Every week I meet a few analysts during my office hours and they talk about a number of things like- careers, hobbies, dreams, managers, colleagues, family, higher education, fitness, balancing priorities and off course, Bangalore traffic. Of late, one common theme is about AI and its impact on Analytics and Data Science. They ask questions like:
Q1 : Will AI make my role redundant?
Q2: Why should I train my replacement?
Q3: What will the Analytics org look like and what will my role shape into?
I wish I could predict the future with as much certainty as they expect but I am not a fortune teller. However, a few pieces are emerging-
The analyst of 2030 will not be doing what the analyst of 2025 did . Not because the work disappeared, but because a machine will learn to do most of it faster, continuously, and without needing a weekly review meeting to present the results.
What comes next is not the end of analytics as a function. It is the most consequential reinvention it has ever undergone.
THE BIG SHIFT
From Humans Doing Analysis to Agents Running It
The defining change is autonomous analyst agents handling the full analytics lifecycle end-to-end: anomaly detection, root cause analysis, experiment readouts, simulation, recurring reporting. No ticket raised, no query written, no slide deck built. The agent monitors continuously, diagnoses in real time, and pushes insights before anyone thinks to ask.
Think about what a senior analyst spends their time doing on a random Tuesday – pulling data, spotting the dip, tracing the cause, writing the summary, answering follow-up questions in the review.
Each of those steps is learnable by a well-governed AI agent with access to a clean semantic data layer. The weekly cadence of reviews and monthly deep-dives exist because humans need time to process. When that constraint disappears, so does the rhythm.
What makes the next generation more interesting is proactive intelligence.
An agent that notices Bhogali Bihu is two weeks away, cross-references last year’s demand patterns, detects a supply gap, and alerts the category team before demand hits without a human in the loop. This is not science fiction. It is a systems design problem, and the most forward-thinking orgs are already building toward it.
“The analytics org’s value will shift from doing analysis to ensuring the quality of analysis done by everyone and every agent.”
THE RISK
Bad Data at Machine Speed Is a New Kind of Failure
There is an uncomfortable flip side to autonomous analytics. A single bad metric definition doesn't just produce one wrong slide, it produces cascading wrong decisions across the business before anyone notices. Governance stops being a compliance exercise and becomes genuinely existential.
The solution is a single governed data layer - one path through which every human or agent accesses business truth, with centrally owned, version-controlled, machine-enforced metric definitions .
Data quality is treated as a live system with production-grade SLAs, not a warehouse that gets spot-checked on weekends. Every org that wants to deploy autonomous analytics has to build this foundation.
Nothing else works if the data underneath is ambiguous.
This is something that is not “cool work” and most organisations feel it’s easy to skip but just as deep learning models depend on millions of labelled examples- images, text, voice and video- AI agents will rely on clean, well annotated and governed data. This is more than half of the work for building Agentic AI.
“Clean and governed data will differentiate successful autonomous agents vs failures. Not so much the choice of LLMs or agentic platforms. In my view that offering will become commoditised."
THE ORG
The Pyramid Inverts
Today’s analytics pyramid is wide at the base: many analysts running queries, building dashboards, writing readouts, presenting in reviews. A smaller senior layer. A thin leadership band at the top.
Agents replace the base first and most completely - structured, high-volume, repeatable work is exactly where AI compounds fastest.
This can mean two things –
- We require fewer analysts
- Analysts will become 10x more productive and do complex work
The future roles in Analytics will broadly be two personas:
1. Decision Scientist : Frames the right questions, validates causal models, designs experiment frameworks, catches what agents miss. Owns the intellectual contract between data and business strategy. Basically defines what “good” looks like.
2. Platform Analyst : Designs, governs, and evolves the autonomous analytics systems- metric registries, agent pipelines, the semantic layer. An analytics engineer who thinks like a product manager.
The generalist in between – decent SQL, solid dashboarding, good at presenting is the most exposed profile .
Total headcount in analytics will in some cases shrink, but leverage per person will expand dramatically. One decision scientist governing a fleet of agents produces more analytical output than an entire traditional team.
The economics of the function change permanently: smaller, sharper, higher-paid, higher-impact. We will see a 10x gain in productivity.
The competitive moat will shift accordingly. When every company has the same LLMs and agent frameworks, faster insights stop being an advantage. What becomes hard to replicate is what’s inside the loop: proprietary data, domain-specific causal models, governance frameworks that enable action at speed, and a compounding effect where every decision makes the next one smarter.
"In the future there might be no “cut-lines” or priorities moving to next quarter/year because of analyst or tech bandwidth. We will be able to experiment and scale everything that drives growth but is currently held back by headcount constraints."
FOR YOU
How to Be Future-Ready
A lot of Agentic AI organisations are claiming that in future you won’t need analysts or engineers. News of lay-offs in many large tech companies is adding to that fear. Fear is not necessarily a bad thing. It keeps us alert and in some sort of a survival mode where we are more open to adapt. Adaptation is the key to surviving and thriving in the future.
If you’re an analyst reading this, the transition is not a cliff; it’s a slope with handrails. Here’s where to put your hands.
1. Master causal reasoning : Agents learn correlations easily. Causation is where human judgment remains essential. Learn to design proper causal frameworks, identify confounders, and know when running an experiment is the only honest answer.
2. Go deep on experiment design: Designing an experiment that actually measures the right thing accounting for spillover, novelty effects, and business context – requires judgment that won’t be automated soon. This skill compounds.
3. Own a metric completely: Pick the most important metric in your domain and know everything about it: its definition, lineage, edge cases, known biases, and every metric it touches. Deep metric stewardship is irreplaceable and it is the foundation of becoming a decision scientist.
4. Build AI and agent literacy: You don’t need to become an ML engineer. You do need to understand how agents fail - where hallucinations arise in structured data contexts, what makes a system prompt fragile, how governance gaps propagate. The platform analyst of 2030 thinks about AI behaviour the way a senior engineer thinks about system reliability.
5. Volunteer for governance work : Metric standardisation, semantic layer design, data quality frameworks – the projects nobody fights over. They feel like overhead today. In 2030, they are the most critical infrastructure in the company. The analysts who built them will be the ones who designed the system everyone else relies on.
“ The last analysts as we know them aren't being replaced. They are being promoted to architect, judge, and be the conscience of a system that will do more analysis in a day than a team does in a year. The question is simply whether they’ll be ready for the job.”
How we are preparing for this shift at Meesho
This is one of the most exciting areas we are exploring at Meesho, building systems that make insights more accessible and easier to act on. We are working towards evolving how metrics and data definitions are managed, with clearer context and metadata that can support more intelligent systems. We are also experimenting with platforms that can help teams explore data more intuitively, enabling them to understand how metrics are moving and investigate potential drivers with greater ease. Another area of focus is improving how experiments are designed and evaluated, with systems that can assist in structuring experiments and surfacing insights, while keeping humans closely involved in decision-making.
Over time, we see the role of analysts evolving towards guiding and improving these systems, helping them become more reliable, more context-aware, and more useful in solving a wider range of problems.
