By Daniel W. Rasmus, Serious Insights

Rasmus highlights key lessons from industry leaders on successful AI deployment in organizations. Drawing from discussions with experts like Saanya Ojha of Bain Capital Ventures, Moveworks President Varun Singh, and thought leaders from TigerGraph, Semedy, and TopQuadrant, the article emphasizes designing AI projects with clear success metrics, including measurable ROI. Despite the MIT NANDA report noting that most AI pilots fail to reach production or deliver ROI, the takeaway is not AI’s failure but the need for organizations to ground AI initiatives in business realities. Success hinges on narrow project scopes, workflow integration, robust data foundations, and scalable design principles that ensure accuracy and governance. Knowledge graphs, enhanced by techniques like GraphRAG, play a vital role in contextualizing and scaling AI to meet enterprise needs, offering a practical playbook for achieving sustainable AI returns.

By Rob Enderle, The Enderle Group for TechSpective

In the complex world of enterprise IT, decisions around software support often feel like a Faustian bargain. Companies invest millions in mission-critical applications from giants like Oracle, SAP, and now, increasingly, VMware. With that investment comes the assumption that the vendor’s own support will be the most effective lifeline. However, a growing number of enterprises are discovering a powerful alternative: third-party support providers like Spinnaker Support. Far from being a niche solution, third-party support is emerging as a strategic imperative, offering significant advantages, particularly as organizations navigate the transformative landscape of Artificial Intelligence (AI).

By Daniel W. Rasmus

Technological revolutions rarely arrive on schedule. They come with fanfare, bold promises, and heavy investment, but their impact often hides in the shadows before it bursts into the open. Artificial intelligence now finds itself in this awkward in-between space. Boardrooms are abuzz with talk of generative AI strategies; enterprises are pouring billions into pilots and partnerships; employees are experimenting with chatbots and copilots. Yet in measurable business terms, most of these efforts appear to fail to deliver returns on the investments.

By Teodora Siman - Research Manager, C-Suite Tech Agenda Program

How are CEOs leading through AI, volatility and risk? IDC’s 2025 CEO Survey reveals 5 tech priorities shaping leadership in an AI-driven world.

By Daniel W. Rasmus

Since the late 1990s, mind mapping has been a digital proxy for representing Tony Buzan’s freewheeling paper-based maps, giving them enhanced features, first for editing and then for all manner of activities from financial control or project planning.

By Bob Violino for CIO.com

CIOs with an eye for AI’s promise of improved productivity often find their efforts hindered by data quality, skills gaps, and sabotage, among other factors.

Artificial intelligence — and generative AI in particular — is fast proving to be a useful solution for increasing productivity across the enterprise but several common barriers to success remain.

The sooner IT leaders can identify and overcome these issues, the faster they will enable their organizations to get more value from AI-based systems.

By Rich Staples 

With all the buzz around Generative AI, it’s easy to forget that it’s just one type of artificial intelligence. Time Series, Geospatial, Computer Vision, and Graph AI are also growing fast—sometimes even faster than GenAI— particularly in sectors where real-time predictions and pattern recognition are business-critical.

Take Computer Vision: you've seen it in action in manufacturing, retail, and in medical imaging. But Time Series AI? It's not grabbing headlines, yet it's quietly driving massive outcomes behind the scenes. So What Is Time Series AI?

By Daniel W. Rasmus

As organizations embrace the democratization of AI, they often overlook the messy realities lurking just beyond the pilot projects and training sessions. Giving end users the tools to build and deploy their own AI agents sounds empowering—and in many ways it is—but without careful design, clear governance, and shared intent, it also invites a wave of unintended consequences. Before celebrating the creativity unleashed by user-developed agents, it’s worth stepping back and asking: how might this new autonomy create conflict, confusion, and risk inside the enterprise? Here’s a closer look at how agents can go wrong.

By Rob Enderle for TechSpective

Artificial intelligence (AI) is rapidly transforming our world, impacting everything from healthcare and transportation to finance and entertainment. As AI systems become increasingly sophisticated, it is crucial to ensure their development and deployment align with ethical principles.

By Security Advisor Rex M. Lee

In this third installment of our AI series, we unpack the rapid rise of artificial intelligence (AI) since 2000, spotlighting breakthroughs, risks, and the future of advanced systems like Artificial General Intelligence (AGI), Artificial Superintelligence (ASI), and quantum AI. Whether you're new to AI or a seasoned user, understanding its trajectory is critical to navigating its impact.