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CONFIDENCE UX

Risky drilling ops,

now a beacon of
confidence

Safely drill for oil. Orchestrates globally dispersed teams making hundreds of high-stakes environmental and billion dollar economic decisions. I made risk legible at the moment of the decision.

Modern Design Drilling Ops

Enterprise UX

Oil & Gas

system Design

Bolt on AI

0 to 1

20

decisions
and design moves

40+

risks & hesitation signals
reduced or eliminated

Fragmented tools ⟶ single source of truth

DOS report cycle months to hours

Projected $22 M / yr cost reduction

Lead Product Designer

2025 - Present

My Contribution

Owned design from research to delivery

Established object model, system and interactive design

Architected UI across 6 surfaces

Research and strategic sprint facilitation to create insights and clarity

Owned design decisions and partnered with product and delivery on roadmap

Led handoff and interaction with engineering & PM

Millions $

per run

Millions $

per run

at stake

risk of environmental devistation

risk of injury and loss off life

strong industry competition

Background Story

Why it mattered

A bottom-hole assembly is a string of tools run miles into the earth to strike oil reservoirs. Meer minutes of non-production time, hesitant decision making and human error were million dollar threats to the ops pipeline, however project ops was being performed by manually stitching together spreadsheets, PDFs and tribal memory.


I was brought in to collaborate with a project team to design a unified system and workflow that to reduce the workflow time to hours.

North star

Reduce workflow from month to hours

Problem • What I heard

Today I estimate we need 50% additional headcount just because of inefficiency in software and process.

— Halliburton Drilling Engineer

My take

These were seasoned engineers, fear of the tool was less a hesitation point than the weight of being responsible for errors and disaster and the labor of crude collaboration.

5

Separate Apps

No data sync. Redundant user tasks. Heavy context switch.

Market Pressure

Competitors were automating Halliburton needed a leap forward to remain competitive.

Limited Scale

Legacy platform siloed from other applications, regions or internal orgs outside of drilling.

Low

Productivity

Work lost due to system crashes. Manual reports creation cost 80% production time/wk.

100+

Complex Tasks

The workflow involved 15-20 individual contributors, distributed globally.

High

Risk & Regulatory

Mistakes in planning can cost time, damage equipment, create legal threat.

Discovery Approach

Ambiguity stalled the project...

Harsh industry domain knowledge

I required deep learning of industry positioning and contextual details.

Solution bias

Stakeholder resistance, users held onto expectations around legacy tool workarounds

Evolving direction

An ambiguous north star, make a modern experience; AI changed workflows.

I designed clarity that got the team unstuck

Role

Interview/Testing Facilitator

Discovery lead

UX storyteller

UX roadmap Owner

measurement Strategy

Deliverable stack

1.UX Deliverables

2.UI Deliverables

3.Handoff Artifacts

4.System Priorities

5.Design Feature

6.Product Feature

Discovery Outcomes

50+

Touchpoints

4

User Roles

12

Key Screens

100+

Required tasks

Legacy System Profile
Complex non-linear app suite

7 apps; each step involves switched between apps

Serves 20 Countries/ 5 global regions

13 collaborators

8 Regulated checkpoints

Future System Profile
Unified collaborative platform

Need modern feel

Need reduce time to complete

Need forward-thinking interactions

Design Strategy

Three leadership decisions that built

Confidence

Role

UI Designer

Design Director

UX & Interaction Strategy

AI-Generated erodes design authority

the Push

A design lead proposed polished AI-generated screens days before sprint close. Accepting the work as is would have been faster and avoided debate.

My Decision

I challenged the design in ways the team hadn't accounted for: wholistic system impacts, tradeoffs and how frequency changes user expectations. I led the team toward a workflow grounded in system logic.

Demonstrated

✓ Design Judgment

✓ Systems Thinking

✓ Cross-functional Influence

✓ AI Generated Design Review

Balancing competing goals with taste

the Push

Development lead optimized performance. Product pushed for a modern experience. The initial UX direction favored a single interaction pattern.

My Decision

I introduced context. I challenged the assumption that consistency always improves usability and I weighed interaction patterns to task frequency and consequence.

Demonstrated

✓ Exception by design

✓ Trade-off resolution

✓ Risk-calibrated interaction design

Define ambiguous requests

the Push

Product asked for a "confident yet modern" feel with no measurable direction. The tension was design toward a mood, not a spec.

My Decision

i turned a vague aesthetic ask into a structural system. i cut friction by reduce data-entry effort across the product. I made progress personal and rewarding, not just a status bar.

Demonstrated

✓ Motivation-driven interaction design

✓ Translate ambiguity to system

✓ Design to unify system

✓ Apply Confidence UX

Approach

Injecting system design transformed engineering decisions

Role

UI Designer

Design Director

UX & Interaction Strategy

Page Banner

Semantic:

Page Banner

Tab Bar

Semantic:

Tab

DETAIL

System Status

Page Banner Function: User can identify or change project from single screen

Well Section Tab Bar: Manage varying project complexity

21

App consolidation

usage

Stay on page

DETAIL

Version Control

DETAIL

User Decision

DETAIL

Reduce Error

Decision making under tension

The product manager expressed low confidence in the aesthetics, and asked me to explore making the design feel “modern”.

Navigating ambiguity

Tab 1 of 4: Tab 1

Decision

Add modern visuals

prompt

Hover state on buttons & cards

Micro-animation: lift

Style: smooth pulse

Avoid

Flip on hover

Micro-animation: trace

Style: glow

RESULT

Product Owner - Drilling Engineer

4/16/2026 10:32 AM

This is is so cool, I feel like I am in the year 3000 😄

Challenging system design - BHA Tool

These 4 moves reduced complexity

Role

UI Designer

Design Director

UX & Interaction Strategy

Handoff & deployment

Building a BHA tool was a technical and non-linear workflow
- 6 steps completed by up to 7 uses in tandem.

Building a BHA tool was a very
technical and non-linear workflow
- 6 steps workflow completed by up to 7 uses in tandem.

Drilling Tool

BHA Tool

Add Tool
components

Logistics

Customize

Preview Tool

Analyze
Measurements

Load Project

1

2

3

4

1. Increase certainty

Human-centered approach to ease learning

User-friendly micro-copy, interactive motion on triggers and intuitive decision points enabled new users to easily navigate the screen.

Eliminated hesitation

175

input fields

Cognitive Load

Challenging system design - BHA Tool

AI-Ready design champion

AI ready means a designer has adopted AI into their design workflow through giving direction and contributing, but also a designer can seamless design AI patterns into systems.

Role

UI Designer

Design Director

UX & Interaction Strategy

01 Approach to Design Workflow with AI

3 strategies

How we used AI

intent-based Design stack

Agentic
Setup

5 steps from idea to handoff

hybrid design stack 3 day to prototype

Led Design with Taste & Rigor

Module
a

Module
b

Module
C

Scalable interaction patterns

critique-led decisions

Design Thinking & expertise

🏞️

Use SCAMPER to explore designs for an AI copilot screen.

Precise prompts reduce tokens use

design-led prompts reduced token waste

02 Building AI Design patterns for users

2 patterns

AI CoPilot & AI Chatbot

I didn’t just bolt on AI.
I designed for the hesitation around using it.

Objective

User needs to identify risks & challenges to the drilling job

Reduce risk of error

Usage ↑ - no need to leave screen

Update legacy to modern feel

Risk Visibility

Inline AI in the Knowledge panel

Risk Score

Engineers are rightly to be skeptical of AI suggestions. I made the risk visible on the screen - severity grade, frequency counted, rationale expanded. No need to open spreadsheets to calculate.

Engineers are rightly to be skeptical of AI suggestions. I made the risk visible on the screen - severity grade, frequency counted, rationale expanded. No need to open spreadsheets to calculate.

High

Lost circulation — depleted fields

82% Confidence

Found in 7/7 Wells

LEM-2543

|

EQWR-WX-003

+5 Wells

Rationale

Severity Tag - color coded red [High], yellow[Medium], green[Low] by risk level

Severity tag - color coded by risk level
red [High], yellow[Medium], green[Low]

Frequency - count how often this risk occurs across other wells

AI Confidence Score - the AI model reports its own certainty

Rationale - reveals the rationale behind each risk severity tag

Hover Rationale button to preview AI reasoning

Restratint

Inline AI in the Knowledge panel

Deliberate control of AI

Because constant screen refresh creates distraction and stacks up token costs, AI runs only when a button is clicked. Engineer stays in control.

Review

Initiate AI suggestions

More Results

Adjust filter get more results

Refresh

3

Add AI new suggestions

Reversible Decision

Inline AI in the Knowledge panel

Frictionless control

Because fear of irreversible actions stalls use, every AI action is undoable and the view stays user-editable

Edit Risk content

Undo Inserted Risk

Design Result

projected number of DOS tasks

to reach confident assisted decision

users reach confident assisted
decision on DOS tasks

~ 80%

Pending Measurement

Legacy App - No AI

Modern - With AI

User Confidence

Workarounds;
low trust in tool

The risk grade
builds trust

Decision Quality

High Variance by

user experience

Score standardizes

decision making

Tools Required

Abandon app;
switching tools required

Single app;

reduced switching

Approach to Design Workflow with AI

3 strategies for Design workflow with AI

intent-based Design

5 steps from idea to handoff

Agentic Setup

Design Thinking

Prompts reduce tokens use

🏞️

Use SCAMPER to explore designs for an AI copilot screen.

Lead with Taste & Rigor

Patterns speed up shipping

Module
a

Module
b

Module
C

Summary

Confidence designed with end-to-end leadership

Confidence UX domonstrated by end-to-end design leadership

Confidence designed with end-to-end leadership

before

Result

Single modern cloud platform + global shared intelligence + live collaboration

Scalable confident decisions.

Alignment time from Weeks → Hours

Report published in 3 clicks

Modern

Look & Feel

Look & Feel

Modern

Personalized

Workflow

Workflow

Personalized

Expert Approach

Design Thinking

Platform

Single System

40+

risks & hesitation

signals reduced or eliminated

Outcomes

Establishing user trust
reduced resistance to change

Established user trust →
reduced resistance to change

Sigals

Decision quality - established standard for understanding AI results

Eliminated spreadsheet workaround - reduced context switch

Risk Visibility pattern

high

Lost circulation

82% Confidence

Found in 7/7 wells • [Rationale]

Made risk legible with 3 layer pattern

Key decision about friction across patterns

Key decision about friction across patterns

restraint

AI Copiolt

Autosave

Review

refresh

AI

EDit

Ready

Completed

user • Date • Time

Human controls on AI
reduced errors, task time, costs

Human controls →
reduced errors, completion time, costs

Sigals

Conserve costs from AI token usage

Design consulted on tradeoff decisions - more clicks vs frictionless workflow

Reduced error - user clarity on versions, AI decisions reversible, fewer rework

Scalable design components
boost team confidence and alignment

Scalable design →
boosted team confidence and alignment

Sigals

Turned unmeasured request into a principled north star

Tasteful and adaptive decision making when team had got stuck, errored or was uncertain

Owned design system - repeatable patterns, wholistic system

Navigated Ambiguity in key moments

Owned design direction

Owned design direction

Discovery alignment agent

Discovery alignment agent

Discovery

Prompts using Design Thinking

Prompts using Design Thinking

Design

DS imagery & motion components

DS imagery & motion components

Design

Extend DS with 2 AI Patterns

Extend DS with 2 AI Patterns

Design

Adapt to AI product team workflow

Adapt to AI product team workflow

Deploy

ImplementedTesting & metrics

Implemented testing & metrics

Deploy

Systems thinking

1

5 apps
1 screen

2

Muliti-user
Collaboration

3

Role-based/
Personalization

4

Flow: Weeks Hours

Efficient system
focus quality and collaboration increased

Efficient system →
focused screens, collaboration, single source

Sigals

Cognitive load: Multiple application + multiple screens -> All-in-one system

Reduce complexity: Multiple application + multiple screens -> All-in-one system

Standard output and consistent knowledge share in under

Clearer screens for non-linear workflow 17 -> 5 steps

Globally multiple users use same screen live simultaneous; cloud file managment