Find Practical AI Help for Your Business or Team

A plain-language guide to choosing AI support

How to find the right kind of AI help

Many people begin with a product name because it is familiar. A more useful starting point is to name the task that causes difficulty. For example, “our weekly report takes two days to prepare” is a better starting question than “should we use an AI agent?” It shows the work, the people involved and something that can be examined. A comparison of tools is helpful later, once the task and its information requirements are clear.

Use this directory to identify the next decision rather than to assume that AI is right for every activity. A business may need an introductory workshop, a workflow map or a small supervised test. A larger implementation only makes sense once someone owns the process, the data being used is suitable, and the team can verify what the technology produces. A responsible plan also includes decisions not to proceed.

1. Choose the problem in ordinary language

Write down what is happening now: who starts the task, what information arrives, how long it typically takes, where mistakes appear and who signs off on the result. Keep the description understandable to someone outside the department. A busy project manager could write, “we receive meeting notes from six people and it is difficult to keep the action list consistent.” A finance team could write, “we need to explain monthly changes but must verify every calculation.” These questions lead to different forms of support.

Ask whether the work is repetitive, well documented and easy to check. A draft summary from approved notes may be a sensible first exercise. Automatic decisions about people, confidential information or important entitlements require a different level of oversight and may not be suitable for experimentation at all. The aim is to identify what a small test could teach you before changing how work is performed.

2. Separate learning from implementation

Some enquiries are about learning to use an existing product better. These can begin with a lesson, a guided exercise or a role-based training plan. Other enquiries concern a work process that crosses files, systems or teams. Those need mapping, authorization checks, test cases and a responsible process owner. An individual learner does not necessarily need a custom system. A team considering automation does not necessarily need a general introductory course.

Training is easier to evaluate when it uses actual task types without exposing restricted information. For example, colleagues may practise writing a short public-facing summary, then compare the result with an agreed answer checklist. More advanced participants may examine a simulated document search workflow and identify unsupported claims. The point is not to promise that everybody will reach the same level; it is to make the skills and review responsibilities visible.

3. Know which information can be used

Before any trial, check who owns the information, which tools are approved and whether private records may leave their existing environment. A task using fictitious examples is different from one that requires access to customer records, confidential contracts or employee data. Do not assume that a chatbot automatically has access to your organization’s documents or that a particular connector is available in your account. Product capabilities, plans and administrative controls change, so confirm the relevant official documentation.

If the required information cannot safely be used, change the test design rather than bypassing the restriction. A simulated input may still allow the team to learn whether a proposed method is understandable and reviewable. If the task depends on live systems, an authorized technical owner must check access, logging, failures and any action that the AI would be allowed to perform. Reading information and changing information are distinct permissions.

4. Test ordinary cases and difficult cases

A useful AI evaluation does not stop when one demonstration looks convincing. Create several examples with expected answers or outcomes. Include ordinary inputs, missing information, contradictory information and cases that should be refused or referred to a person. For a reporting task, verify the underlying totals independently before assessing the written explanation. For a document question, record which approved source supports the answer. For a meeting summary, check whether decisions and owners are accurate.

Keep a visible record of what was tested, which version or setup was used, who reviewed the result and what would trigger a stop. These practices are consistent with NIST’s voluntary AI risk-management approach and with security guidance on untrusted content. Neither framework proves that a particular product or service will perform well in a given organization; they support careful planning and evaluation.

5. Decide what sort of help fits the next decision

A first-use-case review can identify a short list of worthwhile tasks. A workflow map can expose missing rules before automation. Team training can help people practise specific skills. A pilot charter sets out test boundaries and a decision point. An answer-quality review examines whether outputs meet agreed criteria. Tool selection checks competing options against identical sample tasks and user permissions. A broader action plan puts dependencies and accountable owners in sequence.

These are different decisions, even when a project uses more than one of them. Begin with the narrowest useful step. If a team cannot explain how its current process works, it will be difficult to justify automating that process. If the team has no way to check the results, a pilot needs an evaluation method before it needs more functionality. If staff have not been trained to handle sensitive material, training and internal guidance may take precedence over integration.

6. Compare the options without confusing them

7. Use responsible questions at every stage

Ask who has authority to approve the work, who may access the information and who reviews results before they affect anyone else. Clarify what the AI is allowed to do on its own. A draft that requires sign-off is different from a system that sends messages or changes records. Require a simple route for employees to report mistakes, unexpected outputs and data-handling concerns. Maintain a record of why the process was approved or rejected, not only a list of tools.

Some subjects, such as regulated services, employment, finance or healthcare, require domain-specific legal and professional analysis. A general directory cannot determine compliance for an organization or replace the judgment of a qualified professional. For example, the European Union’s AI literacy obligations depend on the applicable legal framework and organization context. General training material should not be presented as a certificate of compliance.

8. Select one practical next step

At the end of a first discussion, a team should ideally have a clearer written problem, an owner, an approved type of example and an initial review criterion. That is enough to choose among the pages in this directory. If a solution is not suitable, documenting that decision is useful too. If further expertise is needed, the proposed conversation should define scope before making claims about tools, timelines or outcomes.

Continue to the free learning guides on AI Coach in Asia if your immediate need is to understand a concept or practise an individual skill. Use the contact route for an enquiry about team training or a clearly defined business problem. This separation makes the directory useful both to visitors who want to learn independently and to organizations evaluating practical help.