AI Local Content Officer (LCGPA) for Saudi 2026: The IKTVA Maximizer (Complete Technical Guide)

AI Local Content Officer (LCGPA) for Saudi 2026: The IKTVA Maximizer

The Price Preference

Two companies bid for a SAR 10M Gov Contract. Company A: Bid SAR 10M. Local Content Score 20%. Company B: Bid SAR 10.5M. Local Content Score 50%. Winner: Company B. Why? The Government gives a 10% price preference to high Local Content scores. The Math: Calculating this score (Salaries + Goods + Assets + Depreciation) is a nightmare.

This guide explains how Contractors in Riyadh use Custom AI Agents to maximize their score and win tenders.

1. The Scorecard Matrix

• LCGPA: Local Content and Government Procurement Authority. The regulator.
• Metrics:
  • Workforce: Salaries paid to Saudis vs Expats.
  • Goods: Using Saudi Steel vs Chinese Steel.
  • Capacity: Training spend.
• Audit: You must submit a certified audit report. Errors = Disqualification.

2. High-Value AI Workflows

Workflow A: The "Spend Analyzer"

Target: Maximization.

Scenario: Monthly Procurement.

1. Scan: AI scans all Purchase Orders (POs).
2. Flag: "You are buying Tables from Supplier X (Foreign). Supplier Y (Saudi) sells same tables for 5% more."
3. Advise: "Buy from Y. The Local Content boost is worth more than the 5% cost difference."
4. Switch: Updates PO to Supplier Y.

ROI Impact: Score increased by 15 points.

Workflow B: The "Audit Prep Bot"

Target: Certification.

Scenario: End of Year Audit.

1. Compile: AI pulls Payroll (Saudis), Asset Depreciation, and Supplier Invoices.
2. Map: Maps every line item to LCGPA template.
3. Verify: Checks if Supplier X has a valid "Local Content Certificate". If not, spend doesn't count.
4. Generate: Creates the Draft Audit File.

ROI Impact: Audit time reduced from 3 months to 2 weeks.

Workflow C: The "Bid Simulator"

Target: Sales.

Scenario: Tendering for a Ministry Project.

1. Input: Competitor assumed prices.
2. Simulate: "If we commit to 40% Local Content, we can bid 8% higher and still win."
3. Strategize: Helps pricing team find the "Sweet Spot".

3. Real-World Use Case: Aramco IKTVA

An Oil & Gas Supplier.

• Program: Aramco's IKTVA (In-Kingdom Total Value Add).
• Goal: Reach 70% IKTVA to get "Preferred Status".
• Action: AI analyzed Aviation Valley">supply chain. Found that 30% of "Local" purchases were actually "Imported" by the local vendor.
• Correction: Switched to true manufacturers.
• Result: Hit 72% Score. Won a 5-year contract.

4. ROI Analysis

Case Study: IT Services Co (Riyadh).

• Revenue: SAR 50 Million.
• Tenders Lost: Lost 3 tenders (Value SAR 20M) due to low score.
• Procurement: Haphazard buying.

With AI Local Content Officer:

• Score: Improved from 15% to 45%.
• Wins: Won 2 extra tenders per year (SAR 15M revenue).
• Cost: Only SAR 100k investment in virtual agent.
• Net Benefit: SAR 15 Million (Revenue Growth).

5. Development Roadmap

Phase 1: The Calculator (Weeks 1-4)

• Real-time score dashboard based on ERP data.

Phase 2: The Advisor (Weeks 5-8)

• Supplier recommendation engine ("Buy This, Not That").

Phase 3: The Auditor (Weeks 9-12)

• Auto-generation of audit packs.

6. Technical Stack

• Data: Integration with Etimad (Tender Portal).
• Database: Database of "Golden List" suppliers (High LC score vendors).
• Lang: Python for complex financial modeling.

7. Cost of Development

• Tier 1 (Calculator): $25k.
• Tier 2 (Procurement AI): $55k.
• Tier 3 (Bid Strategy): $85k+.

Conclusion: Buy Local, Win Big

Local Content is not charity. It is strategy. Build your supply chain to build your nation.

Score High.

Contact Procurement AI Team