For over 15 years, Jennifer Stirrup has been sharing insights and practical advice on Business Intelligence, AI Leadership, Data Strategy and Data Platform expertise. We are collating videos that Jennifer has done herself, or has collaborated with other thought leaders. You can check out the Video page here.
Security needs to be a first-class citizen in every AI project. This means integrating security into every phase of the AI development lifecycle rather than relying on post-deployment fixes. It cites statistics indicating significant financial losses from AI security incidents and highlights real-world cases illustrating the risks of neglecting lifecycle security, urging organizations to adopt proactive, secure-by-design principles for responsible AI deployment.
A 158-year-old UK transport company, Knights of Old, collapsed due to a cyberattack resulting from a single compromised password, leading to 700 job losses. This incident highlights critical cybersecurity failures and emphasizes that all businesses, not just large ones, must prioritize data protection to avoid devastation from similar risks.
A UK government data breach exposed sensitive details of 19,000 Afghan nationals due to human error in spreadsheet management. The leak, caused by a hidden tab in an email attachment, jeopardized lives and cost millions. It emphasizes the need for robust data governance and secure systems to protect data as well as humans from making mistakes.
Comet, Perplexity AI’s innovative Chrome competitor, enhances web searching by integrating AI assistants for tasks like reserving restaurants. As enterprises evolve, they are transitioning from using isolated AI models to orchestrating specialized agents, which improves efficiency across functions. This shift aims for optimal value from AI investments, resulting in better decision-making and customer experiences.
In today’s data-driven business world, organizations often struggle to derive meaningful insights from their metrics, making data storytelling essential. This approach combines data, visualization, and narrative to bridge the gap between numbers and decision-making, enabling clearer communication and inspiring action, ultimately enhancing business outcomes and ROI from analytics investments.
Successful organizations in the AI landscape achieve a competitive edge by harmonizing technological advancements with human judgment. This synergy enhances customer satisfaction and innovation. While AI excels at automation, human empathy remains vital for meaningful connections. A balanced approach fosters improved outcomes, as seen in companies like Starbucks and major retailers.
Did you know that 60% of AI projects are projected to fail by 2026. Will your organisation host one of these projects? Read more to learn how to make your project one of the 40% that succeed.
Despite significant investments in AI, a 2025 crisis reveals that most organizations face an “AI execution gap,” struggling to achieve results. Research indicates 60% of AI projects will fail by 2026, often due to weak data foundations rather than technology. Addressing data challenges is vital for successful AI implementation and outcomes.
Multimodal AI is rapidly transforming business intelligence, evolving faster than anticipated. Organizations are leveraging its capabilities to integrate text, images, audio, and video, enhancing decision-making and customer experiences. Successful implementation requires focus on specific use cases, robust data infrastructure, and proactive governance to harness its advantages and improve operational efficiency significantly.
Shadow AI is one of the hidden areas of today’s workforce. But what about customers potentially manipulating your LLMs behind your agents? Learn how FIDES, a deterministic enforcement system, protects LLM-powered workflows from prompt injection attacks. Discover best practices for AI data security in customer-facing enterprise applications.
If you are balancing AI project expectations versus actual outcomes, then this post is for you. Have you considered process or methodological issues rather than technology? This post critiques legacy frameworks like CRISP-DM for being misaligned with modern needs and advocates for more agile, collaborative methodologies. The silent revolution emphasises the necessity for frameworks to meet business objectives effectively.