Points of View

Don’t Fall for These Six Cognitive Assistant Myths

Sep 10, 2018 Melissa O'Brien

It is critical for enterprise leaders to understand the capabilities and business cases for cognitive assistants and how they can be used as building blocks in the digital enterprise, but unfortunately, hype, myths, and misunderstanding are rampant in this emerging area. Falling prey to these myths will inhibit the potential for your business unit to take advantage of these opportunities.

 

In our research for the cognitive assistants Top 10, we explored the emerging market in the provider ecosystem for services that we call cognitive assistants: the intelligent, automated interactions that replace or augment human customer-facing transactions and processes or internal enterprise interactions and processes.

 

The service providers we profiled in our recent Top 10 report have services that help their customers frame business problems and opportunities for cognitive assistants to help with. We dove into case studies and customer conversations and spoke with the experts to uncover the realities and the potential behind the mystical cognitive assistant.

 

Myth #1: Cognitive assistants are just chatbots

 

In today’s world of automated conversations, it is not uncommon to hear people lamenting about the dinosaurs—and often roadblocks—that traditional chatbots and IVRs have become. Enterprise-level cognitive agents, while still very nascent, have greater learning and processing capabilities that transcend those basic conversational tools.

 

True cognitive agents can self-learn, self-remediate, and execute business processes. They can also often understand structured and unstructured data and then use natural language processing to learn, comprehend, and recommend next steps. Advanced cognitive agents may also enable predictive decision making using real-time analytics. This distinction is significant as many people use the terms “cognitive agents” and “chatbots” synonymously. While cognitive agents are a less mature capability, interest and adoption are growing rapidly—and their impacts are far greater than traditional automated tools.

 

Myth #2: Cognitive assistants are just for customer service

 

Front-office deployments are common, but their AI implementations are not as mature as examples often found in HR, finance and accounting, and help desks. The majority of case studies we saw in the Top 10 research involved the front office, particularly in sales and customer service. These are often the starting points or the low hanging fruits where enterprises will decide to test the use of cognitive assistants. But, the capabilities for cognitive assistants go well beyond the front office, assisting in various elements of the enterprise such as HR, finance and accounting, and the help desk. While the front-office examples are ubiquitous, more mature use cases are often found in other areas where cognitive assistants can execute on processes such as ordering equipment for an employee during onboarding or creating and resolving a help desk ticket autonomously.

 

Most of the examples we’ve seen tackle common problems around very horizontal processes in HR, contact centers, and help desks, which don’t vary much from one industry to another. Completely verticalized case studies are nascent. Domain expertise is still an important client consideration, and service providers that demonstrate prowess in industry verticals fare better with cognitive assistant services adoption.

 

Exhibit 1 illustrates that adoption across industries and functions is varied, with some industries embracing certain functionalities for cognitive assistants at higher rates and with more maturity than others. This heat map reflects the 55 cases studies evaluated as part of the research methodology, with sales and customer service having the highest level of adoption overall. While mid- and back-office use cases are less mature, we did find some examples outside the front office—for instance, we learned of one financial services company that automated its first-level vendor support with the deployment of a chat assistant on its vendor management portal. The chat assistant integrated with the client’s enterprise databases and utilized natural language processing techniques to respond to vendor queries with information.

 

Exhibit 1: Maturity of cognitive assistant functions by vertical 

 

 

Source: HFS Research 2018, 55 cognitive assistant deployment and pilot examples

 

Myth #3: Cognitive assistants are just for <insert channel here>

 

Many people have a bias about cognitive assistants and assume they only focus on one channel or another. In reality, we learned there is a breadth of cognitive assistant deployments across various communication channels, and the channels vary based on use case and function. The more sophisticated service providers we profiled have deployments across all of the communication platforms, but some have doubled down to focus only where they see most demand (most commonly web chat). Because of the greater maturity of customer-facing customer services and sales deployments in consumer-facing industries, the channels with the highest adoption are typically consumer channels such as voice, web chat, and messaging apps.

 

The heatmap in Exhibit 2 illustrates channel adoption using the same 55 deployment examples represented in Exhibit 1. Exhibit 2 illustrates that there are opportunities even in some of the most oblique seeming channels. For example, one use case features a cognitive assistant embedded in a restaurant drive-through kiosk; it employs customer vehicle and biometric recognition along with digital signage and analytics to offer personalized offers and up-sells.

 

Exhibit 2: Cognitive assistant adoption by communication channel 

 

Source: HFS Research 2018, 55 cognitive assistant deployment and pilot examples 

 

Myth #4: Cognitive assistants are all about finding the magic bullet technology

 

Many of the service providers in this study cited a “unique” approach with “best-in-breed” technology providers. The reality is that the technology is advancing so rapidly that there’s really no such thing as best-in-breed and having a partner ecosystem is hardly unique. Those leading in this market will develop strong relationships with well-known players (e.g., IBM Watson, IPsoft’s Amelia, and Nuance for NLP), which is essential to having a flexible and client-friendly environment—but will keep a keen eye on up-and-comers. Integration with other systems (e.g. ServiceNow for ticketing, HCM platforms for recruitment and onboarding, or CRM systems for customer data) is also important.

 

Almost all of the service providers we spoke to have a technology-agnostic platform (except IBM, which partners for some technology but leverages the Watson platform heavily) which enables them to leverage their clients’ existing investments and be flexible to clients’ needs and modular with building the tools. The key takeaway here is about the need for service orchestration, which our Top 10 service providers have developed well.

 

Myth #5: Cognitive assistants will replace human interaction

 

In some cases, yes, automation tools can often replace a human interaction—we see this a lot in self-service, especially in the case of straightforward, focused inquiries. Tools can typically free the employee to do something less transactional and more valuable to the customer, and more “human.” However, with cognitive assistants, the capabilities are more powerful and therefore more nuanced. Generally, the successful use cases we’ve seen are about making people more efficient and effective; often that means that the bot is working side-by-side with the employee as a kind of assistant, synthesizing and presenting data, aimed at making their lives easier and processes more intelligent and agile. Think of throwing away scripts and decision trees to focus on pieces of interaction that are more valuable, empathetic, nuanced, and meaningful. Human interaction will still exist and make that conversation more, well, human!

 

Myth #6: Cognitive assistants will destroy the contact center business

 

On the contrary, cognitive assistants are augmenting agent work and providing opportunities for these service providers to stay relevant. The contact center BPO companies (Convergys, Sitel, Concentrix, and Teleperformance) we profiled had less mature capabilities and fewer actual client case studies; two reasons are that contact center BPO companies are finding that it is difficult to fit cognitive assistants into their bread-and-butter business and that automating customer interactions brings with it revenue cannibalization. However, for front-office use cases, there is a tremendous opportunity for these players to take the lead given their wealth of customer data and customer experience expertise.

 

By embracing cognitive assistants, these service providers have the opportunity to carve out a differentiated capability for a blended bot and human model, providing seamless transitions to human agents and harnessing the power of their core capability—while potentially breaking out of the legacy FTE models that have dampened innovation and profitability for years. Two ripe areas for further developing cognitive assistants for contact center companies are in use cases that employ bots internally for recruiting and hiring and those that augment agents. Companies that use these tools internally to their best advantage will create differentiation in their service delivery.

 

Bottom line: Cognitive assistants are powerful, but will only work if enterprise leaders get educated on the value, possibilities, and realities of this emerging capability.

 

Uncovering these opportunities often involves design thinking and bringing in people with expertise and different perspectives. Service providers can bring technical expertise, talent, and robust partnership platforms to help enterprises navigate the world of digital technology as it relates to cognitive assistant capabilities. As an executive from an entertainment company put it: “It's a different way of thinking. You have to measure things and develop a road map to get a good sense of the customer experience and what should be the next thing.”

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