In another post a short while ago, I looked at the steps organizations need to take to prepare for a robotic process automation (RPA) implementation. In the context of our recent “Reimagining Finance for the Digital Age” report, I’ve been receiving loads of emails with questions about how to get started on full-scale finance automation. In this blog, I write about the most relevant aspects.
It comes as no surprise that what you need is a comprehensive plan. Just like when synapses are formed in the human brain to create new neural networks as a child (or adult) learns to perform a new task or activity, so automation implementation requires the input of every part of the organization. Enterprise-wide digital intelligence in finance creates new connections, with new consequences – consequences that can only be addressed and exploited if everyone has the chance to anticipate them and execute successfully.
Planning for intelligent automation of the finance function goes beyond what’s needed for RPA. While robotic process automation is predicated on applying simple rules to structured data, intelligent automation requires us to address unstructured data processing and, for example, decision-making, as well as training the machines to comprehend, decide, and remember. This gives rise to functions such as predictive modeling, probability analysis, and data discovery through self-learning, as well as natural language processing and insight-driven knowledge. To do this, we’ve developed the “Five Senses of Intelligent Automation,” which focuses on outcomes for the finance function rather than pure robotics-driven automation.
It is at this point that RPA meets cognitive computing. I’ll return to this later.
Meeting the “Masters”
The plan needs not only to address the above AI-related considerations, but also tactical implementation issues and human factors. In short, it needs to cover everything; it has to be staged and managed (here we apply our ESOAR methodology – Eliminate, Standardize, Optimize, Automate and Robotize – to re-engineer processes); and it must be communicated to all affected stakeholders and team members, to ensure that everyone understands and is on board for the ride.
This kind of joined-up thinking was borne out by our study. In it, we identified organizations that are setting the pace in intelligent automation implementations in finance. We called them “Masters” – and we found that as many as 85% of this group said the business as a whole was pursuing an automation strategy, while more than half said automation was being led at enterprise level by a dedicated team.
Getting started – a checklist
If we were to have got this far, with a plan in place, and we were now embarking on a large-scale finance intelligent automation project, what ought to be on our to-do list?
An intelligent automation target operating model is probably a good place to start, which to-date has been defined by the seven levers of our Global Enterprise Model© (GEM)(see Christopher Stancombe’s article on “Seven Laws of Workplace Robotics). This has now evolved into Digital GEM (D-GEM), which comprises aspects such as:
- Grade Mix – defining the right team structure consisting of both a virtual and human workforce.
- Location Mix – locating a virtual workforce that doesn’t need office space, but still has a need to define where it resides and operates – for example, due to legal and regulatory reasons.
- Competency Model – clarifying the sophistication of the virtual workforce. Does it just copy and paste, classify incoming information, and participate in the decision-making process using proven algorithms?
- Processes – defining the new normal when it comes to technology-driven best-in-class process execution.
- Technology – taking advantage of APIs, selecting the right mix of applications, and embedding software architecture into the IT ecosystem.
- Pricing strategy – defining the right pricing approach to optimize total cost of operations.
- Governance – organizational structure, roles, and responsibilities, including Center of Excellence integration into the overall organization, policies, approval levels, governance bodies and escalation paths, compliance, and relevant KPIs, etc.
We will cover D-GEM in a more detailed manner in later posts.
There are some other key organizational elements that are required to move fast:
- Design authority – to oversee and establish design principles, to provide project management, and to ensure consistency with the main thrust of our plan.
- Automation academy – to train not just those implementing the program, but end-users too – because in our experience, the more users learn about the potential of automation, the more great ideas they have about how it might best be utilized. This training can be a mix of classroom-based and online sessions, overseen by knowledgeable service providers, software vendors and the enterprise itself.
- Automation factory – to automate the processes we’ve qualified as opportunities.
- Transitions model – to manage the transition fro human to a virtual workforce and establish a new way of collaboration between the two.
- Operations and apps hub – to ensure the stable and scalable delivery and deployment of all technology components.
When all these foundations are in place, it’s then that we can start to build. It’s then that we can start pipelining day-to-day transactions into our model as it develops, so that we can start to realize benefits long before completion.
And it’s only then, perhaps, that we would be on the right track to join the Masters. Good luck!
Read the full “Reimagining Finance for the Digital Age” report.
To learn more about how Capgemini’s intelligent automation can deliver enhanced value to your finance function, contact: email@example.com
Learn more about how Capgemini’s Finance Powered by Intelligent Automation offering helps you navigate the myriad of products, tools and services, enabling your business to benefit from an intelligent solution that combines automation, digital platforms, know-how and insight.
Dr. Adam Bujak is an expert in strategic management, business process automation and finance transformation. He heads Capgemini’s Business Services’ Technology Transformation team, helping multinational clients to optimize operations by deploying intelligent automation solutions.