How to Calculate the ROI of AI Admissions Automation?

University admissions teams are being called upon to react more quickly, to make it more personal, to deal with increasing numbers of enquiries and to regulate costs. This makes AI admissions automation ROI a sound management issue, rather than just a technology figure. A good comparison is between the financial benefits of the automation that can be measured and the total cost of deployment, integration, supervision and enhancement of the automation.

AI admissions automation ROI should be measured in terms of the direct savings, staff capacity, speed of response, progress of applications, and quality of service for admissions leaders. It shouldn't make the mistake of equating all automated engagements. Despite the common use of AI today, very few institutions are measuring ROI for AI in the workplace, with only 13% of those surveyed saying they do, according to EDUCAUSE. This guide covers the steps to a repeatable approach to calculating AI admissions automation ROI, picking meaningful numbers, establishing a baseline, and steering clear of assumptions.

What Does AI Admissions Automation ROI Mean?

AI admissions automation ROI is a key indicator that determines whether the financial and operational value of using admissions AI outweighs its expenses. You can use the basic formula:

ROI (%) = (Total measurable benefits – Total AI costs/Total AI costs * 100

But in order to be credible, a university's AI ROI calculation must be more than the cost of AI software subscriptions. It should cover implementation, integration, training, monitoring, governance, data preparation and continual optimisation.

ROI in higher education is not just about making a profit; it can also relate to technology choices, preparing students for the workforce, and student performance. That's why there are financial metrics and specific operational metrics that must be a part of admissions technology ROI. 

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Why Universities Need a Baseline Before Calculating ROI?

How admissions work performs before automation is the basis for proving improvement in a university. Set a baseline for enquiry handling, follow-ups, staff effort, conversion stages, and service levels.

To get a good baseline for AI admissions automation ROI, track a minimum of four to eight weeks in a row, depending on the seasonality.

• Log the number of enquiries received, enquiry response times, staff hours and follow-up attempts, as well as completed application outcomes, on a monthly basis. 
• Make sure that repetitive admin tasks are clearly set apart from discussions that need counselling, judgment, empathy, exceptions and/or complex decision making. 
• Before adding up the costs of automation, capture existing technology, staffing, outsourcing, telephony, messaging and CRM costs. 
• Determine one reporting period which is uniform between baseline and post-automation for reviews to make it easy to compare performance. 

With a robust baseline, leaders can make their automation savings case since they are now able to compare like with like.

Step-by-Step University AI ROI Calculation

Step 1: Define the Admissions Workflows Being Automated

Don't do calculations for the value of the workflows across an entire admissions department right away. Ideal workflows can be used in responding to enquiries, qualifying leads, reminding of documents, scheduling appointments, follow-ups on applications, and handling FAQs.

With instant responses, qualification, information gathering, follow-up, CRM updates, and human handover, the voice and chat agents are strategically placed by Sicada.ai. When aligned with the institution's workflow, these abilities can contribute to a focused AI admissions automation ROI model. 

• Select the work that generates a lot of volume and adds measurable pressure on the admissions team due to delays or repetitive work. 
• Ensure that prohibitive decisions and conversations with applicants are carried out under the proper human oversight during the automation of the process. 
• Record automated actions, fields needed, escalation rules and human actions in advance of the deployment. 
• During reviews, designate an “owner” of each workflow, metric, data source and improvement decision who is accountable. 

To avoid combining unrelated outcomes into one number, this workflow definition states that this will not be permitted in the ROI model.

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Step 2: Calculate the Total Cost of Automation

The denominator of AI admissions automation ROI should cover the entire cost and not the licence fee. Gartner recommends that leaders use the cost and value drivers of AI to measure the value, and also mentions that the cost of AI can differ according to usage and scale. 

The benefits of including these items are to make the ROI model more realistic and to avoid underestimating total ownership costs.

Step 3: Calculate DAA Cost Saving

The cost savings of direct admissions automation are due to work that actually doesn't need paid resources after automation. Don't be cavalier about saving any minute that has been recorded as cash saved. Released capacity is then to be used by staff for other higher value counselling, outreach or application support.

A good formula for a practical is:

Capacity value (annual) = hours saved multiplied by the fully-loaded hourly staff cost

Then determine if that value is actually a budget cut, no hiring, decreasing spending on outsourcing, or reallocating budget.

• Compare the handling times of enquiries before and after automation (in comparable periods of reporting and types of enquiries). 
• Do not include overtime or outsourcing avoided unless it is documented in the finance records that the overtime or outsourcing actually was avoided. 
• Avoid double counting staff capacity to achieve value for the redeployment; as it will inflate the internal direct financial return. 
• Change assumptions when there is likely to be a surge in enquiries or staffing needs - for example, in the holiday season. 

The separation creates more believable direct savings in the finance and procurement processes.

Step 4: Carefully assess the measure revenue/enrolment value.

For a lot of institutions, student recruitment automation return on investment relies on the component of whether quicker engagement enhances their progression through recruitment phases. This can be higher conversions to qualified appointments, applications, accepted offers or enrollments.

Don't attribute to automation the value of all of the students that were converted. If possible, compare conversion rates to a baseline/control group, and use a conservative attribution percentage.

Attributed value = Incremental outcomes x Value per outcome x Reasonable attribution factor

When it comes to AI admissions automation ROI, it's important for institutions to articulate the reasonableness of the selected attribution factor. This means that the business case will not claim any outcomes due to marketing, scholarships, counsellors, programme demand or seasonality.

Step 5: Add Value Without Double Counting in Operation

There are some benefits that enhance the service but don't come as cash right now. This could be a quicker response time, higher contact rates, cleaner CRM data, fewer missed follow-ups, and uniformity in getting data captured.

These measures help to boost the ROI on recruiting; however, they should not be double dipping. If the additional time to follow up is already adding to the incremental value of the enrolment, don't take the same improvement and convert it into an additional financial benefit.

The financial ROI and operational performance indicators should thus be split on a balanced dashboard.

Metrics Universities Should Track

When using AI admissions automation ROI, the right metrics will be visible throughout a pilot, and even after it's in use.

The dashboard is also consistent, which helps in reviewing ROI throughout the recruitment cycle.

Worked Example: Calculating AI Admissions Automation ROI

Suppose there was a university pilot. Imagine a hypothetical university pilot. The numbers below are examples and in no way a Sicada.ai performance claim.

Now let's assume that the total cost of the automation is ₹18 lakhs per annum. Annual cash savings of the verified amount is ₹9 lakh. The temporary staffing, which was avoided, is worth ₹5 lakh. The incremental contribution to enrolments can conservatively be attributed to around ₹16 lakh.

Total measurable benefit = ₹9 lakh + ₹5 lakh + ₹16 lakh = ₹30 lakh

AI admissions automation ROI = [(₹30 lakh − ₹18 lakh) ÷ ₹18 lakh] × 100 = 66.7%

The institution may also monitor faster responsiveness, as well as clean the CRM data separately. The indicators have been developed to help guide admissions technology ROI without inflating the financial numerator.

The key for student recruitment automation ROI is to perform sensitivity scenarios. Attributed enrolment value declines by half – the investment case changes significantly. This shows which assumptions are the most important.

Common Mistakes That Distort Admissions Technology ROI

Measuring the AI admissions automation ROI is challenging because it can sometimes look like it is more likely to be a success or a failure than it actually is.

• If payroll is not reduced, it is possible to overestimate the benefits by counting all saved staff time as cash savings. 
• When evaluating the investment denominator artificially, it is too small because the costs related to integration, governance, training and optimisation are ignored. 
• Washing away the effects of marketing, counsellor performance, seasonality, scholarships and programme demand, which are all critical factors in enrolment improvement, is a mistake to credit all the enhancements in enrolment to automation. 
• Results may be skewed when comparing recruitment periods as the number of applicants and programme mix vary significantly between each period. 
• Tracking activity, but not outcomes, incentivises messages, not progress of applicants and the quality of service. 
• Risks of not having exceptions escalated to humans can be introduced when they are automated. 

These controls ensure that ROI quality is maintained and automation savings are achieved on the ground.

Where Sicada.ai Can Fit Into the Measurement Framework?

Sicada.ai is a set of inter-connected AI agents that work across various communication channels, including calls, WhatsApp and chat, and include the ability to qualify leads, capture information, follow up, update CRM and hand over to humans. The platform can be tested against the same baseline and post-deployment metrics that can be used for AI admissions automation ROI for universities considering the workflows. 

It's also on their website with an ROI Calculator as a handy resource. Such a calculator can serve as a model for universities to use, but finance teams should tailor the basis of their assumptions to those of their own institution, including costs, volumes, conversion information, and attribution rules. By maintaining that connection between the ROI of admissions technology and institutional proof, that remains intact. 

• Pilot a single defined admissions process before scaling automation to various stages of the recruitment process or channel (across the entire institution). 
• Ensure that appropriate connections are made between CRM fields to keep the tracking of qualification, follow-up, escalation and outcome measurable throughout deployment. 
• Regularly revisit failed conversations and escalations – workflow quality will have an impact on applicant experience and value. 
• Reserve admissions employees for handling cases that must be interpreted, empathised with, negotiated, exceptions, and/or expert judged. 

These practices can boost recruitment results while not taking automated systems as a substitute for admissions expertise.

A Practical 90 Days Measurement Roadmap for ROI.

Days 1-30: Baseline and design

Identify workflows, baseline metrics, costs, escalation rules and data ownership. Set up initial University AI ROI calculation prior to the pilot.

Days 31–60: Pilot and Validate

Conduct a controlled deployment, track the quality and make comparisons of matched periods. Use finance and staffing records instead of estimates to validate cost savings of admissions automation.

Days 61–90: Review and Scale

Measure AI admissions automation ROI, understand sensitivity cases, and workflow improvements. Scale only in places where quality, risk and economics are acceptable.

Response quality, response rates and progression of applicants should also be assessed at this stage when considering the ROI of student recruitment automation.

FAQs

So how much could you expect to get back from AI admissions automation ROI?

There is no one-to-one indicator. A successful AI admissions automation ROI goes beyond the approved limit of investment by the institution, without compromising service quality, governance and acceptable risk.

When should Universities calculate the Return on Investment (ROI) for admissions technology?

Monitor the AI admissions automation ROI every month while piloting and at key recruitment cycle events following implementation. Comparisons may only be made on an annual basis, and these comparisons may mask performance differences that exist from one season to the next.

So which aspects of admissions automation should be part of admissions automation cost savings?

The cost savings of admissions automation can be: verified labour reduction, avoided outsourcing, avoided hiring or lower operational costs. Unless approved by finance, do not mix redeployed capacity with other capacity.

What's the difference between student recruitment automation ROI and cost savings?

Student recruitment ROI automation can be measured in terms of recruitment outcomes like incremented applications or enrolments, whereas cost savings are, by and large, a reduction or avoidance of cost.

How to make an AI ROI calculation at a university reliable?

A key component of the reliability of a university AI ROI calculation is a documented baseline, complete costs, conservative attribution, consistent period, validated data sources, and sensitivity analysis.

Do universities have to consider faster response times as return on investment in terms of dollars and cents?

Not automatically. Operational indicators of faster responses are more likely to be responses to the question of how much they have saved or what incremental results they have achieved. They can still further enhance the AI admissions automation ROI evaluations.

Conclusion

Optimistic assumptions are not the best way to calculate the AI admissions automation ROI. The starting point is to have a baseline in place, to capture the total ownership cost involved in a university and to differentiate cash saved and released capacity, while assigning recruitment gains conservatively. They also should monitor service and workflow metrics that provide insight into the changes in financial results.

A compelling, solid university AI ROI calculation provides a common set of facts for admissions, finance, IT and leadership teams. It also enables student recruitment automation ROI and cost savings to be easily measured throughout the recruitment cycles.

Sicada.ai's ROI Calculator may be a helpful tool to begin with when modelling the AI admissions automation ROI. Avoid using generalised data when making investment decisions – use data from your institution instead.