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UX Research + AI Productivity

Research-Driven AI Enablement

Applying UX research methods to AI productivity challenges. Systematic approach proven through personal work.

Available for AI Enablement Projects
Get In Touch
The Problem

The Adoption Gap

Your employees are already using AI.

75%of knowledge workers

Have tried or actively use AI tools at work. Usage grew 485% year-over-year.

Source: 2024 Enterprise Surveys

Most are doing it without permission.

59%of employees

Admit to using unapproved AI tools. Over 90% of AI usage happens in personal "shadow AI" accounts.

Source: Cybernews 2024

Meanwhile, organizations struggle to enable AI effectively.

74%of companies

Struggle to achieve and scale value from AI initiatives. Only 26% move beyond proof of concept.

Source: BCG 2024

The problem isn't AI capabilities. It's the gap.

Individual adoption vs. organizational enablement

Employees use AI daily (75%). Organizations fail to scale it (74%).

Companies either ban tools entirely (creating shadow AI) or pilot without strategy (30% abandoned after POC).

The result: 68% report AI creating division between IT and business teams.

Source: Deloitte AI Trends 2025

You need research-driven enablement that bridges this gap. Discover where AI actually helps. Build solutions for those specific cases. Measure adoption and ROI.
The Approach

Research-Driven AI Enablement

Not generic training. Not just tooling. Not measurement alone. Complete methodology that works together.

1

Discover

Research where AI actually helps your workflows

  • →Workflow research with team members
  • →Identify automation opportunities
  • →Systematic problem identification
2

Build

Custom solutions tailored to your specific workflows

  • →Tailored for job functions
  • →Not generic templates
  • →Iterative refinement based on real use
3

Measure

Track adoption, time saved, quality gains, ROI

  • →Systematic data collection
  • →ROI calculation framework
  • →Prove value, improve adoption

Powered by Hands-On AI Expertise

Research Background

  • B.S. in UX Design (DePaul University)
  • 5+ years applying research methods
  • Google Data Analytics Certificate

AI Experience

  • Daily user of Claude, ChatGPT, Gemini (2+ years)
  • Built AI framework handling 100% of development work
  • Proven hands-on productivity transformation
Proof of Concept

Proof: Personal AI Framework

Problem

AI tools were inconsistent and unreliable for development work.

Solution: Complete Methodology

Used the exact process. Research to find where AI helps. Built systematic framework. Measured results over time.

Outcome

Now handles 100% of development work with measurable reliability. Proven the integrated approach works.

What This Proves

  • ✓Can identify workflow bottlenecks where AI helps
  • ✓Can design systematic solutions that work
  • ✓Can measure whether something actually works
  • ✓Know how to build for real constraints

What This Doesn't Prove

  • •Haven't done this for organizational clients yet
  • •Don't have enterprise complexity experience
  • •No years of consulting case studies

Why Honest Positioning Matters

Many AI consultants sell without hands-on experience. Proven methodology through personal work is valuable. Honest about where I am in building practice.
Engagement Models

How We Work Together

Same methodology. Different commitment levels.

Discovery Engagement

4-6 weeks

Try the complete methodology

  • 1.Discover: Research workflows with team
  • 2.Build: Pilot custom solutions for high-value cases
  • 3.Measure: Framework for tracking impact
Deliverable: Roadmap + ROI estimates

Pilot Project

8-12 weeks

Full methodology with one team

  • 1.Discover: Deep workflow research with team/department
  • 2.Build: Custom AI solutions + workflow integrations + training
  • 3.Measure: Track time saved, quality, adoption over time
Deliverable: Proof of concept + ROI

Advisory/Custom

Flexible timeline

Specific parts of the methodology

  • •AI workflow workshops
  • •Workflow research for specific departments
  • •Measurement framework setup
  • •Strategic AI enablement planning
Deliverable: Custom scope

Pricing: Project-based pricing after discovery call. Transparent about approach.

Let's Talk About AI Enablement

Ready to bridge the AI adoption gap? Let's discuss how research-driven enablement can work for your team.

Building early client engagements. Honest about where I am in developing this practice.

Get In TouchLuis@LuisLadino.com

What to Expect:

  • •30-minute initial conversation about your AI adoption challenges
  • •Understanding where employees use AI vs. where organization struggles
  • •Determining if Discover → Build → Measure methodology matches your needs
  • •Discussing engagement models: Discovery (4-6 weeks), Pilot (8-12 weeks), or Advisory
  • •Custom proposal if there's mutual fit