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AI and future skills resources
Using AI well at work is becoming a distinct professional skill, separate from simply knowing the tools exist. These guides cover practical AI workflows, evaluating AI output critically and the wider capabilities that keep a career resilient as work continues to change.
The advantage is not access to AI. Almost everyone has that now. The advantage is judgement about how to use it.
Start here
Audit where AI could actually help this week
Learn to write a specific prompt, not a vague one
Build one repeatable AI workflow before trying to build ten
Always check AI output against a source you trust
Invest in the skills AI cannot replace for you
Automate versus augment: choosing the right approach per task
Not every task should be handed entirely to AI, and not every task benefits from AI at all. The useful distinction is between automating a task, where AI does the whole thing with light review, and augmenting your own work, where AI supports your thinking but you retain control of the judgement.
- Automate low-risk, repetitive, well-defined tasks: formatting, first-draft summaries, routine correspondence.
- Augment high-stakes or judgement-heavy tasks: strategic recommendations, sensitive communication, anything with legal or financial consequences.
- Common mistake: automating a task that needed a human's contextual judgement, then discovering the error only after it has caused a problem.
Practical AI workflows for everyday work
The professionals getting the most value from AI tend to have a small number of well-refined workflows rather than a scattershot approach.
- Meeting prep: feed AI the agenda and background documents, ask for likely questions and a suggested structure.
- First drafts: use AI to produce a rough version of a report or email, then rewrite in your own voice and check the facts.
- Research synthesis: ask AI to summarise multiple sources, then verify the key claims yourself before using them.
- Decision support: use AI to list options and counterarguments you may have missed, then make the decision yourself.
How to evaluate AI output critically
AI output can be fluent and confident while also being wrong. Evaluating it is a skill in itself.
- Check any factual claim, statistic or citation against an independent source before using it.
- Ask whether the reasoning actually supports the conclusion, not just whether the conclusion sounds plausible.
- Watch for output that is generic rather than specific to your actual situation; that usually signals a weak prompt.
- If something looks too convenient or too neatly resolved, treat that as a reason to check it more closely, not less.
Responsible use of AI at work
Using AI responsibly is not only about accuracy. It also covers confidentiality, transparency and accountability.
- Never input confidential or personal data into tools that do not have appropriate safeguards for your organisation.
- Be transparent with your team or manager about where AI has been used in significant work.
- Remember that you remain accountable for the final output, regardless of what produced the first draft.
Future skills that stay valuable as AI capability grows
As AI absorbs more routine tasks, the skills that remain distinctly valuable are the ones that depend on context, relationships and accountability.
- Judgement: knowing which problem is actually worth solving, and when the data is not enough to decide alone.
- Communication: translating complexity for different audiences, and building trust in how you deliver a message.
- Adaptability: learning new tools and ways of working quickly, without waiting for a formal training programme.
- Ownership: being the person who is accountable for an outcome, not just the person who produced a draft.
Guides in development
The following guides are being written for this topic and are not yet published.
AI skills for work
The practical capabilities that separate effective AI users from casual ones.
Automate versus augment
A framework for deciding which tasks to hand fully to AI and which to keep human-led.
Responsible AI at work
Guidance on confidentiality, transparency and accountability when using AI tools.
AI workflow examples
Repeatable workflows for meeting prep, drafting, research and decision support.
How to evaluate AI output
A method for checking AI output for accuracy, reasoning and relevance before relying on it.
Future skills for professionals
The human capabilities worth developing deliberately as AI takes on more routine work.