Organizations often view AI adoption as an opportunity to invest in new technologies, but one of their greatest assets may already exist in years of accumulated business information. Historical records, customer interactions, operational reports and institutional knowledge provide valuable context that strengthens AI data and improves the quality of AI insights.
Read MoreAn important turning point in the development of cybersecurity is the increasing incorporation of AI into every layer of the digital ecosystem. AI is a ubiquitous operational force that is simultaneously bolstering defensive capabilities and enabling attackers with previously unheard-of speed, precision, and autonomy. It is no longer a theoretical technology or a far-off future.
Read MoreDeepfakes undermine trust. In today’s digital ecosystem, trust is not a soft value; it is functions as our operational infrastructure. When people can no longer trust what they see or hear, the foundations of commerce, governance, national security, and personal relationships start to erode. Generative AI has made that risk a reality.
Read MoreThe question underneath that exchange is worth asking directly: what does an expert consultant actually offer, in an age when AI can produce a plausible-sounding regulatory and clinical development strategy on demand?
Read MoreThe lesson is not to surround every AI action with another approval committee. That recreates the friction automation was supposed to remove. The better response is to make exceptions visible, reviewable, and owned.
As I stated in my piece last February, there aren’t a lot of sure-fire methods to slow down the development of AI. But there are some potential levers that could help. The most commonly discussed are legal and regulatory approaches.
Read MoreTreat explainability as infrastructure. Ask vendors about their substrate, not their dashboard. Hire the librarians. Give the project a year. The AI deployment that wins the next decade is the one whose conclusions you can defend in a deposition. That deployment is not the one with the best reasoning-display screen. It is the one whose foundations were built to hold the trail, and whose builders were given the time to lay them.
Read MoreSmaller banks are often told they are behind on enterprise AI. That is true, but only up to a point. In practice, many of them already run on a more standardized operating base than the largest banks, because so much of their work moves through SaaS and core banking platforms that quietly shape how the bank operates.
Read MoreAI investment is accelerating, yet business outcomes remain elusive. The root cause is fragmentation — siloed automation tools disconnected from AI strategy. This paper presents a stage-by-stage roadmap for enterprise leaders to unify AI and IT automation investments, achieve measurable ROI within 9–12 months, and build the architecture required for agentic, enterprise-scale automation.
Read MoreThe next decade will see AI evolve beyond isolated applications into dynamic intelligence fabrics, exhibiting contextual awareness, cooperative reasoning, and continuous learning across all sectors. Future AI will possess persistent memory, multimodal perception, and long-term planning, creating vast digital workforces that blur the line between software and human employees.
Read MoreI have co-authored several articles over the last year or two that together suggest organizations are not likely to get business value with genAI if their primary focus is improving individual productivity. But everybody doesn’t read Harvard Business Review, and I have never put all the reasons for this in one place. So here goes—a laundry list of explanations for why you’re probably not going to achieve measurable productivity gains from genAI.
Read MoreIBM CEO Arvind Krishna recently made a point in the Wall Street Journal that is worth taking seriously. “AI is not helping your business. It is your business model.” He’s right, because most conversations about AI are still happening at one level below what he’s describing.
Read MoreThe 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 MoreThe irony is hard to miss. SR 26-2 leaves each bank to determine how agentic AI should be governed through its own risk framework and architecture. Inside the workflow, the agent is doing its own version of that: resolving what its inputs mean before it acts. hat is where the hardest problem now sits: not in the model output or the execution record, but in the reasoning layer, where operational meaning forms before action.
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 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.
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