The real impact of AI on analytics

72

Jackson T. Mashinge

LAST weekend, I posted on LinkedIn about how AI is transforming real-time ana­lytics and how Intelligent Process Automation is increasingly becom­ing the operational backbone behind modern decision intelligence. The post drew attention from different players across the world, including leaders, analytics professionals, and architects who were quick to share perspectives from their own envi­ronments. It also reignited a conver­sation that seems to resurface every few months: someone says, “AI is replacing Data Analysts.”

From a distance, that statement sounds plausible. After all, AI can produce SQL, suggest DAX mea­sures, propose data models, gener­ate visualizations, and automate a wide range of repetitive tasks that analysts previously performed man­ually. But that narrative misses the real point. AI is not killing data an­alytics. It’s changing how work is structured, where value is created, and what roles professionals must play to keep organizations compet­itive.

The key misconception is that analytics is being defined as output generation. In reality, analytics is a disciplined, business-driven process of problem formulation, data inter­pretation, verification, and decision enablement. AI is strong at acceler­ating certain steps of that pipeline, especially the mechanics. However, it cannot fully replace the human ca­pability to clarify objectives, inter­rogate assumptions, validate logic against reality, and translate find­ings into actions that make sense in a specific business context.

AI-generated analytics often looks like “answers,” but the hard work is in getting to the right ques­tion and ensuring the answer is re­liable. In most organizations, data is messy, definitions vary between departments, and “truth” depends on governance. For example, one team’s definition of customer acti­vation might differ from another’s understanding of conversion. A query that runs correctly can still be semantically wrong if metrics are misaligned, if joins are incomplete, if filters are improperly scoped, or if the underlying data lineage is un­clear. Analysts are responsible for bridging this gap between technical outputs and business meaning.

That is why the pattern many organizations are now experienc­ing is telling. Instead of eliminating analysts, companies that adopt AI at scale frequently end up doing the opposite. They hire analysts who understand how AI should be used, what checks must be applied, and how to integrate AI into existing decision workflows. In other words, AI reduces the time spent on pro­duction chores, but it increases the need for interpretation discipline.

Consider what analysts actually do every day. They don’t just build dashboards. They define KPI hier­archies, translate stakeholder ques­tions into analytical specifications, design measurement logic, and val­idate whether a metric reflects the intended business outcome. They also examine anomalies and con­firm whether deviations are caused by real operational changes or by pipeline failures, late-arriving data, schema drift, or inconsistent instru­mentation. Even if AI can draft the SQL or recommend a Power BI visualization, the analyst still must confirm the logic is defensible and the interpretation is accurate.

AI’s real contribution is in en­abling a higher-iteration cycle. It compresses the time between “hy­pothesis” and “first draft insight.” A professional can prompt an AI tool to generate a query, propose a segmentation strategy, or suggest a DAX measure, and then refine it quickly. This makes experimenta­tion faster and lowers the friction for exploring multiple analytical angles. However, speed is only valuable when paired with correct­ness. That is where skilled analysts remain essential: they ensure the work is not just fast, but also accu­rate, traceable, and aligned with op­erational truth.

This is where the role of AI in real-time analytics becomes espe­cially relevant. Real-time analyt­ics is not simply about having live dashboards. It is about extracting signal from streaming data under changing conditions and translat­ing that signal into decisions before latency becomes a cost. In that en­vironment, AI can help by enabling automated anomaly detection, pre­dicting outcomes using evolving features, and suggesting actions when patterns shift. Yet, the analyst still owns the governance layer: de­termining which alerts are credible, which thresholds are meaningful, and which operational contexts re­quire human escalation.

In intelligent automation set­tings, the workflow becomes even more sophisticated. AI may not only suggest insight; it may also trigger processes routing cases, adjusting resource allocation, updating cus­tomer-facing controls, or initiating remediation runbooks. That means the analytical layer must connect to business policies and operational constraints. Again, automation in­tensifies the need for expertise, be­cause the consequences of incorrect logic are higher when systems are allowed to act autonomously.

So what does this mean for an­alysts and aspiring data profession­als? It means the skill set must shift from “can I produce output?” to “can I produce validated decision intelligence?” The most valuable data professionals won’t compete with AI on raw generation speed. Instead, they will learn how to or­chestrate AI tools with analytical rigor.

For individuals learning Excel, SQL, Power BI, Python, or Tab­leau, the advice is straightforward: don’t stop learning because AI hype creates a false sense that technical foundations are obsolete. The fun­damentals matter even more in an AI-accelerated world. Knowing SQL syntax is not the goal. Know­ing how to reason about joins, data grain, filter context, and metric defi­nitions is. Knowing Power BI is not the goal. Knowing how to build re­liable models, manage relationships correctly, and avoid misleading vi­suals is.

AI may write SQL, but it can also produce incorrect logic with confidence. That is why analytical thinking, validation habits, and re­al-world project experience remain the differentiator. The professionals who thrive will combine strong tra­ditional capabilities with AI-assisted workflows, using AI as a productiv­ity accelerator while still applying a human standard of review.

In practice, the best analysts will adopt a “co-pilot mindset.” They will use AI to draft, iterate, and ex­plore faster, but they will remain responsible for defining success criteria. They will validate outputs against known business behaviour, confirm data lineage, and ensure the final insight can be acted upon. Their value will increase because AI will handle routine generation, while organizations will require more strategic judgment and stron­ger governance to manage ampli­fied decision velocity.

Ultimately, technology changes. Problem-solving never goes out of demand. The demand will shift toward professionals who can op­erate at the intersection of business domain expertise, data quality gov­ernance, and AI-enabled analytical acceleration. That is not a shrinking role, it is an evolving one.

So the real question is not wheth­er AI replaces data analysts. It is whether it replaces outdated work­ing styles. In many organizations, it will. Analysts will spend less time on repetitive drafting and more time on high-impact interpretation, experimentation design, trust-build­ing, and decision orchestration. What is your opinion? Will AI re­place Data Analysts, or will it make great analysts even more valuable?

l Mashinge has over 13 years of expe­rience in accounting, auditing, and finance. His expertise is in auditing, risk advisory, strategy formulation, project assurance, monitoring and evaluation.