Tanglewood

A strategic concept for AI-first enterprise onboarding where intent, not consultants or configuration screens, drives the system.

Black-and-white line illustration of a human and an AI system facing one another, representing collaboration before structure or solutions are defined.

Overview

Tanglewood is a strategic concept exploring how enterprise configuration and onboarding could work if designed AI-first, rather than retrofitting AI into legacy workflows.
This case does not document a shipped product. Instead, it demonstrates how I approach complex systems, reframe entrenched problems, and design toward scalable, human-centered outcomes, especially in environments where AI plays an active role.

Minimal line drawing of a human and a robot standing side by side, looking toward an open horizon, suggesting shared orientation and intent.

The Problem Space

Enterprise platforms often fail before users ever reach “day one.”

Across finance, HR, and operations platforms, onboarding typically involves:

  • Long setup timelines

  • Fragmented ownership across teams

  • Heavy reliance on spreadsheets and tribal knowledge

  • UX that assumes structure before understanding exists

Traditional design approaches rush toward:

  • Forms

  • Step-by-step wizards

  • Dashboards and validation rules

But these solutions presuppose that users already know what they are configuring. In reality, much of the early onboarding phase is about aligning people, policies, and intent before the system can be meaningfully shaped.

In many enterprise environments, this gap is filled by external consultants who translate organizational intent into system configuration. While effective, this approach is expensive, time-bound, and difficult to scale. Much of the knowledge produced during implementation disappears once the engagement ends, forcing organizations to repeat the process during future changes.

AI is frequently introduced too late, framed as...

  • Autofill

  • Recommendations

  • Help text

...rather than as a core collaborator in the process.

Line illustration of a human surrounded by fragmented documents and disconnected workflows, with an AI observing calmly, representing enterprise onboarding complexity.

Strategic Reframe

Tanglewood begins with a different premise:
Enterprise onboarding is not a setup task. It is a translation problem.

Organizations are attempting to translate:

  • Policies into structures

  • Language into data

  • Human intent into system behavior

Instead of starting with screens, Tanglewood starts with conversation and narrative.

The system’s first job is not to validate inputs—it is to help users:

  • Articulate what they mean

  • See assumptions made explicit

  • Understand consequences before committing

In this model:

  • AI acts as an orchestrator, not a shortcut

  • Users collaborate with the system instead of feeding it

  • Progress happens through dialogue, not clicks

Tanglewood reframes this dynamic by embedding those translation and sense-making functions directly into the system itself. Rather than relying on temporary external expertise, organizations collaborate with an AI agent that continuously performs the same interpretive work: documenting intent, surfacing assumptions, and maintaining alignment over time.

The result is not faster setup alone, but clearer, more resilient configuration.

Black-and-white illustration of a human and a robot jointly aligning a large abstract shape, symbolizing a shift from configuration to shared understanding.