Anyone who has a job that requires intensive analysis will say that the speed boost they can find is like getting an extra 30, 60 or 90 minutes back.

Automation tools in general, and AI tools in particular, can help business analysts who need to crunch large amounts of data and present it in a concise manner.

In fact, a recent Gartner analysis, “An AI-First strategy leads to increased revenue,” says the most advanced enterprises rely on AI to improve the accuracy, speed and scale of analytics to drive three key goals – business growth, customer success and cost effectiveness – and competitive intelligence is at the core of each.

Google just released Gemini 2.0 Flash provides business analysts with greater speed and flexibility in defining Python scripts for complex analysis, giving analysts more precise control over the results they produce.

Google claims Gemini 2.0 Flash 1.5 builds on the success of Flashits the most accepted model for developers.

According to Google, Gemini 2.0 Flash outperforms 1.5 Pro in key benchmarks and provides twice the speed. Flash 2.0 also supports multimodal input, including images, video, and audio, as well as multimodal output, including native images mixed with text and controlled text-to-speech (TTS) multilingual audio. It can also natively call tools such as Google Search, code execution, and third-party user-defined functions.

Get Gemini 2.0 Flash software for testing

VentureBeat gave Gemini 2.0 Flash a series of increasingly complex Python scripting requests to test its speed, accuracy, and precision in dealing with the nuances of the cybersecurity market.

Use up Google AI Studio To arrive at the model, VentureBeat started with simple script requests and worked its way up to more complex requests centered on the cybersecurity market.

What's immediately noticeable about Python Scripting with Gemini 2.0 Flash is how fast it is — almost instantaneously, in fact — at rendering Python scripts and generating them in seconds. It is noticeably faster than 1.5 Pro, Claude and ChatGPT when handling increasingly complex challenges.

VentureBeat asked Gemini 2.0 Flash to perform a typical task for a business or market analyst: Create a matrix comparing a number of vendors and analyze how AI is being used in each company's products.

Analysts often need to create quick spreadsheets in response to sales, marketing, or strategic planning requests, and they typically need to capture each company's unique strengths or insights. This can take hours or even days to do manually, depending on the analyst's experience and knowledge.

The VentureBeat script included an analysis of 13 XDR vendors and wanted to make the prompt request a reality by providing insight into how AI can help listed vendors process telemetry data. As with many requests from analysts, VentureBeat asked Python to produce an Excel file of the results.

Here's what we've provided for Gemini 2.0 Flash implementation:

Write a Python script to analyze the following cybersecurity vendors integrated AI into the XDR platform and create a table showing how they differ in their AI implementation. The first column should be the name of the company, the second column should show the company's products in which AI is integrated, the third column should show what makes them unique, and the fourth column should show how AI can help XDR platforms process telemetry data in detail with an example. . Don't scrape the web. Create an Excel file of the output and format the text in the Excel file so that brackets ({}), quotation marks (') and HTML codes are clean to improve readability. Name the Excel file. Gemini 2 flash test.
Cato Networks, Cisco, CrowdStrike, Elastic Security XDR, Fortinet, Google Cloud (Mandiant Advantage XDR), Microsoft (Microsoft 365 Defender XDR), Palo Alto Networks, SentinelOne, Sophos, Symantec, Trellix, VMware Carbon Black Cloud XDR

Using Google AI Studio, VentureBeat created the following AI-powered XDR Vendor Comparison Python script request, Python code generated in seconds:

Then, VentureBeat saved the code and uploaded it Google Co. The purpose of doing this was to see how bug-free the Python code was outside of Google AI Studio and to measure its compilation speed. The code worked perfectly without any errors and generated a Microsoft Excel Gemini_2_flash_test.xlsx file.

The results speak for themselves

Within seconds, the script ran and Kolab reported that there were no errors. It also gave a message at the end of the script that the excel file was executed.

VentureBeat downloaded the Excel file and found it completed in less than two seconds. Below is a formatted view of the Excel spreadsheet to which the Python script is delivered.

The total time it took to complete this spreadsheet was less than four minutes, including submitting the proposal, getting the Python script, running it in Colab, loading the Excel file, and doing some quick formatting.

A compelling argument for unlocking AI in monotonous tasks

For many professionals working in a variety of business, competitive and market analyst roles throughout their careers, AI is the force multiplier they seek to shave hours off repetitive, monotonous tasks.

Analysts are naturally intellectually curious. By unleashing AI on the simplest and most repetitive parts of their jobs, equipping them to create the comparisons and matrices they're asked to quickly develop is a powerful boost to the productivity of the entire team.

Business managers and leaders, competitive analysis and marketing teams should consider how rapid advances in models, including Google's Gemini 2.0 Flash, can help their teams manage their growing workloads. Helping carry that burden allows analysts to do what they love and do best, which is to use intuition, intelligence, and insight to deliver uniquely valuable ideas.