The Engineering Perspective: Designing AI You Can Trust

Building accurate, secure, and reliable AI for the enterprise.

In The Engineering Perspective: Designing AI You Can Trust, we explore how organizations can build AI that is accurate, secure and reliable enough for the enterprise.

Moving beyond the hype, the series examines the engineering principles behind trusted AI; from deterministic calculations, semantic models, and data architecture to explainability, security and scalable agent design. It looks at how AI can work alongside existing planning environments, how organizations can protect sensitive data, and what it takes to create AI systems that people can confidently use to support better decisions.

Explore the series below:

AI Stability
AI Boundaries
AI Troubleshooting
Enterprise AI Positioning
AI Readiness
Secure AI
Semantic Accuracy

How Do You Balance AI Innovation with Enterprise Stability

Discover how Board’s architecture uses the Microsoft Agent Framework, configurable LLMs, and a decoupled data set API to introduce new agents and upgrade models without affecting existing data cubes, procedures, screens, or planning logic.

How Do You Separate Probabilistic AI from Deterministic Planning

LLMs can explain the numbers, but they’re not always reliable at calculating them. In this video, discover how Board combines the creativity and reasoning of AI with the accuracy of its deterministic calculation engine ensuring that insights are grounded in real data, not generated guesses.

How Do You Troubleshoot Incorrect AI Answers?

AI answers shouldn’t be a black box. In this video, discover how Board’s explainability and reasoning inspection tools help teams trace every answer back to its source, revealing whether the issue lies in the agent, semantic layer, user prompt, or underlying planning model.

How Is Board Positioned in the Enterprise AI Landscape

Discover how AI agents can give CFOs and finance teams the extra firepower to run more scenarios, explore simulations, and make better-informed decisions weekly; not just during the annual planning cycle.

How Should Organizations Prepare for AI Agents?

Learn how Board’s partners, professional services, and proof-of-concept programmes can help you move from preparation to real-world AI adoption faster.

How Do You Protect Data While Enabling AI?

AI agents need access to data, but not all data. In this video, discover how Board protects sensitive information by giving agents access only to carefully defined datasets, while enforcing each user’s existing permissions and entitlements.