COGNITIVE WORLD

Organizations are structuring and curating knowledge so AI tools can find and present the right answers, grounding responses in validated sources instead of plausible-sounding hallucinations. In a RAG implementation, that source material is often the company's own knowledge base and internal documentation, information that doubles as competitive advantage. For retrieval to work, this information must be curated and structured. 

For hundreds if not thousands of years, human intelligence has been a rare and valuable good. Those who had large amounts of intelligence could monetize and achieve high social status with it. Political leaders sought them out. Universities paid them to teach, do research, and publish on the topics about which they were smart. Being a “knowledge worker”—one whose job involved the creation, synthesis, or distribution of intelligence—was a role to which many aspired, and was often viewed as a credential leading to long-term and lucrative employment.

Artificial intelligence is moving from experimentation into everyday enterprise work. Organizations use AI to improve productivity, reduce costs, enhance customer experiences, and support decisions. But technical capability alone does not guarantee success: employees and customers need to understand how a system affects them, what it can do, and how its outputs can be questioned.

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.