Rather than automating existing processes to make them faster and cheaper, true process reimagination forges new alliances between people and machines, enabling them to achieve more together. That’s particularly salient for organizations in the public sector needing to reduce the risks they face.

Machines helping humans

These new relationships are what Accenture calls the ‘missing middle’. Where machines help humans, there are three key dimensions. The first is where machines amplify or augment humans with new insights. One police force in the UK, for instance, is achieving a step-change in their ability to detect and prevent crime. Analytics and machine learning on huge quantities of data is giving officers unprecedented situational awareness.

The second dimension is machines giving user interfaces a personality. We’re seeing this in public service contact centers, where virtual assistants give citizens the services they need faster and more intuitively.

Finally, machines help humans by extending physical capabilities, like front-line troops supported by drones or police and border forces using advanced video analytics to spot criminal threats.

Humans helping machines

What about humans helping machines? There are three dimensions: training machines to perform, making what machines do explainable and making machine learning and AI sustainable. The first is where we see most effort today.

Training involves manual tagging and course correcting outputs from AI applications. Once the AI has learned all the variables of a process it can be reapplied elsewhere. For public service organizations facing talent challenges, machine learning and AI offer ways to capture institutional knowledge and use this to replicate top performers’ skillsets. But instead of replacing people, machines are augmenting their capacity to deliver more and focus their efforts on higher value tasks.

The second critical task is explaining what machines do. This isn’t a technical exercise to unpack an algorithm’s code, but a way to overcome skepticism about machine-learning outcomes.

Trust is fundamental to the third dimension: making machine learning and AI sustainable. Public service organizations are focused on ‘responsible AI’, an approach that’s rooted in key principles like accountability and transparency. These concepts are more fully detailed in the book Human+Machine: Reimagining Work in the Age of AI, published in March 2018 by Harvard Business Review Press and co-authored by Accenture’s Paul Daugherty and Jim Wilson.



The case for change

Public service organizations deploying machine learning and AI are achieving significant gains in process improvement. Ninety-five percent are at least doubling KPIs across some processes and 40 percent record improvements between five and ten times.

But others are going further, achieving breakthrough improvements. However, they’re in the minority: just nine percent claim to be realizing all AI’s benefits.

As all public service organizations strive to satisfy fast-changing citizen expectations, reimagined processes will play a key role. There are two key insights:

  1. Don’t try to reinvent the wheel: Organizations should take advantage of proven approaches that are often openly available at low cost from leaders.
  2. Be selective and don’t be over-ambitious: Instead of automating everything, identify the most common issues and automate for those.

Automation is just the beginning. Public service pioneers in AI go further, replacing traditional steps and sequences with real-time feedback and redesigning to automate process change management. And they are continuously reskilling their workforces and putting the best tools to work to get maximum value out of data. As they do so, they are achieving breakthrough results, with exponential gains in performance.



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