Kisaco Leadership Chart on Intelligent Virtual Assistants 2021 | Kisaco Research

About the Author

Author:

Michael Azoff

Chief Analyst
Kisaco Research

With over 17 years analyst experience, most recently at Ovum/ Informa, Michael Azoff joined Kisaco Research, the company behind the AI Hardware and Edge AI Summit series, in 2020 as Chief Analyst. 

Eitan Michael Azoff, PhD, MSc, BEng.

HQ’d in Kisaco Research’s London office, Michael's current focus is launching Kisaco Research vendor product comparison reports with the new Kisaco Leadership Chart (KLC) analyst chart. The first KLC is also the first analyst chart in the AI chip industry, with 16 vendors having participated in the research.

In his career Michael worked at Rutherford Appleton Laboratory building simulators for electron and hole transport in semiconductors for UK national and European community research projects and published papers in learned journals. He then turned to building neural networks and created a startup selling his Prognostica Microsoft Excel add-in for time series forecasting, and wrote a book on the topic for publisher John Wiley & Sons in 1994.

Since 2003 Michael has worked as an IT industry analyst covering software engineering topics, from agile and DevOps, to application lifecycle management and cloud native computing. He started covering machine learning when deep learning emerged as the most recent wave of interest in AI and left his position as Distinguished Analyst at Ovum/Informa to join Kisaco Research and help build an analyst capability within the company.

My analyst coverage areas at KR Analysis

My first research project at KR was to create the first analyst comparison chart for AI chips. We invited AI chip producers to participate and were fortunate to have 16 vendors participate from across the globe: USA, UK, France, and China, and a mix of established players (Nvidia, Imagination, Intel, and Xilinx, to startups.

Our analysis showed that the market naturally fell into three areas of hot activity:

▪ Data centers and high-performance computing environments (HPC): here large boxes are installed and the aim is to achieve maximum performance for training and inferencing AI systems. The buyers are cloud hyperscalars, national research labs and agencies, and some large enterprises with big investments in AI.

▪ Small edge: the opposite end of the spectrum, building the smallest useful chip possible to sell as cheap as possible and embed in edge devices. AI is inferencing here.

▪ Automotive: an active industry in AI but highly regulated creating hurdles and technology adoption cadences that can be challenging for suppliers. AI is mainly inferencing here (for systems installed in vehicles).

We produced four Kisaco Leadership Charts out of this research.

We are also researching the machine learning (ML) software tools space, and our first report here is ML Lifecycle Solutions. The biggest challenge for enterprises is taking the research AI systems developed by their data scientist and deploying these into production at scale. Using a host of open source tools to achieve this is possible but time consuming to build and maintain, as well as prone to breakdown. This is why the ML lifecycle solution space exists.

Finally, in our first batch of KR Analysis reports we produced the KLC on engineering application lifecycle management (ALM) solutions. While ALM has been in existence as a distinct practice since KR Analysis and Michael Azoff introduction © Kisaco Research. All rights reserved. Unauthorized reproduction prohibited. 4 around 2003, it continues to evolve. We found the engineering and highly regulated industries relying on engineering and compliance oriented ALM to help manage risk and complexity.

  • Motivation

    Intelligent virtual assistants (IVAs) have been transformed by the use of machine learning (ML) to provide more accurate machine to human conversation, whether by voice or text, than other techniques alone. Vendors with expertise in linguistics and semantics have been able to combine the best features of such technologies with the recent breakthroughs in deep learning neural networks and achieve superior performance. Today, all the leading IVA players have embraced ML and this field is an excellent showcase for how ML has moved from the research lab into real world applications.

    This Kisaco Leadership Chart (KLC) on IVAs shows how the leading players in the market compare side by side. The focus here is on the needs of the larger enterprises with more complex end user journeys in contrast with bots that answer shallow questions such as FAQs.

  • What you will learn

    • The IVA market and technology landscape section of the report includes definitions and schematic architecture of a generic IVA solution.
    • Continuing, we look at trends in the IVA market and trends in IVA technology. We provide a view of how the IVA market demarcates between sectors, along degree of end user journey complexity and along degree of general applicability.
    • Our report has assessed six IVA solutions and we provide a heatmap on some of the key features available
    • We compare six leading IVA solutions side by side and assess these in our Kisaco Leadership Chart – our take on the classic analyst chart.
    • We provide a profile on each of the participating vendors together with three strengths and three weaknesses.
  • Contents

    Kisaco Research View 2

    Motivation 2

    Key findings 2

    IVA market and technology landscape 3

    The IVA market landscape has broadened considerably 3

    There are many solutions labeled IVA, but they differ significantly 3

    Independent IVA versus enterprise application in-built IVA 4

    The role for IVAs in enterprises 5

    Improving front line customer experience 5

    Dealing with complex end user journeys 5

    Processing leads: pre-purchase sales assistant 5

    Post purchase customer success assistant 6

    The impact of Covid-19 6

    Remote working patterns will continue 6

    Digital transformation is accelerated 6

    IVA market trends 7

    Addressing the SME market 7

    Citizen developers 7

    Voice and mobile over text 7

    IVA technology landscape 7

    Most IVA solutions take a hybrid approach 7

    Speech/voice, text, STT, and TTS: unravelling confusion in the media 8

    The trend for IVA bot army management and orchestration 9

    The accuracy of an IVA 9

    The paradoxes of IVA technology 9

    The ROI from IVA 10

    Solution analysis: vendor comparisons 10

    Kisaco Leadership Chart on Intelligent Virtual Assistants 2021 10

    IVA solution vendor comparisons 10

    The KLC chart for IVA solutions 11

    Vendor analysis 14

    [24]7.ai, Kisaco evaluation: Contender 14

    Kisaco Assessment 16

    Amelia, an IPsoft company, Kisaco evaluation: Leader 17

    Kisaco Assessment 20

    Artificial Solutions, Kisaco evaluation: Leader 20

    Kisaco Assessment 23

    Conversica, Kisaco evaluation: Contender 24

    Kisaco Assessment 26

    Creative Virtual, Kisaco evaluation: Leader 27

    Kisaco Assessment 29

    Verint Systems, Kisaco evaluation: Leader 30

    Kisaco Assessment 32

    Appendix 33

    Vendor solution selection 33

    Inclusion criteria 33

    Exclusion criteria 33

    Methodology 33

    Definition of the KLC 33

    Kisaco Research ratings 34

    Acknowledgements 34

    Author 34

    Kisaco Research Analysis Network 34

    Copyright notice and disclaimer 35

  • Figures

    Figure 1: IVA market landscape in 2020-21

    Figure 2: Schematic architecture of an IVA solution

    Figure 3: Features highlighting differences between the vendor solutions

    Figure 4: Kisaco Leadership Chart on IVA solutions 2021

    Figure 5: Kisaco Leadership Chart on IVA solutions 2021

    Figure 6: The [24]7.ai customer landing page on the engagement cloud

    Figure 7: Amelia’s brain, showing its NLP centers

    Figure 8: Teneo Languages: building blocks based on real world conversations

    Figure 9: Teneo core architecture

    Figure 10: Overview of Conversica IVA

    Figure 11: V-Person AI – a hybrid approach to machine learning

    Figure 12: Overview of the Verint platform

  • FAQs

    1.  What is the KLC?

    The Kisaco Leadership Chart (KLC) is KR Analysis’s take on the classis industry analyst chart in which vendor products are assessed and their scores plotted on a chart comprising four quadrants: Leader, Contender, Innovator, and Emerging Player. The x-axis represents strength of technical features, the y-axis the strength of market execution and strategy, and the size of plotted circle represents market revenue normalized to the strongest participating player in the research.

    In researching the KLC we receive privileged information from a vendor. As explained in question 3, participating vendors are actively engaged in our research. Confidential privileged vendor information is not disclosed in our report but helps us assess vendors in our analysis.

    2. What is the vendor selection process for a KLC project?

    KR Analysis creates a shortlist of vendors to invite to the research project. The aim is to include the leading players as well as innovative smaller players, across startup and established vendors. KLC research can at best be representative of the market and is not designed to be exhaustive – in some markets the sheer number of players would make an exhaustive KLC unmanageable, in smaller markets we are still dependent on vendors agreeing to participate.

    We do create KR Analysis Technology and Market Landscape reports in which we typically list the players in the markets with thumbnail profiles providing information such as company leadership, location, funding status, and main product(s) details. While we cannot guarantee exhaustiveness, the landscape report does aim to list the most important vendors and does not require vendor participation.

    3. In a KLC what does participating entail for a vendor?

    First of all, we do not charge vendors to participate in a KLC. Participating vendors need to be actively engaged in a KLC research project, this involves completing a comprehensive questionnaire, which we score and use as the basis for positioning the vendor in the report’s KLC. We also hold a deep dive briefing and engage in plenty of Q&A. Finally, we research publicly available material on the vendor and its product(s) to complete our final view of the vendor. 

    4. Why are some notable vendors missing from the report?

    As explained in question 2, we do invite the leaders in a market segment we are researching, however not all such players agree to participate. As explained in question 3, participating involves active engagement and example reasons vendors offer for declining our invitation are, often ending with “...but please consider us next cycle of the report.”:

    • We are in the midst of an event in which our relevant staff do not have the time to engage in your process.
    • We are going through a major change in strategy or product re-architecture and the timing is not right for us to participate.
    • We are about to have our IPO and this is not the right time to participate.
    • We are about to launch our flagship product and the report timing is not right for us.
  • About the Author

    Author:

    Michael Azoff

    Chief Analyst
    Kisaco Research

    With over 17 years analyst experience, most recently at Ovum/ Informa, Michael Azoff joined Kisaco Research, the company behind the AI Hardware and Edge AI Summit series, in 2020 as Chief Analyst. 

    Eitan Michael Azoff, PhD, MSc, BEng.

    HQ’d in Kisaco Research’s London office, Michael's current focus is launching Kisaco Research vendor product comparison reports with the new Kisaco Leadership Chart (KLC) analyst chart. The first KLC is also the first analyst chart in the AI chip industry, with 16 vendors having participated in the research.

    In his career Michael worked at Rutherford Appleton Laboratory building simulators for electron and hole transport in semiconductors for UK national and European community research projects and published papers in learned journals. He then turned to building neural networks and created a startup selling his Prognostica Microsoft Excel add-in for time series forecasting, and wrote a book on the topic for publisher John Wiley & Sons in 1994.

    Since 2003 Michael has worked as an IT industry analyst covering software engineering topics, from agile and DevOps, to application lifecycle management and cloud native computing. He started covering machine learning when deep learning emerged as the most recent wave of interest in AI and left his position as Distinguished Analyst at Ovum/Informa to join Kisaco Research and help build an analyst capability within the company.

    My analyst coverage areas at KR Analysis

    My first research project at KR was to create the first analyst comparison chart for AI chips. We invited AI chip producers to participate and were fortunate to have 16 vendors participate from across the globe: USA, UK, France, and China, and a mix of established players (Nvidia, Imagination, Intel, and Xilinx, to startups.

    Our analysis showed that the market naturally fell into three areas of hot activity:

    ▪ Data centers and high-performance computing environments (HPC): here large boxes are installed and the aim is to achieve maximum performance for training and inferencing AI systems. The buyers are cloud hyperscalars, national research labs and agencies, and some large enterprises with big investments in AI.

    ▪ Small edge: the opposite end of the spectrum, building the smallest useful chip possible to sell as cheap as possible and embed in edge devices. AI is inferencing here.

    ▪ Automotive: an active industry in AI but highly regulated creating hurdles and technology adoption cadences that can be challenging for suppliers. AI is mainly inferencing here (for systems installed in vehicles).

    We produced four Kisaco Leadership Charts out of this research.

    We are also researching the machine learning (ML) software tools space, and our first report here is ML Lifecycle Solutions. The biggest challenge for enterprises is taking the research AI systems developed by their data scientist and deploying these into production at scale. Using a host of open source tools to achieve this is possible but time consuming to build and maintain, as well as prone to breakdown. This is why the ML lifecycle solution space exists.

    Finally, in our first batch of KR Analysis reports we produced the KLC on engineering application lifecycle management (ALM) solutions. While ALM has been in existence as a distinct practice since KR Analysis and Michael Azoff introduction © Kisaco Research. All rights reserved. Unauthorized reproduction prohibited. 4 around 2003, it continues to evolve. We found the engineering and highly regulated industries relying on engineering and compliance oriented ALM to help manage risk and complexity.

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