Brex organizes its AI efforts into three pillars: buying tools for internal efficiency (Corporate), building/buying to reduce operational costs (Operational), and creating AI products that become part of their customers' own AI strategies (Product).

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Instead of standardizing on one LLM or coding assistant, Brex offers licenses for several competing options. This employee choice provides clear usage data, giving Brex leverage to resist wall-to-wall deployments and negotiate better vendor contracts.

Customers are hesitant to trust a black-box AI with critical operations. The winning business model is to sell a complete outcome or service, using AI internally for a massive efficiency advantage while keeping humans in the loop for quality and trust.

Treat AI initiatives as two separate strategic pillars. Create one roadmap focused on internal efficiency gains and cost reduction (productivity). Maintain a separate roadmap for developing new, revenue-generating customer experiences (growth). This prevents conflating internal tools with external products.

Successful AI strategy development begins by asking executives about their primary business challenges, such as R&D costs or time-to-market. Only after identifying these core problems should AI solutions be mapped to them. This ensures AI initiatives are directly tied to tangible value creation.

A successful AI strategy isn't about replacing humans but smart integration. Marketing leaders should have their teams audit all workflows and categorize them into three buckets: fully automated by AI (AI-driven), enhanced by AI tools (AI-assisted), or requiring human expertise (human-driven). This creates a practical roadmap for adoption.

Brex structures its AI teams into small pods, combining young, AI-native talent who think differently with experienced staff engineers who understand the existing codebase, product, and customer needs. This blends novel approaches with practical execution.

An effective AI strategy requires a bifurcated plan. Product leaders must create one roadmap for leveraging AI internally to improve tools and efficiency, and a separate one for external, customer-facing products that drive growth. This dual-track approach is a new strategic imperative.

Instead of building a single-purpose application (first-order thinking), successful AI product strategy involves creating platforms that enable users to build their own solutions (second-order thinking). This approach targets a much larger opportunity by empowering users to create custom workflows.

Brex formed a small, centralized AI team by asking, "What would a company founded today to disrupt Brex look like?" This team operates with the speed and focus of a startup, separate from the main engineering org to avoid corporate inertia.

Large companies integrate AI through three primary methods: buying third-party vendor solutions (e.g., Harvey for legal), building custom internal tools to improve efficiency, or embedding AI directly into their customer-facing products. Understanding these pathways is critical for any B2B AI startup's go-to-market strategy.