Your Spanish-Speaking Teams Might Be Too Dependent on AI. Here's How to Find Out.

You rolled out ChatGPT or Copilot to boost productivity. Now you worry: Are your teams using AI as a smart assistant—or as a crutch that's eroding their skills?

This is especially hard to measure in Spanish-speaking regions. You can't just track login times. You need to understand how people are using the tools.

New research solves this. A team has created and validated a simple Spanish questionnaire that pinpoints unhealthy AI dependency. It measures two distinct risks that require different management responses.

What Researchers Discovered

Psychologists developed the LLM-D12-SP, a 12-question scale validated for Spanish speakers. Their paper, "Developing and Validating the Spanish Version of the Large Language Models Dependency Scale (LLM-D12-SP)", reveals two critical findings for business leaders.

First, AI dependency comes in two flavors.

• Instrumental Dependency: Using AI as a crutch for work tasks and decisions. Think of an employee who can't draft an email without asking ChatGPT first.
• Relational Dependency: Treating AI as an emotional companion or social substitute. This is the employee who prefers chatting with a chatbot over having coffee with colleagues.

You need to know which one you're dealing with. The fix for a team leaning too hard on AI for tasks (instrumental) is different from addressing workplace isolation (relational).

Second, this dependency links to broader problems. The study found both types connect to general "internet addiction." An employee who can't put their phone down might also be the one who can't start a report without AI. This means AI overuse is often a symptom of a wider digital wellness issue affecting focus and productivity.

Most importantly, the tool works. It's been statistically validated for Spanish-speaking populations in Spain and Latin America. You can now trust its readings to audit your global workforce.

How to Apply This Today: Your 4-Step "AI Health Check"

This isn't just theory. Your HR or People Analytics team can implement this within the next quarter. Here’s your action plan.

Step 1: Administer the Anonymous Survey

Get the validated 12-question scale (LLM-D12-SP) from the research paper. Use a simple survey tool like Google Forms, Microsoft Forms, or Qualtrics.

Key actions:

• Translate the survey instructions clearly. Ensure complete anonymity to get honest answers.
• Target specific Spanish-speaking teams or departments where AI tools are heavily used (e.g., marketing, customer support, software development).
• Frame it positively: "Help us understand how AI tools support your work so we can provide better training and resources."

For example: Send it to your 50-person customer service team in Mexico City that uses ChatGPT to draft responses. Track the response rate—aim for at least 60% participation for reliable data.

Step 2: Analyze the Results for Two Risk Profiles

Don't just look at an average score. Separate your data by the two dependency types.

Instrumental Dependency (The "Crutch" Profile):

• High Score = Employees who report they "would find it difficult to complete my work tasks without the AI" or "trust the AI's answer over my own judgment."
• Business Risk: Erosion of critical thinking, decision-making skills, and vulnerability to AI errors ("automation bias").

Relational Dependency (The "Companion" Profile):

• High Score = Employees who agree they "feel the AI understands me" or "prefer talking to the AI over colleagues sometimes."
• Business Risk: Workplace isolation, reduced team cohesion, and potential impacts on mental wellbeing.

For example: You might find your finance team in Madrid shows high instrumental dependency (relying on AI for data analysis), while remote developers in Colombia show higher relational dependency. Your intervention for each group will differ.

Step 3: Design Targeted Interventions Based on the Data

Match the solution to the problem you identified.

For High Instrumental Dependency Teams:

1. Implement "AI-Assisted, Not AI-Replaced" Training. Run workshops focused on how to use AI. Teach prompt engineering for verification (e.g., "Draft three options, and I'll choose the best one").
2. Create Human-in-the-Loop Protocols. For critical tasks (client reports, code reviews, financial forecasts), mandate a human review step before finalizing any AI-generated output.
3. Use tools like Microsoft Copilot's "Draft with me" feature that encourages collaboration with AI, not delegation to AI.

For High Relational Dependency Teams:

1. Address Workplace Connection. This is a management and culture issue. Increase structured team interactions: mandatory daily stand-ups, weekly in-person or virtual coffee chats, peer mentorship programs.
2. Promote Digital Wellness. Include AI usage in your company's digital wellbeing guidelines. Encourage "AI-free" blocks of time for deep work and socializing.
3. Train managers to recognize signs of isolation and have supportive check-ins, not punitive ones.

Step 4: Monitor and Measure Impact

This isn't a one-time survey. Establish a baseline and track changes.

• Re-survey every 6 months with the same tool to measure progress.
• Correlate dependency scores with performance metrics (if possible, anonymously). Are teams with lower instrumental dependency producing higher-quality work? Are teams with lower relational dependency showing better engagement scores?
• Refine your training programs based on what moves the needle.

What to Watch Out For

This tool is powerful, but it has limits.

1. It's a Diagnostic, Not a Cure. The research validates the questionnaire—it doesn't provide proven solutions to reduce dependency. Your interventions need to be thoughtfully designed and tested within your own company culture.
2. Correlation Isn't Causation. A high dependency score doesn't prove someone is a poor performer. It indicates a risk. Use the data to start conversations and offer support, not to make assumptions about individual capability.
3. Sample Limitations. The initial validation used an online panel. Your professional workforce might respond differently. Use your internal data to build your own understanding of what "high" and "low" scores mean for your organization.

Your Next Move

Start by getting the tool. Download the research paper and locate the 12-question LLM-D12-SP scale in the appendix.

This week, try this: Translate the 12 questions. Run a small, anonymous pilot with one Spanish-speaking team of 10-15 people. See what the data tells you. Are they more worried about losing a work tool or losing a companion?

That first pilot will show you the real picture of AI dependency in your company—and give you the facts you need to manage it wisely.

Question for your team: If you surveyed your department tomorrow, which type of dependency do you think would score higher—the need for a work crutch, or the need for a companion?