At Built, we recognize that artificial intelligence is rapidly advancing and that its outputs can occasionally be inaccurate, incomplete, or misleading. We are focused on helping you leverage the benefits of AI while being aware of the risks that come along with it.
Built’s AI is intentionally designed to function differently than a standalone chatbot connected directly to company data. Instead, our AI acts as an orchestrator that coordinates trusted systems, workflows, calculations, and structured organizational information to help generate more reliable and context-aware outputs.
We continuously evaluate and improve our AI systems through testing, monitoring, workflow optimization, validation checks, and evolving safeguards as both customer needs and AI technologies advance.
Our goal is not simply to deliver AI functionality. Our goal is to help organizations use AI confidently and responsibly while maintaining trust in the systems that support critical workforce and organizational decisions.
Understanding AI Limitations
Like all modern AI systems, large language models (LLMs) can occasionally generate incorrect information, commonly referred to as “hallucinations.” Modern LLMs are designed to predict and generate language. While they are extremely powerful, they do not inherently “know” facts the same way traditional software systems do.
As a result, AI models can sometimes:
Confidently provide incorrect answers
Misinterpret ambiguous requests
Generate outputs based on patterns rather than verified facts
Infer information that was never explicitly provided
These issues are commonly known as hallucinations and reinforce the importance of strong oversight in enterprise AI systems. Learn more about AI hallucination.
While this remains a broader industry challenge, we do not view that as a justification for inaccuracy. We view it as an important responsibility that requires thoughtful architecture, continuous evaluation, and strong operational safeguards.
How Built Reduces AI Inaccuracies
For organizations making workforce planning, budgeting, or operational decisions, even small inaccuracies can have meaningful downstream impact. Because of this, Built intentionally designs its AI systems with additional safeguards, validation mechanisms, and structured workflows to improve reliability.
Structured, Position-Centric Data Foundation
Built’s AI operates on top of a highly structured, position-centric data model rather than disconnected or loosely organized data.
Because positions, people, organizational structure, workflows, and planning data are connected in a unified system, the AI has access to more contextual and reliable information when generating responses or recommendations.
This reduces ambiguity and improves consistency, helping customers make decisions with greater confidence.
AI as an Orchestrator—Not Just a Chatbot
Built’s AI is designed to coordinate specialized tools and workflows rather than relying solely on freeform language generation.
For example, instead of asking an LLM to independently calculate organizational metrics or generate workforce projections from scratch, the AI can:
Retrieve structured data from trusted systems
Execute purpose-built calculations
Leverage deterministic workflows
Combine validated outputs into a final response
This approach helps customers maintain greater trust and control over AI outcomes.
Purpose-Built Tools and Logic
Where appropriate, AI in Built relies on deterministic systems, workflows, calculations, and reporting engines that are specifically designed for organizational planning and workforce operations.
Examples may include:
Budget calculations
Headcount metrics
Span and layer analysis
Organizational hierarchy evaluation
Workflow approvals and validations
Using purpose-built systems for critical calculations and workflows helps reduce the likelihood of inaccurate outputs that could impact planning, budgeting, approvals, or organizational decisions.
Testing, Monitoring, and Evaluation
We recognize that AI reliability is not a one-time achievement, but an ongoing responsibility. Built continuously works to identify and reduce AI inaccuracies through:
Internal testing and evaluation processes
Prompt and workflow optimization
Validation checks
Monitoring for unexpected or unreliable outputs
Ongoing improvements as AI technology evolves
As customer usage evolves, we continuously evaluate where additional safeguards, validations, or workflow improvements may be needed.
Human Oversight and Review
AI should assist decision-making—not replace thoughtful human judgment.
We believe customers should maintain visibility and control over important decisions, rather than being expected to blindly trust AI-generated outputs. Built encourages users to review AI-generated recommendations, reports, and analyses—especially for strategic, financial, or organizational decisions.
Our goal is to support and accelerate human decision-making, not remove accountability or oversight from critical organizational processes.
Our Philosophy on AI
Built believes the future of AI in the workplace is not about replacing structured systems with chat interfaces. It’s about combining:
Trusted organizational data
Intelligent automation
Purpose-built workflows
Human judgment
Advanced AI reasoning
AI agents with strict controls and oversight
The result is an AI approach designed to help organizations move faster while still maintaining operational trust, governance, and confidence in the systems supporting their workforce decisions.
Looking Ahead
We recognize that trust must be earned continuously as AI evolves. AI capabilities are improving rapidly across the industry, and Built is continuously evolving alongside those advancements.
We remain committed to:
Improving reliability and accuracy
Expanding safeguards and validation mechanisms
Leveraging advances from leading AI providers
Building AI experiences that are practical, trustworthy, and enterprise-ready
Our commitment is not only to advancing AI capabilities, but to doing so responsibly and transparently for the organizations that rely on Built every day.
Summary
AI capabilities will continue evolving rapidly, and Built is committed to evolving responsibly alongside them.
We recognize that organizations rely on our platform to support important workforce and operational decisions, which is why trust, reliability, transparency, and oversight remain foundational to our AI strategy.
Our approach is intentionally designed to combine modern AI capabilities with structured systems, operational safeguards, validation mechanisms, and human oversight to help customers use AI more confidently and responsibly.
As AI technology advances, we will continue investing in the safeguards, governance, and continuous improvements needed to help customers maintain confidence in the systems supporting their organizations.
Please contact our Customer Success Team for additional assistance.