#Chatbot Archives

What is DevOps? – AWS

Posted on 16/04/202131/05/2021 by Anteelo Master

What is DevOps?

DevOps (Development & Operations), a design and operations system, is an approach that works closely with software developers and operations. DevOps was developed in 2009 with the aim of improving communication and integration development and operations in order to benefit fully from modern software development approaches.

The entire range of application management operations has become considerably more complex in this complex environment. At the same time, software applications must be designed, developed, deployed, architected, and monitored via an ongoing, never-ending lifecycle. DevOps is an agent of change in the current business environment, which supports such complex applications.

The clear answer to the frequently asked question, “What is DevOps” is DevOps is a collaborative, team-based approach to technology delivery that leverages experts with cross-functional development and organizational expertise to address issues relevant to applications.

Characteristics of a good DevOps

Corporations and companies have varying degrees of success in introducing and implementing a DevOps system for their IT departments, these are the common characteristics they show:

1. Short, iterative development cycles

DevOps allows an organization to develop and launch new products and services more efficiently through a build-measure-learn process, beginning with minimally viable products (MVPs) running on the production infrastructure.

2. Driven by Metrics

Use lean startup methodologies, evaluating ideas, and designing of MVPs offers immediate feedback to product or service creators and backers, providing valuable data to decision-makers on the continued feasibility of the current process.

If a pivot is required to align the plan more closely with the organizational and strategic priorities, the indicators will support that decision.

3. Focused on People

Even in library systems with a single member, the DevOps team of a library must always concentrate on the people being served. Professional resources and technology departments of the library need to guide research and decision-making based on the actual use of members of the library system and the interest of library leadership in looking for the edges of their populations.

DevOps working Lifecycle

DevOps principles are not only applied throughout the lifecycle, but they include growth, operations, and business stakeholders at all times. The DevOps framework explores the challenges of serving the dynamically changing and business-critical architectures of today’s technology.

In this model, the DevOps interactions at each stage are as follows:

1. Assess

Business stakeholder feedback (for new services) and service-level agreement reviews, (for existing services) are used to define goals for new business offerings and to modify existing ones.

2. Design

At this stage design and development are given importance. The primary responsibility of design is to incorporate requirements into a software design. Business is responsible for educating production on specific requirements, as well as evaluating the project and signing it off.

3. Develop

Development is ultimately responsible for building software that meets the needs of the business. Operations are introduced when necessary to support development teams in building and testing environments and/or to advise on network requirements and expected delivery times.

4. Test

While Development and QA teams perform unit and integration testing, Operations participates in integration and load testing to assess operational readiness.

5. Release

This is the traditional handoff stage of DevOps, but the handoff in this scenario is a change in leadership roles versus a shift in responsibility. Development and Operations (or DevOps) teams lead this stage, while end-user acceptance processes are conducted by business stakeholders.

6. Manage

Infrastructure, systems and application management tools track manufacturing environments and applications during this phase. Service level management (SLM), quality and availability management, troubleshooting/root cause analysis and resource management solutions all track and assess the output of applications as part of ongoing assessments.

AWS DevOps

AWS DevOps with Amazon security & Amazon cloudwatch provides flexible and speedy DevOps services for companies.

Features

1. Monitoring

Enables monitoring and logs to see how the quality of software and infrastructure affects the end-user experience of their service.

2. ChatOps (AWS Chatbot)

It is the latest feature which easier to review the notification and execute commands in Slack channels and Amazon Chime chat rooms. Chatbot supports:

Amazon CloudWatch
AWS Health
AWS Budgets
AWS Security Hub
Amazon GuardDuty
AWS CloudFormation

3. Microservices

Microservices turn the task of building an application into several microservices. Each system operates independently and communicates with other services via a well-defined interface using a lightweight mechanism.

4. Infrastructure as Code

The cloud-driven API model allows developers and system administrators to communicate programmatically and on a scale with infrastructure, rather than setting up and configuring resources manually.

5. Continuous delivery

DevOps have become essential elements contributing to the rise of continuous delivery. Continuous delivery is a software development practice, the term describes an iterative and continuous process of software development, evaluation, and distribution to the desired endpoint. The destination may be a manufacturing setting, a staging environment or a set of software products.

Advantages of DevOps

Speed: The developers and operation team is able to get their results faster
Scale: Operate and manage infrastructure and growth processes at scale. Automation and reliability allow you to handle complex and evolving processes effectively and with reduced risk.
Reliability: Devops highly reliable because of the quality of application updates
Security: You can use automatic enforcement protocols, fine-grained processes, and configuration management tools to implement a DevOps framework without compromising security.
Collaboration: Collaboration of developer and operation using DevOps team increases efficiency and save time

In this journal, we have discussed the enhanced definition of and vividly taken a deep dive in what is DevOps. We can now understand that DevOps provides software development teams with the ability to control and accountability to deliver software efficiently and often especially for small to medium-sized enterprises in the production environment.

In this case, the capability comes from their ability to acquire new skills from operations-related tasks while operating under pressure at the same time. I also established that DevOps in companies is a long-term endeavor that, in addition to technological methods, requires a supportive culture and a mindset. Such cross-functional collaboration is most effective when supported by senior management and customers.

How to measure success of a conversational AI chatbot

Posted on 30/01/202114/05/2021 by Anteelo Master

Last year when one of our healthcare partners (we refer to our clients as partners) was looking to build a conversational AI chatbot, I was apprehensive about guiding them. I had only worked on the level 2 (out of the 5 levels of conversational AI) type of bots. But this time our partner wanted to build a contextual/consultative AI-powered chatbot assistant.

I was concerned about how the bot would understand end users’ problems. What features can we build to make it more humanistic? Would it be successful in replacing human care and compassion? Would it replicate the same emotions of empathy, compassion, and care?

And even if we managed to do everything, how would we know if the conversational AI chatbot is working the way we designed it? How would we define the ‘success’ of our initiative?

My apprehensions became real when I read a Forbes article about chatbots killing customer service with their clumsy conversations and limited learning capabilities. After reading the below paragraph, I realized the problem-

“The AI didn’t always get it, which was frustrating. Even more irritating — the company using the chatbot seemed to shrug the problem off. I detailed my own experience using Skyscanner’s chatbot, which often misunderstood my requests. Some of the companies I mentioned in the column appeared to shrug off my concerns.”

The problem is with organizations/management who choose to look away and see the importance of data analytics in chatbots for healthcare. They think that understanding the users’ behavior, what disappoints them, what makes them happy, is beyond the scope of their work. Because of this mindset, chatbots are still a lost cause.

Is there a solution in sight?

Yes, indeed there is. We’re at a very interesting place where we hold the future of chatbots in our hands. To make chatbots more welcoming and user-friendly, we not only need to make its software side–engineering, UX design, security– more robust. Rather, we should strive to make data analysis a part of the development process– i.e. we must constantly monitor chatbot’s effectiveness and improve features as per users’ needs.

How can we measure a chatbot’s efficiency?

Building a good conversational AI healthcare chatbot is a daunting task. Even after launching it as a service, one can’t be sure of its success. That’s why it’s crucial to measure every interaction with the end users.

There are certain indicators that one can track to see if the chatbot is serving its purpose.

Botanalytics

If you’re looking for a tool that gives you an overview of the user’s lifecycle, then Botanalytics is for you. It’s a great tool for identifying bottlenecks in the user’s journey. You can deep dive into every conversation (transcripts are available for each conversation) and see where your bot failed to respond.

You can set various goals and categorize chats into conversation paths. This is a great feature as it helps you examine which conversation attained its goal and which didn’t.

For example- if your goal is to make users download your mobile app through the link provided in the chat, then this tool will show you how many conversations ended up in accomplishing that goal.

You can also set conversation paths and check how many conversations were successfully handled by the chatbot.

Grafana

Grafana is not a bot-analytics tool. Rather, it’s an open source platform that can be used to monitor applications, websites, and even custom data sources. We integrated it with our platform to use it as a chatbot analytics tool.

One of the advantages of using Grafana is that it’s very easy to customize and you can tweak its dashboard to suit your needs.

If you have a chatbot where there is a lot of data to understand, analyze, and dissect, then you must explore Grafana. Icing on the cake? It’s free. And like I mentioned earlier, highly customizable. You can create dashboards, add panels, change visualizations as per the need of evaluators and stakeholders.

Chatbase

Chatbase is a free cloud-based tool that allows you to integrate your bot into the analytics platform. One of the best features of Chatbase is that it helps you both analyze and optimize your bots.

In the analytics part, Chatbase has every possible feature that you can imagine- session flows, creating funnels, the grouping of not-handled messages, chat transcripts, and so on. The UI of the dashboard is quite similar to Google Analytics. So if you’re a GA user, you’ll find it easier to use it.

In the optimization part, Chatbase provides insights to understand your users by tracking how they behave and what works (or not) for them. This is especially helpful when you want to target a specific audience and you want to improve your messaging and promotions according to specific inputs from the analytics tool.

When it comes to building chatbots, including analytics in the strategy is often sidelined. It’s considered an additional responsibility, something that can be easily avoided. However, measuring the performance metrics of a chatbot must be included in the development strategy because it’s the only way to define if your chatbot is working as you imagined it to be.

I hope you learn to integrate these tools and use analytics to enhance the experience of the chatbot for your end-user. In case you begin to feel that it’s a sponsored post, let me tell you it’s not. All these recommendations are personal and I have learned through trial and error. I hope you find the best tool suited to your needs.

Secret to Patient Encounter: Never Skip Small Talk!

Posted on 07/11/202014/05/2021 by Anteelo Master

Small talk is delightful. It’s an easygoing, inconsequential conversation where you’re not running to reach an answer. It flows like wind, from one talker to the next. Small talk appears trivial, but it’s a way for strangers to know each other and for friends to bond over little details.In office, people indulge in small talk during watercooler run-ins, in elevators, and in big meetings where they don’t know each other. Small talk is used to break the ice with casual questions like ‘how are you?’, ‘How’s the weather in your city?’, ‘How was your day?’.In human conversations, small talk serves many purposes. It’s like a bonding ritual. If the two people are meeting for the first time, it acts as a conversation starter. But if they already know each other, it serves as a conversation kickstarter before diving into the real conversation. Small talk is the foundation of good conversations and great relationships.

In a chatbot, small talk enhances the user experience by bringing a feeling of connection. By adding answers to inputs like ‘How are you?’ and ‘Are you really a robot?” in the chatbot’s architecture, we make it less robotic and more humane. We give our chatbot a human character that builds an invaluable connection with the user.

How can small talk help in a healthcare chatbot?

A good healthcare chatbot is the one that answers users’ questions with accuracy, timeliness and empathy.

But a great healthcare chatbot is the one that understands the moods of users, urgency of the situation and answers accordingly.

For instance, when you’re panicking about waking up to bloodshot eyes, the chatbot calms you down with some first-aid steps. If you’re in a rush to book an appointment, the chatbot sends you proactive information on which doctor is nearest to your location. If you’re feeling low, the chatbot brightens up your mood with some light-hearted conversation. Just as a good friend would do.

The highlight of the above conversation is the personal touch in the conversation. It doesn’t feel like we’re reading a conversation with a chatbot. That’s the magic of small talk. Phrases like ‘that’s dreadful’, ‘wish you a speedy recovery’, add a human element and creates a connection of care between the bot and the end-user.

Small talk in a healthcare chatbot also helps create trust. It eases them through important and time-sensitive tasks— like emergency calls to ambulances — with empathy. Contrary to this, robotic responses in yes or no and apathetic comprehension of chatbots frustrates users and they go elsewhere in search for answers.

But how can chatbots mimic human-like conversations and engage users in delightful conversation? By leveraging the power of AI and NLP.

How to implement small talk?

There are certain guidelines that one can follow to tailor such conversations.

Decide the voice and tone of your chatbot. Every response should reflect the personality of your chatbot.
Initially, you’ll have to hardcode some of the small talk in your chatbot. So, brainstorm ways in which users can ask questions. Do some research and make some educated guesses to decide on the most relevant questions that your targeted audience may ask. Cover the edge cases as well– from casual questions to genuine queries that users might ask.
Write answers to each question as per your understanding.
Keep the small talk brief and clear. Don’t go too far from the crux of the conversation.
There can be multiple levels of small talk. Each parent question can have a child query. The child query can only be asked when the parent question has been asked by the user and the chatbot has responded to it.
Small talk should always be accompanied by a call to action or a solution. Example- You: “Do you really exist?” Chatbot: “Yes I exist as a computer program. An intelligent human created me, so you can trust me. How can I help you?”
Train your chatbot to learn from the conversations of small talk.
Small talk is language-specific. So give users an option to talk to the chatbot in their native language.

Small talk makes the interactions with chatbot intuitive. By adding a small talk feature, we can increase the number of conversations and engagement levels of the user with the chatbot. So, if you have a healthcare chatbot, consider incorporating small talk. It doesn’t even require you to do a complete overhaul of your chatbot development.

A Quick Overview of Chatbot Development : Benefits and Drawbacks

Posted on 03/10/202011/06/2021 by Tushar

According to ComScore’s 2017 US Mobile App Report, users spend 90% of their mobile app time in their top 5 apps. Surprised? Well, more alarming is that nearly 50% of their time is spent on a single app, leaving 18% lesser time for the second and other three apps. This means that until and unless your app is the first or second most used application, there’s no way to get valuable users’ attention. But, is it so easy to become the first choice of the users? With Google, Facebook and Apple leading the market in terms of user engagement, this seems to be nearly impossible. What to do then? How to rise above these tech giants? Well, the answer to these questions is Chatbot.

Chatbot- The New Face of App Market

“The tech industry continues to undergo critical changes. The Websites replaced the desktop Operating system, mobile apps trumped over the websites and now, the Chatbots are all set to surpass mobile applications.”

Chatbots are becoming a hype in the tech industry! Also called Bots and Messaging bots, Chatbots are considered among the best ways to reach the customers and keep them hooked. As per our experts, chatbots with the help of messaging applications are providing newer opportunities to brands for user attraction and engagement. It is offering a promising future to the businesses, and in fact, is supposed to surpass mobile apps by the year 2020. So, what’s your plan now? Ready to invest in the technology used by popular companies like Hyatt, Spring and HealthTap? To make it easier to decide, here we are sharing the merits and demerits of Chatbot Development.

Benefits of Chatbot Development

Reach a Wider Customer Base

As the messaging applications are gaining huge momentum over social networks and other applications, a Chatbot built into a chat platform like Facebook Messenger can help you to reach a wider customer base across the globe.

Increased User engagement

Chatbots are usually text-driven, with images and unified gadgets. It enables the users to get all the information under the same roof, without going through the whole content. In short, the bots screen out the content users which to know and deliver to them in no time. With the help of Artificial Intelligence, Natural Language Processing and Machine Learning, the chatbots learns from the user’s behavior, previous interactions and offer customized services/products. Also, it keeps the vital details like name, address, credit details, etc. into its storage which implies the user need not to provide these information again and again. Apart from these, the Chatbots keep consulting the users like humans about their previous experience, which helps the customers get emotionally linked to the bot.

“According to Gartner’s report, 85% of user communications will be managed without human involvement by 2020.”

Round the Clock’ Customer Service

Another reason why businesses are looking ahead to shaping the future with bots is its potential for ‘round the clock’ customer service. This empowers the customers to communicate regarding any product/service even in the midnight and get a response immediately.

Faster & Customizable Facility

The websites take a few seconds or minutes to load, while the mobile apps consume your storage space. However, the chatbots loads instantaneously and do not ask for separate phone’s memory storage. Thus, they win the race as users prefer mobility solutions with faster loading time and lesser memory consumption.

Besides this, the bot provides a wider array of services- ranging from diagnosing your health to buying a gift to booking a flight ticket at the same platform. This is one of the prime reasons users love bots over the mobile apps.

No More Stress of App Downloads

One of the challenges faced by the companies is getting their app downloaded. According to a report, though the number of app downloads is going to exceed 197 billion by this year, the rate of app downloads in the developed countries like the USA is declining to zero. In such a scenario, a Chatbot can be the ultimate solution for you! A Chatbot is integrated within the messaging apps, and need not be downloaded for usage. That means, your targeted audience can take the perks of your chatbot without compromising with their phone’s storage.

Lower Development Cost

With Chatbot development, you can be available on all the mobile platforms and devices; you no more need to rely on platform-centric development resources. You need not spend weeks and months on developing an app or wait for approval from the app store. Also, building a chatbot is much faster and cheaper. According to our developers, a top-class Facebook bot can be built at about half the cost of a mobile application. Isn’t that amazing?

Limitations

Immature

There’s no exaggeration to the fact that the Chatbot is the fastest flourishing technology in the field of app development. But still, it is not mature enough to give tough competition to humans and mobile applications. According to the top mobile app development companies, the bots gain knowledge by the interactions and might need a human assistant to handle the situation when something goes wrong.

The very best example of this is Microsoft’s chatbot Tay which turned from human-loving bot to a racist freak within 24 hours.

Can Replace Promotional, Retail & Content-focused mobile apps only

Although there are millions of applications on the App Store, about 75% of revenue is driven by games. Various mobile app development agencies have integrated the gamification into the bots to attract the younger audience, but there’s still tough competition in the market. The applications like Prisma, Pokemon Go and MSQRD are still ranking on the top, proving that mobile apps are still in business.

Conclusion

Now, as you are familiar with the advantages and disadvantages of Chatbot development, take the right decision for your business. For any further query or get a bot for your business, contact us!

Incredible Chatbot Design Strategies to Turn Your Bot into an Interaction Ninja

Posted on 26/09/202011/06/2021 by Tushar

48% of global consumers accepted that they would rather connect with a company over chat than any other source of contact. 35% of the users would like to see more companies incorporating chatbots. As for the business side of the story, chatbots save around £6 billion per year and a simple chat option in the mobile app brings up the revenue by over 30%.

These are only a snippet of thousands of statistics that are all around the internet and lying on a million office reports hinting towards how chatbot app development has turned out to be the best business decision.

Done right, chatbots can single-handedly make your company approachable and open to business even when you sleep. Done wrong, it leaves users unsatisfied and complaining about how your brand is slow in the logic department.

What helps in making a chatbot effective, besides of course passing all your chatbot success worries to a chatbot app development company, is having a right strategy in place.

1. Have a Purpose

Knowing what purpose your chatbot would solve is the first step in designing a working, revenue-generating chatbot design strategy in 2018.

The last thing that you would want is to join the chatbot world just because every business is on it. While it is true that it benefits a range of different industries, it is also true that not all businesses need it, especially the ones that are working only around a particular geographical location i.e. in the same time zone.

But if yours is a business that would benefit immensely with Chatbots, think of the process that you would like to give your bot the autonomy of. The scale of the process that you are looking forward to leaving in the hands of chatbot will help decide which bot type you have to invest in (more on this later).

2. Conversation Flow

The flow in which your chatbot would converse is one of the most important things for your chatbot design guide to cover.

The ideal situation when it comes to deciding how the conversation should flow is that the bot should start with a general salutation followed by asking questions and then pitching the product when the user put in the respective action words like ‘Tell me More’ or ‘Give more information’.

The biggest mistake that businesses make is pitching in the product as soon as the user becomes active on the website or page when they should go with a more human-like conversation flow.

3. Bot Personality

The only way to make your bot more human-like is to give it a personality. Now when I say personality, I am not implying you to create an avatar and anime man. No, when I say personality, I mean giving it a tone, a difference in the US and UK English, slang, and age-appropriate responses.

Now, in the normal scenario, a business doesn’t have a specific exact type of customer base, so would you keep making bots aiming for each of them to meet specific criteria? The answer is obviously No. What you can do is ask your users’ background and change the initial conversation level of the bot.

4. Give Context From the Beginning

Make the purpose of your chatbot clear from the first two messages itself. Aim for a scenario where your user knows exactly what the bot would be helping them with and have no misinterpreted expectations and then a bad customer service instance.

The first two dialogue to and fro should be enough for your user to know what is to be expected from the chatbot. If you miss this step, the chances are that your bot dashboard will get filled with repeated questions and frustrations of not getting answers.

5. Share before Publicizing

So you have developed an amazing bot that converses with masses effortlessly, specializes in giving the users the exact information that they need, which even varies from context to context. What’s next?

And the answer does not make it live on your website or integrate into your mobile app right away. The answer is to first share it with your in-house teams to see if it’s actually able to answer different questions and different contexts.

By sharing it with the team and taking their feedback, you will be able to track exactly how people are reacting to it and what changes would make the conversation more meaningful.

6. Choose your Words Wisely

The most important element of strategic chatbot development according to chatbot design trend 2018 is choosing the words right. Your copywriting technique should be on point to not just entice the users to start the conversation but keep them hooked enough to convert into your site’s loyal customer or subscriber.

Know that your chatbot copy has the power to make your chatbot a conversion magnet and it should be treated just like that – as a mix of emotions and conversion statements.

So, here were the pointers that would help you draft a killer, high revenue generating chatbot design strategy in 2018. The one that would result in a chatbot that people love to converse with and pay to.

Now that you know what an ideal chatbot should operate like, it is time to look into the three types of chatbots that you should invest in developing once you have your chatbot design guide planned.

The Three Types of Chatbots

1. Menu Based Chatbots

These are one of the most common types of chatbot available in the market today. Using this chatbot, brands give multiple options to the users to select from. These are used mainly when the users are looking for a direct answer.

One of the biggest challenges that businesses face when it comes to menu based chatbots is that the process that users will have to follow to come to the point when they get the desired answer is quite long. They have to click through several buttons and options to come on one point.

2. Keyword Recognition Based Chatbots

These bots utilize the blend of AI and keywords to determine the appropriate response. They lack in performance when they are asked a lot of similar questions with similar sounding keywords, even when the context is different.

A new set of bots are now present in the market that is hybrid. They automatically switch to the menu based type when they are unable to answer users’ queries through AI.

3. Contextual Chatbots

The most advanced of the chatbot family, these bots remember the interaction and its outcome and keeps growing on it to give better result to the users over time. They work strictly on Machine Learning and Artificial Intelligence to help users come to a decision.

As they are based on ML and AI technologies, they come with a self-learning ability, which helps in not just customer relationship management but also a positive sales conversion rate.

The ultimate decision of which chatbot type to invest in depends entirely on your business need. If yours is a simple Q&A type app where you aim at giving a quick in and out experience to your users, answering their questions to the point, menu-based chatbot app development is what you should go for. But, in case you have an assistant like mobile app idea, then you will lose your pockets a little and invest in Contextual chatbot development.

User Adoption for a Chat Support Channel

Posted on 11/09/202010/06/2021 by Tushar

In general, chat adoption rates are still not as strong as clients would like. Those who simply built a chat option with the expectation that user would adopt it by themselves are seeing between 7% to 12% of their volume via chat.

Most of these organizations are doing nothing to promote chat as a support channel, assuming that users would gravitate to chat as many of them already have in their personal lives. That’s a mistake because, when it comes to social behavior adoption patterns, it is important to realize that users do not behave the same way in the workplace as in their private lives. User who would potentially prefer chatting will fall back to calling the service desk if that is what most of their coworkers do. After all, one of our strongest drivers as human beings is our desire to fit in amongst our group of peers. The weight that the old adage “When in Rome, Do as the Romans Do” has in our decision-making is often underestimated.

The key, then, to overcome adoption barriers, is to find ways to modify the behavior of a small percentage of the population – enough that the social trend starts to pull the rest toward the new desired behavior. (This is why group subscriptions to gyms are often more successful than individual promotions). Studies by Alex Pentland and the MIT MediaLab suggest that the frequency of contact and level of trust between the individuals is much more important than the pure number of people pushing the adoption of new behavior. One of their research studies found that user adoption of a new tool (a company social network in this case) was almost guaranteed if the user received at least three invitations from their trusted peers in a short period of time.

So, what does it take to specifically increase the chat channel adoption? Here are a few approaches:

Promote it. Remind users not only that the chat support channel exists, but that it’s good. (You should, of course, also make sure that it actually is a good service.)
Identify service advocates within the user community. Make them part of the promotion of the new channel and reward them for getting people to use the service. This can generate a domino effect, helping the social trend accelerate over time.
Make chat very accessible and easy to use. If picking up the phone on my desk is faster or easier, nothing will make me stop that behavior when I have an urgent issue. So, instead of giving chat the usual set of barriers — accessible only through a service portal that requires you to open a browser, find the URL, log in with credentials you may not remember, find the button on the page, provide basic info all before getting in a queue — why not allow direct access to support on a medium where users already chat, like Skype? This way, you tap into the built-in mental pattern that tools like Skype generate in our brains (I need to talk to someone, so I look for them in Skype, I chat with them).
Add innovative features that can make chat more appealing. For instance, AI virtual agents and automated translation would make service available 24×7 while improving quality, speed and accuracy.
Expose users to metrics on their personal contact behavior to incentivize the new behavior. For example, a campaign that makes them feel “out of touch” with the social norm can help. Instead of telling them “you should chat” or “chat is great,” it’s better to tell them “most users in your department chat with the service desk as the first channel” or “80% of your peers found chat more effective at resolving IT issues.”

With these recommendations, voluntary adoption of chat can increase from those averages around 10% to those around 20% or 25%. And as virtual assistant technology becomes more available, embedding a chat function within a Skype or Microsoft Teams environment becomes a more viable option. These virtual assistants contain a good library of responses built into an AI engine and will greatly enhance usability.

As things continue to evolve, many contact channel policies will change. We expect to see a day when all IT issues are first sent to the virtual agent before trying any other channel. Some companies may not even make other channels readily available to users. Instead, built-in integrations with a virtual assistant could enable users to request a chat with a live agent as a secondary option, or automatically log a ticket without human interaction. When paired with automated translation services, this will result in a smooth, seamless “chat-first” channel, and radically change how we think about global service desks in the future.