Essential Data Skills for the PostLLM Era
A practical guide for HR leaders to build workforce capabilities that keep AI useful, safe, and differentiating
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Why data skills are now a workforce strategy
Generative AI is no longer “emerging.” It’s rapidly becoming an expected layer in how work gets done. Bain reports that 87% of companies have already deployed or are piloting generative AI. McKinsey’s global survey similarly reports 65% of respondents say their organizations are regularly using gen AI.
But adoption is not the same as value realization. Gartner predicts at least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025, citing factors that include poor data quality and inadequate risk controls. This is why HR leaders are increasingly central to AI strategy: the constraint is moving from access to models to skills, governance, and execution.
At the same time, the skills baseline is moving quickly. The World Economic Forum estimates 39% of workers’ core skills will change by 2030 (WEF, Future of Jobs Report 2025
The same WEF section highlights that analytical thinking is expected to be a core skill for 70% of companies by 2025, and that AI and big data is among the fastest-growing skill areas.
Even where organizations recognize the need, capability building is lagging. BCG reports that only 30% of managers and 28% of frontline employees have been trained in how AI will change their jobs.
Forrester’s research indicates that just over half of employees “should be data literate” to achieve strategic outcomes, yet organizations report only 41% are data literate on average, implying companies need 1.3x more data-literate employees than their current state.
Our position is clear: in the postLLM era, data skills become foundational infrastructure for business performance. We designed this framework to help HR leaders operationalize that reality across job architecture, learning strategy, governance, and culture—grounded in cross-functional collaboration, continuous learning, and leadership-driven change.
The Five Essential Data Skill Areas
We synthesized the core data skills required in the postLLM era into five essential skill areas:
- Determining Unique Data Assets
- Ethics and Compliance in AI and Data
- Basic Statistical Literacy
- Understanding the Cost and Risk of PoorQuality Data
- Embedding a MetricsDriven Way of Working
This is not a “data team only” agenda. These skills must exist across functions, because AI-enabled work increasingly requires employees to interpret, govern, and act on data—with accountability.
Skill Area 1: Determining Unique Data Assets
What this skill area means now
In the postLLM era, competitive advantage shifts toward organizations with unique, high-quality data assets and the ability to use them effectively, especially as advanced AI models become widely accessible.
Why it matters for HR leaders
This shift changes the profile of talent you need—not just data engineers and scientists, but also roles that can identify, steward, and monetize data as an asset (e.g., data strategists and data product managers).
Granular skill map and recommended proficiency
Principle to anchor in job design: “No effective business strategy exists without a supporting data strategy.”
Skill Area 2: Ethics & Compliance in AI and Data
What this skill area means now
As AI usage scales, ethical and regulatory readiness becomes a workforce capability, not a policy document. Our framework emphasizes governance, transparency, privacy, and risk controls as skills embedded across roles—not limited to legal or compliance.
The gap is material. In a MIT Sloan Management Review / BCG study, 75% of executives said it was “very important” to have ethical guidelines for AI, yet only 6% of organizations had developed such guidelines.
Why it matters for HR leaders
Ethics and compliance must translate into role expectations, training, and operating practices—especially in areas such as bias, privacy, and explainability.
Granular skill map and recommended proficiency
Skill Area 3: Basic Statistical Literacy
What this skill area means now
Statistical literacy is not optional when decision cycles compress and AI-generated outputs proliferate. We explicitly define statistical literacy as a workforce requirement because it enables employees to interpret variability, distributions, and evidence—and to communicate insights responsibly. Basic statistical literacy is quickly becoming as essential as computer literacy for many roles.
Why it matters for HR leaders
If employees can’t interpret uncertainty, they can’t challenge flawed outputs, detect misleading narratives, or make high-integrity decisions. This is amplified in AI-enabled work, where plausibility can mask inaccuracy.
Granular skill map and recommended proficiency
Source: our “Basic Statistical Literacy” skill framework.
Skill Area 4: Cost & Risk of PoorQuality Data
What this skill area means now
Poor-quality data is no longer “just” an operational issue. It is a material business risk—especially when AI systems amplify data issues at scale.
Gartner states that poor data quality costs organizations at least $12.9 million per year on average. Gartner also notes that many D&A and AI initiatives fail because of poor data quality, and that generative AI will impact how data quality is ensured.
Why it matters for HR leaders
This is a skills issue because teams need to
- assess quality
- quantify business impact
- diagnose root causes
- steward governance
- monitor ongoing risks.
Granular skill map and recommended proficiency
Skill Area 5: A MetricsDriven Way of Working
What this skill area means now
Tools do not create data-driven organizations—behaviors do. We define a metrics-driven way of working as the set of skills required to build KPIs aligned to strategy, make decisions using data, drive adoption, collaborate cross-functionally, and continuously improve.
A persistent barrier is clarity and alignment on success metrics. Everest Group reports that CIOs’ top challenges to scaling gen AI initiatives include “lack of clarity on success metrics (73%)”.
Granular skill map and recommended proficiency
How to use this framework in HR: practical application model
This framework is designed to be integrated into the mechanics HR already owns: job architecture, capability frameworks, learning strategy, and governance. We recommend an operating rhythm built around three moves:
- Baseline capabilities by role family using the five skill areas and granular skills.
- Set target proficiency aligned to your strategy (e.g., data products, AI governance, operational efficiency).
- Embed skills into work through leadership expectations, role design, learning journeys, and measurable adoption—because cultural shift is central to durable data capability.
How we operationalize this with our Talent Intelligence Platform
We built our Talent Intelligence Platform to aggregate and analyze public profiles, job descriptions, company disclosures, labor market signals, and internal skill taxonomies to deliver insights on workforce trends, role evolution, and skills architecture.
- To keep skills current as demand shifts, we also continuously map roles to the external labor market and deconstruct work into its underlying skills:
- We unify internal role data and break roles into core workloads, functional tasks, and underlying skills (root, core, soft, technical).
- Our AI scans millions of live labor market signals (including job postings, project data, patents, and learning content) to identify emerging and declining skills by role, function, and region.
- We flag which skills are rising (“sunrise”) or fading (“sunset”), with confidence scores tied to demand and adjacent career paths.
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