post examines the abilities required to work effectively with AI, mainly specializing in consumers of AI systems. Within the text below, I’ll dissect the AI skills for the Business Competency Framework developed by The Alan Turing Institute, exhibit how the framework’s foundation is rooted in timeless skills, and recommend areas for upskilling amongst non-technical individuals.
My impression is that we entered the worldwide pandemic of hearsay by spreading headlines and 1000-character-long-AI-generated summaries (or as much as LinkedIn permits) on topics that concern us all.
Opinions pile on top of opinions concerning the way forward for the workspace and topics resembling education, security, and even human extinction within the AI era. Supported, unfortunately, often, by essentially the most recent non-peer-reviewed research, which was superficially red and understood. In some cases, understanding shouldn’t be even the goal one desires to optimise its function for. The goal is to earn a whole bunch or 1000’s of likes and get dozens of recent followers.
Panem et circenses can be found with every recent feed refresh, fresh (mis-) information served, so we don’t need to interact our grey matter to find the “truth.” Whatever this implies today, when basic research efforts are getting outsourced to AI, and the great enough truth is slowly creeping toward becoming a brand new standard.
Nonetheless, the market demands that we get a correct set of…
AI Skills
For many of us working closely with AI developments, once we step out of our IT circle, we realise people don’t talk or care as much about generative AI as we () do.
But, one thing they do care about is the correctness of the outputs produced by AI: is it good or not? Or to reframe it in my sister’s, aka math teacher, words: “”
And yet, a number of days ago, it was reported that Gemini with Deep Think achieved a gold-medal standard on the International Mathematical Olympiad.
So, where’s the gap here, or more precisely…
Let’s begin with the concepts that everyone seems to be attempting to re-package now, and that’s — a skillset framework mixed with some version of the responsibility project matrix.
Although these frameworks are questionable classifiers, as they have an inclination to “box” the people and their abilities with no proper assessment, they supply a useful start line for orientation.
That said, I’ll use an example of an AI skills for Business Competency (Meta-) Framework developed by The Alan Turing Institute, which outlines skill levels targeting predominant learner personas across dimensions representing a set of competencies, behaviours, and responsibilities👇🏼.
Diverging barely from the post topic, I would like to notice my top-of-mind, evident shortcomings within the framework’s mapping of skill levels to personas, resembling:
- It’s disconnected from the market’s need for M-shaped professionals from the “AI Employee” persona, where the designation of a “” level for dimensions like “” or “” falls wanting real-world requirements. This is very true in regulated industries, where every worker handling sensitive data is predicted to have strong knowledge of GDPR and compliance frameworks — a mandate that may probably extend to understanding AI risks and biases.
- Or, how framing the “AI Leader” as an “” within the “” dimension is misleading, because it suggests they need to possess deep technical expertise. Nevertheless, this is usually not the case; many leaders rely on their AI-savvy teams to bridge the gap with when making decisions.
And, there’s more to it, but let’s give attention to the AI competencies. To accomplish that, I’ll share another table to enrich the needed understanding of the learner personas:
Now, we’ll assume how all of us managed to search out our “spot under the Sun” and map ourselves to one among the above-presented personas. The subsequent query that comes up is…
Which skills are timeless, and where are the gaps in the present skills vs. AI skills?
The proof to the primary query is (in some way) straightforward: if we analyse Image #2 with no give attention to the term “AI”, it becomes clear how the listed AI competencies are the applying of existing, ones, resembling:
Nevertheless, the novelty comes from applying them to AI. The context of AI introduces different challenges, which require these skills to be adapted and deepened. For instance:
- “” shouldn’t be recent, but addressing the risks of biased language models or autonomous decision-making presents a brand new set of challenges to mitigate.
- “ ” shouldn’t be recent either, but applying it to discover model (mis-)use, or job displacement because of automation, presents entirely recent dilemmas.
Subsequently, the gaps lie within the foundational, domain-specific nuances that allow a collective to effectively leverage AI as a tool somewhat than be “used” by it.
With this in mind, there are already learning paths being offered to accumulate the AI “nuanced” skills, and these can show you how to kick off your learning journey.
My recommendations for each non-tech and tech individuals who don’t primarily develop AI solutions can be:
- Master high-level understanding of various language models (e.g., LLMs vs. SLMs vs. other specialised models, “pondering” vs. “non-thinking models”, etc.), prompt them and when to make use of them (what are the pros and cons of using AI). Get an understanding of what AI agents are and where we stand on the AGI path, so that you get a sense of what form of tools you might be coping with.
- Understand “failure modes” and learn evaluate outputs. Learn the ways models can lie and manipulate, resembling bias, hallucinations, or data poisoning, so that you avoid resolving problems AI created in seconds. For this, you’ll have to develop an evaluation checklist (from input to output) for specific (sorts of ) problems and be certain that outputs are critically reviewed and tested before they reach the masses.
- Create, don’t just eat AI products. While soft skills are an incredible asset, constructing practical hard skills is just as vital. I consider everyone should start mastering the AI features accessible within the tools we use day by day, e.g., AI tools in Excel. From there, I might recommend you begin learning no-code and low-code solutions (e.g., Copilot Studio or AI Foundry) to develop custom AI agents with an easy “clicky-clicky” method. Mastering these workflows will boost your performance and AI domain knowledge, making you more competitive in the longer term job market.
To finish this post, one takeaway I hope you’ll get is that all of us have to put within the mental effort to complement our current skills with AI ones.
Because AI effectiveness relies on how thoughtfully we interact with it, and that requires the identical critical pondering, risk assessment, and ethical judgment we’ve at all times needed, just applied to recent challenges. Without these foundational skills to judge outputs and avoid over-reliance, we risk being led by AI (or by individuals who know use it) as an alternative of using it to our advantage.
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This post was originally published on Medium within the AI Advances publication.
