Intelligent Automation: The Next Frontier in Finance Transformation

Advancements in technologies and the exponential growth of data are presenting massive opportunities for the finance function to embrace intelligent automation, aggregate and analyze data from disparate sources and ease reporting and analysis.

Today, we are witnessing technology evolve at different rates, providing a spectrum of capabilities ranging from simple, rules-based and deterministic process automation to smart machines that can learn and adapt. Thus, the potential to deploy automation in the finance function is significant.

Intelligent Automation as the Way Forward

To meet today’s finance challenges, finance chiefs have to embrace digital transformation, harness new technologies to supplement or automate the tasks undertaken by knowledge workers, drive better process effectiveness and cost efficiencies in ways not previously possible.

Process automation is not a new thing. Automation has for a very long time proven itself as an operational efficiency driver, quite a number of organizations have successfully automated their Order-to-Cash, Procure-to-Pay, Record-to-Report and Cash Management processes and achieved their stated objectives.

However, in today’s digitally-enabled environment, businesses have to think beyond basic automation and embark on intelligent automation.

That is transition from automation of basic tasks that are administrative, repetitive, and mostly transactional within a workflow to also utilizing more advanced technologies such as AI and ML to analyze unstructured or complex information.

Important to note though is that digital transformation is not simply implementing new technologies alone, but also investing significant time to understand the art of the possible. In other words, developing a deeper understanding of the “why” and “what” of automation.

With intelligent automation, repetitive and rules-based tasks are automated, workflows are streamlined, and humans are freed up to spend more time on value-adding tasks or projects that require critical thinking, problem solving and collaboration capabilities.

Also, machines augment human skills and capabilities to deliver new business solutions that would not otherwise be possible.

Intelligent automation thrives when it’s augmented with people to drive better outcomes as humans have to educate machines the reasoning steps necessary for transforming raw data into valuable and actionable insights.

Adopting Intelligent Automation

Prior embarking on this journey, it is critical to have a thorough understanding of the intelligent automation technology landscape.

At the lower end of intelligent automation spectrum is Robotics Process Automation (RPA) which many finance executives are familiar and comfortable with.

RPA systems act as a virtual agent to execute tasks and imitate the same manual path through an application would take using a combination of user interface interaction or descriptor technologies. They are good solutions for aggregating data, performing basic analysis and then visualizing the data.

The substantial benefits of an efficient RPA process makes the technology very attractive to finance teams. Its best applications are generally highly manual, transactional and rules-based processes that have a low exception rate and are subject to high operational risk, for example, accounts payables/receivables, claims processing, invoice processing, regulatory reporting, journal processing, periodic accounting books closure, contract/SLA compliance and account reconciliations.

RPA technology allows accounting and finance teams to configure computer software to reason, collect and extract knowledge, recognize patterns, learn and adapt to new situations or environments. Think of this as your everyday excel macros but at a more advanced level.

Another added advantage of RPA is that it helps finance functions achieve cost efficiencies by lowering the Full Time Equivalent (FTE) costs.

Successfully implemented, a robot or computer software program reduces the number of full time personnel required to run the finance function. Also, the system can be configured to perform tasks during hours of the day humans are not available.

More sophisticated than RPA systems are Machine Learning (ML) and Natural Language Processing (NLP) systems. ML involves computers improving their performance as a result of being exposed to large datasets and developing learning capabilities by themselves without the need to follow explicitly programmed instructions.

The smart machine automatically discovers patterns in data and uses these patterns to make predictions. The more transaction data a machine learning system processes, the better its predictions are expected to become, to the point where it can predict situations just before they actually happen. Machine learning capabilities can be used to enhance sales forecasting and fraudulent transaction detection.

On the other hand, NLP is a field in which computers process and interpret human language by. Applications include speech recognition, sentiment analysis and also analysis of large sets of information such as legal documentation.

NLP technology has the capacity to organize and structure data in real-time on a large scale from a multiple sources and can facilitate an employee’s work by carrying out demanding tasks such as going through client information to single out discrepancies or summarizing lengthy financial regulation documents.

Accounting and finance teams can leverage NLP to explore unstructured data to gather market intelligence, comply with regulations or provide procurement surveillance. For example, NLP queries can identify possible incremental sales/purchase orders and flag out any potential red zones.

In addition to the above, when combined with other digital technologies such as RPA and machine learning, NLP can help finance organizations make accurate customer and pricing decisions.

For instance, RPA can help aggregate all the required customer information from multiple systems and build customer profiles based on a scoring model. NLP technology analyzes and interprets the structured data, prepares the reports in an easily readable format, then communicates relevant customer pricing insights in a narrative form.

Establish a Small Business Use Case

As much as understanding the intelligent automation technology landscape is critical, this alone is not enough. You also need to build a coherent business case for adopting intelligent automation.

One of the hurdles faced by finance executives when trying to implement new technologies is lack of buy-in and support from other senior executives. So often these initiatives are greeted with less enthusiasm and labelled costly and time-consuming investments.

Instead of initially embarking on wide-scale deployments, finance chiefs are advised to first start with the discovery mode. This involves selecting an individual process or processes for automation and use these as a yardstick for success.

The rationale behind starting small is that you want establish quick wins and business use case, tell a compelling story, then build on that success.

There are certain factors that need to be considered when identifying the process candidates for automation and these include – the amount of time spent on each activity, the number of steps or people involved as well as what systems already exist to perform some of these tasks.

If an activity or process requires more time to complete, is labour intensive and has many steps to follow, then automation might be worth the investment.

Regarding systems already in place to perform some of these tasks, it is important to consider if there are any other alternatives for achieving process efficiency that the organization is bypassing and that could yield better outcomes.

If the results of the pilot program are evident enough and positive in terms of the amount of time savings, the level of operational costs reduction, better outcomes and what the business is able to achieve now that wasn’t possible before intelligent automation, then it becomes less of a daunting task to secure buy-in and support from the project sponsors.

Organizational Culture and Communication are Key Ingredients 

The tendency for employees to resist change can derail a move towards wide-scale deployment of intelligent automation. This is often the case if employees have concrete belief that their jobs are being taken over by machines leaving them redundant.

With intelligent automation, smart employees are freed-up from monotonous, non-value-adding work, and empowered to focus more on higher value work and tasks that require creativity, critical thinking and judgment to achieve increased individual productivity and greater employee satisfaction.

Thus, in order to secure employee buy-in, business leaders must be proactive in commencing this process. They must engage employees and communicate the change and benefits of new technologies, offer to retrain or redeploy those affected and provide a clear path towards new roles as well as prepare employees to work alongside automated processes.

To be successful, change strategies should be established and reinforce the connection between intelligent automation and ongoing employee development, and the ability of new technology to augment existing roles, while also giving rise to new and engaging ones.

Reinforce Collaboration Between Business and Technology Teams

A significant number of new technology failures are attributed to the lack of collaboration between business and IT teams. Many business leaders are often under the incorrect assumption that adoption of intelligent automation systems does not require extensive IT support.

As a result, they tend to ignore the input of these specialists leading to immense project failures. The opposite also holds true. In the case of IT-extensive projects, IT leaders often undermine the contribution of business teams and fail to take process nuances into consideration.

To be successful and yield better outcomes, intelligent automation should be a combined effort between business subject matter specialists and technologists with an operating model that defines the roles and responsibilities of each player.

Business subject matter experts play the critical role of defining operational requirements, leading process design initiatives and monitoring performance, while technologists focus on ensuring effective data security and governance, systems integration as well as monitoring identity and access and control.

Intelligent automation should be tackled as part of a bigger digital transformation initiative contrary to being delivered in isolation. Senior executives, leadership teams and employees must all share an integrated automation vision, commit to its success, and develop measurable goals across the organization against which performance can be assessed.

As technology evolves at an accelerated pace, finance leaders have no choice but to concede that automation in all its forms is already playing a role in their organization’s future. Aligning technology investments to customers’ needs and business outcomes is now a critical endeavor than before.