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.