What do AI product roles actually require? We went through the briefs so you don't have to. These roles impact how AI behaves and the candidates are required to understand the basics of product management, AI/ML systems, user experience and content/ logic design concepts. Here are the three skills that showed up the most: 1. Prompt design & task structuring Appeared in ~100% of briefs This skill is about telling AI exactly what to do, how to do it, and what a good output looks like. What this looks like in practice: a. Designing clear, structured prompts for different use cases b. Breaking down complex tasks into step-by-step instructions c. Iterating prompts to improve accuracy and consistency 2. Evaluation frameworks & rubrics Appeared in ~90% of briefs Teams need structured ways to measure AI performance. Defining “what good looks like” is critical. What this looks like in practice: a. Creating clear criteria to evaluate AI outputs (accuracy, relevance, completeness) b. Building scoring systems or rubrics to assess output quality consistently c. Reviewing outputs to identify patterns in errors and edge cases d. Using evaluation results to refine prompts and improve performance AI products are built as much through prompts and evaluation as through code. Anyone can write a prompt. Not everyone can measure and improve it. Take any AI output you've generated this week: a summary, an email, anything. Now score it on three things: Was it accurate? Was it relevant to what you actually needed? Was it complete, or did it miss something? Then go back and rewrite the prompt that generated it. Run it again. See if the output improves. You just practised the two most in-demand AI skills: prompt design and evaluation without any course or certification. At Crossing Hurdles, we help candidates find opportunities that fit their skills and expertise. What's the one skill you think is missing from this list? Comment down below!
One thing I think people underestimate is giving AI agents memory reusable templates and clear success metrics Model quality matters but the real value may come from systems that learn from feedback remember useful context and improve with repeated use The products that win will likely be the ones that fit into real workflows save time and deliver measurable outcomes not just generate good responses.
I’d add one more future-facing skill: explaining human context to machines. A lot of things are obvious to experienced people, but not obvious to AI systems. What is official, what is current, what matters, what should be inferred, and what must be confirmed. Humans absorb this through experience. AI systems need someone to structure it.
I’d suggest Workflow Orchestration. Most AI isn't just a single prompt anymore; it’s a chain of multiple prompts and tools working together. Understanding how to 'agentically' link these tasks is becoming a massive differentiator for AI specialists.
Great IM agree detail AI maker from start with prompt, my skill sistem making prompt to making video or photo and drama about thing we needed. tks
Useful list. Though the missing skill may be knowing when not to use AI at all. Some polished outputs are just complex answers to simple problems. We can probably see a bit of that here too.
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Great distillation of the shift we’re seeing. You’re spot on: while anyone can write a prompt, few can build repeatable, high-quality systems that survive an enterprise audit. With that, I’d add a critical fourth skill: Systems Thinking & Strategic Governance. The missing link in most AI briefs is connecting "prompts" to the P&L. To lead, the next generation must master: - Operational Sovereignty: Ownership of the IP within the logic. - Human-in-the-loop Friction: Treating rubrics as change management, not just scoring. - Technical Literacy: Translating accuracy into de-risked growth. - The Human Element: As industry veterans, our role is to mentor the next generation to look past the hype and understand systems architecture. We must teach them to build products robust enough for a scale, profitability, optimisation or even exit. Excellent starting point to bridge the gap between tinkering and transforming.