Most organizations today use artificial intelligence (AI) primarily for isolated productivity tasks. Employees ask models to summarize reports, draft emails, generate presentations, analyze spreadsheets, or answer questions. These applications create measurable gains, but they often automate only fragments of a larger operational process.
Read MoreAI is beginning to transform IT operations in significant ways and impacting the bottom line. This article will discuss how IT operations can be transformed by embedding AI into IT Operations. Key use cases impacted by AI across IT operations such as infrastructure & application deployment, management of deployed environment and remediation of issues will be discussed. An example will then be provided so that reader has a better understanding on how to transform IT Operations with AI.
Read MoreThe allure of AI in supply chain management is real. Executives envision chatbots that instantly answer questions about shipment status and delivery exceptions, and knowledge graphs that surface hidden relationships between suppliers, routes, and delivery outcomes. In last-mile logistics where conditions shift by the minute these are not fantasies, they are the future of supply chain intelligence.
Read MoreIn March 2023, the failure of Silicon Valley Bank exposed what practitioners had long understood: operational risk governance failures at individual institutions can cascade into systemic crises. The Federal Reserve’s post-mortem found that SVB had 31 unaddressed supervisory warnings at the time of its failure — triple the average of its peer institutions. The root causes were not exotic. They were failures of basic risk identification, control documentation, and management oversight.
Read MoreOver the past ten years, every executive who has sanctioned a cybersecurity budget can detail the vendor risk process. A SaaS contract triggers a SOC 2 review, sub-processor reviews, data handling reviews, breach notification clauses, etc. NIST and ISO 27001 codify it. It is a developed field.
Read MoreThere is increasing consensus that to get value from AI, you have to redesign your business processes and embed AI within them. A McKinsey 2025 survey, for example, found that redesigning workflows is the factor most highly correlated with getting value from AI. A 2026 paper from MIT researchers argues that AI benefits will come from supporting “chains” of business activities, i.e. processes. My friend Erik Brynolfsson, head of Stanford’s Digital Economy Lab, has long argued that in a “J curve” situation, productivity with AI initially lags as companies re-engineer processes, but then increases significantly once AI is fully integrated into new, redesigned workflows.
Read MoreArtificial intelligence (AI) has created a paradigm shift for Cybersecurity. AI and machine learning (ML)-powered computing systems are now essential to cyber operations. They assist security teams in keeping an eye on large networks, spotting irregularities instantly, and reacting more quickly than is humanly feasible. By automating tasks that would otherwise overburden under-resourced teams, AI levels the playing field in today's threat landscape, which is characterized by sophisticated ransomware, social engineering, and malware.
AI has brought significant advances in automation, decision-making, and content generation, but these benefits carry inherent risks that demand robust security measures. AI security spans data privacy, model integrity, adversarial robustness, and regulatory compliance. This article examines the primary threat vectors targeting AI systems, the key domains requiring protection, and the security controls organizations should put in place to address them
Read MoreThe primary drivers behind BPO decisions haven't changed dramatically: cost reduction, access to specialized talent, scalability, and the ability to focus internal teams on core business. What has changed is how AI reshapes the calculus on each of these.
Read MoreEvery time your organization deploys an AI system, a critical decision gets made — usually by a developer, sometimes by a vendor, rarely by anyone with accountability for the outcome. That decision is: what kind of AI are we using? And in most enterprises today, the honest answer is: we don't actually know, and we don't have the language to find out.
Read MoreIt's good work. Five forces - technology, economics, geopolitics, demographics, climate - each with their own core shifts and key uncertainties.
The work isn’t prediction. It’s perseverance. It’s staying with the complexity long enough to recognize patterns instead of imposing them. It’s building systems that make it easier for the parent with the 13-point cognitive tax to access the same quality of care, information, and decision-support as the investor reading Amy Webb’s report at $10,000 a seat.
That’s not a technology problem. It’s not even an AI problem. It’s a recognition problem — who we see, what we count, and whether we’re willing to build for the ground conditions that already exist instead of the convergence we hope is coming.
Read MoreBut generative AI is clearly changing the process of data analysis. I’ve been experimenting with quantitative data analysis using ChatGPT for several years now. I was certainly impressed by the LLM’s ability to generate Python code to analyze structured data and create machine learning models.
Read MoreI’m reading Jill Lepore’s book If/Then about the origins of analyzing human behavior data with computers. One interesting aspect of it is the automation paranoia arising from the introduction of the IBM 704 mainframe computer in 1954 (the year I was born). The book even includes an image from an automation-focused campaign leaflet for John F. Kennedy’s 1960 presidential campaign—see it above.
Read MoreAs the common logic goes, a smooth road can make you sleepy. A bumpy road keeps you alert. Organizations are increasingly deploying AI to automate discrete activities and sub-processes. Examples are AI copilots that draft, summarize, and decide, and increasingly, AI agents that execute multi-step work with minimal human input. The cumulative logic is irresistible: less friction at each step means faster throughput and higher productivity for all.
Read MoreArtificial intelligence is changing cybersecurity faster than most companies expected. It is helping security teams catch threats earlier, sort through overwhelming volumes of alerts and respond more quickly. But it is also making life easier for attackers, who can now produce more convincing phishing emails, better impersonation scams and more targeted attacks at much greater scale. This is what makes the current moment so important. AI is not just improving cybersecurity tools. It is changing the nature of the fight itself.
Read MoreQuantum computing stands at an intriguing but early stage of development. The technology is advancing, and there are credible signs of progress across hardware, algorithms, and ecosystem readiness. However, the leap from controlled pilots to mainstream enterprise adoption remains substantial. For now, quantum computing is best understood not as an immediate disruptor, but as a strategic, long-term investment—one that organizations should monitor closely, experiment with cautiously, and prepare for thoughtfully.
Read MoreAfter years of anticipation, quantum computing is no longer a distant promise. It has moved decisively into the center of the technology conversation, joining artificial intelligence as one of the defining breakthroughs of recent years. The shift has been driven by an acceleration in scientific progress and a surge in corporate investment that has pushed quantum computing from a laboratory experiment to a strategic priority.
Read MorePricing is one of the most important decisions for organizations and individuals. We may pay 20 dollars for a glass of wine in a restaurant while the same bottle costs the same at a grocery store. The liquid is identical. The value is not. We are paying for context, service, timing, and experience. Price is not a static number. It is a quantified expression of perceived value at a particular place and time.
Read MoreDigital infrastructure serves as the foundation for national security, the economy, and everyday life in today’s hyper-connected world. Artificial intelligence (AI) and quantum computing are examples of emerging technologies that inspire creativity. However, these technologies also magnify risks posed by sophisticated attacks, black swans, gray swans, economic volatility, and geopolitical tensions. At this point, resilience—the ability to anticipate, endure, and recover from disruptions—is absolutely necessary.
Read MoreA great deal has been written about readiness for artificial intelligence (AI). In Factors influencing readiness for artificial intelligence: a systematic literature review, the authors examined 52 papers to study AI readiness factors.
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