The AI dashboard shows you the exact cost of a chatbot interaction, but not the business value of that interaction. That gap is about to become more than a budget problem. It is becoming a competitiveness problem, and most businesses have no pipeline in place to close it.
Read MoreMuch of the effort and attention around AI for the last several years has been around technical developments. New model announced! New benchmark surpassed! New contract for massive data centers! New world-class technologists hired! You know the drill.
I am happy to say, however, that things are beginning to change in this regard. AI companies are beginning to realize something that many corporate executives knew intuitively. What matters isn’t the technology—OK, that’s important too—but the ability of organizations to deploy it effectively and get value from it.
Read MoreFew companies deliberately integrate process management and knowledge management practices when deploying AI. Process management (PM) and knowledge management (KM) typically reside in separate silos where process often sits in operations with an emphasis on Lean Six Sigma and knowledge management often rests in HR. Meanwhile, AI initiatives are regularly led by data/IT teams. Major opportunities are missed as these three initiatives are rarely integrated.
Read MoreMost 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 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.
The 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 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 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.
Read MorePeople outside regulated industries often assume the hardest part of the job is making the right decision. In practice, the harder part is explaining that decision later to someone who was not there, does not know the context, and is paid to be skeptical.
Read MoreThis is the season of holiday overeating and over-drinking—despite the fact that moderate consumption of food and alcohol is widely believed to lead to a better life. Although I sometimes agree with Oscar Wilde in advocating “moderation in all things—including moderation,” I am beginning to think that AI—particularly the generative variety—is no different than food, alcohol, or other good things that become problematic when used excessively.
Read MoreThe technology that dazzled in a controlled environment struggles to deliver consistent value when deployed across the enterprise. This is not a technology problem. It is an architecture problem. And until executives recognize the difference, organizations will continue investing millions in AI capabilities that never achieve meaningful scale.
Read MoreOver the past eighteen months, corporate language shifted from curiosity about AI to impatience with results. Adoption is widespread. Enterprise impact is not.
McKinsey’s State of AI 2025 found that about 88% of companies now use AI in at least one function and 62% are experimenting with AI agents. Only around 23% report scaling an agentic system somewhere, with single-function scaling rarely breaking into double digits. Only 39% report enterprise-level EBIT lift.
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