imperative for companies to act on these initiatives before any new market disruptors jeopardise the competitive edge the organization may hold in the industry.

This demands the leadership of organisations to have a pragmatic and precise understanding about AI’s capabilities and its trajectory into the future. Having a strong academic foundation and practical experience in AI among the leadership allows organisations to avoid misinterpretations and misleading myths about AI.

Adopting AI just for just a few functional areas will be ineffective in making a great impact on the organization as a whole. Hence, companies must try to incorporate an effective blend of embedded, edge and centralized intelligence systems across all functions and teams.

Adopting AI and related technologies in building an intelligent business environment will strengthen the alliance of humans and intelligent machines and produce a powerful workforce for the future. Companies must acknowledge that humans and machines will continue to be the indispensable building blocks of the new workforce and plan to utilise their combined strengths effectively.

Acuvate believes that organizations need AI to accelerate their digital transformation journey. Our range of solutions and services around conversational AI, digital workplace, business intelligence and RPA are built with a strong AI foundation. If you’d like to learn more about AI and its capabilities, please feel free to get in touch with one of our AI experts for a personalized consultation.

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RPA Bots: Understanding The Chatbot And RPA Integration

Rakesh Reddy — Mon, 10 Dec 2018 17:40:00 +0000

RPA Bots: Understanding The Chatbot And RPA Integration

“AS AI BECOMES MORE COMMON, APPLICATIONS THAT EMPLOY IT MUST WORK EFFECTIVELY WITH OTHERS EMPLOYING SIMILAR TECHNOLOGIES, WHICH WILL RESULT IN CHAINS AND MESHES OF AI SYSTEMS THAT WORK SIMULTANEOUSLY TOWARD THEIR INDIVIDUAL GOALS IN A COOPERATIVE BUT DECOUPLED FASHION”
– GARTNER

If you’re following the latest trends in the enterprise AI ecosystem you’re probably aware that chatbots and RPA are two of the most popular and widely adopted AI technologies today. In the 2019 Gartner CIO Survey, CIOs identified chatbots as the main AI-based application used in their enterprises. But have you ever wondered about the possibility of integrating these two powerful technologies together to build an even advanced technological powerhouse?

We’ll explore this thought further in this article and discuss how this super-interesting combination of AI technologies can solve some major challenges for enterprises.

While there are several trends impacting the chatbot ecosystem, an extremely important and interesting trend is the integration of a chatbot with Robotic Process Automation (RPA). Enterprise chatbots already solve many challenges pertaining to customer service, employee self-service, and scalability that organizations are facing today. Now, by combining the power of automation from RPA and cognitive intelligence from chatbots the entire experience can be taken to the next level, improving productivity and business advantage. This can happen when chatbots are integrated with RPA to provide an intelligent, automated and end-to-end customer and employee experience.

As chatbots are increasingly being used to perform a range of tasks across various functions, they will need back office RPA robots that are capable of quickly finding information and performing transactions on behalf of users.

Before learning more about chatbot and RPA integration, let’s have a quick overview of RPA.

RPA and its growing importance

So how does RPA work? RPA is the use of AI software and machine learning capabilities to perform diverse, repeatable tasks such as handling queries, record transactions, entering data, perform calculations etc. that were so far being manually performed by humans. RPA is currently being used to streamline and automate back-office operations in HR, Finance, accounting, etc.

According to Gartner, Robotic process automation (RPA) software revenue grew 63.1% in 2018 to $846 million, making it the fastest-growing segment of the global enterprise software market. By the end of 2019, it is expected to reach $1.3 billion. RPA is being adopted so widely today that Gartner estimates RPA software spending to total $2.4 billion in 2022.

Implementing RPA software does not require changing the existing infrastructure and systems. RPA software sits on top of your existing IT infrastructure – as a layer, as opposed to being a part of it.

Hence, RPA technology can quickly be integrated with the existing infrastructure and systems.

But what makes RPA different from a traditional IT automation system?

Unlike traditional IT automation, RPA technology is capable of adapting to changing circumstances and new situations. Once trained to understand the actions of specific processes in the required applications, RPA software can then manipulate data, trigger responses, initiate new actions and communicate with other systems autonomously.

chatbots integration with RPA

Chatbots (front office bots) converse with customers or employees to send information, complete tasks or capture their requests. Based on the use case, a bot needs to integrate with and access information from different enterprise systems. These systems can be around help desk, intranet, CRM, Business Intelligence, LOB, HR knowledge bases and so on.

If these systems have modern APIs, then the chatbot can access the required information independently and without any issue. However, if the systems lack modern APIs, the chatbot may not be able to integrate and retrieve information.

This is where RPA comes into play. Integration of RPA helps chatbots effectively navigate through legacy enterprise systems that do not have modern APIs.

The RPA-chatbot integration is a powerful combination and serious game-changer for two reasons

An RPA powered chatbot can integrate with disparate and multiple back-end enterprise systems. RPA enables chatbots to retrieve information from these systems and handle more complex and real-time customer/employee requests and queries at scale.
In the same way, chatbots, upon a user’s request, can trigger RPA to perform specific mundane tasks without routing them to a human agent.

The combination of chatbots and RPA can solve several common problems enterprises are facing today. It helps organizations to meet the rising customer expectations at lower costs by combining the automation capabilities of RPA and the self-service features of a chatbot. This also results in increased agent productivity as agents don’t have to spend time on mundane and routine activities like gathering customer data, copying information, completing paperwork, etc.

In addition, with RPA, bots can become more proactive and take the personalization of customer experience to the next level. By leveraging machine learning, RPA bots can harness and analyze large volumes of customer data and send meaningful, contextual and individualized up-sell and cross-sell offers to customers.

business benefits

Some key benefits of RPA bots include

Improved employee and customer experience
Reduce business costs
Reduced time to complete tasks
Increased employee productivity
Increased competitive advantage

the road ahead

Both RPA and bot technologies are still evolving. As the technology of RPA bots matures further, organizations can create intelligent conversational experiences for customers and employees, reduce business costs and increase the productivity of agents. RPA and chatbots complement each others’ functionality. Chatbots need the power of RPA to complete complex tasks and support meaningful conversations and RPA needs the conversational interface of chatbots to take automation to the next level – self-service automation.

Acuvate helps enterprises with a range of RPA and chatbot solutions and services.

If you’re interested to explore this topic further and want to discuss the possibility of deploying RPA bots for your organization, please feel free to get in touch with one of our RPA and chatbot consultants.

If you’d like to learn more about this topic, please feel free to get in touch with one of our experts for a personalized consultation.

The post RPA Bots: Understanding The Chatbot And RPA Integration appeared first on BotCore.

machine learning Archives - BotCore

Top 5 Ways Intelligent Virtual Assistants Augment Customer Contact Centers in Europe

Top 5 Ways Intelligent Virtual Assistants Augment Customer Contact Centers in Europe

Five Ways Customer Support Chatbots Augment Contact Centers in Europe

1. Deliver exceptional customer support across geographies and time zones.

2. Make omnichannel chatbots the new gold standard in customer support in Europe.

3. Address inquiries and grievances in customers’ native tongue in the land of many languages.

4. Providing scalable self-service options and data-enriched proactive engagement.

5. Cost is always a significant factor!

How can Acuvate help?

Generate 5X ROI on chatbots with a personalized engine

Generate 5X ROI on chatbots with a personalized engine

Introduction

The Perils of not Personalizing!

Artificial Intelligence in the CX World

How are AI chatbots driving personalized CX?

Use-cases of AI chatbots in personalizing the customer journey

1. Reach:

2. Acquire:

3. Retain:

4. Support:

5. Loyalty:

Enjoy 5X returns on chatbots with a personalized engine

Let’s take a quick example of SAL, POND’s personalized skincare expert.

How can Acuvate help?

Using AI to Accelerate Competitive Advantage

Using AI to Accelerate Competitive Advantage

Moving from exploration to true AI implementation

How AI accelerates competitive advantage: A peek into the use cases

1. Acting as a smart assistant

2. Boost marketing ROI

3. Data security

Case studies: How companies are using AI to stay ahead

A) Ask Wilbur: Centrica’s chatbot for support agents

B) M&S’s AI-enabled advanced analytics

C) How ESNEFT hosted its RPA platform on Microsoft Cloud

Tips for leaders to scale AI

How is AI Transforming Enterprise Software Applications

How Is AI Transforming Enterprise Software Applications

A Glimpse at AI for Enterprise Applications

1. Embracing conversational AI to simplify data analytics consumption

2. Securing Every Aspect of Enterprise IT through AI

3. Transforming IT through AIOps

4. Making Intranet Smarter with AI

5. Combining AI with CRM

6. Optimizing Supply Chain Management Through AI

7. Simplifying Vendor Billing through AI

Get Started

Further Insights:

9 Components Of A High-Performing Chatbot

9 Components Of A High-Performing Chatbot

Conversational UX