---
sourceDocument: Australia Enable AI
sourceDocumentLink: https://www.servicenow.com/docs/r/australia/intelligent-experiences

 Release :

    - australia

ft:locale :

    - en-US

ft:publication_title :

    - Australia Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Cost and net AI return

# AI cost and net AI return {#ariaid-title1}

Release version: Australia  
Updated November 5, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 4 minutes to read
Summarize  
![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.  

## Summary of AI cost and net AI return

The AI Control Tower (AICT) in ServiceNow enables you to track the financial impact of your AI systems by measuring both their productivity gains and associated costs.
It calculates the net AI return by subtracting the cost of running AI systems from the productivity value, expressed in currency.
This insight helps you understand the profitability and value of your AI investments comprehensively.
Show full answer Show less  

## Key Features

* **Cost tracking by vendor:** AICT automatically tracks costs for integrated vendors (e.g., ServiceNow, Vertex AI, Amazon Bedrock, Azure Foundry) using token consumption or direct cost inputs.
* **Cost calculation methods:** Costs can be defined per million input/output tokens or as a direct total cost without unit consumption.
* **Currency value configuration:** You can set hourly labor rates (single or persona-based) to translate productivity gains into monetary savings.
* **Detailed cost breakdown:** Costs are broken down by vendor, AI system, department, and time period for granular financial visibility.
* **Real-time tracking and reporting:** Token usage and AI Assist agent costs are captured automatically, with dashboards providing consolidated daily or hourly summaries.
* **Cost configurations with effective dates:** Allow managing future, current, and past cost rates to maintain accurate financial reporting over time.

## Key Outcomes

* **Complete financial visibility:** Connect productivity hours saved by AI to actual monetary savings and costs, enabling clear answers to business questions about value and profitability.
* **Net AI return analysis:** Determine if AI investments are profitable by calculating net returns (savings minus costs), including breakeven and payback period assessments.
* **Informed vendor management:** Identify which AI vendors and systems contribute most to costs, supporting cost optimization efforts.
* **Accurate financial metrics:** The framework relies on precise productivity data, accurate hourly labor rates, and comprehensive cost capture to ensure valid comparisons over consistent time periods.  
AI Control Tower tracks the cost of your AI systems and subtracts it from the productivity value to show the net AI return. Cost is tracked by vendor from token consumption or a direct cost.

## AI cost and net AI return overview {#mc-ai-cost-net-ai-return__section_overview}

The AI Control Tower (AICT) reports the productivity value of your AI systems as productivity gains, which it derives from your value templates and can express in currency. It also tracks the cost of running those AI systems.
The net AI return is the money saved minus the cost.

## Key benefits {#mc-ai-cost-net-ai-return__section_benefits}

AI cost tracking provides the following benefits:

* Show the return of your AI systems by comparing value against cost.
* Track cost automatically for integrated vendors through the trace integration.
* Record cost as token consumption or as a direct cost.
* Break down cost by vendor and by AI system.
{#mc-ai-cost-net-ai-return__ul_qmm_tvb_lkc}

## How cost is calculated {#mc-ai-cost-net-ai-return__section_how_it_works}

The AI Control Tower calculates cost at the vendor level, because usage details for external vendors are available for the vendor rather than for each agent. For each vendor, the calculation uses the unit consumption, such as
tokens, and the cost. A breakdown at the AI system level appears where that data is available.

Integrated vendors, such as ServiceNow, the Vertex AI service, the Amazon Bedrock service, and the Azure Foundry service, use the trace integration to capture token consumption. When the API endpoint and the validation key
are configured, the AI Control Tower tracks cost for those vendors automatically.

You can define cost in two ways. Input and output token cost applies a separate rate to every million input tokens and output tokens. Direct cost applies a total cost that you enter, without a unit consumption.

To express value in currency, you configure an hourly rate, which is the total workforce cost divided by the total workforce hours. You can set a single hourly rate or set a rate for each persona.  
Note:  
* Cost configurations use effective dates. A configuration for a future date is a draft, the current configuration is active, and a past configuration is expired.
* Cost is calculated for the previous day, so a new cost configuration appears on the dashboard after the next scheduled run.
{#mc-ai-cost-net-ai-return__ul_tcm_lxb_lkc}

## Why cost tracking matters {#mc-ai-cost-net-ai-return__section_why_it_matters}

Cost tracking connects the productivity hours that AI systems save to their financial impact, so that you can answer key business questions:

* Are we getting value? How much money are we saving with AI?
* What are we paying? How much do the AI systems actually cost?
* Is it profitable? Are savings exceeding costs?
* How long to payback? When will AI investments pay for themselves?
* Where are the costs? Which vendors cost the most?

{#mc-ai-cost-net-ai-return__ul_smm_tvb_lkc}

Without cost tracking, you can see hours saved but can't connect them to financial impact. With it, you have complete visibility into AI net returns.

## Three pillars of the Cost framework {#mc-ai-cost-net-ai-return__section_pillars}

The Cost framework rests on three interconnected pillars that together enable complete financial visibility and net
return analysis:

Savings

:   Quantifies the value (benefits) delivered by AI systems. The Savings pillar converts productivity into currency. AI systems are evaluated for the number of work hours they save. An average hourly rate defines the
    labor cost per hour. Money saved is calculated by multiplying the hours saved by the average hourly rate.

    For example, 40 hours saved × $100/hour = $4,000 in weekly savings.

Cost
:   Tracks all expenses associated with running AI systems, across LLM token costs, Assist costs, integration costs, and infrastructure costs.

Net returns

:   Calculates the net financial benefit as savings minus cost. A positive result is profitable, zero is breakeven, and a negative result means the investment is not yet profitable. For example: $4,000 savings − $500
    cost = $3,500 net returns (profitable); $2,000 − $2,000 = $0 (breakeven); $1,000 − $2,000 = −$1,000 (not yet profitable).

## Cost categories tracked {#mc-ai-cost-net-ai-return__section_cost_categories}

The Cost pillar tracks the following categories of expense:

LLM token costs
:   Charges from LLM providers based on token usage. Token costs vary by the model used (more advanced models cost more), the usage volume (more interactions mean higher cost), and vendor pricing (different providers
    have different rates).

Assist costs
:   ServiceNow AI Assist charges for running AI agents and assistants. These recurring costs are based on the number of active agents, the frequency of usage, and the complexity of tasks.

## How costs are tracked {#mc-ai-cost-net-ai-return__section_how_tracked}

The system tracks costs automatically as follows:

1. Captures all LLM API calls and token usage from integrated systems.
2. Records token costs based on vendor pricing models.
3. Aggregates Assist costs from configuration and usage tracking.
4. Groups costs by system, vendor, department, and time period.
5. Calculates trends and period-over-period comparisons.

{#mc-ai-cost-net-ai-return__ol_vmm_tvb_lkc}

Costs are updated in real-time, although dashboards typically show consolidated daily or hourly summaries.

## Complete calculation flow {#mc-ai-cost-net-ai-return__section_calculation_flow}

The Cost framework calculates financial metrics in the following sequence:

1. The system collects productivity metrics (hours saved from AI automation).
2. The system looks up the average hourly rate configured for your organization.
3. The system calculates money saved as hours saved × hourly rate.
4. The system aggregates total cost as token costs + Assist costs.
5. The system calculates net returns as money saved − total cost.

{#mc-ai-cost-net-ai-return__ol_wmm_tvb_lkc}

All of these calculations feed the dashboards and reports used for stakeholder analysis.

## Key assumptions and considerations {#mc-ai-cost-net-ai-return__section_assumptions}

The Cost framework relies on several assumptions. Verify them regularly and adjust configurations as needed:

* Productivity measurement accuracy: Hours saved must be captured accurately from AI system activity logs.
* Hourly rate accuracy: The average hourly rate must reflect the true labor cost, or a documented business rationale must support different rates.
* Cost completeness: All relevant costs must be captured.
* Time period consistency: Savings and costs must cover the same time period for a valid comparison.
{#mc-ai-cost-net-ai-return__ul_xmm_tvb_lkc}