What is an AI Worker?

An AI Worker is an autonomous AI agent that works like a real teammate: it has a personality, specialized skills and memory, runs tasks end to end and integrates with your tools (Slack, Teams, Gmail, Notion…).

How many AI Workers are available?

15 specialized AI Workers are available, covering marketing, development, design, data, human resources, finance and customer support.

Can I try it for free?

Yes, a free trial is included with no commitment. You can add or remove AI Workers at any time and cancel whenever you want.

Yes. Your data is encrypted end to end, hosted in isolated environments, and never used to train models. Workers Delos is GDPR-compliant and SOC 2-certified.

Rapports de Conseil par l'IA : Réduire les cycles de semaines à jours

Par Delos Intelligence — 2026-08-25

Board pack preparation takes 3-6 weeks and 120 hours manually. AI workers cut this to 4 days and 22 hours while achieving 98% data source coverage.

The Board Reporting Bottleneck: When Weeks Is Not Fast Enough

Corporate secretaries and CFOs at global enterprises spend 3-6 weeks per quarter preparing board packs. Data must be manually extracted from 8-15 systems, narratives written from scratch, and multiple review cycles completed. By the time the pack reaches board members, some data is already 6-8 weeks stale.

!AI Board Reporting Pipeline

How AI Transforms Board Reporting

1. Automated Data Aggregation from ERP and BI Systems

AI workers connect directly to SAP, Oracle, Workday, Salesforce, and Power BI. They automatically pull KPIs, financial statements, operational metrics, and risk indicators according to the board pack template. Data coverage increases from 55% (manual) to 98% of relevant sources.

2. Narrative Generation for Board Packs

LLM-based narrative engines draft executive summaries, variance analyses, and strategic commentary from structured data. CFOs report that AI-generated narratives require only minor editing in 78% of cases, saving 80-100 hours per reporting cycle.

3. Variance Analysis and Exception Highlighting

AI workers automatically identify significant variances, correlate them with operational drivers, and draft explanatory commentary. The system flags items requiring board attention vs routine performance updates.

4. Governance-Grade Auditability

Every data point, calculation, and narrative statement is traceable to its source system with complete data lineage. Audit-ready documentation complies with SOX, FRC Governance Code, and local corporate governance requirements.

!Board Reporting Impact Metrics

Measurable Business Impact

| :--- | :--- | :--- | :--- |

| CFO review cycles | 4.5 | 1.2 | -73% |

| Data source coverage | 55% | 98% | +78pp |

Implementation Roadmap

Phase 1 (Weeks 1-4): Integrate ERP, BI, and reporting systems. Establish master KPI library.

Phase 2 (Weeks 5-8): Deploy AI narrative generation and variance analysis for one business unit.

Phase 3 (Weeks 9-12): Expand to full board pack automation with governance sign-off workflows.

This article was written with AI assistance.