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Take a deep dive into Gartner’s evaluation of the most important attributes of enterprise conversational AI platforms for customer service from their new Critical Capabilities report. We’ve also got learnings from the top ranked platform’s real-world orchestration of conversational AI, which allowed one national retailer to prop up a new call center and cut their inbound traffic by 50% in just ten weeks. 

Rewriting Customer Service with Conversational AI
  • Conversational AI is an inevitable market state and enterprises are in hot pursuit of platforms that will enable them to successfully orchestrate the associated technologies—like NLU/NLP, code-free design, RPA, and machine learning. Gartner’s 2022 Critical Capabilities for Enterprise Conversational AI Platforms report is the first of its kind, providing a comprehensive breakdown of the technical requirements for entering this new paradigm for productivity.
  • The best way to succeed in customer service applications of conversational AI is by incorporating a broad range of channels (touching on every possible way a customer might try to engage with a business) as part of an open system that allows for integration with tools and solutions from other vendors and involves human agents in its operation and evolution.
  • Flexible open platforms allow businesses to rapidly identify and capitalize on the massive opportunities conversational AI brings while adapting their own operations around the fluctuating nature of the associated technologies. In essence, future-proofing their operations.
  • The highest-ranking company in the Customer Service Use case was OneReach.ai. Using their platform for conversational AI, a large national retailer was able to create a new call center and cut their inbound traffic by half in just 10 weeks.
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7 min read
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AI will likely be a key foundational element as the technology matures into the metaverse. Here are some more predictions on AI trends.

AI predictions trending in 2022
  • For business leaders it’s important to learn how new developments can transform the competitive landscape in their industry.
  • The author shares his presumptions on AI trends in 2022:
    1. AI will fully converge with data and cloud — demanding a new management approach
    2. Simulations will unleash AI’s power in supply chains, the metaverse, and more
    3. There will be no more “messy” data: AI will let you find, use and monetize it
    4. You’ll be able to assess and forecast AI’s full value, not just cost savings
    5. AI’s ESG impact will demand your attention
    6. AI will be too important for AI specialists to govern
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6 min read
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An ultimate guide to conversational UX (CUX). Conversational UX principles.

Conversational Design
  • The author defines “conversational UX as a user experience that combines chat, voice or any other natural language-based technology to mimic a human conversation.”
  • The author looks at the following conversational UX Principles:
    •  Affordances
    • Signifiers
    • Feedback
  • Conversational user interface & principles:
    • Cooperative Principle (discover hidden intentions)
    • Turn-Taking (give users a space to interact)
    • Context-aware (in context / out of context)
  • While designing virtual assistants, the author suggests taking two things into consideration:
    1. How to set user expectations and educate users about what their assistants can do
    2. How to help these users
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8 min read
Conversational Design

Green Design Principles that every designer can use to combat climate change

Digital = Physical
  • Recent Microsoft’s Green Design Principles can prevent us from fueling a climate crisis:
    • Get started backpack on digital sustainability — the climate crisis doesn’t happen in a vacuum, big change starts small, talking about climate can be hard, digital is physical
    • Think bigger before you start — challenge the status quo, put care first
    • Build better by default — optimize, transparent, adaptable
  • “The Cloud” doesn’t exist — every design has physical implications and this one is a massive warehouse causing the environment harm.
  • Well-intentioned designs often result in unintended consequences that’s why designers need better ways to visualize their impact.
  • Designers can train AI in order to preserve the planet by employing the energy-intensive cloud and the AI it enables to understand environmental impacts.
  • Some of the most engrained paradigms of the tech industry are the least sustainable and product makers need new ways of working.
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8 min read
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Artificial intelligence (AI) could soon surpass human intelligence. Having both advantages and disadvantages, it still creates powerful opportunities and produces more accurate customer-behavior models.

Time to reflect on my future in the age of AI
  • Since AI-driven processes can create powerful opportunities to improve producing more accurate customer-behavior models, many traditional businesses will soon transform their core processes and business models to take advantage of ML.
  • Sonia P., People-Centric Design Enthusiast, brigs up such questions related to the role of AI in the future:
    • What’s exciting about AI?
    • What’s worrying?
    • How will we work with machines?
  • In order to make machines that behave better for humans is for UX designers to take all factors into considerations, bridge the gap and merge the knowledge from all sides to define the best solution.
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6 min read
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Historically, the values of gender-caste-based minority have been systematically excluded from even being tallied, resulting in gender-biased or gender-invisible prior statistics.

AI is getting better and better — at being biased.
  • The values of gender-caste-based minority have been systematically excluded from even being tallied, resulting in gender-biased or gender-invisible prior statistics.
  • Data scientists have said that there are two main ways that AI perpetuates gender bias: one is caused by Algorithmic and design flaws and the other is the reinforcement of gender stereotypes through new digital products that project a technological gender.
  • Paula Stenholm, a user experience designer at Ellos Group, gives a lot of reasons why datasets are skewed:
    • Real world patterns of health inequality and discrimination
    • Discriminatory data
    • Application injustices
    • Biased AI design and deployment practices
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7 min read
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