A Reflection on My Two Years of Journey Working in the Area of Conversational AI
A spate of recent developments promises to drive great changes in the global economy. Technologies such as Robotics, Artificial Intelligence (AI), and Machine Learning (ML) have advanced to the extent that the world is ripe for a great leap into the age of intelligent automation. What does this intelligent automation portend, and why is this different from the earlier eras?
For one, robots and computers of today can perform most routine physical work activities better and more cheaply than humans.
Two, they are also proving increasingly capable of accomplishing activities that include cognitive capabilities that were once considered difficult to automate successfully, such as making tacit judgments, sensing emotion, or even driving.
A new McKinsey Global Institute Report estimates that with the current level of advancements, automation could raise productivity growth globally by 0.8 to 1.4 percent annually. What’s unique about the study is the finding that despite the substantial potential of applying Deep Learning techniques to use cases across the economy, there are also continuing limitations and obstacles, chiefly owing to improper understanding of the technologies. In a sense, the value of AI is not to be found in the models themselves, but in companies’ abilities to harness them. It is important for global enterprises to clearly understand the trends that shape the industry and how they could be best leveraged.
For example, Conversation AI — text-based or voice-enabled interactive communication — reached peak interest in the enterprise as the most common uses for AI. The underlying Natural Language Processing (NLP) technology’s dependence AI and linguistic understanding creates unique opportunities, be it improving customer experience or reducing costs. As much as we can draw hope from its continued advancement, it’s also important to understand its detailed impact.
We bring the top 20 trends in the market and its implications for all of us:
1. Enterprise-Grade Platforms on the Surge
Today, there are many kinds of providers offering NLP-driven conversational chatbots – from pure-plays to giants. But the underlying trend clearly is that enterprises are now opting for purpose-driven deployments of Conversational AI to serve specific, high-impact and profitable use cases, rather than assembling a couple of dozen bots (for such things as customer support, billing, sales, package tracking and fulfillment, scheduling, etc.) from a couple of dozen vendors.
Says, research firm Opus: “The practices associated with enterprises launching a couple dozen bots (for such things as customer support, billing, sales, package tracking and fulfillment, scheduling, etc.) from a couple dozen vendors are giving way to purpose-driven deployments of ‘Conversational AI’ to serve specific, high-impact and profitable use cases”.
If you need to bring automated virtual agents into the mainstream at scale then enterprise-grade platform models are preferred as they can be leveraged by a wide range of stakeholders. Such platforms come with tools and administration consoles that a team of diverse subject matter experts, bot developers, computational linguists, dialog designers and other specialties (apart from common job descriptions for customer care or contact center administrators) can collaborate on.
For its part, Gartner predicts that by 2025, 30% of major enterprises will have selected a single, enterprise-wide, conversational platform that will be leveraged as a front-end by business applications, both for customer service and to improve employee effectiveness. This confirms the continuing trend of Conversational AI turning mainstream in 2020 and, in turn, helping enterprises become intelligent.
IDC Innovators Report on Conversational AI also underscores the significance of a platform model from a customer experience endpoint. “Conversational AI software platforms enable communication with applications, websites, and devices in humanlike dialect through voice or text. Enterprises deploy conversational AI technologies to automate customer-facing touchpoints across social media, company websites, mobile applications, and other communication channels. Chatbots have a more limited text-oriented implication whereas Conversational AI is more inclusive of AI technology covering Voice bots, and Voice and Text assistants. The Conversational AI chatbots and software platforms are helping businesses to engage more deeply with customers and achieve a higher conversion rate. Chatbots and Conversational AI platforms help enterprises address uncertainties in the digital era as quickly as possible.”
2. Platforms With a Strong Vertical Focus Are Preferred
Everyone knows conversational platforms are good in the long run, but you also need returns that are immediately realizable. Typically, platforms help enterprises deploy and maintain multiple chatbots, and are architectured to include capabilities for Omnichannel deployments, knowledge-base management and dialogue routing for multi-case scenarios. Platform models also help to continuously hone NLP and data to maturity. But enterprises are also realizing the joys of content accelerators, in the form of small talk, or vertical-specific use case bots, that help let business leaders bootstrap your chatbot implementations.
Pre-packaged bots with inbuilt data are precisely tested and trained for a specific industry vocabulary and domain terms, and deployed to enable customers to achieve a faster time to market. In domains such as Banking, IT Services Management, HR Management System and Finance, many such bots are being commonly used by internal employees as well as directly by the end-users.
According to Gartner, by 2022, 30% of customer service experiences will be handled by conversational agents, up from 3% in 2017.
3. Barriers for Entry Are Getting Lower Leading to Democratization of AI
With Cloud services, platforms and frameworks, it is possible to create, deploy and integrate chatbots to a large number of channels in short timeframes. In an age when low-code/no-code platforms are ruling the roost, it’s relatively easier for even a non-technical user to create, test and refine a simple prototype chatbot by using an existing web API and integrating it with Slack, Amazon Echo/Alexa and Skype in no time. While such a solution may not represent a production-ready case, it reflects the relatively low barrier to enter to explore the use cases for chatbots as part of your customer, user and employee experiences. In essence, success with conversational experiences using chatbots is not in mastering the technology, but rather in identifying use cases where natural voice or text-based interactions are a good fit and designing an engaging and efficient conversational flow.
As Gartner comments in its 2020 Top 10 Tech Predictions: “The model will shift from one of the technology-literate people to one of people-literate technology.” The overall trend points to more capabilities becoming available for less development and less data science effort. This, in a way, is leading to the democratization of AI.
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4. No UI Is the Best UI
Conversation — text-based or voice-enabled interactive communication — is the next UI that’s seeing massive uptake. No UI is the next UI as they say. As this technology matures and becomes mainstream, we are witnessing interesting trends. For one, there’s a shift towards feature-rich conversational platforms that provide capabilities to develop, deploy and maintain multiple chatbots at an enterprise scale. Second, we are already contemplating the next-gen of conversations that will blur the line between conversational UI and graphical UI to give the most convenient user experience – something of a UI-Chat symphony. In a Voice-based channel, this could involve controlling things like the tone, pace of audio or removing content ambiguity (obviously, a telephone number needs to be read differently from a literal number). In text-based channels, the channel provider will often allow rich formatting of text and embedding of UI elements such as images, links, and buttons.
5. Conversational Interfaces as Agents of Execution
Conversational interfaces represent a radical change from graphical user interface (GUI). With GUI, the user is the operator of technology, while in a conversational interface, the responsibility for performing the action rests with the agent, which interprets the user’s intent in terms of what they want done, and turns around to execute that intention. In effect, the burden of translating intent will move from the user to the computer. This is a radical way of interacting with technology, while for time being it’s being limited to text input or conversation, increasingly, multimodality is being added and, in time, the conversational interface will transform to a multimodal interface and become the dominant interaction model.
According to Gartner, by 2022, leading vendors will have intent tuning tools for Voice and Text conversational assistants that are suitable for use by competent business users, but that application design will still require advanced skills.
6. Conversational Collaboration Across Communication Tools
While there are many variants of conversational tools out there: chatbots, virtual personal assistants (VPAs), virtual consumer assistants (VCAs), virtual employee assistants (VEAs) – each distinct from the another in terms of platform architecture, integration models and the request-response nature of interactions – it’s difficult for consumers, users and even business stakeholders alike to easily distinguish among them.
For example, chatbots and VPAs like Amazon Alexa, Google Assistant, and Microsoft Cortana or even their enterprise alternates (like Kora) have independent and differentiating capabilities, as do communication and collaboration platforms like Slack, Facebook Messenger, Microsoft Teams and Skype. In future, users should be able to navigate easily from one agent to another seamlessly and chatbots should provide the extension mechanism to allow conversations to connect to services beyond the core capabilities offered by the platform providers.
7. Conversational Analytics Is Highly Valued
The trends towards using data captured from chatbots and conversational platforms that yield useful analytics and metrics to make data-driven decisions for scaling and tuning the platform is likely to show an uptrend. Some metrics currently being captured are conversation engagement, best engagement times, user behavior, filtering user conversations based on their engagement steps, failures, and roadblocks during chats, user pain points, instances of human rep intervention rep, event tracking.
Also, by providing a visual representation of the dialogs bots have with users, you can map popular user paths, tasks, and exit points within a visual context, helping to identify patterns, trends, and correlations that might go unnoticed using text-based data analysis methods.
While most chatbot frameworks come with their own support for conversational analytics, there are third-party services that integrate and track conversations, capturing valuable insights for reporting. Google chat base and Kore’s Conversational Analytics are a few platforms that provide an insightful inference to the user’s journey using the chatbot. Of course, another advantage is the cross-sell or upsell opportunities by understanding the user journey. In 2020, enterprises that adopt chatbot technology would be keen on using these analytics to understand the user better and strategize accordingly.
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8. NLU is Maturing to Make Conversations Look Even More ‘Uber-Cool’
At a time when the enterprise world is tending toward natural and flexible interactions with customers and workforce – typically it suits Gen X and Millennial generation who value terse communications – NLUs are being well trained to easily process such messages. Users shun long, complex answers for short, simple questions in messaging apps and collaboration tools, even when interacting with human users. We must say user expectations of response are being well-managed by the conversational channels.
Richly formatted and interactive responses are helping improve user experience, or what is coming to be known as “conversational experiences”. Gartner estimates that more than 2 billion people will be using conversational AI to interact with VPAs, VCAs, AI-enabled connected devices on a regular basis.
9. Complexity of Use Cases Is Steadily Rising
Beyond delivering answers to simple inquiries, conversational AI will begin to support move complex questions and a broader range of nuances that include tone, consumer personality, previous engagements, stage of customer journey, etc. The result will be highly personalized conversations that are not mechanical in tonality.
Contextual engagement will become the norm. When customer shifts from one channel to another, or from a chatbot to human, the system will not just remember but all insights collected; these will be retained and made part of the ongoing conversation and future delivery of solutions.
As AI’s application expands, so does the number of options that consumers will be exposed to – from machine, to voice, to human, and more. And mind you, it’s going to be the customer who will determine what his choice is.
10. Pre-Trained Bots and Third-Party Data Sets
As data powers AI, huge amounts of high-quality training data is needed to train Machine Learning algorithms to get predictive and yield good analytical results. We will see enterprises able to choose from a variety of free and paid algorithms and models that cover a variety of categories, including Computer Vision; Natural Language Processing; Speech Recognition; Text, Data, Voice, Image and Video analysis; and Predictive analysis. 2020 will be the year that pre-trained Machine Learning models, third-party data sets and models, and open source training data will pick up.
11. But, ‘Conversation Experiences’ Are Still Emerging
Let’s get it straight: even as they progress, become more intelligent and sophisticated, chatbots and conversational experiences are still not the primary interface of engagement. Their UI, design, and technology is evolving and maturing, and use cases are exploding and multiplying with a lot of experimentation, but chatbots are often a supplemental interface to existing services and products that a customer uses. It’s more seen as a convenience, and a value-add.
No doubt it’s more prevalent in the consumer technology space, driven by mobile device and connected speaker support for VPAs, as well as some desktop OS integration. But on many other channels (for example, enterprise collaboration tools and messaging platforms), users still need to discover your chatbot, register or connect to it on first use and remember to use it. The best practices for promoting and driving the use of chatbots in these various channels are in the process of evolution.
12. Human or Machine – Get the Right Mix
In spite of rapid strides conversational interfaces are making, you still need a combination of both – human and AI to deliver outstanding user experience. Most simple questions can be answered by chatbots. Open-ended tasks, like researching, or comparing various analytical questions are still better handled by humans.
While bots can be built to help answer queries and perform a transaction for a user, in some cases there can be a need to fall back to Human Agent to help the user complete the interaction. For example:
- User asks a question or says something which the bot is not prepared to answer
- User prefers to chat with Live Agent rather than the bot
- The developer prefers the conversation to be taken over by a Human Agent on a specific condition (example – based on specific user type)
Hence the trend of customer service representatives taking over the conversation without loss of context when AI reaches its limits will continue to exist and is likely to become more seamless.
13. Bot Personality Is Critical
Dialog differentiation and ‘personality type’ is going to be an inherent feature of any conversational interface going forward. To create a great conversational experience, you must create a brand personality to guide the writing across all intents and, ultimately, multiple chatbot implementations. Gartner finds that enterprises are increasingly paying attention to developing a personality for their chatbot and virtual assistants. This has got to the point where some highly creative teams have hired dialogue writers out of the TV and gaming industries. By 2022, 20% of large enterprises will have dedicated dialogue designers as part of their user experience teams, up from under 1% today, it says.
Companies are expected to assess the personality types that suit their brand image based on studying the brand guidelines or references for dialogue design of a virtual character agent. They need to follow it up with training on finer aspects like the tone of voice, preferred word choices and recognizable speech patterns. The companies are even bringing our living guidance documents on bot script writing and training writers through practical writing exercises, evaluations, and corrections from trainers with deep knowledge of dialogue writing.
14. Voice Enablement
Voice will be the most essential capability for future conversational platforms because the potential savings from automating Voice communication are greater than automating text-based chat. Today, more than 43 million Americans own a digital assistant or a smart speaker such as Amazon Alexa or Google Home (Source). There have been significant product releases from Amazon, Apple, Microsoft, Google, Samsung, Baidu, and others, with vendors flooding the market.
Consumers use these digital assistants to get news and weather updates, play music, control devices, order food or other items, listen to audiobooks and podcasts, and get flight information, to name a few.
Voice conversations tend to be more nuanced, and specific capabilities need to be built into the interfaces to understand interruptions, cues and tone-of-voice signaling, which is currently beyond the scope of most implementations.
While the focus has mostly been on consumer audience, it may shift gear in 2020 with businesses moving to leverage Voice-based assistants for enterprise functions. These include workplace tasks, such as scheduling, basic information searching and assisted conference calls, as well as more complex operations including handling email, processing expense reports, and providing Augmented Intelligence capabilities and other deep conversational features.
According to IDC, one-third of enterprises will use conversational speech technology for customer engagement by 2022. Research from Salesforce shows that already 75% of business buyers say emerging technologies (including Chatbots and Voice Assistants) are changing their expectations of companies. Also, businesses collectively spend $1.3 trillion annually servicing 265 billion customer calls and Conversational AI can be a great technology to purposefully solve contact center problems and achieve larger efficiencies.
15. Conversational AI-Powered Search
More users for AI-powered assistants, and in new ways, implies that sooner than later demand for advanced conversational AI-powered search will crop up significantly. Voice search will revolutionize the way consumers search online.
Consider, for instance, you are looking for restaurants in a new city you have just relocated to. Instead of typing in a search query like “Italian restaurants Boston,” consumers will be able to speak their search queries using a more conversational phrase, like, “How many Italian restaurants are there in Boston?”.
AI-powered search engines will do more than just providing users with a number of eat-outs; they’ll also receive more conversational answers. Search engines could follow up with questions to provide more detailed solutions by asking, say:
- How many tables do you want to book?
- What location would you prefer to dine at?
- Would you prefer a pool-side table?
16. Conversational AI to Boost Automation
Conversational AI is going to be a vital cog in the AI-enabled Application Automation landscape. A convergence of 3 technologies – conversational interfaces, automated business processes, and event-driven application architecture – is predicted to change the way consumers and workforces engage with enterprise applications. Conversational platforms powered by language services that are enabled by AI have an inherent appeal that results from the ability to use a service without having to learn the UI or controls inherent in an application. By 2019, 40% of enterprises will be actively using chatbots to facilitate business processes using NLU.
Macro-economic conditions are also favoring application of automation as a way of dealing with the hardships imposed by an impending economic downturn. Among the things predicted in a recession are fall of long-term interest rates and corporate investments owing to increasing instability from global tensions, conflicts, and trade wars. For organizations with an automation first mindset, the economic downturn is an opportunity to transform and benefit their businesses, their shareholders, and their employees.
17. Cognitive Search on Upswing
Another exciting thing within conversational AI is cognitive search. According to Forrester, a cognitive search is the new generation of enterprise search that uses AI to return results that are more relevant to the user or embedded in an application issuing the search query. The cognitive search market is exploding — it’s anticipated to be worth $15.28 billion by 2023, up from $2.59 billion in 2018 — and it coincides with an upswing in the adoption of AI and Machine Learning in the enterprise.
18. Chatbots Bringing Augmented Reality Closer to Customers
Among the many technologies that conversational AI is tapping is AR. Chatbots are able to provide a happy harmony with AR is helping redefine customer engagement and experience. The current crop of mobile/web apps are unable to support AR as it’s a relatively newer technology and also users are not accustomed to its usage. A chatbot within an app can facilitate the usage of this technology. Depending upon the behavior of the user and his stage in the buying cycle, they can be prompted by the use of AR by the bots.
Gartner, in its Top 10 Technology Predictions for 2020, cites that by 2021 at least one-third of enterprises will have deployed a multi-experience development platform to support mobile, web, conversational, and augmented reality development.
19. Image Recognition
Improved image-recognition has worked wonders in various fields. Recently, Google demonstrated an advanced imaging system for grading prostate cancer that does it more accurately than trained pathologists, and a Stanford team has achieved similar success with skin cancer. Image recognition when clubbed with conversational interfaces expands the scope of their work manifold. With image recognition, virtual assistants can sift through information much faster than humans and are less error-prone.
Meanwhile, building facial recognition into virtual assistants helps industries such as Banking, FinTech, and Insurance implement better security mechanisms.
20. Data Privacy Issues Will Become More Significant
Organizations must enforce stricter norms when it comes to ensuring user data and the privacy of their data and that of their users and remain in compliance with any relevant legislations or regulations. When your customer base is giving away their data to you, it comes with an expectation of securing its privacy. The onus rests on organizations to make chatbots secure channels for sensitive data. Data breaches are a tricky affair and can happen in the most innocuous ways. So be on guard, anticipate these breaches and take steps to avoid them. If you get to the point of requiring personal data from your customers, one good practice is to have the bot link them to a secure channel or even a human representative.
Conversational AI will break fresh ground every year, whether it is unique breakthroughs, disruptions or new product models. Sit back, reflect and plan for taking a giant leap into an exciting future that’s beckoning you!
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