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AI agents no longer belong only in research labs. You can now build agents that sit on top of your existing tools Excel, SQL, and Power BI and help you understand, explain, and act on your data.
But we need first to know exactly:
What AI agents mean in a data context
An AI agent is an AI system that can plan and execute a series of steps on your behalf, not just answer a single question.
In data work, that means an agent can read your data, choose the right operations, run them, check results, and present insights back to you.
Instead of you manually clicking through every step, the agent automates parts of the workflow while you stay in control of the goal.
This shifts your role from doing every detail yourself to supervising, validating, and refining what the agent produces.
How AI agents work with Excel
Excel is still the default place where many teams store and explore data.
Modern AI agents can:
- Generate and debug formulas based on plain language instructions.
- Clean and transform messy tables, such as fixing formats, filling missing values, or splitting columns.
- Build PivotTables and charts and even assemble full reports from raw data.
Some implementations, like Excel’s Agent Mode, go further by planning and executing multi‑step analysis tasks inside a workbook, checking their own work as they go.
You still decide what question to ask and whether the result makes sense, but the agent saves you from many repetitive steps.
How AI agents connect to SQL
Your most important data often sits in databases, not just spreadsheets.
AI agents that connect to SQL can:
- Translate business questions into SQL queries.
- Run those queries against your databases or Power BI datasets.
- Return summarized results that feed reports, alerts, or further analysis.
In more advanced setups, agents also help schedule regular loads, move data from Excel to SQL, or orchestrate flows that keep your reporting layer up to date.
You still need to understand tables, joins, and filters, because you are the one who checks whether the queries and results match the business reality.
How AI agents sit on top of Power BI
Power BI already centralizes a lot of your models and dashboards.
AI agents built around BI can:
- Query existing datasets for you, without you building new visuals each time.
- Monitor KPIs and trigger alerts when something unusual happens.
- Generate narratives that explain what changed and why, based on the latest data.
This moves you from static dashboards to what some call “agentic BI,” where agents continuously scan data, surface insights, and suggest actions.
You still design the models, measures, and dashboards; the agent helps you and your stakeholders use them more actively.
What you need to build and supervise AI agents
Even with powerful AI agents, strong fundamentals remain non‑negotiable.
To build and oversee agents on top of Excel, SQL, and Power BI, you need to:
- Understand data types, relationships, and basic modeling concepts.
- Work confidently with Excel formulas, PivotTables, and Power Query.
- Write and read SQL queries, at least for selecting, filtering, joining, and aggregating data.
- Design clear Power BI models and visuals that reflect real business questions.
AI agents amplify these skills; they do not replace them.
Your value comes from knowing what to ask, how to check results, and how to turn outputs into decisions.
How IMP’s Diploma prepares you for AI‑driven analytics
If you want to use AI agents effectively, you need a solid base in data analysis and BI tools. IMP’s Data Analysis & Business Intelligence Diploma gives you this foundation in a structured way.
Across the diploma, you:
- Build data literacy and descriptive statistics skills, so you understand and trust the data that agents will work on.
- Learn Excel for data analysis, including formulas, PivotTables, Power Query, and data models, which are the building blocks for agent‑assisted work.
- Use Power BI to design data models, measures, and interactive dashboards that AI agents can later query and explain.
- Study SQL for data analysis, giving you control over the queries and datasets your agents rely on.
- Practice storytelling with data, so you can turn any agent‑generated insight into a clear message and recommended action.
This mix of skills lets you work with AI agents as a professional, not as a passive user.
Contact the IMP team to see how the diploma fits your background and career plans.
