Experts on employment trends everywhere from the CIPD to Accenture believe the gig economy to be the biggest change in the workplace for decades. The growth of short-term, statement of work projects delivered by independent freelancers, is outstripping every other area of the employment market.
But the gig economy is only a reaction to much larger changes in the way we live, shop, consume and interact with each other.
In this guide, we look at how people, organisations and technology could combine in the workplace of 2020.
In this guide:
The first influence on the future of work is how everything we interact with will be connected.
The Internet of Things, where objects like household appliances and vehicles are connected to web services, hit the mainstream in 2016.
We already have smart meters that know when to adjust the heating in your home and smart fridges that know when your milk is going to pass it's use-by date, but the proliferation of intelligent objects will continue until the majority of objects we interact with on a daily basis will be able to make decisions on our behalf autonomously.
Apple's HomePod is the latest addition to the smart home assistants line-up, joining Amazon's Alexa and Google Home. VoiceLabs predicted 24million units will be sold in 2017, and the installed base could grow to be in excess of 128million units by 2020 according to RBC Capital Markets.
With an intelligent assistant in most consumer's homes, all connected to the same web services as our other objects and appliances, there will be an unprecedented amount of consumer behaviour data available to companies upon which to make strategic product, sales, manufacturing and marketing decisions.
This realtime, interconnected management information, coming from a network of smart homes and devices, will enable organisations to make effective resourcing decisions, with data providing insight that may lead them into previously unconsidered talent strategy.
Retailers have been using data to solve supply chain management issues for decades. Being able to deliver grocery orders in under an hour requires the likes of Amazon and Tesco to utilise thousands of data points to manage its stock holding and delivery networks.
The widespread adoption of intelligent assistants and web-enabled appliances gives businesses access to an extraordinary amount of consumer behaviour data to complement their decision making data points. Having management information enriched with real-time, connected consumer data, makes it easier to predict shifts and changes in demand for products and services - again, providing organisations more guidance on where their talent strategy needs to take them.
Smart assistants also create the potential for technology to affect much more than a business's manufacturing, supply-chain management and sales functions. Collecting usage data and real-time consumer feedback could also see artificial intelligence influencing product development and innovation.
Using AI and data to remove some of the guesswork from product development, could lead to increased confidence (and spend) in R&D. This could speed up the use of machines and automation in manufacturing and engineering as more human resources are deployed into creative and innovation positions.
Our lives are already on-demand, with TV, music, shopping, knowledge, hotels and taxis available by simply loading a mobile app. The growth of the gig economy is partly attributed to the desire of the modern professional to have more control over how, where and with whom they work.
This fascinating shift in our attitudes towards where and how we work will continue, and as discussed in a great article by levels.io, there could be in excess of 1 billion digital nomads by 2035 as a result of increasing mobile internet speeds, faster, more affordable international travel and falling rates of marriage and home-ownership.
As the traditional job hunt became more and more digital-first, the recruitment and HR community followed suit. From the first job boards of the 1990s, through to the aggregators and search engines of today, we've seen a trend where a couple of powerful generalist platforms are slowly replaced by a range of niche, sector, vertical or location specific platforms.
As the preferred mode of work for the majority of the workforce (50% of the US workforce will be freelancing by 2020), so will the default mode of resourcing. Job boards will start to be replaced (or merged with) freelance marketplaces, where it is possible to hire freelance talent directly quickly and simply.
In today's traditional employment market, there is the notion of the passive job seeker. They aren't actively looking, but they could be persuaded to move with the right role. The challenges the passive job seeker create for HR are:
In the future, the supply and demand equilibrium of the gig economy will effectively solve the passive 'freelancer' challenges:
The workplace of the future could be one that turns to AI to plan its workforce too. The vast amount of real-time consumer data and management information, coupled with an accurate picture of freelancer availability provided by freelance marketplaces, could provide AI the tools it needs.
With information on the skills required and the number of man-hours needed, it is conceivable a machine could resource a business's projects with access to an acquisition channel, like a freelance marketplace.
Shortlisting could be replicated by an algorithm that matched the requirements of a project with the skills listed on a candidate's profile, or associated with a candidate's previous role.
Competence testing could be replicated by assessments like questionnaires and VR role-playing 'games' or simulators. Here's an example of VR technology already being used to gamify/assess a user in a job simulator:
There is an interesting ongoing debate as to whether a machine could resource a project as effectively as a human.
A human is said to make the better decisions because:
Let's look at the mechanics of how an AI-powered machine could (regardless of whether they should or not) make resourcing decisions as effectively:
Experience is only gained from evaluating both positive and negative outcomes from an experiment. Humans learn what is a 'good hire' from recalling a selection of memorable outcomes, both positive and negative, from dozens of personal resourcing 'experiments' over the course of a career.
A machine could evaluate the outcomes of thousands of hires, combined with manager and candidate feedback, and sentiments/results from annual reviews in real-time, without the factors of bias or memory affecting the decision.
A human's ability to judge cultural fit is a subjective product of experience and the representativeness heuristic. "This candidate will be a good cultural fit because they seemed bold and outgoing, which is the same as the client's line manager" is a typical example of a human's tendency to use representativeness to make a decision where there is an uncertain result.
Machines are already capable of assessing an individual's compatibility with another person or a group. Dating websites, Facebook, Google and hundreds of advertising platforms are already extremely effective at using an individual's traits and behavioural patterns to connect them with other like-minded people. It is feasible for this technology to be applied to the workforce, where social and behavioural preferences and patterns can make machines as accurate with their cultural fit estimations as their human counterparts.
In this future reality, it may appear an organisation's HR function is in the control of machines and artificial intelligence.
Whilst, the day-to-day tasks HR fulfil today might be undertaken by machines, it doesn't replace or reduce the importance of humans being responsible for an organisation's HR strategy and direction.
In the workforce of the future, the skills economy will be fiercely competitive - the number of devices, coding languages, media and platforms will continue to expand - and the human brains in HR will need to put more thought into retention.
In order to access the range, depth and diversity of skills it needs, an organisation will be forced to turn to the flexible resourcing model provided by the gig economy. But in doing so, an organisation exposes itself to a risk factor of resourcing projects with independent, self-employed freelancers and contractors: the absence of mutuality of obligation. In other words, when a talented freelancer is no longer motivated by your project, it is very easy for them to move on.
By focusing on developing engaging Freelance Value Propositions and creating freelancer-inclusive talent strategy, HR can ensure they are retaining the 'mission critical' freelance talent they need for their organisation.
With AI taking care of most of the reactive, day-to-day, heavy lifting, it enables HR to be more forward thinking, more proactive and more involved in the strategic direction of the organisation.
With enough data an AI algorithm can be extremely effective at making decisions for right now. "Person A is the best choice for Project X". But an algorithm is not good at foreseeing the unknown. Predicting and assessing the big change that's brewing just beyond the horizon is still best left to the humans.
Discover the 5 essential, must-have, components to look for when shortlisting Freelancer Management System (FMS) vendors in your RFI process.
Understand the importance of including on-demand workforce channels in your strategic spend management.
Freelancers are not employees. They are not bound, or managed, by the same rules and it is common for hiring managers, line managers and operational teams to experience challenges as they start to engage a larger number of freelancers.