February 19, 2026
8:00 PM - IST
Enterprises invested aggressively in modern data platforms, cloud migration, analytics modernization, and AI initiatives throughout 2025. Data lakes expanded, pipelines matured, governance tools improved, and AI pilots demonstrated promising results.
Yet despite this progress, enterprise data continues to fall short of the trust required to scale AI with confidence.
The trust gap widened.
In 2026, the constraint is no longer data availability. Most organizations possess more data than ever before. The real challenge lies in whether that data is complete, explainable, governed, and operationally reliable enough to support AI-driven decision-making at scale.
In this recently concluded session, QKS Group analysts examined why enterprise data still fails AI trust tests, where structural and operational constraints persist across the data-to-AI lifecycle, and what CDOs must redesign as organizations move from AI experimentation to AI accountability. Drawing on real-world enterprise scenarios and emerging data operating models, the discussion translated trust breakdown patterns into actionable guidance for data and AI leaders.
The webinar delivered an operating-level assessment of why data modernization does not automatically translate into AI readiness. Participants gained clarity on how fragmented ownership, inconsistent governance, brittle pipelines, and limited accountability undermine production-scale AI outcomes, and what structural redesigns are required to make AI dependable, scalable, and defensible in 2026.
Why Enterprise Data Still Isn't Trusted for AI
Examination of how data platforms are optimized for access and analytics, not AI reliability, with trust often assumed at ingestion rather than continuously validated.
Where AI Architectures Break in Production
Analysis of how AI pilots succeed in isolation but fail at scale due to real-world variability, regulatory scrutiny, inconsistent lineage, and operational dependencies.
The Limits of Traditional Data Governance Models
Assessment of legacy governance frameworks that cannot keep pace with AI-driven data usage, manual stewardship processes that do not scale, and policy enforcement gaps.
How AI Redefines Data Trust
Insights into the shift from static quality checks to continuous trust validation, where lineage, explainability, context, and model-level assurance become mandatory.
Where CDOs Encounter Structural Constraints
Exploration of fragmented accountability across data engineering, governance, security, and AI teams, weak feedback loops between AI outcomes and upstream data correction, and rising regulatory pressures without proportional control mechanisms.
Operational Metrics Data Leaders Must Track in 2026
Identification of execution-focused KPIs such as AI-readiness validation coverage, lineage completeness, data-to-model traceability, trust validation frequency, and feedback-loop remediation speed.
Operating Model Redesign for AI Accountability
Discussion on aligning data teams with AI outcomes rather than platform uptime, automating trust controls, embedding governance into pipelines, and redefining shared accountability across data, AI, risk, and business stakeholders.
Analyst Recommendations & Best Practices
Practical guidance for designing AI-ready data architectures, implementing continuous trust validation frameworks, strengthening lineage and explainability controls, and transitioning from data availability metrics to outcome-driven trust assurance.
This session was designed for Chief Data Officers, CDAIOs, Heads of Data Governance and Quality, AI and Advanced Analytics Leaders, and Risk and Data Security executives responsible for scaling enterprise AI responsibly.
Viewers gained exclusive access to QKS Group's research-driven insights on data trust and AI execution readiness. The discussion provided structured clarity on where trust breaks down across the data-to-AI lifecycle and the architectural, governance, and operating-model changes required to enable dependable, scalable, and accountable AI in 2026.
Associate Director & Principal Analyst
Amandeep Singh is a Principal Analyst & Associate Director with deep expertise in Communication & Collaboration, Customer Experience, and AI. He is a trusted advisor to industry leaders, offering strategic guidance that empowers decision-makers to navigate the complexities of Conversational AI, CCaaS (Contact Center as a Service), UCaaS (Unified Communications as a Service), CPaaS (Communications Platform as a Service), Customer Communication Management (CCM), and cutting-edge customer experience solutions. Amandeep’s comprehensive analysis spans a vast array of vendors within the CCM, CCaaS, UCaaS, CPaaS, and Conversational AI ecosystems. He provides invaluable insights that shape product strategies, drive market expansion, and refine competitive positioning. His meticulous monitoring of market dynamics ensures that vendors align their innovations with the evolving needs of businesses and end-users. In his pursuit of innovation, Amandeep has established pivotal frameworks in Generative AI adoption, including the Digital AI Leap Framework (DALF) and the Enterprise AI Maturity Model. These frameworks have been instrumental in guiding organizations through the intricate journey of AI integration, helping them achieve scalable growth and operational excellence. As a recognized expert in Generative AI solutions, Amandeep’s research expertise spans advanced technical areas such as Large Language Models (LLM), Retrieval-Augmented Generation (RAG) models, and Natural Language Technologies (NLT). He is deeply involved in the development and application of these cutting-edge technologies, focusing on their integration into AI-driven communication strategies. His deep knowledge and innovative solutions have fueled the growth of numerous companies, enabling them to leverage AI for competitive advantage, enhanced customer experiences, and optimized communication frameworks.
Principal Analyst - Data Management
Arun U. is a Principal Analyst at QKS Group, specializing in the Data Management domain. His role involves planning, managing, and conducting global strategic market outlooks, market insight reports, technology and user-survey guides, SPARK Matrix Analysis, Vendor Review guides and user consulting assignments. Arun's expertise lies in strategic consulting within Data Management, BPM and Process Automation, and Data Analytics domains, focusing on areas such as Data Preparation, Data Integration, Data Governance, Data Catalogs, Data Fabric, Intelligent Process Automation, and Decision Intelligence. Currently managing global assignments in Data Management and Business Process Management and Process Automation domain. Arun have worked as a lead analyst for global assignments including Multimodel Data Platforms, Intelligent Data Catalogs, Enterprise Data Fabric, Enterprise Information Archiving, Event Stream Processing, Intelligent Process Orchestration, Digital Decisioning Platforms, Intelligent Business Process Management Suites such others. He is passionate about observing and monitoring the impact of AI and machine learning on BPM and Data analytics technologies. He tracks over 200 vendors in the data management and business process management space and advise them on product strategy, market expansion strategy, and competitive positioning analysis. He handles both individual consulting and research projects and collaborates closely with a team of analysts within the organization. Arun has extensive experience working with clients across various industries, domains, and global regions on projects that include technology architecture planning, cloud transformation, vendor selection strategies, and operational due diligence. In addition to his core responsibilities, Arun is an active participant in industry events, conferences, webinars, and talk shows on various social media platforms. He regularly shares his expertise and contributes to thought leadership discussions with industry leaders and domain experts.
