Tracking Where AI Is Actually Being Implemented

There is no shortage of companies talking about artificial intelligence. What remains far less clear is where AI is actually being implemented in a way that changes how organizations operate. This page exists to track that distinction in real time.

This project is AI analyst coverage focused on listening to a defined set of earnings calls to understand where companies sit on the real adoption curve of AI. Not in theory. In execution. Not who mentions AI, and not who positions themselves as “AI-first,” but who is integrating it into workflows, decision-making, and operations in a way that holds beyond buzz words.

This is also a working GenAIOps and Applied AI and project for me, building on 5 years of past work as a hobby listening to earnings calls and extracting signal (Googleable). As each call is reviewed, the same question is applied: where are systems breaking, and how could GenAIOps and Applied AI be used to address those breakdowns in a practical way? The goal is not speed. The goal is signal. Insights and observations (via audio format) will be added here and on YouTube as the project develops, with select insights shared on LinkedIn.

The Coverage Model: Why These Companies

This project is structured to follow AI across the full system of modern organizations: infrastructure, enterprise workflows, financial decision-making, physical operations, consumer demand, regulated environments, and the energy required to support it all. AI isn't a vertical. It is a layer moving across each of these domains. Understanding its impact requires following it across the system, not isolating it within one sector.

I. AI Infrastructure and Core Systems

Where AI is funded, built, and scaled

NVIDIA

Microsoft

Alphabet (Google)

Amazon

Meta Platforms

Apple

Broadcom

Oracle

IBM

AMD

Why this group is being tracked:

  • Capital allocation toward AI (compute, data centers, infrastructure)

  • Evidence of real product integration vs positioning

  • Enterprise adoption signals beyond narrative

II. Enterprise Software and Workflow Layer

Where AI meets day-to-day operations

Salesforce

ServiceNow

Workday

Snowflake

Palantir

Adobe

Why this group is being tracked:

  • Whether AI is embedded into workflows or layered on top

  • Measurable efficiency gains versus continued experimentation

  • Alignment between pricing and delivered value

III. Financial Systems

Where decision-making is visible and measured

JPMorgan Chase

Goldman Sachs

Morgan Stanley

BlackRock

Visa

Mastercard

Block (XYZ)

Why this group is being tracked:

  • AI’s role in decision quality (risk, fraud, operations)

  • Discipline and clarity in executive communication

  • Where AI shows up in measurable performance

IV. Industrial and Physical Execution

Where AI is tested against reality

Tesla

GE Aerospace

Caterpillar

Honeywell

Union Pacific

Deere & Company

Why this group is being tracked:

  • Gaps between AI investment and operational outcomes

  • Bottlenecks across supply chain, manufacturing, and logistics

  • Alignment between technology and real-world execution

V. Consumer and Platform Scale

Where demand reflects execution

Walmart

Costco

Nike

Starbucks

McDonald’s

Yum!Brands

Why this group is being tracked:

  • AI impact on pricing, logistics, and personalization

  • Whether AI is influencing performance at scale

  • Real-time demand signals tied to operational decisions

VI. Healthcare and Regulated Systems

Where AI meets constraint and governance

Eli Lilly

UnitedHealth Group

Pfizer

Why this group is being tracked:

  • AI adoption within regulatory and compliance constraints

  • Where implementation slows or is deliberately limited

  • Balance between innovation, risk, and governance

VII. Energy and Infrastructure Backbone

What supports AI at scale

ExxonMobil

Chevron

NextEra Energy

Why this group is being tracked:

  • Energy demand implications of AI scaling

  • Infrastructure readiness versus narrative growth

  • The underlying cost structure supporting AI expansion

What You’ll Find Here

This page will evolve as insights are added and patterns begin to emerge. Some observations will be immediate. Others will take shape over time as multiple calls reveal consistent themes. The intent is not to summarize earnings, but to surface patterns across companies, sectors, and operating environments specifically related to AI.

If you are following these companies as well, or seeing similar dynamics inside your own organization, this is an open thread. The value of this project increases with perspective, not volume.

A Note Before You Listen

This is a working analysis and it will develop over time. Patience is part of the project. The goal here is not to rush toward conclusions, but to pay close attention, identify patterns, and think carefully about where GenAIOps and AppliedAI could be used in practical ways.

This is not investment advice. This project is not produced by a licensed investment professional and is not intended to guide financial decisions. It is an analytical exercise focused on AI implementation, operational signal, and systems thinking inside public companies.

Clarifying further, this is not legal or investment advice. Meisa Bonelli is not a licensed legal or investment professional.