Digital Employees and the future of Service Delivery

I attended an incredibly useful webinar today hosted by Amelia on the topic of 'HyperAutomation to Accelerate Business Value'. This post is a reflection on the discussion points that resonated with me.

What is HyperAutomation?

A lovely new buzzword from Gartner, HyperAutomation is effectively the end-to-end automation of an organization’s business processes. This will often involve multiple technologies such as robotic process automation (RPA) which I have spoken about previously, intelligent business process management (IBPM), machine learning (ML) and artificial intelligence (AI). The difference, is that Hyper Automation expects this to be delivered under a single, unified system rather than the myriad of individual systems that organisations seem to be deploying.

Why should I care?

The technologies involved here can transform your organisation and support what the panelists today referred to as the 'Digitally Oriented Workforce'. It allows organisations to blend the efficiency and effectiveness of the automation environment with the creativity and skills of the human workforce. It has value in delivering more dynamic customer experiences and so increases in customer satisfaction, but also enables a level of agility for the organisation as the holsitic nature of the technology provides an environment of constant evolution, learning, adaptation and improvement.

I have posted previously on the impact of RPA and ML in the context of content services and the efficiencies, quality improvements and risk reduction that they can provide. These have typically though, been achieved via disconnected and independent solutions - RPA from one vendor that optimises and automates contract management processes, Chatbots from 5 vendors (specific to solutions), Machine Learning for information classification from another vendor etc. But is it time to bring this together under a single platform? Possibly...In Gartner's report on Top 10 Strategic Technology Trends for 2020 analysts have predicted that HyperAutomation will reduce as much as 69% of the manager's workload by 2024.

COVID-19 taught us to be brave.

Post COVID-19 we are seeing a great many organisations pivot their business models, operational delivery models as the future of the workforce, working practices, skills and customer expectations evolve at a never before seen pace. We have been talking about this internally - what it means for our working practices, our sense of culture, our alignment and responsiveness to clients. In the session today there were a couple of other points made.

1. The move to Cloud has enabled organisations to leverage modern 'infrastructure' and is the first step in achieving the benefits of hyperautomation.
2. We know we can move quickly. The accelerated nature of many programs was driven by necessity - we had to use a 'number 8 wire' Kiwi mentality and focus on getting things done. For many that meant executing their collaboration and content strategies an awful lot earlier than expected.
3. We work anywhere, anytime, and need the tools to enable and support that - not legacy systems that hold us back because we need to be 'in the office, find the piece of paper'.
4. Customers expect immediacy. If you're not providing immediacy you are not delivering the service. That puts extreme pressure on the people we do have, and given the changing nature of customer expectations, it is unlikely to be a model that can be sustained with humans alone
5. Offshoring, while delivering cost-effectiveness to many organisations is now proving challenging given that many of the offshore countries we work with have significant challenges with COVID and so there is a move to reverse that trend.
6. The tools that support us are evolving, with technology that goes beyond the bots that have been existence since the 1960's - yes they go back that far! https://nlp-addiction.com/eliza

Put simply - post COVID we have to work smarter not harder.

Is it just a private sector thing?

When we talk about HyperAutomation, AI, Machine Learning etc there is often an expectation that these things affect the business world not the public sector. While it's true that many of the use cases for automation tools revolve around customer engagement and interaction, servicing a customer need (purchase/support) there are strong use cases for the public sector also.

The conversation reminded me of the e-Health programmes in Estonia where the digital medical history, is widely used. The medical history combines significant information including medical conditions, test results, treatments, prescriptions etc but it is predominantly in unstructured text form - the use of AI tools would provide opportunities to assist Clinicians with analysis and allow them to focus on key concerns and prioritise the precious time they have with patients.

While that is a future goal, there are current uses of AI technology. The FDA (Food and Drug Administration) in the USA for example, authorized the first AI device to diagnose diabetic retinopathy without a physician’s help in April 2018.

So where do you generate value?

I posed this question to the panel - as I am conscious of the potential of these tools, but in many environments - specifically those that are more risk averse, where do you generate value such that you can obtain the investment needed to improve and automate?

First understand the problem you are trying to solve and iteratively build to reach the desired outcome

For some organisation the best return may be to start in the call centre, and leverage AI in the complex questions that are issued to customer service staff. For other organisations, the greater need may be the use of RPA and Machine Learning to automate and streamline application processing for faster, more reliable outcomes and increased satisfaction.

Whatever the use case, COVID-19 has shown us that starting this automation journey is not as scary as we once thought it was - nor does it need to take 12 months. The technology platforms available today make the adoption of technologies much faster so explore a use case and start - learn from that initial concept and evolve.

My sincere thanks to panelists Andrew Winlaw, Jonathan Crane, Stacey Tomasoni, Amberjit Endow and Chetan Dube for a fascinating and incredibly useful discussion.