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ROI calculator

Save time and money with AI.

Calculate what automating your content processes in the CMS returns — per use case, over time. Every assumption below is yours to adjust. Note on the data basis: one operator, one project, two laps (first project vs. steady state). Constants such as briefing and spec are factored out; the time savings are anchored to published benchmarks and set conservatively.
Calculate your return

ROI Calculator

Use cases in this calculation

  • Content creation 40 % Connect the briefing ⇢ page & sections ⇢ copy ⇢ fill catalogs Baseline 65 min (copy 45, assembly 20) · benchmark: Noy & Zhang, 2023 (40%) · measured lap 2 ~2 min (upside)
  • Translation 43 % Field by field in context ⇢ fill the target language ⇢ native-speaker review Baseline 30 min (~500 words) · benchmark: Plitt & Masselot, 2010 (43%) · measured lap 2 ~24 sec (upside)
  • FAQ creation 40 % Connect sources ⇢ ~90 questions ⇢ distill 8 ⇢ answers ⇢ accordion Baseline 30 min · benchmark: Noy & Zhang, 2023 (40%) · mechanism: the collection round-trip disappears
  • Template development 50 % Template set (use-case, link, page, technical templates & scripts) — in parallel, in one step Inventory: 20 use-case, 5 link, 3 page, 15 technical templates & scripts · only the section template is measured (115 min, benchmark Peng et al. 55.8%); the other times are adjustable assumptions Use-case / section templates | 20 | 115 Link templates | 5 | 30 Page templates | 3 | 60 Technical templates & scripts | 15 | 45
  • SEO & GEO 30 % Metadata & keyword alignment & GEO analysis No baseline, no measured value · capability unlocked (no SEO specialist, GEO without benchmark)
  • Content release 30 % Check completeness, grammar, accessibility, SEO, compliance Not measured · capability unlocked · review cost is mostly approvals, which AI does not remove
Estimated total savings over {months} months An estimate based on measured task times and published benchmarks — not an offer.
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Cost without AI —of work
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Manual work; initial build, new pages and ongoing upkeep
With AI —saved
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AI tooling plus the remaining manual review
Pages created on top within the chosen horizon — they run through the same use cases as the existing ones.
Click a use case in the list to adjust its assumptions — the switch next to it turns it on or off.

The numbers are illustrative estimates based on your inputs — an estimate, not an offer.

Methodology

How the calculation works.

A data basis that respects practice, draws on published research and follows your inputs.

Measured baseline

Per use case one measured, comparable manual task time — e.g. 65 min for a campaign page (copy and assembly; briefing and assets stay outside as constants), 30 min for a translation, plus a monthly repeat for the share of pages that change, plus the pages newly created within the horizon. Template development instead scales with the template inventory, not with pages.

Benchmark anchor

The time saved per use case is anchored to published research — text to Noy & Zhang ( 40% ), translation to Plitt & Masselot ( 43% ), code to Peng et al. ( 55.8% ). Where a measurement beats the benchmark, the study value stays the anchor and the measurement is upside.

AI tooling cost & net

The AI tooling cost is one pool across everything in use — external agent, REST & skills, model usage, connectors, AI Suite — over the horizon, plus one hour of setup and the manual review time the AI does not cover. If the cost with AI exceeds the manual work, the calculator says so right above the total.

Confidence levels

Every use case carries a level: benchmarked or provisional . SEO & GEO and release are provisional — there capability unlocked counts, not a minute figure. The measurements were made with tooling that does not ship like this yet; a human stays at the wheel in every step.

The manual task times come from one measuring round with one operator on one project (FirstSpirit.com), in two laps: the first builds the scaffolding, the second shows the steady state. Read as representative, not statistically typical. Constants such as briefing, asset sourcing and spec are removed from every saving so the comparison measures equal lengths. Sources: Noy & Zhang (Science, 2023, 40%) · Peng et al. (2023, 55.8%) · Plitt & Masselot (2010, 43%).