The true test of intelligence:
Using AI to test AI

Businesses need to trust that the core AI algorithms and logic produce the expected outcomes.

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Goodbye hype. AI is real.

Businesses are increasingly adopting AI applications like Microsoft Copilot and Salesforce Einstein to revolutionize their operational frameworks, creating a more efficient, innovative, and customer-focused environment. But what happens when AI isn’t working as expected? How do you ensure your AI isn’t hallucinating?

A Simple Guide to AI Testing Tools

Generative AI risks

Trust and reliability issues

Errors erode users' trust in the AI, degrading customer experience and business performance.

Data exposure

Malfunctions may lead to inadvertent exposure or mishandling of sensitive data.

Compromised quality

Incorrect suggestions or completions can result in errors impacting business operations.

Compliance risks

Errors in AI processing could lead to non-compliance with data privacy regulations, posing legal risks.

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Build trust in your AI apps with Leapwork

You want your teams to deliver higher-quality software faster and more effectively. To meet this challenge, you may be using or considering AI-augmented testing tools. Let us show you how you drive continuous quality across your business – including your AI apps.

AI Validate

Compares AI-generated responses with expected outcomes to ensure accuracy.

AI Transform

Allows for complex data manipulations tailored to specific needs, enhancing data utility within tests.

AI Extract

Simplifies the extraction of data from various inputs, making your data handling more streamlined.

AI Generate

Creates realistic and varied datasets for more comprehensive testing, simulating real-world scenarios.

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“The world of enterprise software is going to get completely rewired. Companies with untrustworthy AI will not do well in the market.”

Abhay Parasnis

Founder and CEO, Typeface

Source: Salesforce+ Series – Ask more of AI

AI and Software Quality: Trends and Executive Insights

This report provides decision-makers with a comprehensive overview of AI adoption, highlighting the critical need for testing AI applications and the growing role of AI-augmented testing tools.

It offers essential insights and solutions for businesses to adapt and consistently deliver exceptional use and customer experiences at scale.

Download the report

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AI application use case examples

Customer service

Natural language processing (NLP) enable chat-bots to understand and respond to customer queries effectively.

Supply chain optimization

Machine learning models analyze historical sales data, market trends, and other factors to forecast demand and optimize stock levels, reducing overstock and stockouts.

Process automation

Robotic Process Automation (RPA) combined with AI automates routine and repetitive tasks such as data entry, invoice processing, and employee onboarding.

Data analysis and insights

AI analyzes large datasets to uncover patterns and insights that inform decision-making.

Fraud detection and cybersecurity

AI algorithms detect fraudulent activities by analyzing transaction patterns and identifying anomalies and identifying potential threats in real-time.