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Currently driving AI adoption at AT&T

AI adoptionis behavior change.At enterprise scale.

I'm Chris Toth — 14 years across market research, consumer insights, and marketing, now focused on AI adoption inside AT&T. I translate insight muscle into the operating rhythms, enablement, and behavior change that make enterprise AI stick.

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Research & insights
14yr
Enterprise adoption
AI
Current focus
AT&T
Disruptive tech
MBA
◆ Point of view

AI adoption is a behavior-change problem.

The technology matters. But inside a large organization, the hard part is turning curiosity into repeatable practice, and repeatable practice into a new way of working.

  • Adoption beats access.

    Buying tools creates availability. Changing how teams plan, write, analyze, decide, and collaborate creates transformation.

  • Use cases start in real work.

    The strongest AI workflows come from the friction inside existing jobs, not from vendor demos or abstract capability maps.

  • Research instincts matter.

    AI adoption is full of human signals: resistance, confusion, confidence, shortcuts, incentives, and behavior change.

  • Enablement needs operating rhythm.

    Prompt libraries, governance, champions, rituals, and feedback loops have to work together or momentum fades after launch.

  • Measurement has to mature.

    Usage is the floor. Durable adoption shows up in workflow depth, role coverage, repeat behavior, and business-relevant outcomes.

◆ Adoption OS

From tool access to operating advantage.

My lane is the adoption layer: the connective tissue between strategy, enablement, workflow design, and measurable behavior change.

Signal

Find the work that matters

Start with friction, repeat decisions, knowledge bottlenecks, and handoffs where AI can change the way work moves.

System

Design the operating rhythm

Turn loose enthusiasm into rituals, ownership, governance, prompts, examples, and feedback loops.

Enable

Build role-level fluency

Give teams practical patterns they can reuse in the flow of their job, not abstract training they forget by Friday.

Measure

Read behavior over time

Track depth, confidence, repeat usage, role coverage, and qualitative signal before calling adoption real.

Adoption becomes durable when the system makes the new behavior easier than the old one.

The goal is not novelty. It is a repeatable way for people to think, decide, and produce better work.

◆ How I work

The work behind real adoption.

Most AI rollouts stall in the gap between executive ambition and day-one fluency. My work is about closing that gap with practical systems people can actually use.

  • 01

    Strategy

    Turning AI ambition into practical roadmaps: where it belongs, which workflows matter, and what has to change for teams to use it.

    • Use-case discovery & prioritization
    • Adoption frameworks & maturity models
    • Governance & change management
  • 02

    Enablement

    Training, prompts, playbooks, and champion systems that turn access into confidence and confidence into repeat behavior.

    • Role-based curricula
    • Prompt libraries & playbooks
    • Community & champion programs
  • 03

    Measurement

    Using research instincts to read what is working, what is stalling, and where adoption is becoming part of the operating model.

    • Behavioral signals & feedback loops
    • Workflow depth & role coverage
    • Measurement that goes beyond pilot
◆ Why this lens

The human layer is the hard part.

Living it in real time.

Not theoretical. Right now I’m working on AI adoption inside AT&T, where the challenge is not access to tools but making new ways of working stick.

Research to adoption.

Fourteen years in market research and consumer insights gives me a rare muscle: designing AI use cases around how people actually behave.

Disruptive by training.

MBA candidate at CSULB in a program built around Disruptive Technologies — AI, Blockchain, Sustainability. Strategy fluency to back the execution.

◆ Case themes

Where the thesis becomes practice.

These are public, privacy-safe themes from the work, framed around operating patterns instead of proprietary company data.

All projects
◆ Expertise

The stack behind the work.

The toolset is just the surface. What actually matters is knowing which tool fits which problem — and which behavior change has to happen for it to land.

Research & insights
14yr
Enterprise adoption
AI
Current role
AT&T
CSULB · Disruptive Tech
MBA
  • Claude
    Reasoning & long-context work
  • ChatGPT
    Daily-driver LLM
  • Copilot
    Microsoft 365 enablement
  • Gemini
    Google workspace AI
  • Notion
    Knowledge systems
  • n8n
    Workflow automation
  • Tableau
    Insights visualization
  • Qualtrics
    Research at scale
◆ Track record

Fourteen years studying behavior across categories.

Entertainment, automotive, retail, gaming, tech, music, telecom — the through-line is how people decide, adopt, resist, and change. That is the muscle I bring to AI adoption.

  • FOX Broadcasting

    Phase 01

  • UPS Store

    Phase 01

  • VH1

    Phase 02

  • Sony Television Studios

    Phase 02

  • Samsung

    Phase 03

  • Snapchat

    Phase 03

  • Universal Music Group

    Phase 04

  • FOX Broadcasting

    Phase 01

  • UPS Store

    Phase 01

  • VH1

    Phase 02

  • Sony Television Studios

    Phase 02

  • Samsung

    Phase 03

  • Snapchat

    Phase 03

  • Universal Music Group

    Phase 04

  • Hyundai

    Phase 01

  • ABC Studios

    Phase 01

  • Warner Bros. Studios

    Phase 02

  • FOX Sports

    Phase 02

  • Activision

    Phase 03

  • Airbnb

    Phase 03

  • AT&T

    Current

  • Hyundai

    Phase 01

  • ABC Studios

    Phase 01

  • Warner Bros. Studios

    Phase 02

  • FOX Sports

    Phase 02

  • Activision

    Phase 03

  • Airbnb

    Phase 03

  • AT&T

    Current

◆ FAQ

Questions, answered.

Don't see what you're looking for? Just ask.

  • Less about the model, more about the muscle. Real adoption is a behavior-change problem dressed up as a technology one: use-case discovery, role-based enablement, prompt libraries, governance, and measurement that goes well beyond pilot vanity metrics. I work backwards from how teams actually do their jobs, not from the tooling roadmap.