AI Use Cases For Communities & Networks

networkArtificial 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.

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.

Interested in participating in the research? Take the survey here.
Have questions, or interested in a briefing? Please reach out.

Gratitude: I wanted to give a shout out to Venessa Paech. The motivation to start the C3A3 project was inspired, in part, by conversations with her at, and following, the 2017 SWARM Community Management Conference. Be sure to read Venessa’s thought piece on AI and Community Management. I also highly recommend the SWARM conference, being held in Melbourne this year, August 30-31.

 

Announcing the C3/A3 Project: The Connected Future of Communities, AI & Automation

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The C3/A3 Project

C3: Community, Crowd, Collaboration
A3: AI, Agents and Automation

Humans instinctively seek meaning, connection and resources through community. Driven by near ubiquitous access to broadband and the rapid adoption of smartphones, our new and ever-expanding digital world offers instant access to a rich tapestry of social experiences, fundamentally changing the way we seek, find and participate in community. 

Currently, individuals and organizations are struggling to adapt to our evolving digital world, particularly the social technologies we use to connect and communicate with each other. Complex human networks are springing up on and across myriad social media, social network and topic-based communities, forming community ecosystems that transcend technological, geographical and organizational boundaries.

Looking forward, it seems things are about to get even more complicated. A new set of technologies is emerging to augment human cognition (AI), enhance human agency (Agents) and shape digital experiences and outcomes by taking advantage of a rich set of tools and APIs (Automation). We see these three technological forces (AI, Agents and Automation) as the next immediate wave of disruption in digital experience, and we see Community, Crowd and Collaboration as the social contexts in which technology and humanity will interact for the betterment (or detriment) of humankind.

The C3/A3 Project Overview

Our hypothesis is that this combination of social technologies with human augmentation technologies will usher in a new age of digital community experiences. These new experiences will present unprecedented opportunities and challenges for organizations and individuals, and complex policy issues for society.  The C3/A3 project will explore the technological, business and societal implications of this next wave of change and offer a helpful path forward.

Focal areas:

  1. Technology: The current and emerging technology landscape
  2. Business: Corporate strategy, competence, needs and level of readiness
  3. Individuals: Customer (a.k.a. Community member) needs, expectations and likely challenges

Key Components of the Project:

  1. Community Executive survey – February 26th
  2. Technology landscape analysis
    1. Community platforms
    2. AI technologies ( ex: Watson, Einstein)
    3. Agent interfaces (ex: Cortana, Alexa, Obindo)
    4. Automation platforms
  3. Executive interviews with select technology providers, early adopters and startups
  4. Mass practitioner survey
  5. Customer (Community End-user) survey

Reports and Mastermind

The output of the project will be a series of reports throughout 2018 that publish key findings. An executive mastermind group for brands and select startups will be formed to deeply explore relevant topics.

Getting Involved

The first wave of research launches on Monday, February 26th with an invitation-based survey to Executives who own community and social media experiences for their respective companies. Detailed results will be shared privately amongst this group, and summary data will be shared publicly.

If you would like to participate in the research survey and subsequent Mastermind discussion, please send me a note: bill@structure3c.com.

Reminder: This phase of the research is open to Executives at large organizations (5000+). No agencies or consultancies please.

How AI Can Help Solve The Biggest Problem With Crowdsourcing

Starlings at Dusk

The concept of engaging “the Crowd” through digital platforms has been around for some time. Howard Rheingold coined the term “Smart Mob” in 2002 to describe the phenomenon of people acting in concert “because they carry devices that possess both communication and computing capabilities”. The concept was carried forward in 2005 by the editors of Wired to coin the term “Crowdsourcing” (crowd + outsourcing) to describe production with the a digitally connected marketplace. In the 15 years since the concept of Crowdsourcing was introduced, we have seen a wide range of crowd-based business models emerge: Wikipedia (collective knowledge), Lego Ideas (design your own kit), Kickstarter (crowd funding), Local Motors (crowdsourced vehicles), and Dell’s Ideastorm (the original social suggestion box).

With the wide range of crowdsourcing experimentation, we’ve also seen the limits of what the current platforms and practices can produce, and it isn’t pretty. Consider:

  • On average, less than 30% of Crowdfunding campaigns reach their goals. On some platforms it can be closer to 10%.
  • Quirky, once the darling of crowdsourced consumer goods, filed for bankruptcy in 2015.
  • Dell, an early pioneer in crowdsourcing, has been able to implement only 2% of the ideas submitted on IdeaStorm.
  • Independent crowdsourcing research, including a recent study by the Swiss Federal Institute of Technology, discovered that social influence can cause “herding towards a relatively arbitrary position.”

What are the key challenges?

The most common limiting factors to Crowdsourcing initiatives are one, or a combination, of the following:

  1. Engaging the right crowd: Perhaps the most critical challenge in crowdsourcing is finding, and then engaging, the members of the crowd with the knowledge, skill and motivation to participate. Without domain knowledge and skill crowdsourcing produces only low quality results. Without motivation, you have unrealized potential.
  2. Creating an iterative development process: One of the early corporate adopters of crowdsourcing, Dell’s Ideastorm, learned early on that creating an experience that solicits ideas without giving the community the ability to refine and evolve the ideas is a waste of time. After collecting over 10,000 ideas in the first 2 years of IdeaStorm, Dell was left with 9,750 that couldn’t be implemented, causing frustration for the company and their crowd. By introducing multi-staged challenges dubbed “Storm Sessions”, Dell was able to source and develop products with their crowd, most notably Project Sputnik, the first Linux-based laptop for developers.
  3. Developing short and long-term feedback loops: The process and infrastructure required to support short-term feedback loops is difficult and labor intensive, requiring personal interactions and manual data management. Longer term feedback loops that include market data are currently next to impossible.
  4. Creating intelligence from crowd data: The amount of data a typical crowdsourcing initiative produces is overwhelming, and managing this data to create knowledge and insight, even moreso. Consider the amount of manual processing and scoring overhead associated with the 25,000+ ideas in the previously mentioned Dell IdeaStorm example.

How A New Take on Collective Intelligence Can Help

Collective Intelligence, a disciplined approach to the “wisdom of the crowd”, is defined as the “science of scaling insight from multiple knowledgeable perspectives and experiences into predictions”. We’ve traditionally thought of “The Crowd” as exclusively human, but what if we expanded the collective “we” to include the rapidly evolving domain of Artificial Intelligence? The combination of expert communities and artificial intelligence is the core of a new approach to Collective Intelligence being developed by a new startup named CrowdSmart. Specifically, CrowdSmart technology creates a means to predict startup success factors by engaging an expert community of investors to score and provide critical feedback to early stage startups. Investors save time on research and improve the quality of their deal flow, and Startups get critical and timely feedback to help increase their odds for successful outcomes.

What is uniquely valuable about the CrowdSmart approach is leveraging Artificial Intelligence to detect the statistically significant ranked comments behind any given score. These ranked comments are the “drivers” that produce a specific score. The qualitative “wisdom of the crowd” becomes quantitative intelligence that grows in value over time.

According to Tom Kehler, Chief Data Scientist at CrowdSmart, “Collective Intelligence significantly outperforms individual expert intelligence at predicting the success of a new products, services and startups.” If Tom is correct, the application of Collective Intelligence will have far-reaching effects on the future of Crowdsourcing, paving the way for a more disciplined approach and more successful outcomes.

Disclosure: CrowdSmart is a Structure3C client.