Hidden costs of artificial intelligence that affect the ROI

The real cost of artificial intelligence doesn’t end with licenses, models, or tokens. Data, integration, RAG infrastructure, security, monitoring, follow-up work, and adoption can substantially alter the TCO. To protect the ROI, a company must budget for AI as an ongoing operational capability and measure financial value from design, not after deployment.

Taxonomic definitions

1. Total Cost of Ownership of AI — AI TCO

The Total Cost of Ownership of artificial intelligence represents all the costs necessary to design, deploy, operate, control, maintain, and eventually replace an AI solution during its lifetime.

It should not be limited to the cost of the model.

A practical formula is:

TCO IA = development + data + integration + infrastructure + model consumption + security + governance + monitoring + human support + change management + overtime + exit costs

2. Cost of inference

It is the cost generated when a model processes inputs and produces results during its use. Depending on the architecture, this can be determined by:

Entry tokens.
Exit tokens.
Size of the context.
Model selected.
Reasoning required.
Volume of consultations.
Use of external tools.
Grounding.
Document processing.
Audio, images or video.
Caché.
Competition.

Although these costs have decreased considerably, they do not represent the production cost in themselves.

Stanford reported that the cost of consulting a model with performance equivalent to GPT-3.5 on MMLU has gone from approximately USD 20 per million of tokens in November 2022 to USD 0.07 in October 2024, a reduction of more than 280 times.

3. Adjusted ROI of AI

It is the calculated return using the financial benefit actually attributable to the system after considering its full TCO.

An executive approach is:

AI ROI = [(financial benefit attributable − AI TCO) / AI TCO] × 100

For more mature investment decisions, the expected cost of risk must also be incorporated:

Expected cost of risk = Event probability × financial impact

This prevents a seemingly profitable project from relying on ignoring operational costs, risks, or human labor that still exist after automation.

Comparative table

Cost	What is usually budgeted	Cost that usually remains out of the equation	Financial impact	An indicator that should be monitored
AI model	License or tokens	Contextual growth, retries, tools, and premium models	OPEX and margin of the use case	Cost per transaction
Data	Storage	Cleaning, classification, permits, updating and quality	Implementation time and personnel cost	Cost per embedded source
Integration	Initial API	ERP, CRM, IAM, legacy systems, exceptions and maintenance	CAPEX/OPEX and time-to-value	Cost per integration
RAG	Embeddings	Vector base, indexing, storage, queries and updates	Recurring OPEX	Cost per consultation
Security	Authentication	DLP, permissions, auditing, testing and monitoring	Financial and regulatory risk	Incidents per 1,000 operations
Quality	Initial tests	Continued evaluation, drift, hallucinations, and regressions	Overtime and operational risk	Cost per valid response
Human supervision	Pilot review	Exceptions, approvals, and escalation	Hours-man	Human minutes per transaction
Adoption	Initial training	Process redesign, accompaniment and resilience	No ROI captured	Adoption rate
Operation	Technical support	Observability, FinOps, updates, and contingencies	EBITDA/OPEX	Monthly cost per user/case
Risk	Conventional security	Shadow AI, intellectual property and third parties	Losses, interruptions and fines	Expected economic exhibition

Why the model's cost does not represent the real cost of AI

One of the most common mistakes when building the business case is to use the model price as the main reference for the budget.

That approach may work in a small test. In production, it becomes insufficient.

The market itself shows an apparent contradiction: models are becoming increasingly affordable, but controlling the spending on AI has become an entrepreneurial discipline.

The State of FinOps 2025, based on 861 responsible participants collectively responsible for approximately USD 69 billion in public cloud spending, found that 63% already managed spending related to AI, compared to 31% the previous year.

The financial conclusion is important: Lowering the model does not necessarily lower the system. When AI moves from the laboratory to the operational process, other layers appear:

Data.
APIs.
Vectorial bases.
Networks.
storage.
identity.
Security.
Observability.
Evaluation.
Human supervision.
Managing change.
Support.
Continuity.
Government.

The correct budget should be built around the complete business transaction, not just around the call to model.

The hidden costs that must be included in the business case

1. Data and preparation: the cost appears before the first prompt

An entrepreneurial initiative rarely comes from perfectly structured information.

For a copilot, agent, or predictive model to operate on corporate information, activities such as:

Identifying sources.
Extracting information.
Eliminate duplicates.
Homologate catalogs.
Resolve inconsistencies.
Define owners.
Classifying sensitive information.
Establish permissions.
Create pipelines.
Keep up with updates.
Documenting lineage.
Design quality rules.

The initial cleaning should not be confused with a one-time expense either.

If the source system changes, a new product category is added, the ERP structure is modified, or new documents are added, the pipeline also needs maintenance.

Therefore, the indicator should not be just “cost of preparing the data”.

It is advisable to measure:

Monthly cost of maintaining a source available, governed, and usable by AI.

For a CFO, this difference matters: an investment that seemed to be CAPEX for implementation can partially become permanent OPEX.

Risk to the ROI

If the expected benefit from the project is $X per month, but maintaining the information ready for AI requires recurring hours of engineering, administrative, and business work, those hours must be deducted from the benefit.

Not doing so creates Accounting ROI in PowerPoint and negative ROI in production.

2. Integration: when a proof of concept becomes a business system

A pilot can operate using:

Downloaded files.
A limited dataset.
Manual access.
One single user.
A single flow.
An API.

The productive operation needs another level of integration.

It can involve:

ERP.
CRM.
Service Management.
Data warehouse.
Legacy systems.
Active Directory or IAM.
External APIs.
Supplier systems.
Documentary bases.
Collaboration tools.
Internal applications.

In addition, integrations fail, change versions, have consumption limits, and require monitoring.

Therefore, the correct cost is not:

“How much does it cost to connect the ERP?”

The financial question should be:

“How much does it cost to keep the full flow running for the next 36 months?”

That horizon allows for better comparison of AI with traditional alternatives to automation.

3. Inference, RAG and infrastructure: the cost that changes when usage increases

The unit cost of the models has decreased, but an enterprise architecture contains additional components.

A particularly illustrative case is RAG —Retrieval-Augmented Generation—.

AWS publishes an example of a generative query architecture with approximately 8,000 daily interactions. With the incorporation of Knowledge Bases, the estimated scenario reaches approximately USD 1,300 monthly.

Within the example, generating embeddings represents approximately USD 9 per month, While a basic configuration of OpenSearch Serverless used as a vector store represents approximately USD 691.20 monthly.

It should not be interpreted as a universal price: it depends on architecture, provider, region, volume, and configuration.

It does demonstrate a critical issue for the budget:

The apparently central component of AI may represent a fraction of the total cost of the architecture that enables its use.

Costs that must be modeled

Before approving a RAG solution or agentic AI, it is necessary to estimate:

Monthly consultations.
Average entry tokens.
Average output tokens.
Documents recovered through consultation.
Embeddings.
Vector storage.
Reindexation.
Data transfer.
Tool calls.
External APIs.
Grounding.
Caché.
Observability.
Development, QA and production environments.
High availability.

Recommended metric

Do not administer alone Cost per million tokens.

Administer:

Cost of AI per completed business transaction.

For example:

Cost per resolved ticket.
Cost per processed order.
Cost per generated report.
Cost per analyzed file.
Cost per executed forecast.
Cost per effectively resolved consultation.

That connects infrastructure to EBITDA.

4. More powerful models used where they are not needed

Another silent cost appears when all tasks are sent to the most capable model available.

Not all operations require the same level of reasoning.

A financially responsible architecture can separate:

Simple tasks

Classification.
Extraction.
Normalization.
Simple summary.
Routing.

Complex tasks

Multivariable analysis.
Planning.
Research.
Decisions with multiple restrictions.
Advanced reasoning.

The solution can be used model routing to assign each operation to the model that meets the minimum level of performance required.

The goal is not to use the cheapest model.

It is to be used the lowest-cost model that achieves the required level of quality for that transaction.

That nuance avoids two mistakes:

Saving at the expense of precision.
Buy the precision that the process does not need.

5. Retraining and human supervision: the cost of an incorrect response

Automation does not generate value simply because it produces a response.

It generates value when it produces a response:

Correct.
Usable.
Timely.
Complying with policies.
With an appropriate level of confidence.

That is why the cost of rework must be measured.

Suppose an agent generates 50,000 transactions per month.

An incomplete assessment could only calculate:

50,000 × technological cost per operation.

The executive assessment should add:

50,000 × exception rate × human review minutes × cost per minute

The following should also be included:

Reprocessings.
Reattempts.
Escalations.
Corrections.
Validations.
Complaints.
Downstream impacts.

The most useful indicator could be:

Cost per valid result

Cost per valid result = total monthly cost of the system / number of results accepted without rework

This metric allows you to compare AI against:

Manual processing.
Traditional automation.
Outsourcing.
RPA.
Specialized software.

6. Security and governance: The cost of controlling AI is less than the cost of not controlling it

Security should not be added after the pilot as an extraordinary measure.

It must be part of the TCO from the design stage.

The Cost of a Data Breach Report 2025 by IBM He estimated the average overall cost of a gap in USD 4.44 million. The same study found that 13% of the organizations analyzed reported incidents involving their applications or AI models; among those organizations, 97% lacked adequate controls for access to AI..

The phenomenon of shadow AI It also has financial implications. IBM reported that the gaps related to shadow AI added up to approximately USD 200,000 at the average cost of a gap within their sample.

The controls may include:

Identity management.
Roles and permissions.
Classification of information.
Protection of prompts and responses.
DLP.
Activity registration.
Management of secrets.
Evaluation of suppliers.
Controls over plugins and APIs.
Safety evaluations.
Abuse monitoring.
Response to incidents.

NIST explicitly includes government, pre-deployment testing, content provenance, and incident disclosure among the critical areas for managing the risks of generative AI.

The translation to the CFO

The security of AI should not be justified solely as compliance.

It should be evaluated as reduction of expected loss.

7. Regulatory compliance: a small cost can turn into a material exposure

For organizations with operations subject to specific jurisdictions, the cost of compliance also needs to appear in the business case.

An example is the European Union.

The enforcement framework of the AI Act provides for sanctions depending on the infringement, which for certain prohibited practices can reach up to €35 million or 7% of global annual turnover, the greater of the two.

This does not mean that all projects face that exposure.

This means that the regulatory classification of the use case cannot be left for the end.

Before production, the following must be known:

Affected jurisdictions.
Type of processed information.
Affected users.
Automated decisions.
Transparency obligations.
Data retention.
Intellectual property rights.
Auditing obligations.
Contractual liability of third parties.

The cost of preventive regulatory action is usually quantifiable.

The cost of non-compliance may not be known until the incident has already occurred.

8. Monitoring and continuous evaluation: AI does not become “finished” when it enters production

Traditional software can repeatedly execute an explicit rule.

An AI solution also requires checking that it continues to respond within acceptable parameters.

It must be monitored:

Precision.
Hallucinations.
Grounding.
Latency.
Cost.
Availability.
toxicity.
Compliance.
Drift.
Tooling errors.
Quality of recovery.
Special rate.
User feedback.

That involves evaluation datasets, tools, telemetry, and personnel.

This cost rarely appears attractive in a business case, but it is essential because it allows you to detect when the supposed savings are being eroded by:

Longer answers.
More attempts.
More escalations.
Greater human revision.
Higher consumption.
Lower quality.

9. Adoption and redesign of processes: potential productivity does not mean captured productivity

One of the biggest differences between technical benefit and financial benefit is what happens with the time released.

Saving ten minutes does not automatically generate ten minutes of EBITDA.

The benefit appears when the organization can convert that time into:

Higher volume.
Less overtime.
Lower incremental hiring.
Less outsourcing.
Higher sales.
Greater operational capacity.
Less mistakes.
Better utilization of personnel.

McKinsey found that More than 80% of the participants still did not observe a tangible impact of generative AI on the overall company-wide EBIT.; Only 17% attributed 5% or more of the EBIT over the last 12 months to the use of generative AI.

The same analysis found that Redesigning workflows It was the factor with the highest correlation with the reported ability to generate impact on EBIT from generative AI.

This difference is fundamental.

Buying AI does not transform a state of results.

Changing the process can indeed be done.

10. Not measuring the ROI also costs money

There is another hidden cost: implementing projects without a baseline.

McKinsey found that Less than one in five respondents reported that their organization tracks well-defined KPIs for generative AI solutions..

Without a baseline, it is difficult to answer:

How many hours were actually reduced?
Has the incremental headcount been reduced?
Has the throughput increased?
Has the cost per transaction decreased?
Did errors decrease?
Did it increase conversion?
Did the margin improve?
Has the inventory been reduced?
Did it reduce downtime?

Without those answers, the organization knows how much it spent on AI, but not how much value it captured.

And that is probably the most dangerous hidden cost.

How to calculate the actual TCO of an AI project

Step 1. Define the economic unit

Don't start with tokens.

Start with the operation:

Ticket.
Invoice.
Order.
Diagnosis.
Report.
Customer attended.
Revised contract.
Forecast.

Step 2. Build the baseline

Measure before automating:

Volume.
Time.
People involved.
Cost.
Mistakes.
Retraining.
SLA.
Associated income.

Step 3. Separate initial and recurring costs

Initial letters:

Discovery.
Design.
Development.
Integrations.
Data preparation.
Security.
Tests.
Training.

Recurring:

Models.
Infrastructure.
Vectorial bases.
Storage.
Observability.
Support.
Data.
Evaluation.
Security.
Supervision.
FinOps.

Step 4. Propose three scenarios

At least:

Expected volume.
High volume.
Volume of stress.

For each scenario, calculate:

Volume × total cost per transaction

This way, an architecture whose unit economics works with 10,000 requests but stops working with 500,000 can be detected.

Step 5. Incorporate the residual human cost

Size:

Special rate.
Average time for review.
Labor cost.
Percentage of corrected responses.

Step 6. Incorporate economic risk

Models relevant scenarios from:

Interruption.
Security incident.
Information exhibition.
Non-compliance.
Operational error.

Step 7. Calculate the captureable benefit, not the theoretical benefit

If AI saves 20,000 hours per year, it identifies what will happen with those hours.

If there is no economic response, there is still no demonstrably financial savings.

How to protect the ROI before moving to production

Before authorizing the deployment, the committee should require at least:

Financial baseline.
Business owner.
Operational KPI.
Financial KPI.
12, 24 and 36 month TCO.
Unit cost.
Scenography of scale.
Data architecture.
Security model.
Risk assessment.
Monitoring plan.
Maximum acceptable error rate.
Maximum threshold for human review.
Operating budget.
Adoption plan.
Criteria for stopping or redesigning the project.

The recommended financial strategy

A project should not move forward simply because the technical proof works.

It must simultaneously demonstrate:

Technical feasibility + viable economic unit + adoption + control + measurable impact.

That is the difference between an AI experiment and an entrepreneurial ability.

Predictive conclusion

Over the next few years, the nominal cost of models will likely continue to lose relative weight compared to the cost of operating entire AI ecosystems. The historical decline in inference prices already points in that direction.

The new financial debate will not be simply “How much does using AI cost?”, But rather:

“How much does it cost to produce a reliable decision, transaction or automation using AI?”.

That will force CIOs, CFOs and transformation leaders to connect three disciplines that have traditionally operated separately:

Architecture.
FinOps.
Government of AI.

Organizations that only control tokens can reduce their technology costs without necessarily improving their ROI.

Those who administer Cost per valid result, captured benefit, and economic exposure They will be in better condition to climb.

The natural evolution will be to move beyond measuring. AI spending to administer AI unit economics.

And that will be one of the filters that will separate the projects that reach production from those that actually reach the stage of being results.

FAQ

1. What is the hidden cost that can most affect the ROI of an AI project? There is no one universal one. In business projects they usually focus on data, integration, recovery infrastructure, human supervision, and continuous operation. The correct way to detect it is to calculate the total cost per useful transaction, rather than observing only licenses or tokens.

2. How can a CFO know if the AI budget is incomplete? You should check if it includes costs ranging from 12 to 36 months for data, integrations, infrastructure, models, security, monitoring, support, human review, and adoption. If the budget ends up being “development + model license,” you’re probably measuring implementation rather than TCO.

3. Which indicator is more useful for monitoring the ROI of AI in production? The cost per valid business outcome. It should be supplemented by captured financial benefit, exception rate, human review cost, and monthly operating cost. This way, it can be determined whether automation continues to create value as volume increases.

Before scaling AI, discover how much it really costs to produce value with it A project may work technically but still not justify its investment.

At MaIA by Scanda We start from technical and business feasibility, work on data integration, cleaning, and governance, and design the architecture according to the use case and the environment of each organization. Scanda operates with an agnostic platform approach and can work with local, cloud, or hybrid architectures. If you are evaluating an artificial intelligence project, let’s talk before the cost appears after the pilot. With MaIA by Scanda, we can help you identify the TCO, risk points, and variables that need to be controlled to build AI applied in an organized and measurable way that has a measurable impact on the business.

The hidden costs of artificial intelligence that can affect the ROI