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Implementación de IA

How to Prepare Your Team to Adopt AI in the Workplace

By Luis Flores

23 October 2026

How to Prepare Your Team to Adopt AI in the Workplace

Adopting artificial intelligence in a business involves much more than enabling a tool or teaching employees to write better prompts. To make AI useful in everyday work, organizations need to prepare their teams, identify specific problems the technology can help solve, and establish clear guidelines for responsible use.

The challenge is becoming increasingly relevant. In research published in July 2026, The Conference Board found that 55.1% of surveyed workers used generative AI or AI agents daily or weekly, while 33.3% had participated in employer-provided AI training during the previous six months. The research was based on a global survey of nearly 1,300 workers and interviews with 35 business leaders.

These findings illustrate an important gap: using AI does not necessarily mean knowing how to apply it effectively. Closing that gap requires an approach that combines training, ongoing support, changes to workflows, and outcome measurement.

1. Why Is It Important to Prepare Your Team to Adopt AI?

Artificial intelligence tools can help summarize information, organize ideas, analyze data, prepare drafts, and speed up certain tasks. However, their benefits depend on how they are integrated into workflows, the quality of the instructions provided, and people's ability to review the results.

When an organization introduces AI without an adoption strategy, several problems can arise:

Employees do not know which tasks AI can help with and which it cannot.
Different teams use tools and methods without shared guidelines.
Outputs are accepted without checking their accuracy.
Confidential information is shared without evaluating the risks.
The company measures how many people have access to a tool but not whether it improves their work.

AI adoption should therefore be treated as an organizational process rather than an isolated technology purchase. OpenAI's leadership guide recommends aligning strategy, activating teams, sharing lessons, enabling execution, and establishing appropriate governance.

2. Assess Your Company's Readiness

Before organizing training or choosing new tools, understand the current situation across your workforce and business processes. Not every team has the same level of experience or faces the same challenges.

Identify Skills and Needs

Conduct a short survey to understand which tools employees use, how familiar they are with AI, and which tasks consume the most time. Ask about their concerns regarding privacy, output accuracy, and potential changes to their responsibilities.

Google Workspace's guide to preparing for AI adoption recommends establishing a baseline, identifying role-specific use cases, and defining measurable goals before expanding implementation.

Identify Opportunities in Existing Workflows

Look for tasks that are frequent, repetitive, or information-intensive. For example, a customer service team might use AI to categorize questions and prepare draft responses. Marketing teams could explore using it to organize research or generate initial content drafts. Operations teams might test it for summarizing documents or structuring reports.

These are potential applications that must be validated within each organization. Before automating a task, consider how often it occurs, how much time it requires, the consequences of an error, and the sensitivity of the information involved.

Establish a Baseline

Document how each selected process currently works. Where possible, measure the time required, output quality, error frequency, and satisfaction of the people involved. This information will help you compare performance before and after introducing AI.

3. Design a Training Plan for Each Team

Effective training should go beyond explaining what artificial intelligence is or sharing a list of prompts to copy and paste. Employees need to learn how to apply AI to real tasks, evaluate its outputs, and recognize when it should not be used.

Start with Essential Skills

Everyone should understand how to provide clear instructions, supply relevant context, evaluate responses, identify potential errors, and protect the information they share. They should also understand that a convincing answer is not necessarily a correct one.

Adapt Exercises to Everyday Work

Training should reflect the responsibilities of each department:

Marketing: organize research findings, develop initial proposals, and adapt messages for different audiences.
Sales: summarize authorized sales information, prepare meeting questions, and structure follow-up communications.
Administration: classify documents, prepare drafts, and organize information for reports.
Human resources: develop training materials and structure internal communications while maintaining human review and protecting personal data.
Operations: document procedures, summarize incidents, and explore improvements to repetitive tasks.

These applications must be adapted to approved tools, job requirements, and internal policies. Sensitive processes, such as hiring or employee evaluations, require additional safeguards. Organizations should not delegate important decisions solely to an AI system.

Combine Training with Practice and Support

After each training session, ask participants to apply what they have learned to a work-related example, review the output, and document what worked and what needs improvement. More experienced colleagues can share examples and support those who are just getting started.

Gallup's research on manager support and AI adoption highlights the importance of demonstrating the technology's value for each role, providing role-specific training, and establishing clear guidelines for responsible use.

4. Integrate AI into Everyday Workflows

Once employees understand the capabilities and limitations of the tools, the next step is to test them in specific processes. It is usually better to begin with a small number of use cases rather than attempting to transform every activity at once.

Select Use Cases with Clear Criteria

Evaluate each opportunity using five factors: expected benefit, task frequency, implementation effort, associated risk, and the ability to measure results. Prioritize activities where AI can help without compromising sensitive information or removing necessary controls.

Define a Review Process

AI can produce incomplete, inaccurate, or outdated information. Establish who will review the output, which sources must be checked, and which decisions require human involvement. The level of review should reflect the consequences of a potential error.

Document Lessons Learned

When an application works well, document the problem it solves, the process steps, useful instructions, identified limitations, and quality criteria. Share these lessons in an accessible space so other teams can adapt them without repeating the entire experimentation process.

OpenAI's leadership guide similarly recommends sharing successful use cases and knowledge across teams while removing obstacles that prevent promising experiments from becoming useful applications.

5. The Role of Leaders in AI Adoption

Team leaders play an important role in translating organizational goals into practical opportunities for employees. Announcing that a company will use AI is not enough. Employees need to understand what is expected of them, what support they will receive, and which boundaries they must respect.

Leaders can facilitate the process through four actions:

Communicate the purpose: explain which problems the organization wants to solve and how progress will be evaluated.
Lead by example: share appropriate applications, lessons learned, and mistakes discovered during experimentation.
Make time available: allow employees to practice and receive feedback during working hours.
Listen to the team: identify concerns, access difficulties, and additional training needs.

Support must be practical and consistent. If employees are expected to learn AI without sufficient time, resources, or guidance, it will be difficult for them to adopt new practices sustainably.

6. Establish Guidelines for Responsible AI Use

Training should be accompanied by understandable policies. Employees need to know which tools are approved, what information they may share, which uses require additional review, and whom to contact when questions arise.

As a starting point, organizations can establish the following rules:

Do not enter passwords, sensitive personal data, confidential customer information, or internal documents into unauthorized tools.
Verify important facts, references, and claims generated by AI before using them.
Require human review when outputs affect significant decisions, customer commitments, or public company information.
Respect applicable licenses, copyright requirements, and internal policies.
Document high-impact uses and establish a process for reporting errors or incidents.

These guidelines must be adapted to applicable laws, contractual obligations, and the specific risks of each activity. An effective policy should not prevent all experimentation. Its purpose is to establish clear boundaries so teams can work with confidence.

7. How to Measure Whether AI Adoption Is Working

The number of activated accounts or employees who attended training does not, by itself, demonstrate that AI is improving business performance. To evaluate progress, organizations must measure both learning and the outcomes of their processes.

An initial measurement framework can include four groups of indicators:

Training: participation, completion of activities, and demonstrated improvement in practical exercises.
Adoption: recurring use in relevant tasks, active use cases, and employee feedback.
Performance: task completion time, output quality, identified errors, and the need for corrections.
Impact: verified improvements in productivity, customer experience, or operational efficiency, while accounting for implementation and review costs.

Compare results with the baseline and avoid automatically attributing every improvement to AI. Changes in demand, procedures, team experience, and other factors may also influence performance.

Review indicators regularly. If a tool is rarely used, investigate whether the problem involves training, access, the relevance of the use case, or integration into the workflow. If the tool is used frequently but does not improve outcomes, reconsider how it is applied or whether it addresses the right problem.

8. A 90-Day Roadmap to Get Started

The following roadmap is an example that each organization can adapt to its resources, readiness, and the complexity of its selected processes.

Days 1–30: Assess and Prioritize

Identify team needs and their level of familiarity with AI.
Review policies, available tools, and security risks.
Select one or two use cases with clear objectives.
Record baseline metrics for the selected processes.

Days 31–60: Train and Experiment

Provide foundational training and role-specific exercises.
Test use cases with small groups and appropriate supervision.
Collect feedback and document errors or limitations.
Adjust instructions, procedures, and necessary controls.

Days 61–90: Evaluate and Decide

Compare results with the baseline.
Determine whether improvements justify the time, cost, and risks.
Document repeatable use cases and share lessons learned.
Decide which initiatives to expand, modify, or discontinue.

By the end of this period, the organization should have a clearer understanding of its team's capabilities, the applications that provide value, and the conditions required to expand adoption. If a pilot needs more time, it is better to continue evaluating it than to scale it without sufficient evidence.

Conclusion: AI Adoption Is an Ongoing Process

Preparing a team to use artificial intelligence requires much more than providing access to a tool. It involves developing skills, identifying real needs, adapting workflows, establishing responsible-use guidelines, and measuring whether new practices produce improvements.

Companies can begin gradually: listen to employees, select a specific problem, provide practical training, and evaluate results before expanding the initiative. Leadership support and shared learning help ensure that experience is not limited to a handful of individuals.

The most useful question is not how many AI tools an organization uses, but whether its teams know when to use them, how to verify their outputs, and how to improve their work with them. That ability to learn and adapt provides a stronger foundation for sustainable adoption.

Sources

21 October 2026

20 December 2024