How to measure AI Automation performance metrics and ROI
Surviving in today’s competitive world requires businesses to efficiently use available resources and make appropriate strategic decisions.
To prevent the wastage of precious resources (time, money and staff attention), many organisations are turning to automation solutions.
As with any major change, performance metrics are necessary to make a convincing case for investment in AI Automation.
To give you a better understanding of AI Automation’s ROI, we designed an infographic that contains the shortened version of the 10 performance metrics discussed in this article. You can find it at the end of the article.
What is Intelligent AI Automation for business?
AI Automation is growing more and more popular, due to its wide-reaching and powerful business-changing benefits. It’s being adopted to help eliminate manual errors, speed up processes, create error-free databases, improve employee satisfaction, streamline workflows and so much more.
There are two primary objectives to consider when implementing AI Automation.
- Be sure that you are achieving the ROI metrics set by the project directors in the planning phase (this article is dedicated to outlining these metrics and how to measure them).
- The second is to effectively collect and document these metrics to present them to all stakeholders within the business – because when automation results go unnoticed, management can often overlook how much the business is saving daily.
Primarily, these would be head management and other key decision-makers, who are going to want to know that their investment in this new automation tool is yielding convincing enough operational improvements and ongoing ROI.
However, the biggest problem is that many businesses struggle to identify how to best implement AI Automation, how to best measure it and how to make it more visible throughout the entire company.
This is where a lot of businesses let themselves down, undermining successful AI Automation and putting their competitive advantage at risk of being overlooked.
In the end:
- AI Automation performance metrics are necessary to make a convincing case for automating business processes because they provide a crystal clear quantitative demonstration of its financial, business and operational impact on the company.
- Measuring AI Automation is crucial when it comes to measuring the financial return on investment (ROI) of Intelligent Automation in your company.
- By tracking the ROI of AI Automation, performance metrics also allow more realistic planning of the future of your automation journey towards enterprise-wide use. However, there are also metrics to assess qualitative benefits, e.g., employees’ job satisfaction.
How to measure AI Automation performance metrics
The whole purpose of AI Automation performance metrics is to compare “before” and “after” for automated processes.
Generally speaking, “before” refers to a way to measure the manual effort multiplied by the work cost (i.e., the salary of the employees performing the tasks), while “after” is the sum between the cost of the AI Automation tool and that of the people who handle AI Automation maintenance.
Of course, you aim to show that automation results in cost reduction, where ‘cost’ covers both financial and human-centred costs.
In what follows we will provide short descriptions of some performance metrics that can help you objectively evaluate the consequences of AI Automation deployment in your organisation.
1. Improved accuracy
AI Automation allows you to eliminate the errors that are unavoidable in the case of manual performance, due to boredom, fatigue, lack of concentration, etc.
The proof for this claim comes from comparing the amount of work that needs to be done to compensate for errors before and after the implementation of AI Automation.
The interpretative principle is that the less need to redo work due to errors, means more efficient processing, in less time and for less money.
2. Need for intermissions
Another feature that leads to a ‘robot’s’ ability to increase productivity is their capacity for quasi-continuous work, with infrequent upgrade interruptions.
Hence, measuring AI Automation can be done by comparison of how much downtime human employees need to complete processes, with robots’ downtime.
3. Digitisation of audit trails
By passing on to robots all dull audit tasks, such as carefully scanning previous records, internal control testing, detail testing and reconciliations, employees can more effectively focus their attention on more complex activities.
These complex activities could include investigating abnormalities or estimating fair-value investments.
You can assess progress by contrasting the proportion of digitised audit trails with those that are still manual.
Assessment should be done per individual process, and per specific timeframes.
Here you can watch a video with an AI Automation demo, where a UiPath bot is used for reconciliation purposes.
4. Evaluation of compliance deficiencies due to data entry errors
Compliance issues are one of the major negative consequences of making errors when handling data.
Measuring AI Automation amounts to an appraisal of the compliance deficiencies in terms of the number of errors and the cost of fixing those errors. The evaluation should be done for a certain period before you implement AI Automation, and compared with the numbers you get for a similar period after launching.
You can expect the latter to tend to zero.
5. Engagement in new projects
When software AI Automation is used to handle repetitive tasks, humans can be reallocated to new projects where their more nuanced problem-solving and thinking skills are required.
To measure this, choose a certain time before automation is implemented and document the number and quality of new projects developed by employees.
For the qualitative assessment, you need to consider the design, planning, as well as the new tasks assigned to the employees.
Do the same for a similar period after automation and compare the results.
6. Workforce Impact
This can be tracked by several metrics that take into account the number of labour hours saved per year, the reduction in case workload per tax, and the number of employees reallocated.
Full-time equivalents (FTEs) are the typical way to operationalise labour savings, but you can also look at the costs involved in hiring, training, and salary. We recommend assessment in periods of high demand (e.g., end of the month), because this is when the costs are highest.
The scalability of Intelligent AI Automation can be a valuable asset to be used in such times to reduce expenses. It makes it likely that the marginal cost of scaling the robotic workforce during peak periods is less than the average cost of handling everything manually.
7. Tool utilisation
Before deployment of AI Automation, take stock of the number and kind of (automated or manual) tools that are needed to carry out a certain process, so that you can then estimate the total costs.
To ensure accuracy, be sure you also include all associated licensing fees, maintenance, and development costs, as well as the cost of training employees to use them.
Compare this with a similar approximation of the cost of intelligent automation tools.
AI Automation is going to be lower because the bots operate from a central location and do not need to be operated from multiple individual machines.
8. Cycle time
This is a measure of process velocity, or the amount of time necessary to complete a process.
Since AI Automation bots never tire and never need to take a break to ruminate on the meaning of life, Intelligent Automation results in significantly lower cycle times. You can see when you compare the time it takes human employees to execute a given process with the time it takes a bot.
Since cycle time depends on the total volume of work, which is variable, we recommend that you anchor the two estimations to periods that are similar in this respect.
9. Employee retention rate
Employee turnover is incredibly costly – in so many ways.
AI Automation can support retention by taking over the highly repetitive tasks common among those with the highest turnover. Employees are thus relieved from the burden of tedious tasks, and free to focus on business-critical tasks that often require a degree of nuanced or creative thinking.
This also results in staff feeling more valued, fulfilled and less likely to leave the company.
Contrasting retention rates before and after AI Automation implementation is an important metric. However, it is a longer-term metric because employees’ reaction takes time to form and manifest themselves behaviorally.
10. Employee satisfaction
This is a qualitative measure of employee engagement in the short term.
All you need to do to use it as an indicator of AI Automation profitability is to ask your employees to fill in surveys regarding satisfaction with their roles and workloads before and after automation.
You should initiate surveys in those departments mostly affected by automation. You can expect a correlation between the lower the number of manual repetitive tasks employees must perform, and improved employee engagement and job satisfaction.
A necessary precondition is that the staff is well-trained in what automation can and what it cannot do. This is so that they do not fall prey to the ‘robots will steal our jobs’ attitude, which would make them rather hostile to software robots.
Gaining the confidence of people in your organisation is a top practice for a successful automation journey, which facilitates scaling up.
The bottom line: it’s time to measure the ROI of AI Automation in business
AI Automation performance metrics help you evaluate the progress towards the goals that you set before and during the automation journey.
The availability of such a wide range of metrics serves to make an even more convincing case for the utility of AI Automation deployment. They can provide an exhaustive analysis from the perspective of selected criteria that you consider most relevant for attaining your goals.
Ideally, the outcome of documenting these metrics is to provide persuasive arguments to decision-makers that AI Automation is precisely what your company needs.
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