What Great Analytics Talent Looks Like in the Age of AI

By Amin Sadri, PhD — Principal Data Scientist, Founder of AI4Trips & AI4Convey, and IAPA Top Analytics Leaders alumnus

The analytics job market is transforming faster than ever. AI can now code, visualise data, and summarise insights - yet finding truly impactful analytics professionals has never been harder.

This article is written for hiring managers, who want to identify great analytics talent in this new era, as well as for data professionals and job seekers who want to understand what skills matter most for the future.

The takeaway is clear: while automation handles more technical tasks, human‑centric, strategic, and interpretive skills are becoming the true differentiators.

The Evolving Skill Landscape

Here’s a ranked list of 12 analytics-related skill sets, organised from those that have become more important in the age of AI to those that are becoming less critical due to automation and tool advancements.

Top - More Important After AI (Human-Centric & Strategic)

1. Problem Framing & Structured Thinking
Essential for defining the right questions and guiding AI tools toward relevant, impactful solutions. Strong analysts clarify objectives before coding or modeling, ensuring the analysis solves the right problem.

2. Stakeholder Communication
As AI produces more data and reports, the ability to translate outputs into meaningful business insights becomes crucial. The best analysts simplify complexity, communicate with clarity, and build trust across teams.

3. Domain Knowledge
AI lacks business context. Understanding the organisation’s market, operations, and customer behaviour allows analysts to interpret results correctly and make actionable recommendations.

4. Ethical Reasoning & Governance
AI systems introduce questions of fairness, bias, and accountability. Human oversight ensures analytics and AI are used responsibly and transparently.

5. Data Storytelling
AI can generate visualisations, but humans excel at deciding which story to tell and tailoring it to different audiences. The narrative, not the chart, creates value.

Middle - Still Important, But Evolving in Role

6. Critical Interpretation of AI Outputs
As AI automates model creation, the human role shifts toward validation and sense‑checking, knowing when results don’t make sense and how to investigate.

7. Creativity & Innovation
AI can generate ideas, but it cannot imagine new approaches or products. Human creativity drives experimentation and the leap from data to innovation.

8. Data Wrangling & Preparation
AI tools increasingly automate cleaning and transformation, but humans remain vital in defining data logic, ensuring quality, and spotting anomalies that automation misses.

9. Visualisation
AI can generate visualisations using visualisation tools. Again however, the narrative, not the chart, creates value.

Bottom - Less Important After AI (More Automatable)

10. Technical Coding Proficiency (for Common Tasks)
Writing boilerplate code or standard models is now assisted by AI. What matters more is knowing how to edit, validate, and reason about generated code - not memorising syntax.

11. Tool-Specific Expertise (e.g., Tableau, specific libraries)
Being an expert in one tool is less valuable than adaptability. Tools change - thinking and problem-solving skills don’t.

12. Manual Reporting & Dashboard Creation
Many analytics platforms now automate routine reports. The focus is shifting toward interpretation and decision-making, rather than manual report production.

                                                Ranked Analytics Skills in the Age of AI                                                                               

A Lesson from AI4Convey: Why Domain Expertise Still Matters

While leading development at AI4Convey - an AI platform built to automate legal contract reviews for conveyancers in Victoria - I saw firsthand how critical domain expertise remains, even in a world increasingly driven by advanced technology. Our natural language processing models performed exceptionally well: they could extract key entities, identify standard and non-standard clauses, flag potential risks, and summarise contracts at impressive speed. Technically, the system was doing exactly what it was designed to do. But despite these capabilities, the product couldn’t move forward without the judgment, nuance, and practical understanding that only experienced legal professionals could provide.

Conveyancers and property lawyers contributed insights that AI simply couldn’t replicate - how a minor clause might change risk exposure, how Victorian regulations shaped interpretations, or how a contract nuance could impact a client’s financial future. Their knowledge didn’t just improve the product; it shaped it. They helped us determine what “important” really meant in a legal and business context. This experience underscored a broader lesson for analytics and AI leaders: AI is powerful at scale and speed, but human expertise is what makes it credible, contextual, and actionable. The real impact comes when machines and people work together - each doing what they do best.

What This Means for Hiring Managers

When assessing analytics talent today, prioritise candidates who:

• Think critically about problems rather than jumping straight to code.
• Communicate clearly and influence stakeholders.
• Understand business context and act as translators between data and decisions.
• Demonstrate ethical awareness and curiosity about how AI affects outcomes.
• Show evidence of collaboration, creativity, and continuous learning.

These capabilities cannot be automated - and they’re what separate technically capable analysts from true business partners.

What This Means for Data Professionals

For those pursuing careers in analytics, the message is encouraging - AI isn’t replacing your job - it’s changing it.

Focus your growth on skills that AI can’t easily replicate - problem definition, communication, creativity, and domain knowledge. Learn to use AI tools effectively but never outsource your thinking to them. The best professionals will be those who combine technical fluency with structured judgment and human insight.

Final Thought

The future of analytics belongs to professionals who think strategically, act ethically, and communicate effectively - and to leaders who recognise and nurture those qualities.

As AI takes over repetitive work, human skills are becoming the real competitive advantage. The organisations that hire and develop talent around these capabilities will lead the next generation of data-driven transformation.

About the author

Amin Sadri is a Principal Data Scientist at ANZ Bank and the Founder of AI4Convey and AI4Trips. With a PhD in Computer Science and multiple national mathematics awards, he specialises in applying AI and data science to real-world challenges across financial services and legal technology. Amin is passionate about building products that enhance customer experience and make complex decisions simpler through the power of AI.