1 Introduction and background
2 Research design
Interviewee | Local government | Position | Experience with urban technologies (years) | Experience with local governments (years) | Experience with AI systems (level) |
---|---|---|---|---|---|
Interviewee #1 | Brisbane City Council, QLD, Australia | Information Architecture and Security Manager | 30 | 20 | High |
Interviewee #2 | Brisbane City Council, QLD, Australia | Innovation and Planning Manager | 15 | 20 | High |
Interviewee #3 | Logan City Council, QLD, Australia | Environmental Information Systems Manager | 10 | 5 | Reasonably high |
Interviewee #4 | Logan City Council, QLD, Australia | Innovation and City Transformation Manager | 5 | 10 | High |
Interviewee #5 | Moreton Bay Regional Council, QLD, Australia | Assets Management Department Director | 5 | 15 | Reasonably high |
Interviewee #6 | Moreton Bay Regional Council, QLD, Australia | Chief Digital Officer | 10 | 10 | High |
Interviewee #7 | Redland City Council, QLD, Australia | Governance Services Manager | 10 | 30 | High |
Interviewee #8 | Redland City Council, QLD, Australia | Business Innovation and Development Manager | 10 | 10 | Reasonably high |
Interviewee #9 | Sunshine Coast City Council, QLD, Australia | Head of Information Technology Department | 20 | 20 | High |
Interviewee #10 | Sunshine Coast City Council, QLD, Australia | Smart City Program Director | 15 | 20 | High |
Interviewee #11 | Ipswich City Council, QLD, Australia | Smart City Program Director | 10 | 20 | High |
Interviewee #12 | Pearland City Council, TX, USA | City Administration Manager | 5 | 30 | Reasonably high |
Interviewee #13 | Pearland City Council, TX, USA | City Budget Manager | 5 | 10 | Reasonably high |
Interviewee #14 | Raleigh City Council, NC, USA | City Manager | 5 | 20 | High |
No | Category | Question |
---|---|---|
Q1 | Participants’ backgrounds | Please tell us about your experience in local government management, such as how many years and which positions you have had at the local government level |
Q2 | Please tell us your experience with deploying, using or managing information and decision support systems at the local government level | |
Q3 | Please tell us your experience with leading or contributing technological innovation initiatives at the local government level | |
Q4 | Please tell us your knowledge on and experience with AI in the context of cities and local government services | |
Q5 | Participants’ general views on AI | When do you think AI will affect or reshape the future of local government services (including your local government), and why? |
Q6 | How do you think AI will affect or reshape the future of local government services, and why? | |
Q7 | How useful is AI and how useful will it be (within the next 20 years) in supporting local governments to achieve desired outcomes, and why? | |
Q8 | In which areas should AI be adopted in local government services, and why? | |
Q9 | What are the reasons that make local governments approach to AI with caution, and why? | |
Q10 | Do you think local governments are prepared (e.g. in terms of know-how, technology, finance, regulation, ethics) for AI adoption, and why? | |
Q11 | What are the main roadblocks in AI adoption in local governments, and how can they be overcome? | |
Q12 | Participants’ specific views on AI deployment in their local government | How knowledgeable is your local government on AI and its potentials in transforming the city and its communities (e.g. in the delivery of public services, and so on)? |
Q13 | Which AI technologies, applications and systems are currently being considered by your local government, and how are they used? | |
Q14 | What AI adoption challenges is your local government experiencing, and how are these challenges being addressed? | |
Q15 | How are you evaluating the impact of deploying AI systems in your city and community? | |
Q16 | What are your plans for future deployments of AI in your local government area? |
Node | Sub-node | Relevant interview question |
---|---|---|
Adoption areas | Asset management, Automation, Buildings, Businesses, Communication and complaints, Data analytics, Enforcement, Maintenance work, Public services, Service delivery, Urban infrastructure, Waste management | Q8 |
Cautionary areas | Blackbox nature, Human interaction, Privacy and cybersecurity, Transparency | Q9 |
Challenges | Bias and inaccuracy, Culture, Ethics, Financial management, Innovation, Risk management, Staff redundancy, Unfamiliarity, Validation | Q14 |
Effects | Broder purpose of use, Drag behind some councils, Increased capacity, Increased expectations, Increased experimentations, Increased system maturity | Q6 |
Impacts | Bridging knowledge gap, Increased efficiency, Increased investment, Revenue generation | Q15 |
Knowledge basis | Broad, Intermediate, Limited | Q12 |
Plans | Data, Development, Engagement, Ethics, Management | Q16 |
Preparedness | Not ready and not focused, Not ready but focused, Ready | Q10 |
Roadblocks | Budget restrictions, Change management, Elderly population, Legal issues, Pace of implementation, Trust issues | Q11 |
Technologies | Machine learning, Deep learning, Natural language processing, Neural networks, Robotic process automation, Asset maintenance systems, Automated decision support, Autonomous vehicles, Chatbots, Data analytics, Identification systems, Innovation portals, Smart maps | Q13 |
Deployment timeframes | Long term (in 20 years), Mid-term (in 10 years), Short term (in 5 years) | Q5 |
Usefulness | Automating routine decisions, Creating efficiencies, Improving productivity, Managing repetitive tasks, processes and decisions, Minimising errors, Tackling complexity | Q7 |
City | State | Country | State Capital | Metropolitan Location | Population | Smart City Strategy | AI Agenda |
---|---|---|---|---|---|---|---|
Brisbane | QLD | AUS | Yes | Yes | 2,439,467 | Yes | Yes |
Logan | QLD | AUS | No | Yes | 303,386 | Yes | Yes |
Moreton Bay | QLD | AUS | No | Yes | 425,302 | Yes | Yes |
Redland | QLD | AUS | No | Yes | 160,331 | Yes | Yes |
Sunshine Coast | QLD | AUS | No | Yes | 336,482 | Yes | Yes |
Ipswich | QLD | AUS | No | Yes | 229,845 | Yes | Yes |
Pearland | TX | USA | No | Yes | 122,078 | Yes | Yes |
Raleigh | NC | USA | Yes | Yes | 464,485 | Yes | Yes |
3 Results
3.1 Quantitative content analysis
Node | Sub-node | Sub-nodes mentioned by interviewees | Frequency of sub-nodes |
---|---|---|---|
Adoption areas | Asset management, Automation, Buildings, Businesses, Communication and complaints, Data analytics, Enforcement, Maintenance work, Public services, Service delivery, Urban infrastructure, Waste management | 14 | 76 |
Cautionary areas | Blackbox nature, Human interaction, Privacy and cybersecurity, Transparency | 11 | 30 |
Challenges | Bias and inaccuracy, Culture, Ethics, Financial management, Innovation, Risk management, Staff redundancy, Unfamiliarity, Validation | 13 | 67 |
Effects | Broder purpose of use, Drag behind some councils, Increased capacity, Increased expectations, Increased experimentations, Increased system maturity | 10 | 25 |
Impacts | Bridging knowledge gap, Increased efficiency, Increased investment, Revenue generation | 5 | 11 |
Knowledge basis | Broad, Intermediate, Limited | 7 | 11 |
Plans | Data, Development, Engagement, Ethics, Management | 13 | 31 |
Preparedness | Not ready and not focused, Not ready but focused, Ready | 10 | 26 |
Roadblocks | Budget restrictions, Change management, Elderly population, Legal issues, Pace of implementation, Trust issues | 13 | 42 |
Technologies | Machine learning, Deep learning, Natural language processing, Neural networks, Robotic process automation, Asset maintenance systems, Automated decision support, Autonomous vehicles, Chatbots, Data analytics, Identification systems, Innovation portals, Smart maps | 13 | 47 |
Deployment timeframes | Long term (in 20 years), Mid-term (in 10 years), Short term (in 5 years) | 6 | 8 |
Usefulness | Automating routine decisions, Creating efficiencies, Improving productivity, Managing repetitive tasks, processes and decisions, Minimising errors, Tackling complexity | 11 | 32 |
3.2 Qualitative content analysis
3.2.1 Artificial intelligence adoption prospects
“In the Moreton Bay Regional Council, we have been working with our Arup and Brent divisions since the middle of 2018, about two years now, where we have got a camera connected to a Raspberry Pie with a GPS unit and modem that sits on the dashboard of a garbage truck, and it effectively just captures video as the truck drives along the road. That video is then transferred to some computers through the 4G/5G network, and those computers then run various algorithms over that footage to detect all sorts of different road defects—so potholes and cracking and all that kind of stuff”.
“We are using our smart drones and machine learning to capture issues on the roofs of over 1,700 buildings. Besides, we are also building an underground drone to cruise in the 2,700 km stormwater pipe network to check connectivity, blockages and parts that need maintenance”.
“The council is interested in automating some of the routine tasks to create efficiencies and save time and resources in the long run…. We have realised that robotic parking was a good way to go because it opened up commercial opportunities for the rest of the land”.
“We use AI-driven data analytics on all of the phone calls, letters, complaints, and everything the council receives from the residents and suppliers. This really helps us to understand what our performance was, are people happy or are there particular areas of council performance that people want to see improvements. These analytics process has been very useful to deliver better services… It is also very helpful for improving service productivity and minimising human errors”.
“We conducted thorough sentiment and analyses over the council communication data to work out what is going on with customers. We have done a lot with the structured data as well, like emails coming in to the main council mailbox. It was extremely helpful to improve customer relationships and satisfaction… This gave us an opportunity to first better understand and then tackling relatively complex customer relations matters”.
“We are at an early stage in the service automation journey, however our prior BIM (building information modelling) experience is guiding us in adopting suitable AI applications to automated decisions concerning buildings and infrastructures”.
“We have already got an autonomous lawnmower. So, at our stadium it goes and cuts the grass for us without needing a person to do that for us. This saves time and money”.
“Autonomous shuttle buses are critical solutions for urban accessibility. We already have one trial going on here in Redlands (Redlands Coast Smart Mobility Trial), and getting more of these busses in service, let’s say to and from Cleveland Rail Station or where the boats come in from Stradbroke Island, will help in solving the first and last mile connectivity”.
3.2.2 Artificial intelligence adoption constraints
“The biggest challenge around AI in local government is removing bias in training data for machine learning. Unless this obstacle is removed, AI systems will always generate inaccuracies. Hence, the risk is high for a public entity to adopt AI systems and deploy them in confidence at this instance”.
“You have to explain to employees why you are using AI, you have to explain to residence how they are benefiting from AI, why it is important, council staff has to have the training and skills to be successful operating whatever AI application it is and then there has to be accountability if the AI system fails or generates undesired outcomes”.
“Adoption of new technology such as AI and adoption of change processes are not only challenging for local council personnel, but these are quite difficult and perplexing for the public, particularly for the senior citizens”.
“AI is fast moving, new, and full of exciting promises, however, there is the critical legal side of it. In my opinion, the lack of AI regulations is a big roadblock for local governments to thoroughly deploy AI systems. Just getting all the laws around it sorted is complicated”.
“Government organisations, and certainly local councils such as Brisbane City Council, are very risk adverse organisations, therefore, we have got duty of care to our customers and rate payers, particularly on the matters of AI service user privacy and security. We are highly cautious at the absence of statutory AI ethics frameworks and legislations”.
“We have had a robot, which we had to actually decommission it last year, because its associated code was not the latest generation, and it was not delivering what was expected from it. I am wondering when robotics will reach to the desired human-robot interaction level so we can use them with an ease of the mind”.
“Not financially ready, mainly due to the financial crisis triggered by COVID. Nevertheless, these challenges make us think out of the box and force us to do things differently. Perhaps despite preparedness, this age of digital transformation will speed up the AI uptake at the local governments”.
“It is training, awareness and partnership with industry and academia that lacks in the councils to develop their skill and knowledge basis to be comfortably planning, deploying, and managing AI systems. As the technology continues to grow and becomes disruptive, councils should find ways to build their competencies on AI”.
“Local governments could have financial restraints due to the cost of AI systems, consultancy services, staff training/upskilling, and campaigns for residence acceptance of new AI-driven local services”.
“AI is going to create a major impact in the local government service particularly in increasing efficiency of service delivery in the council, but only those can afford and be prepared for it. The proper adoption will require large financial investment and knowledge skill up among the employees. Also, there might be some resistance in the council employees thinking that they will either lose their jobs in the near future to algorithmic decision systems or need to upskill themselves to be competitive”.
3.2.3 Artificial Intelligence Adoption Choices
“I think citizen overview, or a community where the citizens are a part of the planning committee, is essential. At the least, this creates transparency, fairness and responsiveness in planning for AI and convinces many residents that sensors are not the eyes and ears of a ‘big brother’, rather they are there for responsible uses benefiting them”.
“The adoption is going to happen step by step, and I can see the signs of them already in Logan City Council. But it may take a bit of time, maybe not 20 years but not overnight either. Federal government regulations and state government initiatives at the local level will definitely speed up the adoption process in the local councils”.