AI Use Cases For Communities & Networks
Artificial Intelligence is arguably the buzziest of buzz words these days. Yet, there is a reason for the hype: AI could support a radical transformation of online community management and experience: automation of routine tasks, real-time insight, enhanced personalization and the enhanced agency of an individual in digital ecosystems.
For business leaders shaping online community strategy, AI holds promise to help solve two of the biggest challenges with online communities: 1) Quantifying the value of community investment and delivering timely and actionable insight and 2) Managing large networks of relationships at scale.
To Start: What is AI?
In the context of Community, AI can be thought of as an agent, or set of agents that
is / are connected to real time data sources;
has / have the ability to act in the community (or admin interface); and
has / have specific goals to make progress towards.
From the Wikipedia entry on AI:
"In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".[2] "
"Isn't this just an algorithm?" is the next natural question, and the answer is "well, not really." Algorithms are complex sets of bounded instructions, and they aren't (typically) designed to learn from their environment and evolve.
Where are we on the map?
Clearly, interest, investment and experimentation in AI by corporations is increasing year over year. According to Harvard Business Review, which surveyed over 3,000 organizations, 20 percent of companies used AI in a core part of their business model, and 41 percent were experimenting or piloting in 2017 (a total of 61 percent).
Narrative Science partnered with the National Business Research Institute and found the same numbers: 61 percent of surveyed respondents utilized AI in their corporations in 2017 (up from the 38 percent in 2016). The study also found that 35 percent of respondents use AI for interaction with customers (a.k.a. potential community members).
A recent study by Constellation Research found that 70% of the organizations they studied were already investing in AI and that 60% were expecting to increase their investment by 50% or more this year.
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Image Source: Constellation Research 2018 Artificial Intelligence Study
Community Leaders and Community Platform Providers have been leveraging simplistic AI tools for more than a decade, primarily for automating community moderation tasks and supporting member personalization. An early example: we launched TechRepblic.com in '99 with an overly-complex community and content personalization function and wound up pulling back on the functionality in subsequent releases because of the technical overhead.
Emerging Use Cases for AI
We (Stucture3C) are in the midst on a year-long research project, C3/A3, studying how organizations are using / planning to use AI in their online communities. In our first wave of research with 40 Community Professionals at large organizations, we asked what types of advanced technologies they are considering or implementing, including AI and related technologies. Personalization, bots / agents and analytics topped the list.
tech_consideration
Digging deeper, we wanted to understand the most valuable use cases under consideration: We found that corporations are either piloting or planning to use AI in three key areas: Customer Experience, Community Management, and Analytics / Insights.
Customer Experience (for Community Members)
Examples include:
Advanced personalization based on profile / activity
Recommendations of people and content
Conversational interfaces, including chatbots
Agents (acting on behalf of a member)
From the write in responses:
"(We are evaluating)... Machine Learning that automates personalization for content, news, interaction models."
Community Management (for Community Managers)
Examples include:
Influencer & Advocate identification
Escalation identification - ID’ing people who need help, like Facebook’s suicide threat technology
Moderation of content and member behavior
Suggested actions (what to do next in the community)
Suggested content (to produce, based on member behavior and other signals)
From the write in responses:
"(We are)...Leveraging machine learning in our peer to peer support community to predict certain kinds of moderation needs, such as suicidal escalations or harassment etc. Better sentiment/text analysis."
"(We are piloting)...AI text analysis to draw insights from unstructured data feeds (with reduced dependency on tagging)"
Analytics / Insights (for Executive / Business Stakeholders)
Examples include:
Community health
ROI measures
Areas of investment
Identifying customer behavior trends
Gleaning insight for product / service enhancement
From the write in responses:
"Predictive - I want to present our users with timely and relevant content, before they even know they need it in some cases. If we know what you're doing with our products and what your behaviors are in community, we should be able to activate that data into meaningful upgrades to the experience in both places."
#TeamHuman vs. the Machines
Swiss Futurist Gerd Leonard characterizes the broad adoption of AI and related technologies as a battle of "Technology vs. Humanity". The statement is hyperbolic, but the intent is spot in: we have to act now to ensure enabling human agency and purpose remains at the heart of any broadly deployed technology, including AI. Australian Online Community pioneer Venessa Paech says it best in a recent article:
"Instead of being replaced, community experts will upgrade. We’ll work to help businesses set up bots and intelligent interactions. We’ll plot behavioural frameworks for machine learning. We’ll spill into HR, marketing, IT, innovation – anywhere there’s a need to understand and optimise social intelligence. Leveraging AI for communities demands we extend our capabilities as social systems engineers. If we get it right, we can see to it that AI augments our best natures, not our worst."
Participants in Wave 1 of the C3/A3 project are also optimistic about the possibilities of AI:
"I'm excited about the shift that AI could bring - instead of being reactive, let's be proactive. I'd also like to use this tech to identify the things that we can flatly stop doing and redirect those efforts into more valuable activities."
"I'm really excited to see how AI & ML augment and enhance a community member's experience rather than replace any of the human aspects!"
Conclusion
Essentially, we think the value of AI is threefold for Community Professionals:
AI will allow for the automation of routine community tasks and processes so that focus can be put on more valuable activities;
AI will provide real-time analytics, insight, and specific and contextual suggestions;
AI will shape the community experience for all stakeholders, including members (onsite), prospective members (externally), Community Managers and Executive Stakeholders.
A possible future vision for AI and Communities
We think future communities will thrive with AI if the ultimate goal of the community is enabling member agency and purpose. Perhaps paradoxically, the future of community management will likely depend on Community Managers becoming comfortable with, and knowledgable about, intelligent agents and automation, while doubling down on the art and science of human interactions and group facilitation.
Have questions, or interested in a briefing? Please reach out.
