Size the case for an AI-enabled IT service desk.
Best for password and access requests, software provisioning, device and connectivity issues, and how-to questions handled by a central service desk today.
Your inputs
Use ticket volume from your ITSM tool and the fully loaded cost per ticket your finance team already reports.
Incidents and service requests across all channels
Password, access, provisioning, connectivity, how-to
Analyst time per ticket, including after-ticket work
Labour, tooling and overhead per handled ticket
Resolved by self-service or an agent without human touch
From grounded knowledge, summarisation and guided resolution
Mean time to resolve, all severities
Platform, ITSM and identity integration, knowledge remediation
Service desk cost avoided from deflected tickets and analyst time returned on the rest.
Estimates are directional. Baselines should come from your own ITSM data before any target is committed.
Three-scenario view
Finance reviewers expect a range. These scenarios flex adoption and implementation cost around the model you entered.
Slower adoption, higher integration effort
Your inputs as entered
Strong sponsorship, clean data, phased scale-up
How do you calculate ROI for AI in the IT service desk?
IT service desk AI ROI is annual ticket volume multiplied by the repeatable tier-1 share, multiplied by the deflection rate you can evidence, valued at your fully loaded cost per ticket — plus analyst time saved on the tickets that still need a human, offset by year-one implementation cost. The tool returns tickets deflected, hours returned, indicative MTTR and payback.
Ungated — results appear instantly, no email required.
What you enter
- Monthly IT ticket volume
- Share that is tier-1 or repeatable (%)
- Average handle time per ticket (minutes)
- Fully loaded cost per ticket
- Target deflection on tier-1 (%)
- Analyst time saved on remaining tickets (%)
- Current MTTR and year-one investment
How it is calculated
- 1.Annualize ticket volume and apply the tier-1 share to size the addressable pool.
- 2.Apply the deflection target to that pool to get tickets resolved without an analyst.
- 3.Convert deflected tickets into analyst hours using average handle time.
- 4.Add time saved on remaining tickets from knowledge assist and guided resolution.
- 5.Value all recovered hours at the hourly rate implied by cost per ticket, then derive payback.
What you get back
- Tickets deflected per year
- Analyst hours and FTE equivalent returned
- Indicative MTTR after assist
- Scenario range and downloadable PDF
Built for: CIOs, IT service owners and digital workplace leaders funding employee-facing AI.
What deflection rate is realistic for an AI IT service desk?
It depends far more on knowledge quality and identity context than on the model. Enterprises with governed knowledge and real entitlement data deflect a large share of password, access and how-to tickets; those without either mostly move the ticket rather than remove it.
Does the model assume service desk headcount reduction?
No. It values recovered analyst hours as released capacity. Most IT organisations redeploy that capacity into backlog, major-incident work and deferred hiring rather than reductions.