Technology – specifically Automation and Artificial Intelligence – has shifted the balance in how workforces are designed, employed, and engaged. We are already seeing the impact for workers, but what about organisations and how they engage and roster their workforce to optimise the gig economy?
It’s been a long time coming but as the advance of technology shifts organisations away from traditional workforces, so too has a shift occurred in the relationship between workers and their employers.
Where once there was a modicum of loyalty in the old-school ‘master-and-servant’ type of labour exchange between organisations and their workforces, tied by regular wages and employment benefits, the advent of the ‘gig’ economy has changed both the relationship and the dynamic. Many of us now sell our services into our ‘customers’, via time-limited contracts.
Recent Australian stats show that the balance of workers in full-time employment is at its lowest, as the rise in “insecure” work (e.g. part-time, casual, and gig workers) reshapes the workplace. Whether it’s being driven by business looking for workforce efficiencies or individuals seeking a different way of working, technology and cost management is a key driver in the changes that are infiltrating how we all work.
The explosion of Artificial Intelligence (AI) into the business – and personal – world has wider and deeper penetration than many of us realise. As more permanent workers are displaced by technology an ever-widening pool of ‘gig’ workers are now offering their wares to the broad marketplace – or alternatively creating their own start-up and nimble businesses in direct competition with their previous employers (disruption).
But what of the impacts to both the business, its institutional knowledge, and worker engagement? How can organisations stay ahead of the curve when it comes to managing the impacts of AI and automation on their workforce?
Looking for Competitive Advantage with AI
Pulling back the covers for a moment, the reality is that many organisations will only be successful if they offer a product or service to the market that is deemed to be of greater value than its competitors.
Designing for competitive advantage leverages a range of touch points, such as:
- Efficient production via automation driving down cost to improve quality
- Reach via the breadth and depths of its channels, logistics, supply chain, and speed of delivery
- Product differentiation via personalisation, unique selling point, or quality
It’s easy to see where AI can play a role in all of these elements alongside automation. As we swap out humans for machines that are faster, quicker, and cheaper, while making somewhat intelligent decisions and predictions, it’s possible to gain competitive advantage.
But focusing on efficiency and cost effectiveness alone has its drawbacks and it isn’t without consequences.
The Morphing Workforce & the Risk to Knowledge
One of the oft-discussed potential – and actual – consequences of the move towards automation and AI is the consequences for actual jobs for people. As workforces move toward a demand-driven or more dynamic style of human capital management, one element of competitive advantage that comes under threat is an organisation’s intellectual property (IP).
There is more to products and services than the recipe. There’s a know-how or tacit organisational knowledge that each and every worker possesses to varying degrees. It is unscripted, learned, felt, and managed in conjunction with documented or automated processes. It’s the human element that knows how to get things done in that environment and is really very intangible.
As IP, and thus people, still play a starring role in successful organisations – and cannot, as yet, all be replaced by machines – the perhaps less obvious challenge is how to create, capture, and protect the organisational knowledge when moving to an increasingly contingent workforce. And how to best leverage the existing workforce to ensure the inherent IP is not lost.
Central to this challenge is work Force Management (WFM) and more specifically rostering and the creation of value add machine-based predictive rosters.
A Personal and Technical Interplay: Who Wins?
As we’ve written about in previous articles on rostering, there is an interplay that exists between the rosterer and the worker. It’s a vital relationship offering insights and tacit knowledge to the business that impacts building a roster.
Software will certainly deliver an academic roster, based on the data available, which on the surface is optimal. However, it will not be able to incorporate the tacit knowledge about what is happening in the lives of the workers and the impact the roster may have on them. The optimal roster combines both.
Play this forward with the shift to gig workers and we have a workforce that may or may not be available based upon what gigs are available which makes this an even greater challenge as there is no way the “machine” will have visibility that critical resources that have been previously engaged now have other, potentially better, options.
The Rise of the Gig … and Rodney the Digital Nomad
Let’s consider a practical reality with Rodney the Digital Nomad. He is an expert in technical delivery that is vital to the business, yet he’s engaged on a gig basis. Right now, he has three gig opportunities in front of him, has made himself available to all three, but really will only choose one.
His decision will be based on many different facets, including the age-old filter: “What’s in it for me?” As a gig worker, he’ll choose the gig most appropriate for his current circumstances. For him to choose one gig over another, organisations that want to employ him will now have to factor workforce engagement management (WEM) as a critical influencer.
WEM is increasingly becoming a pillar in strategic workforce management as workforces shift, to enable them to not only attract exceptional talent, but also retain them, train them, and learn from them.
So as Rodney embodies what is rapidly becoming a ‘tribe of one’ i.e. the worker is concerned about only meeting his own needs first and less about the broader business goals of the organisation, how then does a business retain and protect its IP and institutional knowledge, whilst also sustaining its ability to retain or improve its competitive advantage?
These are points that offer up some serious questions for most organisations as they increasingly seek the input and engagement of a contingent workforce. It’s as much about the people as it is about AI or machine learning. In our opinion, the time is now to start addressing how this impacts workforce management strategy and getting head of the changing landscape brought on by the gig economy.
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