The Data Analyst in the Age of AI

From Report Producer to Decision Architect

Photo by Walls.io on Unsplash

I’ve been reading a lot on AI lately – most recently listening to Ethan Mollicks’ audiobook “Co-Intelligence: Living and Working with AI” and also intrigued by Luke Stanke’s LinkedIn article “The Death of the Data Analyst.” Both pieces of work made me realize that the data analyst’s job description has not been formally rewritten. The role is transforming perhaps a bit faster than job titles are catching up. As uncomfortable as it may make you (us), Artificial intelligence is already inside the tools you use every day: the BI platform, the CRM, the ERP’s reporting layer, the institution’s Microsoft 365 environment. The question is no longer whether AI changes the data analyst role. It is what kind of analyst you want to be when it does.

The good news is that the skills that make a great analyst are not being made obsolete. They are being made more valuable. Critical thinking. Institutional knowledge. The ability to translate complex data into a story that drives a real decision. These are the things AI cannot do (at least not yet). They are exactly the things your institution needs you to do more of — if you’re ready.

What’s Actually Changing — Right Now

Let’s be specific and cite some of the research that’s out there: One of the largest and comprehensive AI studies conducted by California State University showed that 74% of staff express concern about AI’s impact on job security. EDUCAUSE’s 2025 AI Landscape Study found that higher education students, faculty and staff are already using AI for at least one work-related task. The most common uses are summarizing content (74%) and brainstorming (71%). In relation to the AI-related skills that staff and faculty need to develop, 90% of respondents cite AI literacy and best practices for using AI to boost productivity as the top two.

Wharton professor Ethan Mollick’s GDPVal benchmark makes the pace of change visceral: AI completed complex professional tasks that took humans approximately seven hours — in five to ten minutes. Blind expert judges preferred the AI output 72% of the time.

What does this mean practically? Routine data cleaning, basic SQL querying, standard dashboard generation, and summary reporting are all moving toward AI-assisted or AI-automated territory. The analysts who recognize this earliest have the most time to redirect their energy toward work that genuinely cannot be automated.

“AI can make the chart. Only the human can make it matter.”

— Cole Nussbaumer Knaflic, Storytelling with Data (2025 edition)

The Real Opportunity: The Work AI Can’t Do

Here is the case for optimism, grounded in evidence: the skills at the top of the analyst’s value stack are the ones AI is least equipped to replace. LinkedIn’s Skills on the Rise: The Fastest-Growing Skills in 2026 named People Skills (Leadership and People Management) the #1 most in-demand skill across all industries. Not SQL. Not Python. People Skills — like Cross-Functional Collaboration, Team Management, and Mentorship. #3 on the list is Technical and Strategic AI (AI that goes beyond coding).

The EDUCAUSE 2026 Top 10 adds a higher-education-specific dimension that every analyst in our community should internalize: Institutional leaders are valuing human connections. A data analyst who can connect institutional evidence to student success, equity outcomes, and strategic priorities is not just doing a job. They are helping their institution rebuild trust.

Ben Jones, CEO of Data Literacy, captures the evolution plainly: the future belongs to analysts who are “keen observers”. These professionals understand the institutional terrain, they ask questions, locate the right data and share it effectively. They know what the data means in context. They ask the question behind the question. And they communicate the answer in a way that actually moves people.

Ethan Mollick refers to the human in the loop principle in his audiobook “Co-Intelligence: Living and Working with AI”. To be the human in the loop means to exercise caution with AI “hallucinations,” and to work with AI but whilst maintaining a critical perspective.

A Word of Honest Caution

Optimism is warranted. Complacency is not. Three cautions deserve your attention:

• The critical thinking risk is real. Jones cites early Carnegie Mellon University and Microsoft Research suggesting that over-reliance on AI can erode critical thinking capacity over time. If AI generates your analysis and you accept it uncritically, you may be losing the very skill that makes you irreplaceable. Stay deliberate. Question AI outputs. Maintain the habit of understanding the data yourself. Be the human in the loop.

• AI amplifies bad data. Gartner’s 2026 D&A Summit delivered a stark message: AI does not fix poor-quality data — it makes poor decisions at scale, faster. EDUCAUSE found that only 16% of institutions say their data functions operate cohesively. If your institution has data quality problems (and most do), AI is a reason to fix them urgently, not a workaround.

• The pipeline is at risk. PwC’s 2026 research shows organizations reducing entry-level analyst positions as AI handles more routine tasks. This creates a real risk: the apprenticeship pathway that has historically developed senior analysts and data leaders is being eliminated. Whether you are early-career or leading a team, advocating for that pipeline is advocating for the future of your profession. Page 1 of the Executive Briefing indicates that despite optimism around enhancing skill development and productivity gains through AI adoption, there is overriding concern around socio-economic inequity and lack of preparedness.

What You Can Do — A Practical Framework

The following table lays out the Analyst Evolution Framework in visual form. The core idea is simple: wherever you are in your career, there are concrete, doable steps you can take right now. The framework has two tracks — one for mid-to-advanced career analysts, one for early-career and aspiring analysts — because the actions are different even though the destination is the same.

Two-column table with mid and early career framework for inserting AI in analyst work. Created by author.

The Bottom Line

The data analyst role in higher education is not disappearing. It is expanding in scope and contracting in routine. The analysts who thrive in the next five, ten, fifteen or so years will be the ones who embraced AI as a collaborator, protected their critical thinking, deepened their institutional knowledge, and learned to communicate data in ways that rebuild — not erode — the trust our institutions desperately need to earn back.

None of this requires waiting for your institution to have a strategy. It requires you to start, right now, with the tools you have and the time you can protect.

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The views and opinions expressed here are personal and do not necessarily reflect the official policy or position of my employer.