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“Let no feeling of discouragement prey upon you, and in the end you are sure to succeed.” -- Abraham Lincoln

The identity mess your customers feel before you do

Over half of organizations rely on developers who are not specialists in . These teams juggle identity work alongside core product duties, which leads to slow progress, inconsistent implementation, and recurring defects. Decision makers admit that they underestimate the time developers spend on authentication. In many organizations, identity work drops down the backlog until a breach, an outage, or lost revenue forces renewed attention. Context switching is common. Developers move between authentication, compliance requirements, and product enhancements, which increases the likelihood of mistakes and slows delivery. ... Authentication issues undermine revenue as well as security. Organizations report that user dropoff during login, delays in engineering delivery, and abandoned transactions stem from outdated authentication flows. These issues rarely show up as a single budget line, but they accumulate into lost revenue and higher operating costs. ... is set to make customer identity more complicated. Automated activity will increase on every front, from routine actions taken on behalf of legitimate users to large scale attacks that target login and account creation flows. Security teams will face more traffic to evaluate and less certainty about what reflects user intent. Attackers will use AI to run high volumes of account takeover attempts and to create synthetic identities that blend in with normal behavior.

Bank of America's Blueprint for AI-Driven Banking

Over the past decade, Bank of America has invested more than $100 billion in technology. "Technology is a strategic enabler that now allows AI and automation to expand across every part of the organization, stretching from consumer services to capital markets," Bank of America CEO said. This focus on scale also shapes how the bank approaches . ... The bank's decade-long AI effort now supports 58 million interactions each month across customer support, transactions and informational requests. has also become an internal platform. Erica for Employees has "reduced calls into the IT service desk by 50%," Bank of America said. This internal role matters because it shows how a consumer-grade AI system can evolve into an enterprise asset - one that assists with IT queries, operational troubleshooting and employee guidance across large distributed teams. ... The bank's CashPro Data Intelligence suite includes AI-driven search, forecasting and insights, and recently won the "" award. These capabilities bring predictive analytics directly into the operational core of corporate treasury teams. By analyzing behavioral cash flows, transaction histories, seasonality and market data, the platform can generate forward-looking liquidity projections and actionable insights. For enterprises, this means fewer manual reconciliation cycles, improved liquidity planning and faster financial decision-making.

Cybersecurity Is Now a Core Business Discipline

Cybersecurity is now a core business discipline, not an IT specialty. When a household name like Marks & Spencer can take a $400 million hit to trading profits after a major cyber incident, we’ve moved beyond “technology risk” into enterprise resilience. I often say the bad actors only need to get lucky once; defenders must be effective 24/7. That asymmetry won’t vanish. The job of leadership is to run with it; to accept the pace of the threat and build organizations that can withstand, respond, and keep moving. ... If bad actors only need to be lucky once, then your business must be designed to fail safely. That means strong identity controls, everywhere it makes sense, segmentation that limits lateral movement, and backups that are both tested and recoverable. None of this is glamorous. All of it is decisive. I’ve yet to meet a breached organization that regretted investing in the basics. Engineer for better human decisions. Traditional awareness training has diminishing returns if it’s divorced from real work. Replace generic modules with just-in-time prompts in the tools people actually use. Add controlled friction to high-risk workflows: payment changes, supplier onboarding, privileged access approvals. Normalize “pause and verify” by making it easy and expected. Culture is created by what gets rewarded and what gets made simple.

Building Your Work Digital Twin Starts With The Video You Already Have

This concept is far from new. We've already seen AI-generated assistants, virtual trainers and automated knowledge bases. But what separates a true from a chatbot or a script is the ability to capture how we communicate and not just what we say. That's where video—where tone, style, facial expression and more are clearly displayed—becomes invaluable. ... The idea of creating another you that actually delivers requires a concerted effort from both individuals and organizations. But it starts with centralizing and organizing the video content that already exists across departments, including training sessions, customer interactions, leadership updates and team calls. Assembling the video is just the start, as curating what matters is key. Prioritize videos that demonstrate clarity, professionalism and authenticity. ... As AI becomes more prevalent, authenticity, not automation, is emerging as a competitive differentiator. Customers, partners and employees still crave the sense of a real, trustworthy voice, and human digital twins give organizations a way to scale that presence. These are not fabricated influencers or AI puppets but extensions of real people, grounded in consent and context. Of course, this shift also demands ethical guardrails: clear usage boundaries, transparency about when digital twins are speaking and secure storage of identity data. When done responsibly, it can be a powerful evolution of human-machine collaboration that keeps people at the center.

AI adoption blueprint: Driving lasting enterprise value in India

The challenge that employees face towards is not rooted in capability gaps or lack of enthusiasm, but stems from insufficient contextual understanding. Organisational experiences reveal that mandating users to move between disparate systems enables them to craft their own prompts or proactively seek AI assistance without much experience, which often results in digital friction, underutilisation or complete abandonment. These challenges intensify across diverse workforces spanning multiple languages and regions. ... Building workforce confidence around AI remains a key hurdle given the uneven distribution of AI fluency across teams—even within digitally advanced Indian IT ecosystems. Overcoming this requires embedding just-in-time learning resources tailored to user roles and scenarios directly inside the applications employees use daily. Offering interactive onboarding, scenario-based microlearning, and guidance in multiple languages not only meets users where they are but respects the linguistic and cultural diversity that characterises India’s workplaces. This approach helps alleviate hesitation, foster trust, and accelerate AI fluency across complex organisations. ... Treating adoption as a continuous process that evolves alongside workflows, user requirements, and business priorities ensures AI continues to deliver value beyond launch phases, achieving sustainable scale.

A CIO’s 5-point checklist to drive positive AI ROI

“Start by assigning business ownership,” advises Srivastava. “Every AI use case needs an accountable leader with a target tied to objectives and key results.” He recommends standing up a cross-functional PMO to define lighthouse use cases, set success targets, enforce guardrails, and regularly communicate progress. Still, even with leadership in place, many employees will need hands-on guidance to apply AI in their daily work. ... CIOs should also view talent as a cornerstone of any AI strategy, adds CMIT’s Lopez. “By investing in people through training, communication, and new specialist roles, CIOs can be assured that employees will embrace AI tools and drive success.” He adds that internal hackathons and training sessions often yield noticeable boosts in skills and confidence. Upskilling, for instance, should meet employees where they are, so Asana’s Srivastava recommends tiered paths: all staff need basic prompt literacy and safety training, while power users require deeper workflow design and agent-building knowledge. ... The resounding point is to set metrics early on, and not fall into the anti-patterns of not tracking signals or value gained. “Measurement is often bolted on late, so leaders can’t prove value or decide what to scale,” says Srivastava. “The remedy is to begin with a specific mission metric, baseline it, and embed AI directly in the flow of work so people can focus on higher-value judgment.”

The coming storm for satellites

Although an uncommon occurrence, the list of dangers caused by space weather is daunting. In addition to atmospheric drag piercing LEO space, Earth’s radiation belt can be changed by the injection of high-energy electrons, plunging geostationary satellites at high elevations into deep-space conditions, unshielding them from the Earth’s magnetosphere. Even inside the relative protection of the planet’s orbits, radiation can damage electronics, charged particles from the sun can electrify the body of a spacecraft, potentially powering a discharge between two differently charged sections, and solar cells can be degraded faster during solar storms. A single space weather event can cause the same wear and tear as an entire year of normal operation. ... Nonetheless, the concern is “that a big solar event could disable a large number of satellites and cause a major increase in the collision risk, particularly in the very busy LEO orbit domain,” Machin says. “We need to ensure that such an event does not risk our ability to continue using space in the future. “We need to always plan for space sustainability.” Machin alludes to the danger of Kesseler Syndrome, a scenario in which debris density in low-Earth orbit becomes so great that the destruction of satellites and newly launched vehicles becomes probable, thereby multiplying debris density, resulting in unusable orbits, and trapping the human race on Earth for thousands of years.

How intelligent systems are evolving: Rob Green, CDO at Insight Enterprises

We operate on a zero-trust model and corresponding policies. An additional advantage of being a major Microsoft partner is that we received early access to ChatGPT, which we deployed internally as “InsightGPT.” We launched it early to develop AI capabilities within our services, solutions, and IT teams. We recognised the need for clear guidelines around AI usage and deployment. Our AI usage policies, first introduced two years ago, ensure employees understand how to implement and experiment with AI responsibly. These policies are continuously updated, our most recent revision was released three weeks ago. Regulatory and compliance requirements vary by region, and our policies are adapted accordingly. ... First, we ensure awareness and education across the organization. Not everyone needs to be an AI developer, but we want employees to be fluent with AI tools and understand how to use them productively. We recently launched the AI Flight Academy, which includes five proficiency levels. A large portion of employees is expected to reach advanced levels. Our mission has evolved, we aim to be a leading AI-first solutions integrator. To support this, my team is building platforms that enable agentic capabilities across shared functions such as finance, HR, IT, warehouse operations, and marketing.

Agentic HR: from static roles to growth roles with AI co pilots

When people cannot see progress, they stop stretching. In many firms the only formal feedback loop is the annual review. That is too slow for real learning and it misses the small wins that power engagement. The alternative is to treat every role as a platform for growth. You design work so that capability increases by doing the work itself. This is where agentic HR comes in. ... Co pilots should live where work already happens. That means chat, documents, code, tickets, and task boards. The system watches patterns, respects privacy settings, and offers context aware prompts. ... People facing AI must earn trust. That starts with shared governance. HR and technology leaders should set rules for data minimisation, explainability, and bias monitoring. They should also be clear on when AI recommends and when a human decides. Two reference points help. The introduces a risk based approach with specific duties for higher risk use cases and transparency expectations for generative systems. This shapes how enterprises should document and oversee AI that touches employees. The NIST AI Risk Management Framework provides practical guidance on mapping risks, measuring impacts, and governing models over time. It is vendor neutral and it emphasises continuous monitoring rather than one time checks. Enterprises can also look to the new ISO and IEC standard for AI management systems.

The Three Keys to AI in Banking: Compliance, Explainability and Control

When a new technology like AI enters an industry, the goals are simple: Save money, save time, and ideally, increase revenue. According to a 2023 report from McKinsey, AI has the potential to reduce operating costs in banking by 20-30% by automating manual processes, cutting down on errors and saving time. ... Finance is one of the most heavily regulated industries, and rightfully so. When you’re managing transactions and people’s hard-earned money, there is little room for error. As banks adopt AI, they need full disclosure for what is happening every step of the way. ... To close that gap, financial institutions need to prioritize not only technical accuracy but also interpretability. Investing in training, cross-functional collaboration and governance frameworks that support explainable AI will be key to long-term success. The banks that succeed will be the ones that use AI systems their regulators can audit, their teams can trust, and their customers can understand. ... Trust is the currency of this industry, which is why adoption looks different here than it does in consumer tech. Rather than rushing into full-scale adoption, many banks are starting with pilot programs that have tightly scoped risk exposure. ... Done right, AI can help institutions expand credit more inclusively, flag risks earlier and give underwriters clearer insights without sacrificing compliance.

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Daily Tech Digest - November 27, 2025