Daily Tech Digest by Kannan Subbiah: satellites

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Daily Tech Digest - March 20, 2026

Quote for the day:

"Nothing so conclusively proves a man's ability to lead others as what he does from day to day to lead himself." -- Thomas J. Watson

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Rethinking Cyber Preparedness in Age of AI Cyberwarfare

The article "Rethinking Cyber Preparedness in the Age of AI and Cyberwarfare" highlights a critical disconnect termed the "," where nearly 80% of IT leaders feel prepared for cyberwarfare despite over half of organizations suffering AI-driven attacks recently. According to Armis’s latest report, traditional defense mechanisms are failing against , which nation-state actors now deploy for rapid reconnaissance and lateral movement. As autonomous agents begin weaponizing zero-day exploits faster than human researchers can categorize them, the attack surface has expanded to include overlooked assets like building management systems and IoT devices. The financial stakes are escalating, with average ransomware payouts reaching $11.6 million, often exceeding annual security budgets. To counter these sophisticated threats, the article emphasizes that organizations must achieve superior visibility into their internal environments and map every network asset. Furthermore, IT leaders should embrace AI-driven security policies rather than ineffective bans to combat the risks of "shadow AI" used by employees. Ultimately, true resilience depends on whether a company knows its own infrastructure better than its adversaries, transforming AI from a liability into a vital defensive tool for modern geopolitical threats.

Are small language models finally having their moment?

The rapid ascent of Small Language Models (SLMs) marks a strategic shift in the artificial intelligence landscape, as enterprises seek to mitigate the immense costs and security risks associated with massive frontier models. Unlike their trillion-parameter counterparts, SLMs operate with significantly fewer parameters—ranging from millions to a few billion—allowing them to run locally on laptops or mobile devices without internet connectivity. This architectural efficiency ensures superior data privacy and regulatory compliance, particularly in sensitive sectors like healthcare, defense, and banking where proprietary data must remain on-premises. While Large Language Models (LLMs) excel at general synthesis and creative tasks, SLMs are increasingly preferred for specialized, rules-based functions such as code completion and document classification. Gartner even projects that by 2027, task-specific SLM usage will triple that of LLMs. Through techniques like knowledge distillation and pruning, these compact models offer a cost-effective, energy-efficient alternative that delivers high performance with minimal latency. Consequently, the industry is moving toward a hybrid ecosystem where SLMs handle secure, specialized operations while LLMs provide broader abstraction, proving that in the evolving world of enterprise AI, bigger is not always better for every specific business need.

What it takes to level up your org’s AI maturity

To advance an organization's AI maturity, leaders must transition from merely "doing AI" to driving substantial business impact through an outcomes-based, AI-first strategy. According to experts Afshean Talasaz and Zar Toolan, this shift requires CIOs to adopt an "innovator-operator" mindset, balancing the need for rapid evolution with the stability required for consistent execution. Maturity is categorized into three levels, with the most advanced organizations enjoying a first-mover advantage led by CEO-backed agendas. A critical component of this journey is the "from-to so-that" modeling, which aligns data and AI initiatives with specific strategic outcomes like trust, business value, and reduced time to value. Winners in this space prioritize long-term infrastructure investments and rigorous data cleanup while securing short-term wins to demonstrate ROI. Furthermore, scaling AI successfully demands an intense focus on granular details rather than abstract concepts; without getting the technical and operational nuances right, true scale remains elusive. Ultimately, the transformation is a "team sport" requiring absolute alignment across the C-suite and a commitment to reducing internal volatility. By preparing thoroughly and maintaining consistent execution, organizations can move beyond operational tools to treat sovereign enterprise data as a powerful competitive moat.

The Power Ladder Architecture—A System For Turning Risk Work Into Decisions, Delivery And Proof

Maman Ibrahim’s article, "The Power Ladder Architecture," addresses the critical gap between identifying organizational risks and executing meaningful change. Ibrahim argues that risk management often fails not because of a lack of effort, but because it fails to convert analysis into "leadership work." Many teams present polished dashboards that provide a false sense of security while stalling when faced with difficult trade-offs. The Power Ladder is proposed as a solution, shifting the focus from mere reporting to three tangible outcomes: decisions, delivery, and proof. First, "decisions" require framing risks as binary choices for leadership, forcing clarity on trade-offs like speed versus security. Second, "delivery" ensures that once a choice is made, it is translated into structured tasks with clear ownership and deadlines. Finally, "proof" demands verifiable evidence that the risk profile has actually improved, rather than just being documented. By implementing this architecture, organizations can move beyond ceremonial risk management and establish a high-altitude system where audit concerns and cyber exposures are effectively neutralized. This approach transforms risk work into a powerful engine for operational resilience, ensuring that every identified vulnerability leads to a documented decision and a validated result.

The espionage reality: Your infrastructure is already in the collection path

Modern enterprises are increasingly caught in the "collection path" of global espionage, not necessarily as primary targets, but because they utilize the same centralized infrastructure as their adversaries. This shift highlights a structural exposure problem where shared dependencies—such as telecommunications, cloud services, and identity layers—become conduits for siphoning data and monitoring authentication. When national telecommunications providers are compromised, attackers can collect intelligence directly from the pathways an organization relies on, rendering traditional internal security measures insufficient. The article emphasizes that security leaders must move beyond internal asset protection to evaluate risk through the lens of . Key recommendations include demanding integrity attestation from providers, reducing implicit trust in external networks, and hardening session layers to mitigate token theft and impersonation. Furthermore, the persistence of advanced persistent threats (APTs) within backbone infrastructure is now influencing the cyber insurance market, leading to higher premiums and stricter exclusions. Ultimately, organizations must integrate intelligence-driven assessments into their governance models, acknowledging that upstream compromise is a structural reality. To maintain resilience, CISOs must treat every external partner as an active component of their threat surface and design systems that degrade safely under inevitable compromise.

A direct approach to satellite communication

The article "" on Data Center Dynamics explores the transformative shift in how satellite systems integrate with terrestrial network infrastructures. It highlights the evolution from traditional, isolated satellite setups toward a more "direct" and seamless integration within the broader data center and cloud ecosystem. The piece details how and advancements in software-defined networking (SDN) are reducing latency and increasing bandwidth, making satellite links a viable, high-performance extension for enterprise networks rather than just a backup for remote locations. By treating space-based assets as reachable network nodes, providers can offer direct cloud connectivity, bypassing complex ground-station hops that previously hampered speed. This integration allows data centers to achieve greater resiliency and global reach, facilitating real-time data processing for edge computing and IoT applications in underserved regions. Ultimately, the analysis suggests that the convergence of space and ground infrastructure is turning satellite communication into a mainstream pillar of modern digital architecture, effectively "cloudifying" the final frontier to support the next generation of global, high-speed connectivity.

AI will accelerate tech job growth - former Tesla president explains where and why

In this ZDNet article, Jon McNeill, former Tesla president and current CEO of DVx Ventures, challenges the "" narrative by highlighting how artificial intelligence will actually accelerate employment in specific sectors. McNeill argues that the growing complexity of AI-driven ecosystems creates an intense demand for human expertise, particularly in infrastructure and networking. As organizations deploy massive server farms and sophisticated GPU clusters, the need for skilled professionals to manage, synchronize, and maintain these resilient networks becomes critical. While AI may handle basic coding and quality control, McNeill emphasizes that high-level architectural design remains a uniquely human domain, requiring "smart computer scientists" to navigate multi-layered model stacks. A core takeaway from his experience is the "automate last" principle, which suggests that businesses must first simplify and optimize their manual processes before introducing automation. By doing so, companies avoid the trap of embedding complexity into rigid code. Ultimately, McNeill urges technology professionals to move up the value chain, focusing on architectural innovation and process optimization, while cautioning against using expensive AI solutions where simpler, human-led methods are more effective and efficient for long-term growth.

Are You the Problem at Work? These 15 Questions Will Reveal the Truth.

In the Entrepreneur article "15 Questions That Reveal If You’re the Problem at Work," author Roy Dekel challenges leaders to look inward rather than blaming external factors for workplace issues like high turnover or low engagement. The piece argues that while many professionals prioritize strategic optimization, the true bottleneck is often a lack of emotional intelligence (EQ). To help leaders identify their blind spots, Dekel presents fifteen diagnostic questions that assess one’s "emotional wake." These include whether a team falls silent when the leader enters the room, how the leader reacts to bad news, and whether they value outcomes over effort. High EQ is framed as the foundation of psychological safety; leaders who possess it tend to listen more, apologize easily, and regulate their emotions under pressure, ultimately making their employees feel "bigger" rather than "smaller." By honestly answering these questions, managers can transition from being a source of tension to becoming a catalyst for trust and innovation. The article concludes that leadership is effectively the environment in which others must work, emphasizing that self-awareness is a learnable skill that can fundamentally transform organizational culture and employee satisfaction.

Aura breach and AI companion app flaws sharpen privacy fears

The recent security report highlighting widespread vulnerabilities in AI companion apps, coupled with a significant data exposure at identity protection firm Aura, has intensified global privacy concerns regarding the management of intimate user data. Aura recently confirmed that a targeted phishing attack on an employee allowed unauthorized access to approximately 900,000 records, including names and email addresses, though sensitive financial data remained secure. Simultaneously, research by Oversecured revealed that seventeen popular AI companion and dating simulator apps—boasting over 150 million installs—contain hundreds of critical and high-severity security flaws. These vulnerabilities, ranging from hardcoded cloud credentials to exploitable chat interfaces, potentially expose deeply personal information such as erotic chat histories, sexual orientation, and even suicidal thoughts. Despite the sensitivity of this data, the report emphasizes a regulatory "blind spot," noting that while authorities have addressed child safety and broad privacy disclosures, they have yet to enforce rigorous application-layer security standards. Together, these incidents underscore the growing risk of a digital era where companies frequently fail to protect the highly personal details they solicit from users. This convergence of corporate breaches and structural app flaws highlights an urgent need for stricter oversight and improved security architectures across the global network ecosystem.

The rise of the intelligent agent: Why human-in-the-loop is the future of AIOps

The article "The Rise of the Intelligent Agent: Why is the Future of AIOps" examines the transformative role of Agentic AI in IT operations through an interview with Srinivasa Raghavan S of ManageEngine. It argues that intelligent agents should amplify human expertise rather than replace it, specifically by automating repetitive tasks and filtering out telemetry noise to provide actionable insights. A central theme is the "human-in-the-loop" architecture, which integrates automation with strict policy guardrails, orchestration, and auditability to ensure engineers maintain control. These systems utilize machine learning for predictive anomaly detection and causal AI for rapid root-cause analysis, significantly decreasing mean time to resolution. By transitioning from reactive monitoring to self-driving observability, enterprises can better align technical health with business goals like customer experience and uptime SLAs. Although hybrid and multi-cloud environments introduce visibility challenges, unified observability platforms help manage this complexity. Ultimately, the article advocates for a phased adoption of autonomous remediation, building trust through transparent, guarded processes that combine machine speed with human oversight to navigate the intricacies of modern digital infrastructure effectively and safely.

Daily Tech Digest - December 04, 2025

Quote for the day:

"The most difficult thing is the decision to act, the rest is merely tenacity." -- Amelia Earhart

Software Supply Chain Risks: Lessons from Recent Attacks

Modern applications are complex tapestries woven from proprietary code, open-source libraries, third-party APIs, and countless development tools. This interconnected web is the , and it has become one of the most critical—and vulnerable—attack surfaces for organizations globally. Supply chain attacks are particularly insidious because they exploit trust. Organizations implicitly trust the code they import from reputable sources and the tools their developers use daily. Attackers have recognized that it's often easier to compromise a less-secure vendor or a widely-used open-source project than to attack a well-defended enterprise directly. Once an attacker infiltrates a supply chain, they gain a "force multiplier" effect. A single malicious update can be automatically pulled and deployed by thousands of downstream users, granting the attacker widespread access instantly. Recent high-profile attacks have shattered the illusion of a secure perimeter, demonstrating that a single compromised component can have catastrophic, cascading effects. ... The era of blindly trusting software components is over. The software supply chain has become a primary battleground for cyberattacks, and the consequences of negligence are severe. By learning from recent attacks and proactively implementing robust security measures like , secure pipelines, and rigorous vendor vetting, organizations can significantly reduce their risk and build more resilient, trustworthy software.

Building Bridges, Not Barriers: The Case for Collaborative Data Governance

The preserves existing structure while improving coordination among teams through shared standards and processes. This is now more critical to be able to take advantage of AI systems. The collaborative model is an alternative with many benefits for organizations whose central governance bodies – like finance, IT, data and risk – operate in silos. Complex digital and data initiatives, as well as regulatory and ethical concerns, often span multiple domains, making close coordination across departments a necessity. While the collaborative data governance model can be highly effective for complex organizations, there are situations where it may not be appropriate. ... Rather than taking a centralized approach to managing data among multiple governance domains, a federated approach allows each domain to retain its authority while adhering to shared governance standards. In other words, local control with organization-wide cohesion. ... The collaborative governance model is a framework that promotes accessible systems and processes to the organization, rather than a series of burdensome checks and red tape. In other words, under this model, data governance is viewed as an enabler, not a blocker. ... Using effective tools such as data catalogs, policy management and collaboration spaces, shared platforms streamline governance processes and enable seamless communication and cooperation between teams.

China Researches Ways to Disrupt Satellite Internet

In an academic paper published in Chinese last month, researchers at two major found that the communications provided by satellite constellations could be jammed, but at great cost: To disrupt signals from the to a region the size of would require 1,000 to 2,000 drones, according to a research paper cited in a report in the South China Morning Post. ... against satellites are being embraced because they pose less risk of collateral damage and are less likely to escalate tensions, says Clayton Swope, deputy director for the at the Center for Strategic and International Studies (CSIS), a Washington, DC-based policy think tank. ... The constellations are resilient to disruptions. The latest research into jamming constellation-satellite networks was published in the Chinese peer-reviewed journal Systems Engineering and Electronics on Nov. 5 with a title that translates to "Simulation research of distributed jammers against mega-constellation downlink communication transmissions," the SCMP reported. ... China is not just researching ways to disrupt communications for rival nations, but also is developing its own constellation technology to benefit from the same distributed space networks that makes Starlink, , and others so reliable, according to the CSIS's Swope.

The Legacy Challenge in Enterprise Data

As companies face extreme complexity with multiple and disparate analytical data assets models owned by the line of business analysts, the decision-making becomes challenging when moving to cloud-based data systems for transformation and migration. Where both options are challenging, this is not a one-size-fits-all solution, and careful consideration is needed when making the decision, as this involves millions of dollars and years of critical work. ... are long journeys, not short projects. Programs typically span 18 to 24 months, cover hundreds of terabytes of data, and touch dozens of business domains. A single cutover is too risky, while endless pilots waste resources. Phased execution is the only sustainable approach. High-value domains are prioritized to demonstrate progress. Legacy and cloud often run in parallel until validation is complete. Automated validation, DevOps pipelines, and AI-assisted SQL conversion accelerate progress. To avoid burnout, teams are structured with a mix of full-time employees who work closely with business users and managed services that provide technical scale. ... Governance must be embedded from the start. Metadata catalogs track lineage and ownership. Automated validation ensures quality at every stage, not just at cutover. Role-based access controls, encryption, and masking enforce compliance.

Through the Looking Glass: Data Stewards in the Realm of Gondor

Data Stewards are sought-after individuals today. I have seen many “data steward” job postings over the last six months and read much discussion about the role in various periodicals and postings. I have always agreed with my editor’s conviction that everyone is a data steward, accountable for the data they create, manage, and use. Nevertheless, the role of data steward, as a job and as a career, has established itself in the view of many companies as essential to improving data governance and management. ... “Information Stewardship” is a concept like and may even predate it, based on my brief survey of articles on these topics. Trevor gives an excellent summary of the essence of stewardship in this context: Stewardship requires the acceptance by the user that the information belongs to the organization as a whole, not any one individual. The information should be shared as needed and monitored for changes in value. ... Data Stewards “own” data, or to be more precise, Data Stewards are responsible for the data owned by the enterprise. If the enterprise is the old-world Lord’s Estate, then the Data Stewardship Team consists of the people who watch over the lifeblood of the estate, including the shepherds who make sure the data is flowing smoothly from field to field, safe from internal and external predators, safe from inclement weather, and safe from disease. ...

Scaling Cloud and Distributed Applications: Lessons and Strategies

Scaling extends beyond simply adding servers. When scaling occurs, the fundamental question is whether the application requires scaling due to genuine customer demand or whether upstream services experiencing queuing issues slow system response. When threads wait for responses and cannot execute, pressure increases on CPU and memory resources, triggering elastic scaling even though actual demand has not grown. ... Architecture must extend beyond documentation. Creating opinionated architecture templates assists teams in building applications that automatically inherit architectural standards. Applications deploy automatically using manifest-based definitions, so that teams can focus on business functionality rather than infrastructure tooling complexities. ... represents a highly effective practice of systematically rebuilding infrastructure each sprint. Automated processes clean up running instances regularly. This approach enhances security by eliminating configuration drift. When drift exists or patches require application, including zero-day vulnerability fixes, all updates can be systematically incorporated. Extended operation periods create stale resources, performance degradation, and security vulnerabilities. Recreating environments at defined intervals (weekly or bi-weekly) occurs automatically.

Why Synthetic Data Will Decide Who Wins the Next Wave of AI

Why is suddenly so important? The simple answer is that AI has begun bumping into a glass ceiling. Real-world data doesn’t extend far enough to cover all the unlikely edge cases or every scenario that we want our models to live through. Synthetic data allows teams to code in the missing parts directly. Developers construct situations as needed. ... Building synthetic data holds the key to filling the gap when the quality or volume of data needed by AI models is not good enough, but the process to create this data is not easy. Behind the scenes, there’s an entire stack working together. We are talking about simulation engines, generative models like and , large language models (LLMs) for text-based domains. All this creates virtual worlds for training. ... The organizations most affected by the growing need for synthetic data are those that operate in high-risk areas where there is no actual data, or the act of finding it is inefficient. Think of fully autonomous vehicles that can’t simply wait for every dangerous encounter to occur in traffic. Doctors working on a cure for rare diseases but can’t call on thousands of such cases. Trading firms that can’t wait for just the right market shock for their AI models. These teams can turn synthetic data to give them a lesson from situations that are simply not possible (or practical) in real life.

How ABB’s Approach to IT/OT Ensures Cyber Resilience

The convergence of IT and OT creates new vulnerabilities as previously isolated control systems now require integration with enterprise networks. ABB addresses this by embedding security architecture from the start rather than retrofitting it later. This includes proper network segmentation, validated patching protocols and granular access controls that enable safe data connectivity while protecting operational technology. ... On the security front, AI-driven monitoring can identify anomalous patterns in network traffic and system behavior that might indicate a breach attempt, spotting threats that traditional rule-based systems would miss. However, it's crucial to distinguish between embedded AI and . Embedded AI in our products optimises processes with predictable, explainable outcomes. This same principle applies to security: AI systems that monitor for threats must be transparent in how they reach conclusions, allowing security teams to understand and validate alerts rather than trusting a black box. ... Secure data exchange protocols, multi-factor authentication on remote access points and validated update mechanisms all work together to enable the connectivity that require while maintaining security boundaries. The key is recognising that digital transformation and security are interdependent. Organisations investing millions in AI, digital twins or automation while neglecting cybersecurity are building on sand.

Building an MCP server is easy, but getting it to work is a lot harder

"The true power of remote MCP is realized through centralized '' where these servers are registered and managed. This model delivers the essential guardrails that enterprises require," Shrivastava said. That said, agent gateways do come with their own caveats. "While gateways provide security, managing a growing ecosystem of dozens or even hundreds of registered MCP tools introduces a new challenge: orchestration," he said. "The most scalable approach is to add another layer of abstraction: organizing toolchains into 'topics' based on the 'job to be done.'" ... "When a large language model is granted access to multiple external tools via the protocol, there is a significant risk that it may choose the wrong tool, misuse the correct one, or become confused and produce nonsensical or irrelevant outputs, whether through classic hallucinations or incorrect tool use," he explained. ... MCP's scaling limits also present a huge obstacle. The scaling limits exist "because the protocol was never designed to coordinate large, distributed networks of agents," said James Urquhart, field CTO and technology evangelist at , a provider of products that orchestrate and deploy autonomous AI agents. MCP works well in small, controlled environments, but "it assumes instant responses between agents," he said -- an unrealistic expectation once systems grow and "multiple agents compete for processing time, memory or bandwidth."

The quantum clock is ticking and businesses are still stuck in prep mode

The report highlights one of the toughest challenges. Eighty one percent of respondents said their crypto libraries and hardware security modules are not prepared for . Many use legacy systems that depend on protocols designed long before quantum threats were taken seriously. Retrofitting these systems is not a simple upgrade. It requires changes to how keys are generated, stored and exchanged. Skills shortages compound the problem. Many security teams lack experience in testing or deploying post quantum algorithms. Vendor dependence also slows progress because businesses often cannot move forward until external suppliers update their own tooling. ... Nearly every organization surveyed plans to allocate budget toward post quantum projects within the next two years. Most expect to spend between six and ten percent of their cybersecurity budgets on research, tooling or deployment. Spending levels differ by region. More than half of plan to invest at least eleven percent, far higher than the and . ... Contractual requirements from customers and partners are seen as the strongest motivator for adoption. Industry standards rank near the top of the list across most sectors. Many respondents also pointed to upcoming regulations and mandates as drivers. Security incidents ranked surprisingly low in the US, suggesting that market and policy signals hold more influence than hypothetical attack scenarios.

Daily Tech Digest - November 27, 2025

Quote for the day:

“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.