Almost all types of organizations have customers, and most of them struggle to effectively scale communications with customers that have questions or need actions taken. Significant amounts of money are spent on contact centers, interactive voice recognition (IVR) systems and websites meant to support a company’s interaction with customers. When the COVID-19 pandemic struck, companies found themselves with an even larger challenge than they had faced previously. COVID-19 placed massive stress on communities and economies around the globe. Healthcare providers, companies, and government agencies were overwhelmed by the number of urgent inquiries and concerns caused by this pandemic. Across the globe, companies in virtually every industry — whether banks, hotels, manufacturers, or retailers — were exploring ways to communicate with customers while their physical locations were closed to the public. Call centers were swamped, as anxious consumers looked for answers about the virus, purchased needed supplies and goods, or attempted to cancel or change travel plans. This situation caused long wait times and dropped connections, preventing callers from getting help when they needed it most. At the same time, call centers were short-staffed as agents called in sick or attempted to transition to remote work.
Throughout all of this, conversational artificial intelligence (AI) tools and technologies proved their worth during the COVID-19 crisis. Organizations turned to conversational AI software platforms, developing customer service applications for help during these unprecedented times. For years, organizations have wished for “virtual” or “digital” assistants that could carry some of this conversational load working with customers. The good news is that recent breakthroughs and improvements in speech recognition, natural language processing(NLP)/natural language understanding (NLU), and conversational artificial intelligence made that wish come true for an ever-increasing number of organizations over the past two years.
Over the past three to five years, due to advances in deep and machine learning, conversational AI applications can understand and respond to conversation in all its various forms, including telephony, voice, text messaging, web messaging, WhatsApp, and Facebook Messenger. The conversational AI software platforms that IDC has evaluated as part of this IDC MarketScape have shown that organizations can develop and deploy sophisticated AI-based conversational agents that can interact with customers, consumers, and the public at large to answer their questions, help them conduct transactions, and provide a wide range of self-service that wasn’t possible only a few years ago.
As part of this evaluation, IDC spoke with dozens of organizations using these conversational AI software platforms to develop and deploy applications that are providing great customer service and generating significant return on investment (ROI). Among the technology buyers IDC spoke with, we noted a range of maturity in implementations of conversational AI, from point solutions to enterprisewide deployments. If your organization is not using or evaluating conversational AI applications for customer service use cases, it should be.
The technologies behind these conversational AI software platforms are good and getting better by the day, but that shouldn’t stop organizations from evaluating and implementing these solutions as soon as they can. Conversational AI tools and technologies are rapidly evolving, and new vendors, products, technical innovations, and acquisitions are a frequent occurrence. Conversational AI platforms can be used for a broad set of use cases related to customer service, for both customer-facing applications and internal-facing agent assistance, and these platforms can vary greatly in terms of prepackaged offerings and templates, low-code/no-code tools for business analysts and line-of-business (LOB) subject matter experts, and customizable developer tools. For example, some organizations will benefit from vendors that offer low-code/no-code tools and other features that can eliminate the need for one or more of the initial three steps in the conversational AI-build process (see Figure 2). Other organizations will need the ability to work directly with one or more of these areas to customize aspects such as language, conversation flows, and workflows.
IDC offers the following advice to technology buyers considering conversational AI:
This section briefly explains IDC’s key observations resulting in a vendor’s position in the IDC MarketScape. While every vendor is evaluated against each of the criteria outlined in the Appendix, the description here provides a summary of each vendor’s strengths and challenges.
After a thorough evaluation of Omilia’s strategies and capabilities, IDC has positioned the company in the Leaders category in this 2021 IDC MarketScape for worldwide conversational AI software platforms for customer service.
Omilia offers Omilia Cloud Platform (OCP) miniApps, a set of modular pretrained software services for building and deploying virtual assistants, including no-code options for business users. OCP miniApps includes prebuilt integrations to platforms such as NICE InContact and Genesys to support contact center deployments as well as pretrained models for specific industries. Omilia supports both text- and voice-based channels with 29 languages and dialects out of the box and is headquartered in Cyprus, with five additional offices in Greece, Czech Republic, South Africa, Ukraine, and Canada. It is venture backed, with $20 million in funding.
Quick facts about Omilia include the following:
Consider Omilia when you are a midsize or large enterprise seeking a vendor with modular conversational AI services that is willing to work closely with your organization to ensure a successful deployment and ROI. Omilia is also a good choice for those looking to include support for voice-based channels, since it has a broad set of voice capabilities including speech to text and text to speech. Combined with its strong developer and dialog capabilities, Omilia’s voice-based features make it a vendor to consider as a comprehensive solution for conversational AI for customer service use cases.
The criteria used for the selection of IT suppliers that were evaluated are the following:
For the purposes of this analysis, IDC divided potential key measures for success into two primary categories: capabilities and strategies.
Positioning on the y-axis reflects the vendor’s current capabilities and menu of services and how well aligned the vendor is to customer needs. The capabilities category focuses on the capabilities of the company and product today, here and now. Under this category, IDC analysts will look at how well a vendor is building/delivering capabilities that enable it to execute its chosen strategy in the market.
Positioning on the x-axis, or strategies axis, indicates how well the vendor’s future strategy aligns with what customers will require in three to five years. The strategies category focuses on high-level decisions and underlying assumptions about offerings, customer segments, and business and go-to-market plans for the next three to five years.
The size of the individual vendor markers in the IDC MarketScape represents the market share of each individual vendor within the specific market segment being assessed.
IDC MarketScape criteria selection, weightings, and vendor scores represent well-researched IDC judgment about the market and specific vendors. IDC analysts tailor the range of standard characteristics by which vendors are measured through structured discussions, surveys, and interviews with market leaders, participants, and end users. Market weightings are based on user interviews, buyer surveys, and the input of IDC experts in each market. IDC analysts base individual vendor scores, and ultimately vendor positions on the IDC MarketScape, on detailed surveys and interviews with the vendors, publicly available information, and end-user experiences in an effort to provide an accurate and consistent assessment of each vendor’s characteristics, behavior, and capability.
Conversational artificial intelligence (AI) refers to product/services that are used to develop conversational solutions, such as chatbots or voice assistants, which users can talk to via a text- and/or voice-based interface. They use large volumes of data, machine learning, and natural language processing to help imitate human interactions, recognizing speech and/or text inputs and translating their meanings across various languages. Conversational AI products/services are used by organizations to create solutions that can communicate like a human by recognizing speech and/or text, understanding intent, deciphering different languages, and responding in a way that mimics human conversation. This evaluation is focused on those conversational AI platforms that can create conversational AI applications for customer service–related use cases.