AI at Work: the Problem Is Application

Most people already understand that Artificial Intelligence is not only "the future". It is present in the writing, research, meeting, analysis and creation tools that enter organisations every day.

The challenge has changed: knowing where to start, what is worth using and how to apply AI without losing quality, safety or accountability.

That was the problem behind a course we designed with ISQ Academy for professionals and leaders who needed to move from demonstrations to real work.

Why does AI application fail?

Many experiments begin with the tool: someone creates an account, tries several requests and shares an impressive result. Days later, usage disappears because it was never connected to a process.

Common causes include:

• a poorly defined problem;
• missing data or context;
• expectations that are too broad;
• no quality criteria;
• uncertainty around sensitive information;
• nobody accountable for the result;
• no measurement;
• training without subsequent practice.

Adoption is not solved by access to technology alone. It requires a method.

A model for selecting use cases

Assess a task across five dimensions:

1. Value: which outcome does it improve?
2. Frequency: how often does it happen?
3. Effort: how much time does it consume?
4. Risk: what happens if there is an error?
5. Verification: is quality easy to check?

Begin with tasks that have reasonable value and frequency, low risk and simple verification. A draft internal communication is a safer starting point than a decision about recruitment, credit or health.

The responsible application cycle

Define

Describe the current task, audience, information used and expected result.

Prepare

Organise examples, approved templates and rules. Decide which data may enter the tool.

Experiment

Test representative cases, including easy, ambiguous and problematic examples.

Validate

Compare the output with the criteria. Record errors, corrections and situations requiring human intervention.

Integrate

Create a simple procedure: who uses it, at which point, with which prompt and who approves.

Measure

Compare time, quality, cost and satisfaction with the previous process.

Example: responding to a customer request

Rather than asking AI to respond autonomously, a team can create an assisted flow:

1. identify subject and urgency;
2. find information in an approved knowledge base;
3. prepare a draft;
4. mark missing information;
5. escalate exceptions;
6. request approval before sending.

The result is faster and more consistent without removing accountability from the team.

Training is not only learning prompts

Good training should enable each person to:

• understand capabilities and limits;
• practise with tasks from their context;
• recognise data they should not share;
• evaluate answers;
• document a useful case;
• know when to stop and seek help.

After the session, the organisation needs follow-up, space for sharing and up-to-date rules. Otherwise, learning remains disconnected from work.

The account of the AI in Action course shows how this approach was translated into professional exercises.

Where to continue

Anyone beginning can assess their starting point through the SuperHumano Academy AI Maturity Index and practise through the Academy's courses. For teams, SuperHumano AI provides generative AI training and executive AI and technology consulting to connect priorities, processes and investment.

Frequently asked questions

Where should I start applying AI at work?

Choose a concrete, frequent, low-risk task that is easy to verify. Record the current process and test the change with a small number of users.

How can an organisation prevent uncontrolled tool use?

Define authorised tools, permitted data types, required review and a channel for questions. The rules must be simple and workable.

Does training solve adoption?

It is essential but not sufficient. Adoption also depends on processes, leadership, access, support, measures and internal examples.

Context note: the original version of this article introduced an ISQ Academy cohort that was about to begin. That edition has ended; the content was expanded to preserve the method.