How to Build an AI-Ready Analytics Career

Imagine you arrive tomorrow at your office to find a request from your team lead waiting: clean a customer dataset, compare monthly revenue, investigate unusual movements, and prepare a dashboard for management. Years ago, this singular assignment could have occupied most of your day; now it takes only a few hours.

Tools such as Claude’s Data plugin, Gemini in BigQuery, Fabi.ai, and Copilot in Power BI can support different parts of that workflow, from profiling datasets and detecting data-quality issues to generating SQL, exploring patterns, creating visualisations, and developing report pages.

Your work has not disappeared, but its centre of value has shifted. These tools can accelerate execution, but they still depend on reliable data, well-defined business logic, and informed review. You must still determine whether revenue should be calculated from invoice dates or payment dates, whether cancelled orders belong in the analysis, and whether the selected customer segment reflects the transaction being examined.

Producing the first draft has become easier, but determining whether it represents the business accurately still requires your expertise. And this formed the crux of a recent dbrownconsulting’s webinar, How to Build an AI-Ready Analytics Career, facilitated by Gbenga Obikoya, AI Project Lead at dbrownconsulting.

Gbenga examined where professional value is moving as AI becomes embedded across data cleaning, coding, statistical analysis, visualisation, and reporting. His recommendation was clear: build your career above and below AI rather than compete with it on routine execution.

Execution Is Cheaper. Judgment Is More Valuable.

AI can generate SQL in seconds, but speed does not guarantee that the query answers the correct business question. Your value lies in understanding the logic behind the request and detecting when the output lacks necessary context.

As analytical execution becomes easier, your attention should move towards questions such as:

• Does the calculation follow your organisation’s definition of revenue?
• Should cancelled orders be included?
• Does correlation genuinely indicate causation?
• Is a statistically significant result commercially important?
• What decision should management make from the finding?

As Gbenga explained, when execution becomes cheaper, judgment becomes more valuable. AI may report that customer churn increased by 6%, but someone still needs to determine why it happened and what the business should do next.

Become the Editor-in-Chief of Analytics

The AI-ready analyst can be compared to an editor-in-chief managing several fast junior analysts. One assistant writes SQL, another handles Python, while others support statistics, research, and dashboard design.

Your responsibility is no longer to complete every task personally. Instead, you must:

• Define the problem that needs to be solved.
• Direct the work using clear context and boundaries.
• Review the methods, calculations, and findings.
• Challenge assumptions that do not reflect the business.
• Approve only what is reliable enough to inform decisions.

You still need a sound understanding of Excel, SQL, Power BI, statistics, and data modeling. Without those foundations, you cannot recognise weak analysis or supervise AI-generated work effectively. How then should the AI-Ready analyst work in this era?

Build Your Career Above and Below AI

AI increasingly occupies the middle of the analytics workflow, where data is cleaned, code is generated, analysis is performed, and reports are assembled. Based on your expertise, we have identified two stronger positions for your career.

Below AI: For newbies to build the foundations AI depends on:

• Data pipelines, warehouses, and lakehouses
• Dimensional and semantic models
• Data quality and testing
• Security, permissions, and governance

Above AI: For experienced analysts to strengthen the capabilities required to direct AI:

• Domain expertise and business context
• Problem framing and analytical judgment
• Output validation
• Stakeholder communication
• Decision support

Organisations cannot build reliable AI systems on fragmented data, conflicting metric definitions, or poor governance. Analysts who can improve those foundations while translating AI output into business decisions will remain valuable.

A Practical Career Plan for the AI Era

If this article has inspired you to take action, here are four things you can begin applying immediately:

1. Build what AI needs: reliable data, sound models, clear definitions, and strong foundations.
2. Direct what AI does: provide context, instructions, tools, and boundaries.
3. Judge what AI produces: validate figures, review logic, and challenge assumptions.
4. Turn the output into business value: connect analysis to better decisions, lower costs, stronger revenue, or reduced risk.

Your next career advantage will come from designing how analysis should be done, teaching AI to follow that process, and ensuring its output improves the decisions your organisation makes. This requires more than knowing how to prompt an AI tool. It requires an understanding of reliable data foundations, repeatable workflows, governance, validation, and business judgment.

dbrownconsulting’s bespoke AI training programmes help professionals and organisations develop these capabilities within the context of their actual work. AI for Work focuses on applying AI to everyday workflows and productivity, while AI for Strategic Leaders help decision-makers identify valuable use cases, guide implementation, and manage adoption responsibly. AI for Board Members equips boards to provide informed oversight across AI strategy, risk, governance, and accountability.

Each programme can be tailored to an organisation’s roles, processes, data environment, and strategic priorities. For organisations seeking to move from casual AI experimentation to structured business application, dbrownconsulting can design a learning experience around the decisions and workflows that matter most.

Send an email to [email protected] for all enquiries and additional information.