COGNITIVE WORLD

Large organizations are already putting more structure around AI consumption. The Wall Street Journal reported that TIAA uses token limits based on employee role and workload, with additional consumption subject to approval. The company is also trying to distinguish valuable, compute-intensive work from activity that simply consumes more compute. Its longer-term direction is more intelligent routing, including matching a task to an appropriate model.

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.

An 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.

Deepfakes 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.

The 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?

The 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.

Treat 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.

Smaller 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.

AI 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.

The 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.

I 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.

IBM 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.

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.

Much 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.

The 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.

Few 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.

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.

AI 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.

The 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.

Over 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.

There 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.

Artificial 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

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.

Every 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.

It'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.

But 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.

I’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.