Productivity through Innovation

Building a Business Case for Intelligent Automation Systems
Jul 15, 2018

When the cost of business process improvement using intelligent automation systems such as Robotic Process Automation (RPA), Machine Learning and Artificial Intelligence (AI) exceeds the signing authority of a manager, funding will come from a capital expenditure (capex) budget controlled by upper management. This means that the proposal may be one of many competing for limited funds allocated across the company. Writing a good business case that demonstrates the value of the proposed capex to management becomes important. A good business case is one that aims to validate a purchasing decision by transforming benefits into concrete, tangible returns. 

An intelligent automation business case becomes compelling when written with the approver in mind. Knowing the audience is reflected by author focusing the business case on the approver's pain point, making it easier to include the right amount of information and level of detail.

In this post, we are going to introduce intelligent automation, – the next big advancement in business process automation, -  and how one can justify the value of implementing this type of technology in their business. 

Understanding Intelligent automation 

Intelligent automation systems combine RPA, Machine Learning and AI  giving them the ability to; sense and synthesise vast amounts of information; automate entire business processes or workflows; make decisions; learn and; adapt on their own. This type of automation can be categorised into three main area’s as shown in the diagram below. Intelligent Automation technologies range from those that can mimic human actions (via their ability to automate rule-based repetitive tasks that a white collar worker would do) to those that think like a human (enabling intelligent action like voice and image recognition, decision making and self-learning). 

Rules: The first category of automation technologies are those platforms that are able to take domain knowledge and characterise them into rules so that computers can study the implication of those rules. This can be script based automation of routine task in one application (i.e. accounting platforms) or those running across multiple applications that mimic human actions (i.e. Robotic Process Automation). 

Learn: The second category includes those automation technologies that have the ability to perceive the outside world through sensors (incl. cameras) to then learn from the data set collected using machine learning techniques. These technologies are very good at recognising patterns in data, allowing them to accurately filter information and make predictions (e.g. marketing system that presents offers to customers based on their profile and market basket analysis or a credit card processing system that identifies and blocks fraudulent transactions).

Reason: The third category of automation are those systems that are able to build underlying explanatory models allowing them to characterise real-world phenomena and mimic "cognitive" functions that humans associate with other human minds. These systems have the ability to make decisions – following similar logic that a human would follow, process human conversation and update its knowledge through self-learning (i.e. Artificial Intelligence). 





As the benefits of intelligent automation adoption continue to grow, greater numbers of organizations are beginning their own deployments. A recent case study from one of our RPA partner Help Systems discussed how a bank in the US successfully implemented RPA to manage customer record validation, employee onboarding and payroll task resulting in a 3800% return on investment within 5 months. 

As Intelligent automation is still subject to much speculation, businesses need a well-designed plan to help them achieve the cost and operational efficiencies that these technologies are promising. RPA is an entry-level technology in the intelligent automation spectrum that is coming of age and becoming a mainstream investment focus across many industries. 

Creating a business case for intelligent automation

Understanding the risks and benefits of an intelligent automation project can help one craft a compelling business case that will get management excited about the potential savings that can be realised from adopting these technologies.

According to a guide from our automation technology partner titled "Automation Operations: 5 benefits of Automation", the primary benefits of operations automation cited most often were cost reduction, productivity, availability, reliability, and performance. However, to achieve these benefits one needs to thoroughly;

•    understand the business problem that these technologies will be solving;
•    be clear about the solution and the intended results and;
•    be clear about the investment and financial return. 

Excitement from achieving one or more of the benefits highlighted above can be enough for operational management or business owner to be curious about these technologies and allow the funding of small pilot projects. 

Knowing that a small pilot without a clear plan will likely fail, how do you approach building a business case that will deliver over the long run?

To build a business case with the least amount of detail possible, situational analysis and financial justification is required.

Situational Analysis

The first step in writing a business case to justify the cost of an intelligent automation solution is to clearly identify and define the business problem that an intelligent automation system will address once deployed within the business. In this sense, it is the diagnosis of a business problem that the proposed technology will address. Using financial measures, state the current process and expose any problems or bottleneck opportunities. In addition, note which departments and/or divisions the current situation impacts and who stands to gain from any improvement. When describing the current situation, incorporate facts that have measurable financial components tied to them.

Secondly, you need to recommend an intelligent automation technology that is a solution to the business challenge. Describe the proposed solution and make sure that you remain on topic and succinct, it is important to reference one or more of the benefits described above. A good practice is to state end results rather than the intermediate returns. For example, if the proposal would reduce cost, in terms of labour hours, state how the time will translate into profit. Would the technology be used to decrease headcount? Would it be used to increase departmental output? You need to make it easy for the decision maker to see how the implementation of this technology can affect the bottom-line results. Finally, you will need to present a cost breakdown of the proposed solution and how long it will take to operationalise. 

Thirdly, you need to show that a sound evaluation was conducted by listing and describing the most obvious alternatives to the proposed solution, and stating why they are inferior options.

Finally, you need to carry out a 'show stopper analysis' to identify the risks associated with implementing the proposed automation solution and show that they are manageable. Include the most obvious risks and how you propose to eliminate or mitigate their impact should they occur.

Financial Justification

A core part of the intelligent automation business case is the financial justification. Every manager funding an automation project are looking to know when they would most likely get a return on their investment. There are two parts to the financial justification;

1) The cost of implementing the intelligent automation solution which includes; 

•    Licensing costs
•    Infrastructure costs (i.e. ongoing cloud hosting fees); 
•    Implementation costs(i.e. one-time costs of consulting, configuration and training fees)
•    Maintenance costs (i.e. costs associated with keeping the software robots up to date with changes in the business process and applications.

2) The financial benefits that could be realised from implementing this technology. This could be in the form of cost reduction and/or revenue increase or the advantages and efficiency gains that occur when outsourced
work is brought in-house. 

These two parts to the financial justification can be combined to form the financial indicators that measure the return on investment. The elements included in the financial justification will depend on many factors, so it is important to understand the decision-making process and the concerns of the decision maker. For example, the goals of the decision makers will dictate whether you should build the justification on increasing revenues or decreasing expenses. In most cases, the strongest approach is to justify the project in terms of cost reductions. 

The projects return on investment can be calculated for a period of three to five years. While it may be beneficial to project the financial gains over a number of years, more focus should be placed on the short-term gains (i.e. gains within the first year). If in doubt on how to correctly ascertain the financial value of automation, you should engage an external consultant that can assist you in properly evaluating your return on investment.

While there are several different approaches that companies can take in developing a business case for intelligent automation, it is important to know that doing nothing is not an option. It is highly advisable to start developing a strategy as soon as possible and secure the necessary funding to at least carry out a proof of concept of one or more of these intelligent automation technologies.


Higher return on investments is more likely to lead to the approval of a capital expenditure proposal for any project, including intelligent automation project. Crafting a business case that includes information, methods, and language relevant to the decision-makers is the secret to guide the approver towards the right decision. While the benefits of intelligent automation are obvious to the team that is making the proposal, it is their responsibility to express that advantage in terms that are understood, appreciated and welcomed by those who control the distribution of funds from the capital expenditures budget. Compiling a defensible, clear and concise justification gives the business case the edge needed to get an agreement to invest in an intelligent automation project.

The number one way to ensure a good return on investment for an intelligent automation initiative is to think ahead about whether the solution will scale to meet future requirements. It’s also important to identify the right processes to automate where sometimes the best candidates are workflows that span multiple departments. These aren’t always obvious since each part of the business is concerned with increasing their own productivities, however many industries now foster a core team of automation experts to look at the business as a whole and find automation opportunities.

Introducing Data Engineering

Are you looking for a partner to start your journey into the world of intelligent automation? Data Engineering is a digital operation partner that combines design-thinking practices, process and domain knowledge, digital technologies and analytics to rapidly prototype intelligent solutions, resulting in quick insights into the size of a return on investment opportunity. 


Kelechi Anyaegbu,
Founder & Principal Consultant 
Data Engineering Pty Ltd

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