How much does it cost to implement AI in a mid-sized company?

It depends entirely on integration depth and review burden, which is why credible providers quote a range after discovery rather than a fixed figure upfront. Merjora publishes its own commercial model transparently on the pricing page.

Is it cheaper to build or to buy?

Buy for commodity capability — extraction, transcription, classification. Build only where the workflow logic is a genuine differentiator. The expensive part is almost never the model; it is the integration and the review process around it.

AI ROI

AI implementation costs: what actually goes in the budget

The real cost structure of an AI implementation — build, integration, evaluation, change management, inference, human review and maintenance — and how each line behaves over time.

Miguel Torres, Founder, Merjora · Published 16 August 2026 · Updated 3 September 2026

In short

AI implementation cost has two shapes: a one-off build cost covering discovery, data access, integration, evaluation and change management, and a recurring run cost covering model usage, human review, monitoring and maintenance. For mid-market process automation, the recurring cost is commonly 25-40% of the first-year build cost annually, and human review is frequently the single largest recurring line — which is why business cases that model inference cost alone understate total cost of ownership.

Key takeaways

Model usage is rarely the dominant cost. Review and integration usually are.
Budget evaluation explicitly, or quality debates will consume the timeline instead.
Run cost grows with adoption; model it against year-two volumes, not year-one.
Maintenance is an operating line, not a project phase.

One-off cost lines

Discovery and baseline measurement
Data access, cleaning and pipeline work
Integration with the systems of record
Solution build and prompt/model configuration
Evaluation harness and acceptance testing
Change management, documentation and training

Recurring cost lines

Line	Driver	Behaviour over time
Model / inference usage	Volume × tokens or calls	Grows with adoption and scope creep
Human review	Items × review minutes	Falls slowly as confidence thresholds tighten
Monitoring and evaluation	Fixed plus per-release	Roughly stable
Maintenance and model updates	Change frequency	Spikes when providers deprecate models
Support and exception handling	Exception rate	Falls with process fixes, not with model changes

Where budgets typically break

Integration effort discovered after approval, because data access was assumed
Quality thresholds negotiated late, forcing a rebuild of the evaluation approach
Review workload never reduced, because nobody owned tightening the thresholds
Scope widened from one workflow step to a whole process mid-delivery

Frequently asked questions

How much does it cost to implement AI in a mid-sized company?: It depends entirely on integration depth and review burden, which is why credible providers quote a range after discovery rather than a fixed figure upfront. Merjora publishes its own commercial model transparently on the pricing page.
Is it cheaper to build or to buy?: Buy for commodity capability — extraction, transcription, classification. Build only where the workflow logic is a genuine differentiator. The expensive part is almost never the model; it is the integration and the review process around it.

Where would this apply in your business?

Merjora maps your workflows, scores each opportunity and quantifies the likely value range before you spend budget.

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