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.
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?
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.
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.
“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
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.
What app? Participate in our survey and
receive the report
Let’s find out what AI applications you’re using and why. This is an open project, so we’ll send you the results and post them here. If you’re still early in your AI journey, take a moment to imagine the use cases that would benefit your business. If you need a little prompting, we’ve added use cases below for you to consider.
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.
Energy management
AI optimizes energy consumption by analyzing usage patterns and adjusting systems for efficient energy use.
Technology and consumer goods
AI accelerates product development by analyzing customer feedback and market trends to suggest new features or product ideas.
Predictive maintenance
Sensors collect data on machinery, and AI algorithms analyze this data to forecast maintenance needs, reducing downtime and repair costs.