Voices from Accenture Public Service


In one of my previous posts, I looked at how Artificial Intelligence (AI) can help human services agencies address some of their most intractable problems and take the speed, personalisation and human-centricity of their services to a whole new level. In this post, I switch the focus to how human services can start to make the most out of AI – and the compelling use cases that offer them the biggest returns on their AI investments. 

Looking across processes and infrastructure, it’s clear that AI can benefit many stages of service delivery – all the way from back-office processes, through to customer-facing channels and – even more importantly, enabling the development of new more personalized service offerings. With augmentation of human abilities and capacity, AI provides agencies with the ability to deploy their workforce where it is most valuable, collaborate seamlessly and securely with third-party providers, and improve service delivery. 

In other words, AI has the potential to help agencies transform services and increase operational efficiency. To show what’s possible, I’d like to highlight three use cases that have the potential to support better and more responsive citizen services. 

The first is in customer and channel management. To meet customer expectations that are both fast-evolving and increasingly fluid, agencies need to offer proactive, automated, lifelong circumstance support that is personalised, transparent, comprehensive and engaging. And they need to deliver this support anytime, anywhere through all channels – including mobile.  

Agencies can achieve all this with the help of virtual advisors powered by natural language processing (NLP), sentiment analysis and automation. This might include digital assistants helping citizens figure out which services they are eligible for and help them verify eligibility and apply. This increases service compliance and efficiency, as well as citizen experience.  

The second use case is in service delivery. Agencies can extend their insight-driven services by using internal and external data and new decision-support capabilities to improve their core processes. By harnessing the power of machine and deep learning, agencies can significantly enhance the judgment-based problem-solving capabilities of their workers. An example of this use case might be a predictive service delivery model that leverages machine learning to estimate fraud risk when people apply for benefits. For instance, relationships between applicants can be better understood and common data points such as the same address or phone number can be more fully investigated.   

The third use case is around back-office functions. The need for more intelligent information processing has prompted many agencies to consider using automation technologies such as robotic process automation (RPA) or cognitive RPA to automate the entire back-office and deliver insight-driven services. Compared to traditional manual processes, this would help to ensure fewer errors and better rule-based decisions. An instance of this could be using a combination of machine learning and RPA to automate back-office processing of standard or low complexity customer claims, while routing the more complex cases to case workers. 

In these areas and more, the case for change is clear. But despite the strong business case for investing in AI, many agencies are still daunted by the transformational complexities that they fear could be involved in fully embracing this technology. 

In my opinion, such concerns are largely unfounded. With the right foundations in place, many AI applications can be implemented incrementally – bringing ideal opportunities to start small and scale up over time. The right foundations for an agency’s AI journey will consist of three elements: 

  • Having the right data in place – including designing a big data strategy and supporting architecture, engineering a flexible data model and virtualised content, and integrating all this within the wider citizen data ecosystem. 
  • Investing in capabilities – mapping the service delivery value chain with relevant AI usage scenarios, building AI capabilities as a platform, and training and improving the AI platform’s self-learning abilities. 
  • Focusing on skills and culture – including defining an AI governance model, building a multidisciplinary workforce with skills in business, analytics and AI, and aiming for fine-tuned AI capabilities within an agile, insight-driven digital organisation. 

With these elements in place, a human services agency is well-placed to embark on its AI journey towards a new era of effective, empowering, human-centered services. 

To find out more about the intelligence-powered human services agency, download our point of view here. 

See this post on LinkedIn: Starting off on the right foot along the journey to AI for human services.

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