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This is a sample. Levanto Software is a fictional 400-person B2B SaaS company. Every number below is an illustrative assumption with the arithmetic shown, not a client result. In a real audit, each figure traces to a named interview and your team validates it before we present. How the math works: terenzo.ai/roi-methodology.
Terenzo
AI Operations Audit · Sample

Findings and roadmap.

Prepared for Levanto Software, a fictional companySample edition · 2026
Scope and objectives

What we looked at, and why.

The finance close runs nine days, the support backlog grows with every new customer, and the monthly board pack is assembled by hand. The question Levanto asked: where would AI actually pay for itself, before Q3 budget planning?

The question

  • Where does AI pay for itself in finance, support, and revenue operations
  • No new headcount; answer needed before Q3 planning

Who we heard from

  • 4 leadership interviews
  • 9 team interviews
  • Finance, customer support, revenue operations

What we assessed

  • AP, close, and reporting workflows
  • Support queue and knowledge base
  • Existing automation: 12 Zapier flows, one abandoned chatbot pilot
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Opportunity matrix

Every opportunity, by impact and effort.

Impact, low to high
Quick wins
High impact, low effort. Do first.
AP invoice matchingSupport response draftingReporting pack assembly
Big swings
High impact, high effort. Phase 2 and 3.
Quote-to-cash rebuildSupport knowledge assistant
Nice to have
Low impact, low effort. Bonus only.
Meeting-notes capture
Deprioritize
Low impact, high effort. Considered, skipped.
Public website chatbot
Effort to implement, low to high
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Roadmap

Sequenced so value lands early.

30 to 90 days
Quick wins
  • AP invoice matching against POs
  • Draft-first support responses
  • Automated reporting pack assembly
Months 4 to 8
Bigger builds
  • Support knowledge assistant grounded in the product docs
  • Quote-to-cash data cleanup and handoff rebuild
Months 9 to 18
Transformational
  • Forecasting assistant on the cleaned pipeline data
  • Order-to-renewal automation across the customer lifecycle
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Quick win deep dive

Reporting pack assembly

Today
•Three analysts export from five systems into one workbook
•Two days of copy-paste and reconciliation every month-end
•The board pack ships late whenever any source changes format
With AI
+Sources land in one reporting table on a schedule
+A model drafts the narrative from the numbers; an analyst edits
+Analysts move from assembling the pack to analyzing it
21 hrs/wk
Time saved (3 analysts × 7 hrs)
$57K
Annual value (21 × $52 × 52)
6 wks
To implement
Terenzo  ·  Sample  ·  05
ROI summary

The money, year one.

Implementation (one-time)
$58K
Annual savings
$128K
Year-one ROI
36%
Year-two ROI
256%

Annual savings = hours saved per week × people × loaded hourly rate × 52. Here: AP matching $32K + support drafting $39K + reporting pack $57K = $128K. Year-one cost = $58K implementation + $36K maintenance ($3K/mo). ROI = (savings − cost) ÷ cost. Every figure on this slide is an illustrative assumption; in a real audit each one traces to an interview.

Terenzo  ·  Sample  ·  06
Terenzo
Next step

Start with the quick wins.

First move: the reporting pack, owned by the CFO's operations lead.
hello@terenzo.ai  ·  terenzo.ai
This is the work product. In a real audit, every number on these slides is yours.