Focusing on Commvault, the recent developments in AI data security at the RSA Conference emphasise the importance of integrating Data Security Posture Management (DSPM) into AI systems. The “last mile” of governance, where sensitive data may be mishandled, is highlighted as a gap that organisations must address to ensure secure AI operations.
The Commvault SHIFT London event highlighted that data resiliency is essential for effective AI and BI strategies. We need to consider what we do with the data that AI produces, not just the data sources. Without a strong data foundation, businesses risk poor decisions and operational failures. The event advocated for integrating security and data management, deploying tools like Commvault Cloud Unity, and continuously testing recovery plans to ensure organisational resilience.
Microsoft’s latest earnings call indicates a strategic shift towards prioritizing high-margin AI software over infrastructure. With 15 million Copilot users, the focus is on transforming enterprise value through active user engagement rather than mere license purchases. The emphasis on intelligence signals the future direction for organizations investing in AI.
The rise of artificial intelligence brings excitement, yet many organizations fall into the “AI Pilot Trap,” where initiatives fail to yield real value. Statistics show 95% of AI tools never reach production and 72% destroy value. To escape this trap, organizations should focus on governance, back-office ROI, and strategic partnerships.
Datasphere technologies are transforming data management for AI by transitioning from siloed structures to interconnected ecosystems that deliver real-time insights, ensure data quality, and democratize product development. These advancements enable organizations to efficiently leverage data, improve AI deployment speed, and enhance innovation, positioning them for competitive advantage in the evolving landscape.
IBM’s $11 billion acquisition of Confluent represents a strategic shift in enterprise AI deployment, focusing on event-driven platforms for resilience and speed. Confluent’s streaming technology enhances data integration across hybrid environments, fostering robust AI ecosystems. This move positions IBM competitively against major cloud providers, emphasising that data infrastructure is just as important for AI scalability and reliability.
Discover why Shadow AI is reshaping the enterprise, often under the radar. Explore essential statistics, real-world risks, and proven solutions to manage Shadow AI, protect your data, and enable secure innovation in your organisation.
Organisations need to be AI resilient to protect and maximise their investments in AI. Here’s a roundup of how Commvault’s new Cloud Unity Platform is transforming enterprise resilience by unifying data security, cyber recovery, and identity protection to help you achieve AI resilience. Learn about key features, real-world benefits, and why this matters for your organisation.
Discover which AI agents actually work in production today and why, and which are still just hype. This post unpacks real-world AI agent successes, common failures, and what every organisation needs for deployment,plus simple answers to the biggest FAQ about AI agents in 2025.
Balancing structured AI and BI project delivery with unexpected business-as-usual demands is vital for data leaders. Discover thoughtful leadership strategies to protect team wellbeing, maintain project focus, and build resilient, responsive organisations.
Trust – but verify! AI hallucinations, which occur when AI generates false information confidently as fact, present significant challenges and risks for organizations. A recent case involving Deloitte illustrates the dangers of unchecked AI, highlighting the critical role of data quality and governance in preventing such issues. Striving for accuracy, relevance, and human oversight is essential to develop trust in AI systems.
Effective AI implementation relies on a well-structured data landscape. Organizations face challenges with “data puddles,” which create silos, “data lakes” that provide organized access, and “data swamps,” which hinder AI initiatives. Success requires governance, metadata management, and purposeful data strategies to transform chaos into a competitive advantage, maximising AI’s potential.