MEMO FROM THE FUTURE: C3 AI

CEO Edition

BOARD STRATEGY SESSION June 2030

TO: C3 AI Board of Directors

FROM: Thomas Siebel, CEO

DATE: June 2030

SUBJECT: Competing with Hyperscalers and the Path to Enterprise AI Dominance

OPENING

C3 AI is an enterprise AI platform company. We've built the only no-code/low-code AI platform that non-tech companies can actually use to build AI applications. But we're facing an existential threat: hyperscalers (Google, AWS, Microsoft, Anthropic) are building competing platforms.

The question is: How do we remain relevant as an independent company when we're competing against companies 100x our size with unlimited capital?

This memo proposes a strategy that doesn't try to compete on raw resources, but on focus, specialization, and deep integration with enterprise workflows.

THE REALITY

Threats: - AWS SageMaker is expanding AI/ML capabilities aggressively - Microsoft Copilot stack includes enterprise AI tools - Google has Vertex AI and LLM infrastructure - Anthropic is building Claude-based enterprise solutions - These companies are giving away or subsidizing AI tools to lock in ecosystem

Our advantage: - We focus on non-tech companies (manufacturing, pharma, utilities, energy) - We've built application templates for specific industries - Our platform is easier to use than hyperscaler tools for non-data-scientists - We have deep relationships with enterprise CXOs who are skeptical of hyperscalers

The strategy: We can't compete on scale or capital. But we can compete on specialization and vertical depth. We become the "operating system for AI in traditional industries."

WHERE WE ARE

• Revenue: $636 million (growing 18% YoY)
• Customer count: 800+ (up from 650 one year ago)
• Average contract value: $2.1 million
• Gross margins: 72%
• Operating margins: -8% (unprofitable)
• Customer retention: 94%

The business model is strong. The question is: Can we achieve scale profitably?

THE OPPORTUNITY

Opportunity 1: Vertical Specialization

The play: Instead of being a horizontal platform, become the dominant AI platform for 3-4 key verticals: manufacturing, energy, pharma, utilities.

How: - Build industry-specific AI application templates (predictive maintenance for manufacturing, geological modeling for oil/gas, drug discovery for pharma) - Embed industry expertise in the platform (not just AI technology, but domain knowledge) - Partner with industry consultants to drive adoption - Build community and certification programs around vertical expertise

Estimated impact: - Become the de facto standard in each vertical - Higher switching costs (customers are betting on vertical expertise, not just AI capability) - Premium pricing (customers pay more for specialization) - Faster sales cycles (customers buy pre-built solutions, not custom platforms)

Timeline: 12-18 months to launch first three verticals

Opportunity 2: Integrated Workflows

The play: Move beyond "AI platform" to "AI-powered business processes" for enterprise customers.

How: - Build connectors into ERP, supply chain, maintenance, and financial systems - Package AI applications into business workflows (e.g., "Predictive Maintenance for Manufacturing" isn't a platform; it's a complete workflow that includes monitoring, prediction, work order generation, and scheduling) - Focus on outcomes (reduce downtime, improve yield, accelerate drug discovery) not on technical capabilities - Move toward outcome-based pricing (customer pays based on value delivered, not software seats)

Estimated impact: - Customers see tangible ROI faster - Stickier relationships (integrated into their operations) - Higher NRR (customers expand to more workflows) - Premium pricing (outcomes-based pricing can be 2-3x higher than platform pricing)

Timeline: 18-24 months to first integrated workflow launches

Opportunity 3: Leverage AI Partnerships

The play: Partner with leading AI labs (Anthropic, OpenAI) to integrate best-in-class LLMs into our platform, rather than building our own.

How: - Integrate Anthropic Claude, OpenAI GPT, Google Gemini into C3 platform - Build competitive moat through vertical integrations and business process workflows - Avoid the capital-intensive race of building foundational models - Focus on application excellence, not model training

Estimated impact: - Access to best AI models without $10B+ in training compute - Faster time-to-market for new capabilities - Lower capex - Focus on what we do best: enterprise application building

Timeline: Ongoing

MY RECOMMENDATION

Pursue vertical specialization as primary strategy, with integrated workflows as secondary. This plays to our strengths and avoids head-to-head competition with hyperscalers on horizontal platforms.

EXECUTION PLAN

Phase 1: Vertical Specialization (2030-2032)

• Launch industry-specific platforms for manufacturing, energy, pharma
• Build templates, integrate domain expertise, certification programs
• Target: 2,000+ customers by end of 2032 (vs. 800 today)

Phase 2: Integrated Workflows (2032-2034)

• Launch outcome-focused AI workflows integrated into enterprise processes
• Move toward outcome-based pricing
• Target: NRR 130%+, gross margins 75%+

Phase 3: Scale and Profitability (2034-2035)

• Achieve operating profitability
• Expand to 5,000+ customers
• Generate $2+ billion in annual revenue

FINANCIAL IMPLICATIONS

By 2035:

• Revenue: $2.0-2.5 billion (from $636M today, 30%+ CAGR)
• Gross margins: 75-78% (up from 72% today)
• Operating margins: 25-30% (from -8% today)
• Customer count: 5,000+
• NRR: 130-140%

Stock target: $80-120 per share by 2035 (from $35 today, assuming horizontal expansion to 18-20x revenue multiple).

Tom

Confidential — Board of Directors Only