The smart use of resources is crucial for the economic growth of one of the most successful products in the United States. Uber, Airbnb, etc. are just some of the examples of innovative products that employ technology to optimise the allocation of existing resources under a set of constraints. The entrepreneurial spirit of that kind spreads to the public sector as well, even though there it has its own challenges.
The so-called optimising economy impacts the public sector in a unique way. Government structures are trying to keep up with the trends, but at the same time, they are extra careful not to disrupt the existing procedures that are designed to maintain public safety. If we look at the government regulatory sector, we’d see a lot of opportunities for optimisation that match the trends in the economy via better resource allocation and asset optimisation.
What’s the common between optimising economy sectors?
Looking at the private sector and optimising economy products such as Airbnb, Uber, Lyft, etc., we can identify three pillars of optimisation:
- Automated data analysis
- Distributed resources.
These three pillars are transferable to the public sector’s optimisation effort as well. But for their potential to be fully realised, some challenges must be overcome.
Challenges for the regulators
The optimising economy, in a nutshell, can be explained in terms of contextualising – to turn an existing process into a better version of itself – to utilise resources smartly and realise the hidden potential of resources that haven’t been used before. However, governments are not good at contextualising, they are good at standardisation (to make rules that are equal for everyone). However, standartisation and generalisation are keeping government at bay to resist customisation – one of the three pillars of the optimising economy. Therefore the gap between the technological advancements in the private sector and the public sector grows.
Optimising the government and the regulators – the example of the USA
The growing gap between customisation in the private sector and the standardisation in the public sector is one of the big challenges for governance in the US economy. It’s interesting to see how a country with well developed economy that relies on technologies such as cloud, AI, machine learning and data analytics is adopting the same technologies to optimise its regulatory resources better.
The US government’s challenges towards resource allocation optimisation
Since the use of computer systems is present in all government structures, one must think that it would be easy to optimise the regulatory inspection process. However, to achieve that, one must find a way to combine databases across the federal government and use AI to turn regulatory inspections into a more efficient process. Looking at this challenge Adam Finkel and Richard Berk at the Penn Program on Regulation have shown that the federal Occupational Safety and Health Administration could improve its targeting of inspection resources dramatically by combining and applying AI, machine learning and data-driven automation to disparate governmental and private-sector databases. This analysis serves as an example of where the road to optimising the regulatory sector could start from and how challenges can be confronted. Some US regulators like the Environmental Protection Agency are considering how remote sensing can be used for improving regulatory monitoring. This on the other hand is an example for creativity as a mean to optimise the government sector.
The major limitations for more optimal government are the resource constraints. Confronting resource constraints is a key factor for optimising public services. Creativity also has a big part in the equation for a more optimised government. Canalix as an inspection optimisation system helps regulators overcome the challenges that stand in their way to growth and efficiency – not only in terms of digital infrastructure but also in the creative part. For example, regulators that use Canalix for inspection planning and resource allocation improved their smart work by introducing remote inspections and self-inspections in the optimisation equation.
Watch how they did it in our video: