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.
Discover how leadership is evolving as humans and AI converge. Learn the essential skills, strategies, and trends for future-ready leadership in the age of artificial intelligence—insights from Jennifer Stirrup.
Discover how intelligent augmentation (IA) and hybrid workforces combine human creativity with AI efficiency. Practical steps and real-world examples for a resilient, future-ready organisation. The Future of Work needs IA as well as AI.
From pilots to enterprise-wide transformation, discover how AI strategy consulting helps organisations scale innovation, achieve measurable business results, and build a sustainable competitive edge. Backed by data from ResearchGate, IDC, and McKinsey.
Executive boards must make informed technology investment decisions, especially regarding AI. In this article, Jennifer Stirrup discusses emerging best practices based on industry research. Essential inquiries include strategic alignment, data quality, risk assessment, ethical considerations, long-term impacts, and governance structure. Proactive oversight ensures competitive advantage and stakeholder trust.
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.
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.
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.