But here’s the reality: When used for audit and assurance, AI’s probabilistic capabilities NEED analytics’ deterministic foundation to provide meaningful value. On its own, AI isn’t there yet. 

What’s more, AI and automation tools are evolving fast. What’s available six months or a year out will be a big step up from what we have now, as vendors and AI models alike learn to optimize outcomes. 

So why not make sure your Internal Audit team has an even stronger foundation of analytics for those AI and automation tools to build upon? 

This was the core message of Jim Tarantino’s June 2026 webinar on using spreadsheets, no-code, low-code, and embedded AI solutions to advance Internal Audit’s use of data analytics. “AI still lacks the logic to provide the 100% precision Internal Audit often needs as an analytics function,” said Jim. That’s why he views data analytics as an “indispensable pillar” providing the reliable foundation of precision, consistency, and determinism upon which AI and automation can operate. 

Jim is a consultant, speaker, and trainer who helps GRC teams use data, automation, analytics, and AI. He’s also the senior instructor for the Internal Audit Collective’s DRIVE course, designed to help any auditor become proficient in creating, executing, and troubleshooting analytics. (The next program starts July 13, 2026; sign up today.) 

As Jim reinforced, today’s analytics tools will ultimately become tomorrow’s AI tools. Building out your analytics capabilities now positions you to use AI to make them faster, easier, and more scalable down the road. What better way to start than with desktop analytics capabilities you may be overlooking? (Note: Feature availability may depend on licensing, configuration, and region.)

Following are several of the tools Jim demoed. (Internal Audit Collective members can access Jim’s slides and watch the webinar on demand.)

Spreadsheet-Based Quick Wins: More Interactive Analytics

Modern enterprise spreadsheet software embeds analytics capabilities auditors may not be exploiting. These tools enable more interactive analytics without requiring additional software. 

1. Explore Existing Spreadsheet Functionality — Like “Analyze Data” 

There’s now a button in Microsoft Excel called “Analyze Data.” I’d wager that, like me, most Internal Auditors haven’t tried it yet. 

You can change that today. “Analyze Data” enables fast exploratory analytics while requiring no real technical aptitude.  

Jim’s demo showed how “Analyze Data” uses automated statistical analysis and behind-the-scenes AI to generate a series of charts based on your selected data, guessing which analytical slices may interest you. You can then:

• Insert them as pivot charts into your workbooks. 
• Ask the tool natural-language questions (e.g., countries with most units sold, top vendors by spend, monthly trends, duplicate payments) to slice and dice the data in new ways. 

Both Microsoft and Google’s spreadsheet solutions have added capabilities for creating more interactive analytics. For example, depending on your version or license, Excel may offer: 

• Slicer and Timeline (under “Insert”). Add filters and drop-down menus, letting you explore your data with, in Jim’s words, “a cockpit of controls that have the look and feel of a Tableau or a Power BI.”
• Forecasting (under “Data”). Get predictions based on historical time-series data. 
• Statistics add-ons. Try utilities for sampling, regression models, and variance checks. 
• Insert Python (under “Formulas”). Run Python scripts within cells to perform statistical analysis and ML calculations. (More on Python below.)
• Microsoft Copilot. Copilot can generate formulas, summarize data, offer interactive assistance, (e.g., data cleaning recommendations), and much more, 

Google Sheets offers:

• Apps Script (under “Extensions”). Leverage cloud-native Javascript automation tools to customize audit macros and other tasks. 
• Google Apps (under “Extensions”). Build out web apps for dashboards and other common use cases. 
• Third-party Gemini integrations to connect with Anthropic’s Claude. Explained Jim, “You can use Claude to reference formulas. It can summarize a column. It can look for data quality issues. It can describe your data and make recommendations about how you can analyze it.” 

2. Use Power Query to Build Repeatable Workflows 

In the Microsoft world, Power Query automates data transformation and acts as a bridge to your enterprise data sources (e.g., Excel or CSV files, SQL servers, SharePoint, APIs, web pages). The tool, available via Get & Transform Data (under the “Data” menu), works the same in both Excel and Power BI. So if you know how to do it in one technology, the skill easily transfers to the other.

As Jim explained, as long as you have the required credentials, Power Query lets you query a variety of file formats and databases. “It’s a very rich way for Excel to extract data. In many cases, I can extract from data sources that have more than a million rows of data. I have to manage down the data to get it into Excel, but I'm not necessarily limited by the size of the source — I'm only limited by what I can show in the Excel data set.” 

But as you run queries and transform your data — importing, filtering, merging, standardizing, and so on — Power Query remembers what you did, creating a repeatable workflow. 

That way, when the data updates and you ask Power Query to refresh, it’ll reach out to the data sources and use its consistent, automated logic to rerun your queries and refresh the logic and reports.

So, why not convert your recurring audit tests (e.g., AP testing, expense reporting analytics, user access reviews) into Power Query workflows? You’ll reduce repetitive manual effort, increase consistency, and save hours you can redeploy elsewhere. 

Graphics and Dashboards: High-Impact Reporting and Monitoring

Enterprise software also embeds impressive AI-enabled capabilities for creating dynamic visuals, narratives, and monitoring dashboards, helping bridge the gap between complex data and executive-ready assurance reporting. No coding experience needed. 

3. Leverage Desktop AI Capabilities to Tell a Better Story

Power BI Desktop offers enterprise quality and connectivity integrated with AI capabilities. Essentially, once you’ve used Power Query to clean up your data, you can drag and drop charts from the Visualizations pane directly onto a canvas and add Slicers (e.g., drop-down menus, custom filters) to enable dynamic viewing. You can also use AI enablers for:

• Q&A visuals. Type a question; instantly get a custom chart. Jim demonstrated how, instead of writing out the specific metric, he could use natural language to ask for what he wanted (e.g., median gross sales). If you save it, it’s added to your list of available charts. 
• Smart Narratives. Request AI-written summaries of a highlighted chart or findings. Jim explained, “I can actually have it describe what I am seeing in a particular chart.” 
• Key influencer identification. Drag and drop the chart(s) containing the data you’d like to predict; the tool applies statistical and ML techniques to identify factors associated with a selected outcome.
• Decomposition. Create an interactive tree that enables you to drill down into root causes. 
• Azure AI integration. Tie into Azure capabilities for direct text analytics, sentiment analysis, and machine learning predictions. 

Google Data Studio/Looker Studio offers an agile, intuitive, cloud-native, browser-based option for creating data visualizations. “It’s basically just Power BI for Google,” said Jim. “It has chart pickers, you drag and drop your fields, you pick your filters, you lay out canvases of dashboards. It works the same way as Tableau.” Features include:

• Direct connection to hundreds of data sources, including Google Sheets, BigQuery, Google Ads, Google Analytics, Microsoft Excel, CSV files, cloud databases, data warehouses, and more. 
• Community visualizations. Beyond the standard built-in charts, you can leverage custom charts created by third-party developers to display complex risk information with high-impact formats (e.g., Gantt charts, heat maps, funnel charts, radar/spider charts, Sankey diagrams, treemaps, gauges, custom geographic maps). 

4. Enable Continuous Monitoring with Dashboarding

Existing desktop tools can help you create dashboards that update in real time:

• Power BI Desktop makes it simple to publish and share reporting dashboards. After using the Publish button to upload the finished report to your workspace, you can simply open the report, hover over the charts or KPIs you want to feature, and click Pin Visual for each. Then, you can create an executive-summary canvas called the Dashboard by pinning your most important visuals from one or more reports. 
• In Google Data Studio/Looker Studio, you can Create a report, connect to your data source(s), verify field types, and design your dashboard by adding visualizations you’ve created, Scorecards to add KPI metrics, interactive controls (e.g., filters, drop-down menus, search boxes), and custom metrics.

Google users can also create custom dashboards or web apps by pairing Google Gemini with Claude to write Google Apps. Jim shared an example in a live demo: “I wanted multiple cards across multiple swim lanes, creating an analytic dashboard. I did not code this out myself. I described what I wanted in [Gemini], and it wrote the code in the Google Apps Script interface.”

The result: a Kanban board organizing tasks into swim lanes (i.e., backlog, not started, in progress, in review, complete) and a dashboard with visuals tracking his team’s pipeline, progress, upcoming deadlines, task ownership, and more. 

“It’s vibe coding. It’s agentic engineering — that’s kind of the cooler way to say it. Either way, it’s using natural language to code out your applications,” explained Jim.

Low-Code Analytics: Reusable Workflows and Big Data Capabilities

Jim also encouraged auditors to begin exploring free, low-code tools to enable higher-horsepower analytics solutions. 

5. Use (Free!) KNIME to Create Reusable Workflows

KNIME is an open-source, low-code visual programming and analytics tool that offers many of the capabilities associated with tools like Alteryx. Jim advised, “This is a viable option when you’re budget-constrained, or you want to start doing advanced analytics but don’t want to learn a whole programming language.” 

KNIME is a good way for auditors to start extending their desktop capabilities given its:  

• Free or low cost. The desktop version is free. (Server access comes with a cost.) 
• Capacity. KNIME can handle row volumes and complexity Excel and Sheets can’t. 
• Flexible functionality for creating reusable workflows. KNIME is a great tool for creating audit analytics workflows (e.g., three-way match testing, journal entry analysis, duplicate payment detection) that can be reused across different engagements. 
• Intuitive drag-and-drop interface. KNIME uses icons called “nodes” that represent different types of inputs, outputs, analytics, and data manipulation. You drag and drop these nodes onto a canvas to sequence your workflow. “It’s like taking Visio icons and dragging them together into a workflow that performs an analytical operation,” said Jim. “The workflow is written out in a picture,” making it self-documenting. There’s also an AI helper to help you use the tool.
• Automation capabilities. Canvases can be automated. “So every time you get a file, you drop it into a directory. Then a KNIME workflow reads the file and runs through the workflow,” said Jim. 

6. Try Python Capabilities Embedded in Enterprise AI Tools

When you’re ready to start using a programming language, Jim recommends considering Python. Python is a great option because of its:

• Big data horsepower, letting you bypass Excel’s 1M row limit. You can use Pandas — an open-source Python library used for working with structured data — to help you import, clean, transform, join, and analyze full-year general ledgers and millions of records. 
• Interoperability. As mentioned, Excel and Power BI support Python scripts for certain data preparation and visualization scenarios. Python can integrate with Google Sheets, Google Colab, and BigQuery.
• Vibe coding. For example, you can describe risks in natural language and have AI draft functional scripts for data cleaning and testing. 
• Minimum-install sandbox — complete with audit trail. You can leverage Jupyter Notebooks or Google Colab notebooks to run Python in your browser, letting you put Python into boxes you can run interactively. The notebooks log your work, keeping your outputs in exportable files you can use as workpapers. 
• Zero-dollar price tag. The main cost is in “sweat equity,” said Jim. “This takes a little bit of technical skill to get used to… but it’s free. Python has no cost. Jupyter Notebooks doesn’t have a cost. You just have to learn to use the tools.”

THE LAST WORD: Today’s Analytics Are Tomorrow’s AI Tools

Jim’s main message: Analytics is the foundation. AI is its amplifier. 

So don’t wait for AI to “mature.” Start using the tools you already have to build a strong analytics foundation that will help you drive more benefit from your AI and automation investments. 

And in the process, future-proof your skill set, becoming the triple-threat auditor the future demands.

Said Jim, “As auditors, we're naturally inquisitive, and I think our time has come to shine — by having some of these natural-language push-button tools that inspire us to think, explore, and use these tools to their greatest advantage.”

So yes, Internal Audit teams must build their internal AI capabilities. But it’s equally crucial to keep developing and reinforcing the data analytics capabilities that will help auditors do more with AI. 

That’s why Jim’s 4-week, 16-CPE course, DRIVE, is free for Internal Audit Collective members. What are you waiting for? Sign up today to get the practical, tactical knowledge you need to make the most of your analytics tools and more effectively integrate analytics in your audits. The next course begins July 13, 2026.

We also worked with Jim to create a fantastic eBook, How to Build a Data Analytics Program That Works, Sticks, and Scales. Download your copy for actionable insights and how-to guidance on avoiding common data analytics pain points, balancing your operating model as maturity increases, strategies for evolving your data analytics people strategy, and more. 

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