The Total Economic Impact™ Of Robin By Atera

Avoided IT Service Desk Costs

Evidence and data. Interviewees reported Robin’s autonomous resolution of routine requests and improvements to operational efficiency led to measurable reductions in service desk workload and associated costs. They said Robin handled up to 60% of service desk tickets within the first 90 days of deployment but realized autonomy rates varied depending on the maturity of the integrations, the completeness of knowledge base content, and the effectiveness of model training.

• A security and infrastructure architect at a retail organization reported that Robin autonomously resolved approximately 34% of service desk tickets during a 30-day period, resulting in an estimated 31% of monthly working hours saved. They also said the solution scaled over time with autonomous resolution rates exceeding 50% of the organization’s total ticket volume in later phases.

• A head of IT, security, and data at a healthcare organization explained that their organization expects Robin to resolve up to 80% of its service desk tickets. This is contingent on completing key integrations with network systems, SharePoint, and other platforms.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization has 2,500 employees in Year 1 and grows at an annual rate of 4%, reaching 2,704 employees in Year 3.

• Employees generate an average of 1.2 service desk tickets per month.

• Prior to Robin, 70% of service tickets required full manual handling.

• The average mean time to resolve a fully manual ticket is 1.5 hours.

• The average fully burdened hourly rate for a L1/L2 service desk specialist is $51.

• Robin handles 60% of fully manual tickets in Year 1, 75% in Year 2, and 90% in Year 3.

Risks. The value of avoided IT service desk costs may vary based on several factors:

• The proportion of tickets fully resolved by Robin, which will influence the level of downtime avoided.

• Delays or limitations in integrating Robin with existing systems, which may constrain autonomous resolution coverage.

• The breadth, accuracy, and continuous curation of workflows, documentation, and model training, which influences the proportion of tickets that can be resolved autonomously.

• Variations in the distribution of low-, medium-, and high-complexity tickets.

• Preexisting mean time to resolve a support ticket.

• Pay rates for service desk specialists.

• Regional labor costs.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $3.3 million.

Service Desk Triage Efficiency Gains

Evidence and data. Interviewees reported that Robin delivered significant value by augmenting the service desk triage process. This reduced the effort required to manage tickets, including those that still require human intervention. Prior to Robin, the organizations spent considerable time gathering information, clarifying issues, and performing initial diagnostics before resolution efforts could begin. This often introduced delays and inefficiencies early in the ticket lifecycle.

The interviewees said Robin consistently acted as an intelligent first-line responder by engaging users immediately, capturing structured inputs, and performing initial troubleshooting steps prior to escalation. They noted this standardized the triage process and reduced variability in categorization, prioritization, and routing. As a result, their organizations experienced more consistent service delivery and improved SLA performance.

Interviewees also said Robin transformed the triage process by enabling real-time, guided interactions at the point of ticket creation. By dynamically prompting users with relevant follow-up questions, the solution captured critical context (e.g., system information, location, user type) and applied initial diagnostics. This eliminated delays typically caused by manual follow-ups from service desk personnel.

• The IT senior manager at a real estate organization noted that Robin provides immediate, 24/7 engagement at the point of issue submission, effectively acting as a Tier 1 support layer. They said the solution initiates the interaction and either resolves the issue or collecting the necessary information for escalation. This reduced back-and-forth communication between end users and IT staff, particularly in geographically distributed environments.

• The security and infrastructure architect in retail reported that their organization previously had triage inefficiencies that significantly impacted service desk productivity. Depending on ticket complexity, IT staff could spend anywhere from 15 minutes to multiple days gathering missing information before beginning resolution. The interviewee said introducing structured, real-time triage improved efficiency across all service requests, including those not fully resolved autonomously.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• As headcount grows, the composite organization processes 36,000 service desk tickets in Year 1, 37,440 in Year 2, and 38,938 in Year 3.

• Prior to Robin, approximately 25% of tickets required manual triage effort before resolution could begin. These tickets involved initial classification, information gathering, and routing by service desk personnel.

• Prior to Robin, each of these tickets required an average of 30 minutes of L1/L2 service desk time for triage.

• The average fully burdened hourly cost for an L1/L2 service desk specialist is $51.

• Robin triages 100% of the composite organization’s applicable tickets.

Risks. The level of avoided IT service desk costs may vary depending on several factors, including:

• The proportion of tickets fully resolved by Robin, which will influence the level of downtime avoided.

• Delays or limitations in integrating Robin with existing systems, which may constrain autonomous resolution coverage.

• The breadth and accuracy of workflows, documentation, and model training, which directly influence the proportion of tickets that can be resolved autonomously.

• Variations in the distribution of low-, medium-, and high-complexity tickets alongside different triage processes.

• Pay rates for service desk specialists.

• Regional labor costs.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $533,000.

End-User Productivity Savings

Evidence and data. Interviewees reported that Robin reduced the time end users spend waiting for IT support and resolving technical issues. This reduced productivity losses associated with service disruptions and helped employees remain focused on business activities. They explained that end users previously experienced delays caused by ticket queues, asynchronous communication, and the need for multiple interactions with IT teams to clarify issues. These inefficiencies extended downtime and negatively impacted employee productivity. However, interviewees also highlighted that a share of employee IT friction never became service desk tickets. Employees frequently worked around slow devices, degraded application performance, connectivity issues, or other minor and recurring technology problems rather than reporting them to IT. In some cases, users expected support requests would take too long, they considered the issue too minor to justify raising a ticket, or they simply adapted their behavior to compensate for the problem.

Interviewees said Robin improved the end-user experience by providing immediate, conversational engagement at the point of issue submission, and they noted this allowed users to progress more quickly toward resolution without idle waiting time. In most cases, Robin fully resolved issues without requiring human intervention. Interviewees also reported increasing value from Robin’s proactive monitoring and autonomous remediation capabilities. They said that rather than waiting for users to report issues, Robin identified and resolved conditions that commonly lead to employee frustration and productivity degradation.

Interviewees reported end-user productivity gains from both faster resolution and the prevention of everyday technology disruptions. The head of IT, security, and data in healthcare described configuring Robin to monitor disk-space thresholds and automatically remove temporary files before users experienced significant performance issues. They said without this proactive remediation, employees would have experienced intermittent slowdowns and degraded device performance, and that proactively identifying and resolving these hidden sources of friction reduced productivity losses that would have been invisible to the service desk while simultaneously accelerating the resolution of reported issues.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• Robin resolves 60% of applicable service desk tickets in Year 1, increasing to 75% in Year 2 and 90% in Year 3.

• Each ticket fully resolved by Robin results in an average of 1.5 hours of end-user productivity regained.

• The composite organization experiences approximately 10,800 to 11,700 unreported IT friction events annually.

• Robin detects and prevents 40% of unreported IT friction events in Year 1, increasing to 50% in Year 2 and 65% in Year 3.

• Each avoided IT friction event prevents one hour of lost employee productivity.

• The average fully burdened hourly rate for an end user is $46.

• The composite organization has a productivity recapture rate of 80%, which means not all recovered time is converted into productive output.

Risks. The level of loss productivity avoided may vary depending on several factors, including:

• The degree to which the organization’s service desk team and end users adopt Robin.

• Potential delays or challenges in system integration, which may restrict the scope of autonomous resolution.

• The breadth and quality of workflows, knowledge base content, and LLM training, which influence the proportion of tickets that can be fully resolved without manual intervention.

• Ticket complexity

• Downtime baseline.

• The average rate for end users.

• The amount of employee IT friction that goes unreported and does not lead to a service desk ticket.

• The amount of recurring IT friction that can be proactively resolved without requiring manual investigation or intervention.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $2.9 million.

Avoided Service Desk Hiring Costs

Evidence and data. Interviewees reported that Robin enabled their organizations to scale IT service delivery more efficiently by reducing reliance on incremental headcount as demand increased. They said they previously relied on labor-intensive service desk models in which increased ticket volume required corresponding increases in headcount, creating cost pressures and limited organizational agility. But they reported Robin’s autonomous resolution and triage capabilities absorbed a significant share of incoming ticket volume, which allowed IT teams to maintain or improve service levels without increasing staff.

• The IT manager and service desk lead at a multiservice organization reported that ticket volumes nearly doubled during a 12-month period, increasing from approximately 300 tickets per month to nearly 600. They said that although the organization was still expanding use of Robin’s autonomous resolution coverage, the tickets Robin did handle significantly reduced pressure on the IT team, allowing the organization to avoid hiring additional service desk specialists.

• The head of IT, security, and data in healthcare noted that Robin’s use of AI enabled their organization to avoid incremental service desk hiring and optimize workforce allocation. They said that Robin provided consistent first-line support, which reduced the risk of service bottlenecks as demand scaled and supported more efficient utilization of existing IT resources.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization employs 30 L1/L2 service desk specialists.

• Organizational growth leads to an increase in annual service desk tickets from 36,000 in Year 1 to 38,938 in Year 3.

• Without Robin, each service desk specialist handles 840 tickets in Year 1, increasing to 909 tickets in Year 3.

• Without Robin, the composite organization requires the addition of one full-time equivalent (FTE) service desk specialist in Year 2 and Year 3 to maintain service levels.

• The average fully burdened hourly rate for a service desk specialist is $51.

• Each FTE works 2,080 hours annually.

• Forrester applied a 1.8 times cost multiplier to labor costs to account for bonuses, recruitment, onboarding and training, management overhead, equipment, software licenses, and corporate overhead allocation.

Risks. The level of service desk hiring costs avoided may vary depending on several factors, including:

• Demand growth rates.

• SLA expectations.

• Staffing models, which may influence hiring requirements.

• Pay rates.

• Geographic cost structures.

• Benefits packages.

Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $241,000.

Shadow IT Remediation Avoided

Evidence and data. Some interviewees reported that Robin’s assistance provided end users with a faster way to resolve issues and discouraged them to independently seek alternative solutions. This reduced the use of shadow IT (e.g., unauthorized software, informal tools, workaround processes), which can contribute to technical debt and increase the complexity and cost of future remediation.

One interviewee described shadow IT as a natural consequence of friction in IT support, but they said Robin’s AI-driven support reduced the incentive to bypass IT. They estimated that a midsize organization could reduce shadow IT driven by support constraints by approximately 30% to 40% but emphasized that the solution’s impact on shadow IT should be assessed with nuance.

Interviewees said shadow IT in large or complex environments is often driven by broader organizational needs (e.g., the desire to rapidly develop new capabilities like AI-enabled tools) rather than purely by limitations in IT support responsiveness. They said Robin’s support accessibility meaningfully reduced certain categories of shadow IT, particularly those related to end-user support, but it did not always eliminate the issue entirely.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization experiences 375 shadow IT instances in Year 1, increasing to 390 in Year 2 and 406 in Year 3.

• The average remediation cost per shadow IT instance is $1,000.

• Robin reduces new shadow IT instances by 35%.

Risks. The scale of this benefit may vary from organization to organization based on:

• The extent of shadow IT use.

• Existing shadow IT management maturity.

• Complexity of shadow IT solutions.

• Number of users affected.

• Complexity of data migration.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $305,000.

Unquantified Benefits

Benefits that provide value for the composite organization but are not quantified for this study include:

• Shift to a more proactive IT service desk model. Interviewees said prior to implementing Robin, their organizations operated in a largely reactive mode, focusing on resolving tickets as they arose rather than anticipating or preventing issues. The composite uses Robin to address routine requests autonomously and reduce the volume of low-value incidents, enabling IT personnel to redirect effort toward root cause analysis and continuous improvement while transitioning to a more proactive and structured service delivery model. This benefit could be quantified by measuring reductions in recurring incidents, ticket backlogs, or unplanned service disruptions.

• More strategic allocation of IT resources. Interviewees noted that a significant share of service desk work previously involved routine requests that did not require specialized expertise but consumed substantial time. By reducing manual workload through autonomous execution of repetitive tasks, Robin allowed their organizations to redeploy IT resources toward higher-value activities. The composite organization shifts the focus of its IT team to strategic priorities (e.g., cybersecurity, infrastructure optimization, business-critical initiatives). This benefit could be quantified by measuring the value of resources reallocated to strategic initiatives.

• Enhanced end-user satisfaction and confidence in IT. Interviewees said Robin improved the end-user experience by providing immediate, responsive engagement at the point of issue submission. They reported that, prior to implementation, delays in initial response often led to user frustration and reduced confidence in IT services. With real-time interaction, the composite organization’s users receive instant acknowledgement and support, improving perceptions of IT responsiveness even before full resolution is achieved. Several interviewees reported measurable improvements in satisfaction scores following implementation, including increases from 3.8 to 4.7 over a four-month period and consistently high satisfaction ratings of up to 4.8 out of 5. This benefit could be quantified by linking improvements in end-user satisfaction scores to employee productivity, retention, or reduced time spent seeking IT support.

• Improved SLA compliance and ticket resolution performance. Interviewees reported that Robin contributed to improved service desk performance by accelerating response times, increasing resolution rates, and reducing ticket backlogs. One of their organizations increased ticket closure performance from approximately 75% prior to implementation to more than 100%, reflecting the ability to resolve both new and previously outstanding tickets. Another interviewee reported SLA compliance improving from 75% to 99%, including a 7-percentage-point increase within the first four months of 2026. Through a combination of autonomous resolution, real-time triage, and structured ticket handling, the composite organization achieves more consistent service delivery along with stronger SLA compliance and ticket resolution performance, contributing to more predictable IT operations. This benefit could be quantified by measuring reductions in ticket backlogs or improvements in SLA attainment.

Flexibility

The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Robin and later realize additional uses and business opportunities, including:

• Cultural shift toward AI and automation. Interviewees observed a gradual cultural shift in how end users interact with IT services. They said that while initial adoption of AI-driven support required change management and user education, behavior evolved over time as users became more comfortable with the technology. One interviewee noted that adoption was not immediate, as some users initially resisted engaging with an AI interface. However, as the system demonstrated value, acceptance improved and adoption moved quickly. The composite organization could expand the use of AI and automation across additional IT processes and service workflows in the future, creating opportunities to further scale operations and realize additional efficiency gains.

Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).