Finance Transformation with Automation

Transforming the Finance Function with Automation

With rapid market shifts forcing the need for business agility, CFOs are being pressured to reinvent their finance and accounting teams from number crunchers into THE competitive advantage for their company.  Before the digital revolution, finance leaders could only throw more bodies at the problem areas, but now businesses can employ armies of digital workers to execute business tasks and processes with speed, efficiency, consistency, and quality.

Digital Workers are the “Robots” in Robotic Process Automation (RPA).  With RPA, digital workers replicate the mouse and keyboard functions of an employee but can go beyond the human interaction aspect and leverage more technological solutions, like running queries, calling APIs and Web Services, and conducting advanced analytics within the robot.  Additional benefits of employing digital workers are the 24/7 work, their ability to rapidly scale to meet demand and flattening the peak demand from your financial close.

What processes and tasks can I automate?

According to a report published by the McKinsey Global Institute, 42% of finance activities can be fully automated and an additional 19% can be mostly automated. RPA is good for simple tasks, like checking for FX rate changes, to complex processes, like reading bank statements for bank reconciliations.  Other examples where RPA is often used can be: Entry of sales orders, cash application, account reconciliations, vendor registration, Purchase Order creation, invoice registration, journal entry uploads, report retrieval, assembly, and preparation.

It is also important to understand that RPA is effectively the entry point for Artificial Intelligent (AI) technologies, so RPA can engage more advanced technologies like OCR and Machine Learning for the reading of invoice documents of a supplier, extracting the information, and registering the invoice into your AP system.  RPA can also be for engaging AI to support analytic models in forecasting process.

The following is a simple way to think about where to employ RPA:

  • Rule-based
  • Easily described
  • High transaction volumes
  • Low exceptions
  • Stable and well-defined processes
  • Low system change
  • Structured data and readable electronic inputs
  • Are people passionate about the process?

Examples where we have used automation in Finance and Accounting:

Financial Control & Reporting
Credit-to-Cash (AR)
Procure-to-Pay (AP)
Financial Planning and Analysis (FP&A)
Cash & Treasury Management
Financial Control & Reporting

  • Journal Entry Creation and Upload/Entry
  • Account Reconciliation
  • Report Assembly and Preparation

Credit-to-Cash (AR)

  • Enter Sales Orders
  • Credit Checks
  • Customer Follow-ups
  • Retrieve Cash from Bank
  • Cash Application / Allocation

Procure-to-Pay (AP)

  • Vendor Registration
  • Create Purchase Order
  • Invoice Registration
  • 2/3 way matching
  • Payment (Batch) Creation
  • Payment Issuance

Financial Planning and Analysis (FP&A)

  • Retrieving reports from internal/external sources
  • Standardizing and cleansing data
  • Consolidating datasets
  • Building standard report outputs and populating PowerPoints

Cash & Treasury Management

  • Generate Daily Cash Positions
  • Cash Forecasting
  • Bank Account Analysis


  • Time-sheet coding validation
  • Run payroll
  • Calculating deductions
  • Auditing reported hours against schedule

When over 50% of your finance and accounting processes are automated, finance functions can focus on the risk identification, business analysis, forecasting, and analytics to accelerate data-driven decision making. With efficient process automation you can transform your finance function.

Related readings:


Joshua Gotlieb

Intelligent Automation Practice Director, Vigilant Technologies

RPA Can Fix Your Data Quality Issues

Robotic Process Automation (RPA)
can fix your data quality issues

According to Gartner, “the average financial impact of poor data quality on organizations is $9.7 million per year.”  In 2016, IBM estimated  the yearly cost of poor data quality in the US alone, to be $3.1 trillion.  Anyone that works with data that has completed its processing journey understands the impacts, but why are we not talking more about the issue with data quality?

While there are many possible explanations for organizations not addressing data quality, there is no identifiable relationship between data quality and business results (i.e. it cannot be quantified) and most business functions do not have awareness of the impacts that poor data has on downstream processes.

The reality is that executives, managers, employees, accountants, et al, just accommodate the bad data and integrate work-arounds as part of their day-to-day work.  Employees accept bad data since there is little/no incentive and it is easier to accept than trying to figure out where bad data originates then work with the upstream teams to correct the behaviors/actions resulting in the bad data.

These bad data accommodations cost organizations in both employee’s time and their expense since a large portion of their time is focused on the low-value tasks associated with data correction and standardization, rather than the higher value tasks of analysis for insights, risks, and opportunities.  Even non-reporting tasks cost business processes efficiency, since simple tasks like selecting a vendor or supplier could result in a series of try/fails until the right entry is found, because of undisciplined data creation practices.

We use RPA to standardize data entry and validate data to ensure data quality.  We look for the root cause of the data issues to correct bad data practices before we apply RPA to business processes to ensure we do not harden accommodations for bad data, since fixing at the root ensures we do not have to automate bad data accommodations at every related downstream process that utilizes the data.  Also, accurate and standardized data means faster processing which translates to faster report generation, which accelerates Close, Financial Analysis, Analytics, and more.

For more information about how hardening bad data practices adds data debt to your organization, read our Process Automation Approach blog


Joshua Gotlieb

Intelligent Automation Practice Director, Vigilant Technologies

Process Automation Approach

Navigating the Automation Roadmap Blog Series

Your Automation Approach is Adding Data Debt to Your Organization!

Process automation positively impacts businesses in many ways, including the elimination of human processing errors, faster processing, 24×7 work time, and the scaling of capacity to flatten peak workloads.  With so much goodness, what could be bad?  How about the data debt that is piling up with each process automation?

With all the talk of robotic process automation (RPA), intelligent process automation (IPA), Hyperautomation, and other fancy terms, at the most basic level of a business process is the movement of data.  Data moves from Point A to Point B, PDF to system, system to system, system to report, etc.  The ultimate purpose of these data movements is to support data-driven decision making at various management levels.  If all goes well, captured data is mined for insights that improve the customer experience.

What is data debt?

Data debt is the cost that comes with a decision to choose fast solutions over the longer, more technically correct solution.  Sometimes these decisions are known at the time or origin, but they can also get established over time, unknowingly.  A relatable example of (inadvertent) data debt is the unstandardized practice for name and address entry.  Also related is the implementation of name and address entry standards after the process has matured, but a decision is made not to clean-up and align legacy data with the new standards.

In both situations, unstandardized name and address databases force downstream processes to contend with data variations.  Now, consider the processing impacts of name and address variations across Procurement, Accounts Payable, Accounts Receivable, Accounting, Sales and Marketing, and Reporting.  Human accommodations for these data deviations might not be fully understood or realized, because it is easy enough for an employee to perform a lookup and use historical processing knowledge to make the right selection.  The fact is, these accommodations are process deviations.

Process deviations across downstream processing create process waste and complexity, but also adds to the data debt that is unrealized by most organizations.

Fast forward to the future when you upgrade your name and address system to a new CRM system.  The organization is now confronted with the decision to standardize the data or convert as-is.  Standardizing the data increases the costs, time, and risk of the project, whereas converting as-is maintains (or increases) the data debt in the organization and perpetuates bad data practices.

How automation contributes to data debt

Automation backlogs consists of process candidates that are typically isolated functions (or tasks) within a larger process (e.g. invoice registration).  Decisions to “green light” or automate a process from the backlog are based on priority, projected returns on investment (e.g. hours, FTEs), and if the ‘gating’ qualifications have been met.  Most qualification guidelines include the identification of ‘known’ process deviations at a higher level (Levels 0 to 3), but it is easy to miss process deviations resulting from human accommodations to support bad data practices at the desktop level (Level 4).

To the trained and untrained eye, a business process might look to be standardized and consistent at the higher levels and even during a process (desktop) walk-through.  Automation development teams will define the process definition document and scope the business process based on the business team walk-through.  Depending on your development methodology (e.g. agile or waterfall), process variations caused by inconsistent data are identified during development reviews or during user acceptance testing (UAT).  Accommodating process variation within the current phase of the automation project might seem insignificant, but they will often add up and expand the scope of the project and in many cases increase the project duration.

Project scope creep and longer project times aside, the deeper issue is that process variations are often solved with hard-coding unstandardized data or by utilizing lookup tables, which really means the automation has now established the bad data practices as standards.

Data debt in practice

An example that exemplifies the data issues is starting with invoice registration for automation.  The AP invoice registration team receives an invoice and manually keys the invoice into the registration system.  Data entry usually entails straight entry and the utilization of field lookups to select and ‘connect’ the invoice with the vendor data and required internal coding structures.  From a top level, the process looks to be consistent and repetitive, but it is easy to miss that the processor is performing multiple lookups to identify the correct vendor or business unit entries for selection because of data inconsistencies.

It is commonplace for the lack of governance over vendor name and address entry or Purchase Order entry, therefore resulting with inconsistent data being keyed into supporting systems.  The issue this causes is that the information is a critical input for many downstream processes that will require process variations to support (subtle variations or more pronounced) the unstandardized data.

Eliminate data debt, create business agility , and transform your organization!

If you are thinking, “It is impossible and impractical to map out every process scenario,” then you are correct, but you are not addressing the root cause of the process variations, which are usually the result of inconsistent data entry standards.  To dig deeper into the issue, we need to examine where the unstandardized data originates from and address at the point of entry into the organization (where possible/practical).  From an automation perspective, this means that automating the process with the highest ROI might not be the right process to start with and you might need to start reengineering for automation further upstream to bring ‘real’ transformation to the organization.

We recommend examining all process inputs during the scoping phase of an automation, then ask if the inputs have a standardized process at their point of entry.  The Yes/No answer will help to determine if an investigation is required to examine the quality and consistency of the data.  Based on the outcome of the analysis you can determine how to best address for your organization.

Parting thought

Transformation of an organization usually does not come from the shiny object in front of you but comes from establishing the right practices to guide an aligned organization.  It is also important to note that transformation occurs though many available tools (levers to pull), such as system upgrades, reengineering, expansion of system functionality/parameters, or automation.  Too often, people want to believe automation is the only answer for transformation, but it highlights an organization that is not aligned on transformation initiatives.

In future articles we will address the important of an “aligned organization” for transformation, but for now I hope this article has been informative and thought provoking.

Please reach out on We would appreciate hearing your feedback or having discussions to learn how we can help support your data governance implementation and execution, or automation initiatives.  Thank you for reading.


Joshua Gotlieb

Intelligent Automation Practice Director, Vigilant Technologies

Expected Benefits of Process Automation

Navigating the Automation Roadmap Blog Series

Are the expected benefits of process automation too high?

Are the expected benefits of process automation too high?

If you follow the RPA market, you will know advisory firms have provided guidance for Robotic Process Automation (RPA) service providers to shift from RPA into more product-based, “Hyperautomation”, to accelerate intelligent process automation deployment.  While this shift is expected to push outcomes, more than the journey of RPA, I cannot help asking if either approach is really achieving the desired benefits.  Otherwise asked, are the expected benefits of process automation too high?

Why the need for automation arises?

Before addressing the desired benefits, please allow me to set the context for why the need for automation arises (generally speaking).  When a company implements an enterprise application, the intent is to provide support for the business transaction.  Inherent to the software is automation to create an efficient and effective processing of the business transaction.  The result is that process teams can focus on higher value tasks because the lower value tasks are reduced because of the application.

Over time, the application’s ability to stay aligned with the business erodes, therefore business teams absorb the system deficiencies and support with manual work-arounds and stop-gaps.  Before anyone realizes, the business teams are largely supporting the work-arounds and manual work, only leaving minimal time to support the higher value tasks.  As the business continues to evolve, business leaders and IT engage in discussions to amend or add onto the enterprise system.  Typically, enhancements usually have a 6 to 9 month development cycle and a price tag into the hundreds of thousands of dollars, which becomes difficult to justify compared to the labor expense (especially if work was moved off-shore).  The result is that manual work becomes the status-quo and to be addressed in the future.

This is the segue where automation enters the picture, since automating the low-value, manual, repeatable tasks is the sweet spot for RPA, but more so, shifts the status quo back towards the business focusing on high value tasks.

Defining benefits

Every organization will have different approaches to tracking benefits, but savings often include: 1) Reduced Labor Costs, 2) Seasonal Coverage Costs, 3) Overtime Elimination, 4) Higher Value Time Replacement, 5) Improved Quality, Regulatory Compliance, 6) Employee Satisfaction, 7) Cost Avoidance – Future Salaries.  Some of these benefits can more easily be measured and tracked over time than others, but these benefits are reasons why processes should (or should not) be automated.  Assuming you can assign an annualized dollar amounts to these benefits, the cost savings calculation is simply:  {Development Costs}  +  {Attributed Operating Expenses}  –  {Business Process Savings}  =  Cost Savings.  Organizations will adjust this basic equation to suit their needs and ability to track, but it provides a basis for understanding the costs and benefits to automating a business process.

Clarity on your automation investment returns

Business leadership will typically make automation decisions based on the prospective ROI for an automation, based on the as-is state of the process.  Unfortunately, we often see automation decisions are made without consideration for the high value work that was being rushed and diminished because of the manual, low-value work that has overwhelmed the team.  Automating processes often just allows the staff to re-focus on the high-value work that lost focus over time, which is a significant benefit, but not necessarily the return on investment leadership was targeting.

My point is that we might not be placing enough value on restoring order to business processing for business teams to focus on high-impact priorities, while doing their jobs more comprehensively and completely, without sacrifice.  We include #4, “Higher Value Time Replacement,” in our benefits calculation, so we enable business leaders to understand and capture the benefits of refocusing team members on the more important work they are currently distracted from.  Without understanding of how work teams are refocused, business benefits can be over-estimated if only focused on the hours returned or the FTE cost savings.  Rather than RPA not delivering benefits, maybe we need to start being honest with ourselves and a bit more realistic that RPA is not a magic pill that will eliminate jobs overnight, but it can be used to create business agility (and cost savings) if used correctly.

Vigilant’s understanding of value and technology not only uniquely positions us to help address restoring of order to a process, but also finding additional benefits trapped in the low and high value manual work.
Thank you for reading.  Our future blog articles will focus on addressing the impact and root causes of bad data perpetuated by ineffective and inefficient business processing, and how solution architects can make or break your automation program.

Please reach out on for a spirited discussion on maximizing the benefits on RPA and how we have found the ‘secret sauce’ for achieving success with automating Oracle EBS Financials and accounting operations.

We look forward to your feedback.


Joshua Gotlieb

Intelligent Automation Practice Director, Vigilant Technologies

RPA Implementation Pitfalls

Navigating the Automation Roadmap Blog Series

Not another “Pitfalls” of RPA

While industry trends are shifting towards ‘Hyperautomation’ to accelerate the automation journey, there are many considerations and reasons to use RPA, but there are also cautionary tales about RPA done wrong, which is not always talked about.  With so many people and service providers posturing about “doing RPA right” and “pitfalls of RPA”, we wanted to present a series of our no-nonsense view of RPA lessons learned from hardened industry veterans.

In our hands-on experience, and after correcting the trajectory of many poorly implemented RPA programs, we offer our top 5 list of considerations.  (We had more to share, but our marketing team forced a limit of 5 points.)

1.    Stop listening to the RPA tool vendor hype and get your RPA teams aligned with the transformation strategy.

Business teams looking for automated solutions are often working around IT, which can work against achieving the desired business outcomes.  Business leaders cannot shy away from IT because IT is feared to be some scary function in the back rooms of the organization.  Business leaders need to embrace IT as a business enabler and help IT better understand their role in supporting business objectives.  Our experience finds that organizations achieve greater results from transformation when the Executive, Business, and IT are all aligned on priorities and objectives.  To be clear, the transformation strategy should set the priorities for the organization, so everyone is aligned on how resources, projects, and obligations are being focused to achieve timely results.

2.    Managing RPA benefit expectations (Learn more)

RPA has been the ‘flavor of the month’ because it holds the promise of creating process efficiencies for business teams to focus on higher-value work.  Many executives hear ‘automation’ and believe it will reduce head count.  The reality is that many legacy business processes have evolved over time, but the business systems have not remained aligned.  Therefore, business teams are manually maintaining lower-value work to support the execution of the business function.  RPA restores balance between diminished system capabilities and (re)focusing business teams on higher-value tasks.  Business benefits are still significant, but not necessarily only for the reduction in headcount executives might be looking for.

3.    Yes, another consideration about selecting the right process candidate for automation.

There are many articles written about how important selecting the right process is for automation.  For an effective automation strategy, additional consideration must be placed on using a building blocks in your automation roadmap to map out reusable components for reducing future development complexity and expense.  This might mean you do not start with the biggest ROI or impact, but foundational building blocks are critical for a successful automation program.  For additional information, give us a call and we can work with you to map out a practical automation program that achieves the transformational benefits you are looking for.

4.    Solution architects will make or break the success of your automation program.  

Quality solution architects know how to better leverage technical capabilities of the robot and maximize the value and benefits of the automation. Skilled solution architects will also know how to incorporate available technology to re-imagine and enhance the automated solution.  Lastly, solution architects will ensure automations are cost-effective when considering reusability, available robot capacity, and capabilities of the automation team.  Underestimating the value of qualified and capable solution architects (and having the right strategy) can be the difference between success and failure of your automation program.

5.    Automating bad data practices is adding to your data debt! (Learn more)

There is a good chance you are creating data debt through the hardening and accommodating bad data practices with automation.  The typical automation approach is to identify a perceived benefit (i.e. ROI) then automate the process (if qualified).  What often gets overlooked is examining the standardization of data inputs into a process to ensure data best practices are being adhered to upstream.  Automating these upstream processes might not be sexy or produce immediate ROI, but they are part of the building blocks to maximizing the benefits of automation and creating business agility.  Be warned, bad data accommodations increase project scope, create technical (data) debt, and increase the cost of support since the automation is built on bad data.

In the coming blog articles, we will drill down into each of the 5 topics and also expand on important points not addressed in this blog, so keep an eye out over the coming weeks and months.
Thank you for reading.  Please reach out on for a spirited discussion on maximizing the benefits on RPA and how we have found the ‘secret sauce’ for achieving success with automating Oracle EBS Financials and accounting operations.  We look forward to your feedback.

Author: Joshua Gotlieb

Intelligent Automation Practice Director, Vigilant Technologies