6 minute read 15 Oct 2018
Women with a laptop robotic hand

8 tips for successful intelligent automation projects

By

EY FS Insights

Minds Made for Financial Services

6 minute read 15 Oct 2018
Related topics AI Financial Services EMEIA

Financial services companies can thrive through intelligent automation – the intelligent use of multiple tools and automation approaches. 

Financial services companies have been embracing automation for some time now. Robotic process automation (RPA) has already made its mark, but artificial intelligence (AI) is now attracting huge interest as businesses explore the potential to unlock value in the form of improved revenue, customer service, efficiency and risk management. 

AI is widely seen as the next business disrupter and advancing quickly. Those working in the AI field believe it is the most disruptive technology we will see in the next three to five years. And there’s more to AI than driverless cars. Financial services companies are already seeing how AI tools can improve revenue, efficiency and risk management. 

However, AI is one part of the future: its real power comes in combination not only with RPA and digitization in general, but also with the input of people. Neither human nor machine alone can outperform human and machine working together. We believe financial services companies can thrive through “intelligent automation” — the intelligent use of multiple tools and automation approaches.

Intelligent automation

Intelligent automation is the intelligent use of multiple tools spanning not just RPA, but Digital and AI enablers, human-in-the loop and “Big AI” concepts. Intelligent automation also means looking for alternatives to automation and deciding what’s right based on a wide range of potential benefits — not just cost.

But, as importantly, intelligent automation is: 

  • Understanding if there are better delivery options e.g., LEAN, Six Sigma or system changes.
  • Understanding the full range of benefits e.g., new revenue, better compliance, reduced fraud.
  • Understanding the options for use of RPA, digital and AI in terms of cost, risk and ROI — i.e., should we use AI to read emails, or have simple digital forms?
Can you combine the power of human and machine through robotics and intelligent automation?

How to achieve best results with intelligent automation projects

  1. Undertake a joined-up opportunity assessment 
    There is a significant body of evidence to show that automation techniques can deliver tangible business benefits across all types of companies, even those with the most archaic IT systems. However, it is important to perform a proper opportunity assessment to find the optimum portfolio of processes with an opportunity assessment. Targeting RPA at a highly complex process is a common mistake, resulting in significant automation costs and low ROI. The effort could have been better spent automating multiple other simpler processes. 

    We typically advise companies to carry out a rapid company-wide or unit-wide opportunity assessment looking at not only RPA but also using digital and AI. We would also recommend looking for non-financial services benefits e.g., customer service or compliance improvement but also the role of BPR, LEAN, Six Sigma and system advancements.
  2. Use cloud technology to support AI tool selection 
    The number of tools available in the automation space is now so great that it can be difficult for organizations to identify which best suit their needs. Using cloud-based services such as RPA, digital, AI as a service can help with selection, enabling organizations rapidly to work out whether a particular tool is fit for purpose without significant upfront costs. Tapping into the power of the cloud can have a big impact on the deployability of AI.
  3. Plan your automation pathway from lab to live carefully 
    Progressing ML from the developmental stage through testing into live operations requires careful planning. The process will vary for every different tool and use case. Lessons can be learnt from experience with RPA projects, where key elements involve creating infrastructure, establishing governance and controls, and developing necessary skills. The particular challenge with AI projects is that each one can involve multiple tools. New approaches to the “lab to live” challenge are being developed, however. For example, distributed algorithms can be run and tested on any device (including mobile). 
  4. Consider your preferred operating model 
    Like RPA, AI implementations can benefit from establishing an operating model that includes a center of excellence (CoE). We believe a business-led CoE is the best way to manage and enhance a virtual workforce supplemented by read key skills including IT, risk and compliance — we suggest migrating towards an integrated automation CoE, rather than creating CoEs for each automation technique or technology. We also see benefits from conducting an integrated opportunity assessment and solution design across all automation technologies.
  5. Prepare a talent plan 
    With so much interest in automation — and particularly RPA and AI, the demand for skilled talent is high and demand is outstripping supply. There is, therefore, a huge shortage of skilled talent in the space. Hiring your way out of the problem is unlikely, instead a combination of strategic hires, external assistance and organic growth will be necessary. 

    Note that one of the common traps of RPA is that with just a day or two of training, most business users can automate simple processes. But the skills needed to create scalable, resilient processes are significantly greater than those needed to create a proof of concept (PoC). Companies should work on the basis of needing at least two weeks of classroom training, then two to three months of hands-on project delivery with supervision and coaching, before an analyst can deliver production-quality automations well. It’s essential not to be economical on teams’ training or skills transfer and support. 

    Because of the transformational nature of IA there could be a significant impact to roles as well as automation anxiety, hence an integrated change plan needs to be initiated as early as possible. Set up an operations control room As RPA technology has matured, organizations have realized the importance of establishing a control room
  6. Set up an operations control room 
    As RPA technology has matured, organizations have realized the importance of establishing a control room to monitor performance. Whenever adaptors e.g., OCR, NLP adaptors, are core to processes, they need to be managed and monitored. Excessive errors or hand-backs to humans would indicate potential issues that need to be addressed. The same need for a control room arises with AI implementations in order to make sure there are high quality outputs. Additional monitoring will be required — for example, to address the risk of bias in algorithms which can skew results. Like the CoE, a unified approach to the control room is recommended. 
  7. Monitor impact 
    Automation projects can have multiple benefits on an organization. These could include reduced risks, the delivery of more predictable services, higher data quality, increased capacity and throughput, and reduced cost and headcount.

    The opportunity assessment performed at the start of the project can be used to identify desired benefits and impacts. Performance in delivering these benefits must then be monitored. This is vital for ensuring that investment in the automation program will continue. Ultimately an automation program must deliver its planned benefits in order to continue to rollout. Focusing on measuring and realizing benefits and providing transparency to executive business leadership is therefore key. 
  8. Engage risk and build controls earlier 
    Risk and controls must not be treated as an afterthought but considered early in the automation process. First, second and third lines need to be actively involved from the time that opportunities are being identified and business cases developed. A range of issues need to be considered, including the potential concentration of automation risks. A strong focus on risk and controls is also vital because, globally, much attention is now being focused on the ethical and regulatory issues raised by robotics and AI. 

    How can regulators and auditors see into the AI “black box” and understand what’s happening there? If ML tools are being used to provide services such as approving loans, how can organizations “wind back the clock” to check retrospectively what may have gone wrong at a date in the past if, for example, clients were mis-sold products? Does this depend on adequate and appropriate human oversight being maintained? Such considerations need to be incorporated in the design of control frameworks.

Conclusion

Combining RPA with AI and digital enablers to create intelligent automation has the potential to transform existing operations and finally deliver end-to-end process transformation even across a legacy estate. However, while the potential of AI is enormous, the practical implications for its delivery today mean that it needs to be well targeted and prioritized. To achieve a rapid and significant ROI from intelligent automation, companies will need a full understanding both of business processes and how they could be transformed, and of the associated benefits for RPA, digital enablers and AI.

Summary

Realizing the promise of intelligent automation lies in finding the right balance of RPA, AI, digital tools and people to maximize return on investment (ROI), while minimizing complexity and risk. Download our survey (PDF)

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By

EY FS Insights

Minds Made for Financial Services

Related topics AI Financial Services EMEIA