AiThority Interview Series with Jessica Groopman, Industry Analyst and Founding Partner at Kaleido Insights

While understanding the capabilities and limitations of AI is important, especially given its role in enhancing (and in some cases displacing) humans’ roles, investments must support specific problems. 

Know My Company

Tell us about your interaction with smart technologies such as AI and Customer Experience platforms such as Voice and Intelligent Assistants.

As a research and advisory firm specializing in emerging technologies, we analyze voice platforms by use cases, market penetration, and integration with other capabilities. For example, how voice is being used as a form of biometric authentication or the use of intelligent assistants in specific contexts such as retail or healthcare.

How did you start in this space? What galvanized you to start Kaleido Insights?

Our team has been conducting market research on emerging technologies for years, across multiple ‘eras’ of technological innovation. We all worked together about a decade ago at another firm, and a few years ago, decided to reconnect and analyze how so many emerging technologies are intersecting and influencing each others’ development (a kaleidoscope of technologies, if you like). This is distinct, relative to many other firms that take a vertical approach to a technology or market.

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How do AI and Machine Learning techniques unlock new horizons for human efficiencies in business operations?

In a research I conducted a few years ago, we identified well over 200 use cases for applying AI across more than 25 industries. Today, most of these applications drive efficiencies and cost savings (e.g. supporting call center agents with automated triage or repetitive tasks, or using machine vision to automate inventory tracking), but in the future, we will see AI unlock altogether new revenue models. It is still very early days for the technology, and user trust still has a ways to go before more widespread adoption, never mind dependence.

How do you differentiate between technologies for AI and Machine Learning?

AI is an umbrella term of multiple techniques used for machines to simulate tasks a human can do. Under the AI umbrella, sit specific techniques such as Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, and a growing array of other permutations. These techniques are often used in conjunction with one another, and all AI applications fall under one of 3 categories:

• Language/Speech
• Vision/Perception
• Analysis/Cognition

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Which sectors are most readily prepared to adapt AI/ML disruptions?

Today, AI applications are probably the most “advanced” in the high-tech sector, as these data giants have the lion’s share of data, the critical fuel for training AI algorithms. Access to big data is a pretty good indicator for AI adoption, so other industries include Financial, Transportation, Telecommunications. That said, the last 5 years have witnessed an explosion of SaaS tools using AI, which is starting to open up AI-enabled features to companies of all shapes and sizes.

Recent Insights and Analytics

Tell us more about the latest report on the role of AI in Automating Content Marketing.

This report was written to help companies understand AI’s impact on content, while significant for marketers, goes much further than marketing. There are numerous implications for content marketing, around workplace, integration, and design efficiencies, and perhaps, most importantly, in its ability to deliver personalization at scale. But our research found these same capabilities open up a host of efficiencies for other business units too.

Take as one example, automated reporting, which uses Machine Learning and NLP to automate the development, design, and delivery of reports based on specific (often multi-variate) criteria. This is useful for marketers, of course, but also for business intelligence, supply chain, accounting, legal, communications, and beyond. The report lays out dozens of use cases and case studies to illustrate this further, but it also points out the numerous risks that emerge when we begin to automate tasks historically done by humans.

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How could AI clear all the clutter from various content platforms, including Social Media and News?

In many ways, AI is only adding to the noise in social media and news, for example using AI to suggest content designed to garner the most clicks (rather than offer full analysis or context). That said, there is an opportunity to use Machine Learning to optimize what content is delivered to whom, when, based on very unique, and even personalized criteria.

Let’s say for example, I was learning to play guitar. Today recommended content is based primarily on my search history. But increasingly companies are working to apply ML and DL to connect and ascertain what sorts of content are most useful to me based on my unique (and evolving) context as a budding guitar player. How have I engaged with past content, how might my musical preferences inspire me to learn, do I have friends and family who could help, are there opportunities to try out local classes or instructors. This is one broad example, but there are many other uses for ML to improve search, discovery, fraud, and local engagement, and more.

Tell us more about the role of voice and chatbot in the automated content landscape.

Automated content is really a catch-all term for many ways data and technology are making each state of the content lifecycle — ideation, creation, generation, curation, distribution, engagement, visualization and optimization — more efficient.  Voice recognition and chatbots are simply two example of a wide array of techniques and interfaces that support content automation.

How do you see the raging trend of including involving AI and Machine learning in a modern CMO’s stack budget?

It’s likely that many existing tools marketers have invested in are beginning to deploy AI-enabled features into their platforms, and this is true across the stack. For CMO’s though, the objective has been to stay grounded in problems and use cases, not technology. While understanding the capabilities and limitations of AI is important, especially given AI’s role in enhancing (and in some cases displacing) humans’ roles, investments must support specific problems. Don’t just invest in AI because it’s trendy.

What are the biggest challenges and opportunities for businesses in leveraging AI and Machine Learning technology to optimize their Customer Support and Customer Success?

Challenges: Data quality, algorithmic accuracy, algorithmic bias, employee reticence, poorly performing chatbots.

Opportunities: Improved efficiencies (e.g. triage, access to insights, resolution time, cases resolved, etc.), improved customer satisfaction, reduced tedium/improved employee satisfaction, cost savings.

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Hacks on Performance Improvement

What is the biggest challenge to digital transformation in 2019? How does Kaleido contribute to successful digital transformation?

One of the biggest challenges we see is that companies are struggling to keep up with, make sense of, never mind invest strategically in the wide range of emerging technologies. AI is one example — from Machine Learning, Computer Vision and Voice to Chatbots, Robotics, Text Analysis and Deep Learning. Not to mention IoT, 5G, Augmented and Virtual Reality, Blockchain, Edge Computing, etc.

Sure, they may be dismissed as buzzwords, but each represents new ways of doing work; collecting, managing, and securing data; and engaging with customers. The pace of technological change driving digital transformation isn’t just accelerating, it’s widening. We help companies develop strategies to account for this new normal.

What is your opinion on “Weaponization of AI and Automation”? How do you promote your ideas?

This is a very serious and real threat that every company must heed. AI’s reliance on data and its ability to automate decision-making means is highly vulnerable for abuse. This abuse can be inadvertent, as in the case of algorithmic bias, or it can be intentionally weaponized. Businesses play a critical role here as the builders and consumers of AI, and stewards of personal, sensitive, and mission-critical data. From a technology POV, security across the stack is a paramount decision-making criterion for any business. But from a product/service POV, one question every company should ask is: What is the worst, most destructive, exploitative possible way our service might be used?

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Thank you, Jessica! That was fun and hope to see you back on AiThority soon.