Scaling AI with confidence
AI Quality Assurance is essential to build trust
The Forrester Consulting Opportunity Snapshot: a custom study commissioned by Cognizant and Microsoft finds that according to senior IT leaders, quality assurance is increasingly important for organizations as AI adoption gains momentum. The right partner provides agility and reduces complexities to navigate this shift.
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<h5>The last mile of AI implementation can create a range of challenges—bias, drift and lack of explainability often emerge when it is scaled. Traditional approaches to software testing are not sufficient for next generation, AI-infused applications. According to Forrester Consulting research, 82% of leaders believe their organizations must dedicate time to an AI quality assurance strategy—because reliable performance at scale is critical. Cognizant’s AI Assurance framework makes this possible. By embedding testability, traceability and reliability across the AI lifecycle, we help enterprises evaluate, test and monitor AI models, agents and application features. Our approach accelerates deployment and delivers measurable business impact.</h5>
<h3><b>Real stories, real impact</b></h3>
<h3>AI Assurance Services</h3>
Data assurance
Consistent, accurate and unbiased data
Data augmentation techniques involve providing the right data for training and testing the AI model—validating early for bias, drift, coverage and realism. Detecting gaps early prevents flawed inputs that can compromise outcomes. This shift-left foundation secures reliable performance before the first prediction is made.
Functional and model quality assurance
Resilient and reliable every time
Beyond checking for accuracy, AI-infused applications are validated across edge cases, stress conditions and changing inputs. Disaggregated metrics reveal hidden weaknesses, subgroup errors and unintended trade-offs. This ensures AI-infused applications aren’t just functional but resilient, reliable and aligned to enterprise expectations in real-world conditions.
Trustworthy assurance
Regulatory compliant and trust-validated
The focus is on testing if AI is fair, explainable and resistant to manipulation. By embedding checks for transparency, privacy, ethics and accountability, we ensure systems not only meet regulations or compliance requirements but also earn lasting stakeholder confidence.
Non-functional assurance
Reliable, secure and resilient
Validation ensures that AI delivers optimal performance, secures PII data without any leakage and remains resilient enough to recover from failures or disruptions.
Latest thinking
<h4><b><span class="text-bold">Responsible AI at Cognizant</span></b></h4> <p>Learn how to engineer your AI systems with integrity, ensuring they are fair, secure and transparent. Our approach focuses on building a foundation of ethical principles that promotes responsible AI adoption and earns lasting trust.</p>
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<h3>Industry recognition</h3>
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Contact us to learn how Cognizant can help you build, fine-tune, validate and deploy AI models faster and better.