Business alignment

I

Pillar I of V

AI Organization

The leadership structure, accountability lines, and risk method behind AI value without surprises.

Most AI projects fail. Models reach benchmark scores. Then they ship to no customer outcome anyone agreed to measure. An OCR engine at 99% accuracy still wastes money if no approval workflow uses the output. Every AI initiative at Bizzi traces back to a business metric you see in your contract.

Context

The cost of AI without alignment is not academic. Industry surveys put the failure rate of AI projects above 70%. The most cited cause is unclear business value, not technical limits. Our domain covers accounts payable, expense management, and e-invoice automation. Each unaligned feature consumes engineering capacity. That capacity moves your Straight-Through Processing (STP) rate when spent well, or moves nothing when spent on a vanity demo.

Principle: The alignment rule

Every AI feature at Bizzi maps to one of three business outcomes: higher STP rate, lower time-to-process, or lower cost per transaction. A proposal without a named outcome and target number does not enter a sprint.

How we implement

Six rules hold alignment in place.

• Strategic contribution. Each AI initiative maps at intake to one of three value axes. STP rate, time-to-process, or cost per transaction. Initiatives outside these axes do not receive funding.
• Prioritized investment. The AI Governance Board reviews the portfolio every quarter. Scoring weighs expected business impact, build cost, and operating risk.
• Risk-aware framing. Bizzi publishes an internal AI risk appetite statement. Squads stay inside the statement or escalate. The AI Board reviews the statement when market or regulatory conditions shift.
• Lifecycle ownership. Every shipped AI feature has one named Business Owner from intake through end-of-life. Engineering owns how. The Business Owner owns whether the feature stays alive.
• Performance integration. A model that hits a technical benchmark but moves no customer KPI is not a successful project. Closing the loop on business KPIs gates the stable label.
• Adaptive alignment. The AI roadmap re-baselines every quarter against customer demand, regulation (Decree 13/2023, ISO/IEC 42001), and competitive context.

Standards mapping

Standards Mapping

ISO/IEC 42001. A.5.2 (AI policy), A.6.1 (Internal organization). NIST AI RMF. GOVERN-1.1 (legal and regulatory requirements understood and managed).

KPIs you see

>85% Straight-Through Processing rate Target: Bizzi customer-wide target

>99% Field-level extraction accuracy Target: Tax ID and invoice total

Example. How the rule blocks a low-value feature

A product squad proposed an AI feature to auto-categorize business travel expenses. Before any code, the proposal answered four questions. How many percentage points does the feature move STP rate? What is the change in cost per transaction? What is the twelve-month ROI estimate? What is the risk envelope on mis-categorization? The squad had no evidence for the first two. The proposal returned to discovery. Two weeks later the squad came back with measured baselines and entered the sprint. The rule did the job.