The gig economy (or platform economy) is commonly defined as digital platforms that allow freelancers to connect with potential clients for short-term jobs, contracted work, or asset-sharing. When referring to the gig economy, we automatically think of Uber drivers or the Deliveroo riders, but the concept is way older than Uber or Deliveroo. In certain professions people have always worked gigs (think of actors and musicians for example). And while the employment status of e.g. Uber drivers has been controversial, this is not true for all freelance professions where most workers are self-employed. New technologies have just enabled a better cooperation between "employers” and freelancers/gig workers.
Members of the Gig economy are moving from early adopters to early majority on the adoption curve. Becoming a freelancer or ordering jobs from a freelancer in general is becoming the new normal rather than a trend. Research shows that approximately 30% of Fortune 500 companies are hiring talent through this channel. The statistical data on gig workers in Europe is extremely limited, but according to the 2018 JRC Science for policy report, approximately 8% of the active population were frequent platform workers in 2017. In the US, 38.4% of the non-seasonal gig workers find their jobs through digital marketplaces. According to Statista, the projected gross volume of the gig economy is expected to reach 455.2 billion U.S. dollars in 2023.
The downside of fast growing online marketplaces is that they can easily become crowded online job platforms where both employers and gig workers have a hard time finding the right match. This is where ML (machine learning) powered algorithms have already proven to be extremely valuable. The algorithms understand the employer’s demands and match them with the best suited gig workers. AI-driven recruitment has even proven to be less discriminatory.
Machine learning has been a hot topic in a number of industries for quite some time and the translation industry is no exception. Most translation companies limit machine learning research to enhance (machine) translation technology and tend to stick to traditional workflows:
Translation agencies (LSPs or language service providers if you will) have a network of freelance translators that will perform the translations. Traditionally the middleman (LSP) receives the task and decides which translator is suitable for the job or which translator is available and whose rates fit within the budget.
In the platform economy it is no longer necessary to let another person decide which translator will work on the project. Instead, the customer himself chooses “the talent” on the digital platform, ideally supported by AI to find the best suited freelancers. This offers advantages in terms of transparency and budget control:
- Direct communication line between end client and translator, boosting the sense of accountability and speeding up query solving;
- Faster turnaround times as there’s no longer a bottleneck;
- If the end client is happy with a translator, he/she can decide to keep on working with that translator;
- Cost-efficient solution, provided that the skills and competences of the gig workers are verified.
Lilo understood the rising need for transparency, budget and quality control, and developed a platform where the ML powered algorithm not only enables streamlined translator selection, but also enables automated, streamlined workflows to support both end-clients and translators. That way a platform can easily become a strategic solution to embed in day-to-day processes, which is currently the biggest challenge for companies: combine on-demand and strategic.
After surveying about 700 senior business leaders, the BCG Henderson Institute stated the following:
“Our research showed that many leadership teams have not yet fully grasped the strategic significance of these talent platforms. They are more than a stopgap, they are a means for resolving the chronic problems companies face while filling their talent needs. Business leaders cannot risk missing a critical opportunity to build a more flexible, resilient organisation.”
The survey shows that an increasing number of managers have been experimenting with talent platforms. But to introduce change on the scale needed to innovate new business models, companies will have to get strategic and appoint a C-suite leader to explore how talent platforms can unlock new sources of value and become a competitive advantage. Talent platforms on their end should keep on investing in features so they can easily become strategic partners.
The Covid pandemic and technological advancements will most probably keep fuelling the growth of the gig economy. And the growing gig economy will allow companies to become more and more flexible and resilient. Is your company ready to embrace this new normal?