The curation of collective intelligence
There’s a classic story about a statistician who, while at a county fair, happens upon a competition to guess the butchered weight of an ox. He compared individual guesses with the median guess of the crowd and found that while individual estimates varied widely, the group as a whole came within 1% from the ox’s true weight of 1,198 lbs. This simple story illustrates how collective intelligence often yields more accurate results than individual.
Tapping into digital groups to glean collective intelligence isn’t a new concept. Platforms already exist to utilize crowd intelligence. Freelance labor marketplaces like Freelancer.com bring together communities of workers from around the globe to fill labor shortages. Talented individuals who were previously “undiscoverable” can be accessed to help companies form a network of skilled workers with more intellectual capital than any one individual could provide.
There’s little doubt that these marketplaces represent the future of work. Some 20-30% of the working-age population in Europe and North America have already left the stable confines of organizational life to engage in some form of independent work.
Turning to the world’s collective intelligence to address skill shortages makes economical and practical sense when considering the 93-97% cost savings compared to traditional labor methods. There is also an accelerated time-to-market when sourcing multiple ideas in parallel — but it’s sometimes difficult to defend the quality of these solutions. Without mechanisms for gatekeeping quality, freelance labor marketplaces can attract both the highly skilled workers, but also the charlatans looking to hoodwink prospective hirers.
Fortunately, user reviews and reputation can surface talent and push the most skilled workers to the top. On freelance labor platforms, workers are reviewed based on their past projects, evaluated on their deliverables, timeliness, communication, and ability to finish a project on budget.
Imagine you’re interviewing an in-house candidate. The only insight you have at your disposal is a carefully crafted resume, and perhaps two contacts the candidate knows who are willing to offer a glowing reference. You have no clear window into their actual day-to-day job performance, the quality of work they’ve delivered, or their practical aptitude.
Now imagine you had access to every 360 performance review from that candidate’s entire working history and a real-time assessment of acquired skills and knowledge presented to you on a digital dashboard when making the hiring decision. Obviously, you will be far more informed and equipped with a level of certainty about the candidate’s claims.
This is what user reviews provide — an objective and immutable snapshot into a worker’s real-world performance. It’s a powerful tool with which to make hiring decisions and lends a degree of transparency to crowdsourced labor that’s lacking in the traditional workforce.
User reviews are only the first step in quality assurance for distributed workforces. Cloud labor platforms have become more sophisticated, using machine learning and artifical intelligence to connect companies with the most qualified freelance candidates for their specific project. Matchmaking is now driven by algorithms that connect the skills, budget, and time frame of a project with only those workers able to execute within those parameters.
Individual human curation also plays a role in this process. As freelance workers garner more reviews from the crowd, they begin to surface to those managing skifreelance marketplaces. Hirers can then create their own database or talent pool of high-quality freelance workers to spotlight on the platforms.
Labor platforms also have the opportunity to create branded communities of elite workers within specific skill sets, vetted and verified by enterprises who have a trusted reputation in that domain. This way companies can certify the quality of the freelancer workforce in their industry or expertise while freelancer platforms provide the liquidity of human capital. For example, Arrow, a Fortune 109 company, joined forces with Freelancer.com to create a branded community of 500,000 IoT, hardware, and electrical engineers.
Connecting quality workers with enterprises
At the moment, this individual human curation is helpful. It connects the highest quality workers with enterprises looking for their specialized skill set. It enables hirers to sift through the millions of workers available to find only the ones best suited to their project. Advancements in AI will further the curation process to aid global enterprise, while addressing needs more rapidly.
It’s at this point where global enterprise assumes its evolved form — lean, agile, efficient, and resourced to take on challenges of a scale previously unimagined. It utilizes a flexible workforce that never lies dormant, awaiting its next project. It will become a worldwide network of geographically dispersed genius connected through a platform and rapidly deployed to address companies’ immediate needs.
We are on the horizon of that future. Collective intelligence could become the supercomputer of human ingenuity that will define the enterprises of tomorrow.
Jin is the program director and senior researcher at the Laboratory for Innovation Science at Harvard (LISH).
John is he executive-in-residence at Harvard Business School’s Laboratory for Innovation Science at Harvard (LISH) and founder and CEO of Open Assembly.
Sarah is VP, Enterprise Services at Freelancer.com and collaborates closely with the Laboratory for Innovation Science at Harvard (LISH).
Jean is the head of content strategy at Open Assembly.
Adam is a content manager at Freelancer.com.
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