Samsung Gave 260,000 Employees AI. Few Will Use It Well.
Giving employees the tools doesn’t change how the work gets done.
On June 9th, Samsung announced that ChatGPT, Gemini, and Claude will be rolled out across every team at Samsung Electronics by the end of this year, with the rest of the Samsung Group to follow. It’s one of the largest enterprise AI rollouts ever announced.
The way Samsung plans to measure whether the rollout works is the part of the story every operator and manager needs to understand, because every other large enterprise AI rollout has hit the same wall: access without a system for using that access well.
What Samsung Just Did for 260,000 Employees
Samsung Electronics has around 260,000 employees globally. By the end of 2026, every one of them will have access to ChatGPT, Gemini, and Claude. The DX Division, which builds phones, TVs, and home appliances, gets access first. Every other Samsung subsidiary, from Samsung Display to Samsung SDI to Samsung Biologics, follows after.
To get there, Samsung is running a top-down training program. About 50 senior leaders are training first. 2,300 executives follow through August. Then everyone else, in waves, through the rest of the year.
The metric Samsung is using to measure whether the rollout worked is full-time equivalent, or FTE. One FTE equals one person working a 40-hour week. Samsung will measure AI success by how many people’s worth of work a single employee can do with AI. If a marketing manager using ChatGPT produces what used to take three people, that’s a 3.0 FTE.
Samsung is the first of Korea’s four largest business groups, called chaebols, to fully adopt external AI tools across all its companies. The other three are SK, Hyundai, and LG. Together, these four groups account for a large share of Korea’s industrial economy. Whatever Samsung does, the other three watch. Whatever Samsung measures, the other three are likely to measure too.
The FTE metric is worrying some Samsung employees, who said AI adoption ‘could be perceived as a measure for ultimately determining how many jobs can be cut rather than being used as a means of improving work efficiency.’
Hey there! 👋🏿👋🏿👋🏿 I’m Hodman Murad, Founder of The Data Letter, Between Thinking and Doing, and Asaura AI. In case you’re new here, here are some recent articles you may have missed:
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I sat down with Katharine Gallagher for her Career Pivot Playbooks series to talk about how I got from data science to building AI for productivity. Read the interview here.
Why Giving Employees AI Doesn’t Change How Work Gets Done
Samsung’s rollout is the largest one announced this year. It isn’t the first to hit the same problem.
In May, Marc Benioff said on the All-In podcast that Salesforce will spend $300 million on Anthropic tokens this year, primarily for coding. He described the efficiency gains across service, support, and marketing as ‘unprecedented.’ Salesforce’s support team went from 9,000 people to 5,000 over the past year as AI agents took over more of the work.
Later in the same interview, Benioff said Salesforce still needs a smarter routing system to connect its employees with the models they use. Right now, every request from an employee goes to the same top-tier model.
Benioff said simpler requests should be sent to smaller, cheaper models, and only the complex ones should reach a frontier model like Claude. Salesforce is spending $300 million a year on Anthropic, and the routing system Benioff wants doesn’t yet exist at his company.
Two weeks before this appearance on the All-In podcast, Microsoft started canceling Claude Code licenses for the engineers who build Windows, Microsoft 365, Outlook, Teams, and Surface. The cancellations will finish by the end of June. The official reason was that Microsoft wanted to consolidate its own GitHub Copilot CLI. The reason underneath was cost. Claude Code usage at Microsoft grew faster than the team’s budget could absorb.
Each of these companies invested heavily in access to AI. None of them built the system that sits between the employee and the model and decides what the model should be asked to do, with what inputs, and scored against what criteria. That system is what’s missing. Without it, $300 million in token spend produces uneven output. Without it, access to frontier models becomes a budget problem. Without it, 260,000 employees with three AI tools each will fall back to the few uses they already know.
What a Scoring System Is
The operators I work with don’t ask the model to make the decision. They write down the criteria a good decision would meet, hand the model those criteria, and ask the model to score the option against them.
Let’s take a simple example: qualifying inbound sales leads. A new lead arrives through a contact form on your website. The sales manager writes down what makes a lead worth a call: job title fit (30 points), industry match (25), company size (20), intent signals in the message (15), tech stack overlap (10).
Each lead gets scored out of 100. The model reads the lead, scores it against the criteria, and returns the number, a tier (hot, warm, cold), and a written explanation of how it arrived at the score.
The sales team trusts the output because they can see exactly how the model arrived at the number, and they can change any single point value within 30 seconds if they disagree. The model applies the same scoring across hundreds of leads a week, faster than any one person could.
The same scoring approach works for almost any decision your team makes regularly:
Evaluating vendor proposals
Triaging customer escalations
Reviewing inbound resumes
Flagging unusual expenses
Prioritizing bug reports
A scoring system is what your team would write down if they had unlimited time to document how they make these calls. It’s the criteria, the point values, and the explanation requirement, written down once and applied consistently thereafter. AI handles the volume. Your team keeps control over the criteria.
Samsung’s training program covers how to use ChatGPT, Gemini, and Claude. From everything Samsung has announced publicly, it doesn’t cover how to build the system that turns those tools from a chat window into a decision engine the team can trust.
Your Next Step
Two days is how long it’ll take you to build the system that Samsung’s 260,000 employees aren’t getting.
This Wednesday at 8:30 AM EST, I’m going live on Substack to build a working scoring system from scratch. Here’s what I’ll be covering:
How to take the way your team already makes a decision and turn it into a written scoring system
The structure of a scoring prompt that returns a usable number every time
How to write the explanation field so your team trusts the output
What to do when a domain expert on your team disagrees with the model’s score
Why a short, plain scoring system outperforms a complicated AI agent
Thursday’s article is the full implementation guide of what we built on the live. Every line of the scoring prompt, the workflow setup, the structured output format, and the integration into your existing tools. By Thursday afternoon, you’ll have everything you need to copy the system into your own stack.
By the end of this week, you’ll have a working scoring system running on your team’s own decisions, in your own tools, with your own criteria. You’ll be the person on your team who built it.