Daily Tech Digest by Kannan Subbiah: ROI

Showing posts with label ROI. Show all posts

Daily Tech Digest - April 02, 2026

Quote for the day:

"Emotional intelligence may be called a soft skill. But it delivers hard results in leadership." -- Gordon Tredgold

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No joke: data centers are warming the planet

The article discusses a provocative study revealing that AI data centers significantly impact local climates through what researchers call the "data heat island effect." According to the findings, the land surface temperature (LST) around these facilities increases by an average of 2°C after operations commence, with thermal changes detectable up to ten kilometers away. As the AI boom accelerates, data centers are becoming some of the most power-hungry infrastructures globally, potentially exceeding the energy consumption of the entire manufacturing sector within years. This environmental footprint raises concerns about "thermal saturation," where the concentration of facilities in a single region degrades the operating environment, making cooling less efficient and resource competition more intense. While industry analysts warn that strategic planning must now account for these regional system dynamics, some skeptics argue that the temperature rise is merely a standard urban heat island effect caused by land transformation and construction rather than specific compute activities. Regardless of the exact cause, the study highlights a critical challenge for : the physical infrastructure required for digital growth is tangibly altering the surrounding environment. This necessitates a shift in location strategy, prioritizing long-term environmental sustainability over simple site-level optimization to mitigate second-order risks in a warming world.

The Importance of Data Due Diligence

Data due diligence is a critical multi-step assessment process designed to evaluate the health, reliability, and usability of an organization's data assets before making significant investment or business decisions. It encompasses vital components such as data quality assessment, security evaluation, compliance checks, and compatibility analysis. In the modern landscape where data is a cornerstone across sectors like finance and healthcare, performing this diligence ensures that investors and businesses identify hidden risks that could compromise return on investment or operational stability. This process is particularly essential during mergers and acquisitions, where understanding data transferability and integration can prevent costly technical hurdles. Neglecting these checks can lead to catastrophic consequences, including severe financial losses, expensive legal penalties for regulatory non-compliance, and lasting damage to a brand's reputation among consumers and partners. Furthermore, poor data handling practices can disrupt daily operations and impede future growth. By prioritizing , organizations protect themselves from inaccurate insights and security breaches, ultimately fostering a culture of transparency and informed decision-making. This comprehensive approach transforms data from a potential liability into a strategic asset, securing the genuine value of a business undertaking in an increasingly data-driven global economy.

Top global and US AI regulations to look out for

As artificial intelligence evolves at a breakneck pace, global regulatory landscapes are shifting rapidly to address emerging risks, often outstripping traditional legislative speeds. China pioneered generative AI oversight in 2023, while the provides a comprehensive, risk-based framework that currently influences global standards. Conversely, the United States relies on a patchwork of state-level mandates from California, Colorado, and others, as federal legislation remains stalled. The article highlights a pivot toward regulating ""—interconnected systems that perform complex tasks—which presents unique challenges for accountability and monitoring. Experts suggest that instead of chasing specific, unstable laws, organizations should adopt established best practices like the or ISO 42001 to build resilient governance. Enterprises are advised to focus on AI literacy and real-time monitoring rather than periodic audits, given that AI behavior can fluctuate daily. While the current regulatory environment is fragmented and complex, companies with strong existing cybersecurity and privacy foundations are well-positioned to adapt. Ultimately, staying ahead of these legal shifts requires a proactive, framework-oriented approach that balances innovation with safety as global authorities continue to refine their oversight strategies through 2027 and beyond.

Agentic AI Software Engineers: Programming with Trust

The article "Agentic AI Software Engineers: Programming with Trust" explores the transformative shift from simple AI-assisted coding to autonomous agentic systems that mimic human software engineering workflows. Unlike traditional models that merely suggest code snippets, agentic AI operates with significant autonomy, utilizing standard developer tools like shells, editors, and test suites to perform complex tasks. The authors argue that the successful deployment of these "AI engineers" hinges on establishing a level of trust that meets or even exceeds that of human counterparts. This trust is bifurcated into technical and human dimensions. Technical trust is built through rigorous quality assurance, including automated testing, static analysis, and formal verification, ensuring code is correct, secure, and maintainable. Conversely, human trust is fostered through explainability and transparency, where agents clarify their reasoning and align with existing team cultures and ethical standards. As software engineering transitions toward "programming in the large," the role of the developer evolves from a primary code writer to a strategic assembler and reviewer. By integrating intent extraction and program analysis, agentic systems can provide the essential justifications necessary for developers to confidently adopt AI-generated solutions. Ultimately, the paper presents a roadmap for a collaborative future where AI agents serve as reliable, trustworthy teammates.

Security awareness is not a control: Rethinking human risk in enterprise security

In the article "Security awareness is not a control: Rethinking human risk in enterprise security," Oludolamu Onimole argues that organizations must stop treating security awareness training as a primary defense mechanism. While awareness fosters a security-conscious culture, it is fundamentally an educational tool rather than a structural control. Unlike technical safeguards like network segmentation or conditional access, awareness relies on consistent human performance, which is inherently variable due to cognitive load and decision fatigue. Onimole points out that attackers increasingly exploit these predictable human vulnerabilities through sophisticated social engineering and business email compromise, where even well-trained employees can fall victim under pressure. Consequently, viewing awareness as a "layer of defense" unfairly shifts the blame for breaches onto individuals rather than systemic design flaws. The article advocates for a shift toward "human-centric" engineering, where systems are designed to be resilient to inevitable human errors. This includes implementing phishing-resistant authentication, enforced out-of-band verification for high-risk transactions, and robust identity telemetry. Ultimately, while awareness remains a valuable cultural component, true requires moving beyond the "blame game" to build architectural safeguards that absorb mistakes rather than allowing a single human lapse to cause material disaster.

The Availability Imperative

In "The Availability Imperative," Dmitry Sevostiyanov argues that the fundamental differences between Information Technology (IT) and Operational Technology (OT) necessitate a paradigm shift in cybersecurity. Unlike IT’s "best-effort" Ethernet standards, OT environments like power grids and factories demand determinism—predictable, fixed timing for critical control systems. Standard Ethernet lacks guaranteed delivery and latency, leading to dropped frames and jitter that can trigger catastrophic failures in high-stakes industrial loops. To address these limitations, specialized protocols like EtherCAT and PROFINET were engineered for strict timing. However, the introduction of conventional security measures, particularly Deep Packet Inspection (DPI) via firewalls, often introduces significant latency and performance degradation. Sevostiyanov asserts that in OT, the traditional must be reordered to prioritize Availability above all else. Effective cybersecurity in these settings requires protocol-aware, ruggedized Next-Generation Firewalls that minimize the latency penalty while providing granular protection. Ultimately, security professionals must validate performance against industrial safety requirements to ensure that protective measures do not inadvertently silence the machines they aim to defend. By bridging the gap between IT transport rules and the physics of industrial processes, organizations can maintain system stability while securing critical infrastructure against evolving digital threats.

Microservices Without Tears: Shipping Fast, Sleeping Better

The article "Microservices Without Tears: Shipping Fast, Sleeping Better" explores the common pitfalls of transitioning to a and provides a roadmap for successful implementation. While microservices promise scalability and independent deployments, they often result in complex "distributed monoliths" that increase operational stress. To avoid this, the author emphasizes the importance of and establishing clear bounded contexts to ensure services are truly decoupled. Central to this approach is an "API-first" mindset, which allows teams to work independently while maintaining stable contracts. Furthermore, the post highlights that robust —encompassing metrics, logs, and distributed tracing—is non-negotiable for diagnosing issues in a distributed system. Automation through CI/CD pipelines is equally critical to manage the overhead of numerous services. Ultimately, the transition is as much about culture as it is about technology; adopting a "you build it, you run it" mentality empowers teams and improves system reliability. By focusing on developer experience and incremental changes, organizations can harness the speed of microservices without sacrificing peace of mind or stability. This holistic strategy transforms the architectural shift from a source of frustration into a powerful engine for rapid, reliable software delivery and long-term maintainability.

Trust, friction, and ROI: A CISO’s take on making security work for the business

In this Help Net Security interview, PPG’s CISO John O’Rourke discusses how modern cybersecurity functions as a strategic business driver rather than a mere cost center. He argues that mature security programs act as revenue enablers by reducing friction during critical growth phases, such as mergers and acquisitions or complex sales cycles. By implementing standardized frameworks like NIST or ISO, organizations can accelerate due diligence and build essential digital trust with increasingly sophisticated buyers. O’Rourke highlights how PPG utilizes automated identity management and audit readiness to ensure business initiatives move forward without unnecessary delays. He contrasts this approach with less-regulated industries that often defer security investments, resulting in prohibitively expensive technical debt and fragile architectures. Looking ahead, companies that prioritize foundational security controls will be significantly better positioned to integrate emerging technologies like artificial intelligence while maintaining business continuity. Conversely, those viewing security as an optional expense face heightened risks of prolonged incident recovery, regulatory exposure, and lost customer confidence. Ultimately, O'Rourke emphasizes that while security may not generate revenue directly, its operational maturity is indispensable for protecting a brand's reputation and ensuring long-term, uninterrupted financial growth in an increasingly competitive global landscape.

In the wake of Claude Code's source code leak, 5 actions enterprise security leaders should take now

On March 31, 2026, inadvertently exposed the internal mechanics of its flagship AI coding agent, Claude Code, by shipping a 59.8 MB source map file in an npm update. This leak revealed 512,000 lines of TypeScript, uncovering the "agentic harness" that orchestrates model tools and memory, alongside 44 unreleased features like the "KAIROS" autonomous daemon. Beyond strategic exposure, the incident highlights critical security vulnerabilities, including three primary attack paths: context poisoning through the compaction pipeline, sandbox bypasses via shell parsing differentials, and supply chain risks from unprotected Model Context Protocol (MCP) server interfaces. Security leaders are warned that AI-assisted commits now leak credentials at double the typical rate, reaching 3.2%. Consequently, experts recommend five urgent actions: auditing project configuration files like CLAUDE.md as executable code, treating MCP servers as untrusted dependencies, restricting broad bash permissions, requiring robust vendor SLAs, and implementing commit provenance verification. Furthermore, since the codebase is reportedly 90% AI-generated, the leak underscores unresolved legal questions regarding intellectual property protections for automated software. As competitors now possess a blueprint for high-agency agents, the incident serves as a systemic signal for enterprises to prioritize operational maturity and architect provider-independent boundaries to mitigate the expanding risks of the AI agent supply chain.

AI gives attackers superpowers, so defenders must use it too

This article explores how artificial intelligence is fundamentally transforming the cybersecurity landscape, shifting the balance of power toward attackers. Sergej Epp, CISO of Sysdig, explains that the window between vulnerability disclosure and active exploitation has dramatically collapsed from eighteen months in 2020 to just a few hours today, with the potential to shrink to minutes. This acceleration is driven by AI’s ability to automate attacks and verify exploits with binary efficiency. While attackers benefit from immediate feedback on their efforts, defenders struggle with complex verification processes and high rates of false positives. To combat these AI-powered "superpowers," organizations must abandon traditional, human-dependent response cycles and monthly patching in favor of full automation and "human-out-of-the-loop" security models. Epp emphasizes the importance of context graphs, noting that while attackers think in interconnected networks, defenders often remain stuck in list-based mentalities. Furthermore, established principles like and blast radius containment remain essential, but they require 100% implementation because AI is remarkably adept at identifying and exploiting the slightest 1% gap in coverage. Ultimately, the survival of modern digital infrastructure depends on matching the machine-scale speed of adversaries through integrated, autonomous defensive strategies.

Daily Tech Digest - February 28, 2026

Quote for the day:

"Stories are the single most powerful weapon in a leader's arsenal." -- Howard Gardner

AI ambitions collide with legacy integration problems

Many enterprises have moved beyond experimentation and are preparing for formal deployment. The survey found that 85% have begun adopting AI or expect to do so within the next 12 months. Respondents also reported efforts to formalise AI governance, reflecting greater attention to risk, accountability and oversight. ... Integration sits at the centre of that tension. AI initiatives often depend on clean data, consistent definitions and reliable access across multiple applications, requirements that legacy estates can complicate. The survey links these constraints to compliance risks, including data retention, access controls and auditability across connected systems. ... Security and privacy concerns featured prominently. Data privacy across systems was cited as a top risk by 49% of respondents, while 48% said they were concerned about third parties handling sensitive data. The results highlight the difficulty of managing information flows when AI systems interact with multiple internal applications and external providers. Governance approaches varied. Fewer than half (47%) said board-level reporting forms part of risk management for AI and related technology work, suggesting uneven executive oversight as AI moves into operational settings where incidents can carry regulatory and reputational consequences. ... Despite pressure to move quickly on AI initiatives, respondents said engineering quality remains a priority.

Striking the Right Balance Between Automation and Manual Processes in IT

Rather than thinking of applying AI wherever possible and over-automating, leaders should think about the most beneficial uses of the technology and begin implementation of the technology in those areas first before expanding further. Automation is a powerful tool, but humans are the most powerful tool in the IT stack. Let’s discuss how today’s IT leaders can strike the right balance between automation and manual processes. ... Even with the many benefits of automation, human-led processes still reign supreme in certain areas. For example, optimal IT operations happen at the intersection of tools and teamwork. IT teams must still foster a collaborative culture, working with other departments to ensure cross team visibility and alignment on business goals. While the latest AI technology can help in these efforts, ultimately, humans must do this collaborative work. Team dynamics can also be complex at times. Conflict resolution and major team decisions are not things that automation can solve. Moreover, if there is a critical system issue, DBAs must be able to work with IT leaders to resolve this issue and forge a path forward. Finally, manual processes are often necessitated by convoluted workflows. Many DBA teams have workflows in which every step is a set of if-then-else decisions, with each possible outcome also encumbered with many if-then decisions cascading through multiple levels of decisions.

Translating data science capabilities into business ROI

The fundamental challenge in demonstrating data science ROI is that most analytics infrastructure feels optional until it becomes essential. During normal operations, executives tolerate delays in reporting and gaps in visibility. During a crisis, those same gaps become existential threats. ... The turning point came when I realized we weren’t facing a data problem or a technology problem. We were facing a decision-making problem. Our leadership needed to maintain operational stability for a multi-trillion-dollar asset manager during unprecedented disruption. Every day without visibility meant delayed decisions, missed opportunities, and compounding uncertainty. ... Speed-to-value often trumps technical sophistication. The COVID dashboard taught me this lesson definitively. We could have spent months building a comprehensive data warehouse with sophisticated ETL pipelines and machine learning-powered forecasting. Instead, we focused ruthlessly on the minimum viable solution that executives needed immediately. ... Strategic positioning creates a disproportionate impact. I served as strategic architect for a major product repositioning — a multi-million-dollar initiative essential for our competitive positioning. My data-backed strategies produced immediate, quantifiable market share gains and resulted in substantially larger deal sizes and accelerated acquisition rates that fundamentally altered our market position.

The reliability cost of default timeouts

Many widely used libraries and systems default to infinite or extremely large timeouts. In Java, common HTTP clients treat a timeout of zero as “wait indefinitely” unless explicitly configured. In Python, requests will wait indefinitely unless a timeout is set explicitly. The Fetch API does not define a built-in timeout at all. These defaults aren’t careless. They’re intentionally generic. Libraries optimize for the correctness of a single request because they can’t know what “too slow” means for your system. Survivability under partial failure is left to the application. ... Long timeouts can also mask deeper design problems. If a request regularly times out because it returns thousands of items, the issue isn’t the timeout itself. It’s missing pagination or poor request shaping. By optimizing for individual request success, teams unintentionally trade away system-level resilience. ... A timeout defines where a failure is allowed to stop. Without timeouts, a single slow dependency can quietly consume threads, connections and memory across the system. With well-chosen timeouts, slowness stays contained instead of spreading into a system-wide failure. ... A timeout is a decision about value. Past a certain point, waiting longer does not improve user experience. It increases the amount of wasted work a system performs after the user has already left. A timeout is also a decision about containment. Without bounded waits, partial failures turn into system-wide failures through resource exhaustion: blocked threads, saturated pools, growing queues and cascading latency.

From dashboards to decisions: How streaming data transforms vertical software

For years, the standard for vertical software has been the nightly sync. You collect data all day, run a massive batch job at 2:00 AM, and provide your customers with a clean report the next morning. In a world of 2026, that delay is becoming a liability rather than a best practice. ... Data streaming isn’t just about moving bits faster; it’s about changing the fundamental value proposition of your application. Instead of being a system of record that tells a user what happened, your software becomes a system of agency that tells them what is happening right now. This shift requires a mental move away from static databases toward event-driven architectures. You’re no longer just storing a “state” (like current inventory); you’re capturing every “event” (every scan, every sale, every sensor ping) that leads to that state. ... One of the biggest mistakes I see software leaders make is treating real-time data as a “table stakes” feature that they give away for free. Streaming infrastructure is expensive to run and even more expensive to maintain. If you bake these costs into your standard subscription without a clear monetization strategy, you’ll watch your gross margins shrink as your customers’ data volumes grow. ... When you process data at the edge, you’re also solving the “data gravity” problem. Sending thousands of high-frequency sensor pings from a factory floor to the cloud just to filter out the noise is a waste of bandwidth and money.

MCP leaves much to be desired when it comes to data privacy and security

From a data privacy standpoint, one of the major issues is data leakage, while from a security perspective, there are several things that may cause issues, including prompt injections, difficulty in distinguishing between verified and unverified servers, and the fact that MCP servers sit below typical security controls. ... Fulkerson went on to say that runtime execution is another issue, and legacy tools for enforcing policies and privacy are static and don’t get enforced at runtime. When you’re dealing with non-deterministic systems, there needs to be a way to verifiably enforce policies at runtime execution because the blast radius of runtime data access has outgrown the protection mechanisms organizations have. He believes that confidential AI is the solution to these problems. Confidential AI builds on the properties of confidential computing, which involves using hardware that has an encrypted cache, allowing data and inference to be run inside an encrypted environment. While this helps prove that data is encrypted and nobody can see it, it doesn’t help with the governance challenge, which is where Fulkerson says confidential AI comes in. Confidential AI treats everything as a resource with its own set of policies that are cryptographically encoded. For example, you could limit an agent to only be able to talk to a specific agent, or only allow it to communicate with resources on a particular subnet.

3 Ways OT-IT Integration Helps Energy and Utilities Providers Modernize Grid Operations

Increasingly, energy providers are turning to digital twins to model and simulate critical infrastructure across generation, transmission and distribution environments. By feeding live telemetry from supervisory control and data acquisition systems, intelligent electronic devices and other OT assets into IT-based simulation platforms, utilities can create real-time digital replicas of substations, turbines, transformers and even entire grid segments. This enables teams to test load-balancing strategies, maintenance schedules or DER integrations without disrupting service. ... Private 5G networks offer a compelling alternative. Designed for high reliability and low latency, private 5G can operate effectively in interference-heavy environments such as substations or generation facilities. When paired with TSN, utilities can achieve deterministic, sub-millisecond communication between protection systems, controllers and analytics platforms. ... Federated machine learning allows utilities to train AI models locally at the edge — analyzing equipment performance, detecting anomalies and refining predictive maintenance strategies — without centralizing raw operational data. For industries such as energy and oil, remote sites can run local anomaly detection models tailored to site-specific conditions, while still sharing insights that strengthen enterprisewide safety and operational protocols.

Even if AI demand fades, India need not worry - about data centres

AI pushes rack densities from ~5–10kW to 50–100kW+, making liquid cooling, greater power capacity, and purpose‑built ‘AI‑ready’ Data Centre campuses essential — whether for regional training clusters or dense inference. What makes a Data Centre AI-ready is the ability to support advanced cooling, predictable scalability and direct access to clouds, networks and partners in a sustainable manner. ... In India, enterprises are rapidly adopting hybrid and multi-cloud architectures as they modernise their digital infrastructure. Domestic enterprises, particularly in BFSI and broking, are moving away from in-house data centres toward third-party colocation facilities to gain scalability, efficient interconnection with their required ecosystem, operational efficiency and access to specialised talent. This shift is being further accelerated by distributed AI, hybrid multi-cloud architectures and a growing focus on sustainability. ... India’s Data Centre market is distinctive because of the scale of its digital consumption, combined with the early stage of ecosystem development. India generates a significant share of global data, yet its installed data centre capacity remains comparatively low, creating strong long-term growth potential. This growth is now being amplified by hyperscalers and AI-led demand. India aims to become a USD 1 T digital economy by 2028. It is already making significant progress, supported by the country’s thriving startup ecosystem, the third largest in the world, and initiatives like Startup India.

Surprise! The One Being Ripped Off by Your AI Agent Is You

It’s now happening all the time: in the sale of location data and browsing histories to brokers who assemble and sell our highly personal profiles, and in DOGE’s and other data grabs across the federal government, where housing, tax, and health information is being weaponized for immigration enforcement or misleading voter fraud “investigations.” With AI agents, it just gets worse. Data betrayal is an even more intimate act. Yet the people who granted OpenClaw access to their accounts were making a reasonable choice—to use a powerful tool on their behalf. ... The data aggregation capabilities of AI add another dimension of risk that rarely gets even a mention, but represent a change in scale that adds up to a sea change, making someone marketed as “productivity” software a menacing vector for data weaponization. The same capabilities that make agents useful—synthesizing enormous amounts of information across sources and acting autonomously across platforms with persistence and memory—make them extraordinarily powerful instruments for state surveillance and targeted repression. An autocratic government could build dossiers on dissidents, journalists, or voters from financial records, social media, location data, and communications metadata, acting in real time: micro-targeting people with persuasion campaigns, swarming targets with coordinated social media attacks, engineering entrapment schemes, or flagging individuals based on patterns no court ever authorized.

What makes Non-Human Identities in AI secure

By aligning security goals with technological advancements, NHIs offer a tangible solution to the challenges posed by AI and cloud-based architectures. Forward-thinking organizations are leveraging this strategic advantage to stay ahead of potential threats, ensuring that their digital remain both protected and resilient. ... Can businesses effectively integrate Non-Human Identities across diverse sectors? Where industries such as financial services, healthcare, and travel become increasingly dependent on digital transformation, the need for securing NHIs is paramount. Each sector presents unique challenges and requirements that necessitate tailored approaches to NHI management. In financial services, for example, the emphasis might be on protecting transactional data, while healthcare organizations focus on safeguarding patient information. Thus, versatile solutions that accommodate varying security demands while maintaining robust protection standards are essential. ... What greater role can NHIs play where emerging technologies unfold? The growing intersection of AI and IoT devices creates a complex web of interactions that requires robust security measures. Non-Human Identities provide a framework for securely managing the myriad connections and transactions occurring between devices. In IoT networks, NHIs authenticate and authorize communication between endpoints, thus safeguarding the integrity of both data and operations.

Daily Tech Digest - February 22, 2026

Quote for the day:

"If you care enough for a result, you will most certainly attain it." -- William James

The data center gold rush is warping reality

The real impact isn’t people—it’s power, land, transmission capacity, and water. When you drop 10 massive facilities into a small grid, demand spikes don’t just happen inside the fence line. They ripple outward. Utilities must upgrade substations, reinforce transmission lines, procure new-generation equipment, and finance these investments. ... Here’s the part we don’t say out loud often enough: High-tech companies are spending massive amounts of money on data centers because the market rewards them for doing so. Capital expenditures have become a kind of corporate signaling mechanism. On earnings calls, “We’re investing aggressively” has become synonymous with “We’re winning,” even when the investment is built on forecasts that are, at best, optimistic and, at worst, indistinguishable from wishful thinking. ... The bet is straightforward: When demand spikes, prices and utilization rise, and those who built first make bank. Build the capacity, fill the capacity, charge a premium for the scarce resource, and ride the next decade of digital expansion. It’s the same playbook we’ve seen before in other infrastructure booms, except this time the infrastructure is made of silicon and electrons, and the pitch is wrapped in the language of transformation. ... Then there’s the cost reality. AI systems, especially those that deliver meaningful, production-grade outcomes, often cost five to ten times as much as traditional systems once you account for compute, data movement, storage, tools, and the people required to run them responsibly.

Chip-processing method could assist cryptography schemes to keep data secure

Just like each person has unique fingerprints, every CMOS chip has a distinctive “fingerprint” caused by tiny, random manufacturing variations. Engineers can leverage this unforgeable ID for authentication, to safeguard a device from attackers trying to steal private data. But these cryptographic schemes typically require secret information about a chip’s fingerprint to be stored on a third-party server. This creates security vulnerabilities and requires additional memory and computation. ... “The biggest advantage of this security method is that we don’t need to store any information. All the secrets will always remain safe inside the silicon. This can give a higher level of security. As long as you have this digital key, you can always unlock the door,” says Eunseok Lee, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this security method. ... A chip’s PUF can be used to provide security just like the human fingerprint identification system on a laptop or door panel. For authentication, a server sends a request to the device, which responds with a secret key based on its unique physical structure. If the key matches an expected value, the server authenticates the device. But the PUF authentication data must be registered and stored in a server for access later, creating a potential security vulnerability.

What MCP Can and Cannot Do for Project Managers Today

The most mature MCPs for PM are official connectors from the platforms themselves. Atlassian’s Rovo MCP Server connects Jira and Confluence, generally available since late 2025. Wrike has its own MCP server for real-time work management. Dart exposes task creation, updates, and querying through MCP. ClickUp does not have an official MCP server, but multiple community implementations wrap its API for task management, comments, docs, and time tracking. ... Most PM work is human and stays human. No LLM replaces the conversation where you talk a frustrated team member through a scope change, or the negotiation where you push back on an unrealistic deadline from the sponsor. No LLM runs a planning workshop or navigates the politics of resource allocation. But woven through all of that is documentation. Every conversation, every decision, every planning session produces written output. The charter that captures what was agreed. ... Beyond documentation, scheduling is where I expected MCP to add the most computational value. This is where the investigation got interesting. Every PM builds schedules. The standard method is CPM: define tasks, set dependencies, estimate durations, calculate the critical path. MS Project does this. Primavera does this. A spreadsheet with formulas does this. CPM is well understood and universally used. CPM does exactly what it says: it calculates the critical path given dependencies and durations.

How to Write a Good Spec for AI Agents

Instead of overengineering upfront, begin with a clear goal statement and a few core requirements. Treat this as a “product brief” and let the agent generate a more elaborate spec from it. This leverages the AI’s strength in elaboration while you maintain control of the direction. This works well unless you already feel you have very specific technical requirements that must be met from the start. ... Many developers using a strong model do exactly this. The spec file persists between sessions, anchoring the AI whenever work resumes on the project. This mitigates the forgetfulness that can happen when the conversation history gets too long or when you have to restart an agent. It’s akin to how one would use a product requirements document (PRD) in a team: a reference that everyone (human or AI) can consult to stay on track. ... Treat specs as “executable artifacts” tied to version control and CI/CD. The GitHub Spec Kit uses a four-phase gated workflow that makes your specification the center of your engineering process. Instead of writing a spec and setting it aside, the spec drives the implementation, checklists, and task breakdowns. Your primary role is to steer; the coding agent does the bulk of the writing. ... Experienced AI engineers have learned that trying to stuff the entire project into a single prompt or agent message is a recipe for confusion. Not only do you risk hitting token limits; you also risk the model losing focus due to the “curse of instructions”—too many directives causing it to follow none of them well.

NIST’s Quantum Breakthrough: Single Photons Produced on a Chip

The arrival of quantum computing is future, but the threat is current. Commercial and federal organizations need to protect against quantum computing decryption now. Various new mathematical approaches have been developed for PQC, but while they may be theoretically secure, they are not provably secure. Ultimately, the only provably secure key distribution must be based on physics rather than math. ... While this basic approach is secure, it is neither efficient nor cheap. “Quantum key distribution is an expensive solution for people that have really sensitive information,” continues Bruggeman. “So, think military primarily, and some government agencies where nuclear weapons and national security are involved.” Current implementations tend to use available dark fiber that still has leasing costs. ... “The big advance from NIST is they are able to provide single photons at a time, as opposed to sending multiple photons,” continues Bruggeman. Single photons aren’t new, but in the past, they’ve usually been photons in a stream of photons. “So, they encode the key information on those strings, and that leads to replication. And in cryptography, you don’t want to have replication of data.” There is currently a comfort level in this redundancy, since if one photon in the stream fails, the next one might succeed. But NIST has separately developed Superconducting Nanowire Single-Photon Detectors (SNSPDs) which would allow single photons to be reliably sent and received over longer distances – up to 600 miles.

Quantum security is turning into a supply chain problem

The core issue is timing. Sensitive supplier and contract data has a long shelf life, and adversaries have already started collecting encrypted traffic for future decryption. This is the “harvest now, decrypt later” model, where encrypted records are stolen and stored until quantum computing becomes capable of breaking current public-key encryption. That creates a practical security problem for cybersecurity teams supporting procurement, third-party risk, and supply chain operations. ... There’s growing pressure to adopt post-quantum cryptography (PQC), including partner expectations, insurance scrutiny, and regulatory direction. It argues that PQC adoption is increasingly being driven through procurement requirements, especially from large enterprises and public-sector organizations. Vendors without a PQC roadmap may face longer audits or disqualification during sourcing decisions. ... Beyond cryptographic threats, the researchers argue that quantum computing may eventually improve supply chain risk management by addressing complex optimization problems that overwhelm classical systems. It describes supply chain risk as a “wicked problem,” where variables shift continuously and disruptions propagate in unpredictable ways. ... Quantum readiness spans both cybersecurity and supply chain management. For cybersecurity professionals, the near-term work focuses on long-term encryption durability across vendor ecosystems, along with cryptographic migration planning and third-party dependencies.

CEOs aren't seeing any AI productivity gains, yet some tech industry leaders are still convinced AI will destroy white collar work within two years

Most companies are yet to record any AI productivity gains despite widespread adoption of the technology. That's according to a massive survey by the US National Bureau of Economic Research (NBER), which asked 6,000 executives from a range of firms across the US, UK, Germany, and Australia how they use AI. The study found 70% of companies actively use AI, but the picture is different among execs themselves. Among top executives – including CFOs and CEOs – a quarter don't use the technology at all, while two-thirds say they use it for 1.5 hours a week at most. ... "The most commonly cited uses are ‘text generation using large language models’ followed by ‘visual content creation’ and ‘data processing using machine learning’," the survey added. When it comes to employment savings, 90% of execs said they'd seen no impact from AI over the last three years, with 89% saying they saw no productivity boost, either. The report noted that previous studies have found large productivity gains in specific settings – in particular customer support and writing tasks. ... Despite the lack of impact to date, business leaders still predict AI will start to boost productivity and reduce the number of employees needed in the coming years. Respondents predict a 1.4% productivity boost and 0.8% increase in output thanks to the technology over the next three years, for example. Yet the NBER survey also reveals a "sizable gap in expectations", with senior execs saying AI would cut employment by 0.7% over the next three years — which the report said would mean 1.75 million fewer jobs.

Observability Without Cost Telemetry Is Broken Engineering

Cost isn't an operational afterthought. It's a signal as essential as CPU saturation or memory pressure, yet we've architected it out of the feedback loop engineers actually use. ... Engineers started evaluating architectural choices through a cost lens without needing MBA training. “Should we cache this aggressively?” became answerable with data: cache infrastructure costs $X/month, API calls saved cost $Y/month, net impact is measurable, not theoretical. ... The anti-pattern I see most often is siloed visibility. Finance gets billing dashboards. SREs get operational dashboards. Developers get APM traces. Nobody sees the intersection where cost and performance influence each other. You debug a performance issue — say, slow database queries. The fix is to add an index. Query time drops from 800 ms to 40 ms. Victory. Except the database is now using 30% more storage for that index, and your storage tier bills by the gigabyte-month. If you're on a flat-rate hosting plan, maybe that cost is absorbed. If you're on Aurora or Cosmos DB with per-IOPS pricing, you've just traded latency for dollars. Without cost telemetry, you won't notice until the bill arrives. ... Alerting without cost dimensions misses failure modes. Your error rate is fine. Latency is stable. But egress costs just doubled because a misconfigured service is downloading the same 200 GB dataset on every request instead of caching it.

A New Way To Read the “Unreadable” Qubit Could Transform Quantum Technology

“Our work is pioneering because we demonstrate that we can access the information stored in Majorana qubits using a new technique called quantum capacitance,” continues the scientist, who explains that this technique “acts as a global probe sensitive to the overall state of the system.” ... To better understand this achievement, Aguado explains that topological qubits are “like safe boxes for quantum information,” only that, instead of storing data in a specific location, “they distribute it non-locally across a pair of special states, known as Majorana zero modes.” That unusual structure is what makes them attractive for quantum computing. “They are inherently robust against local noise that produces decoherence, since to corrupt the information, a failure would have to affect the system globally.” In other words, small disturbances are unlikely to disrupt the stored information. Yet this strength has also created a major experimental challenge. As Aguado notes, “this same virtue had become their experimental Achilles’ heel: how do you “read” or “detect” a property that doesn’t reside at any specific point?.” ... The project brings together an advanced experimental platform developed primarily at Delft University of Technology and theoretical work carried out by ICMM-CSIC. According to the authors, this theoretical input was “crucial for understanding this highly sophisticated experiment,” highlighting the importance of close collaboration between theory and experiment in pushing quantum technology forward.

When Excellent Technology Architecture Fails to Deliver Business Results

Industry research consistently shows that most large-scale transformations fail to achieve their expected business outcomes, even when the underlying technology decisions are considered sound. This suggests that the issue is not technical quality. It is structural. ... The real divergence begins later, in day-to-day decision-making. Under delivery pressure, teams make choices driven by deadlines, budget constraints, and individual accountability. Temporary workarounds are accepted. Deviations are justified as exceptions. Risks are taken implicitly rather than explicitly assessed. Architecture is often aware of these decisions, but it is not structurally embedded in the moment where choices are made. As a result, architecture remains correct, but unused. ... When architecture cannot explain the economic and operational consequences of a decision, it loses relevance. Statements such as “this violates architectural principles” carry little weight if they are not translated into impact on cost of change, delivery speed, or operational risk. ... What is critical is that these compromises are rarely tracked, assessed cumulatively, or reintroduced into management discussions. Architecture may be aware of them, but without a mechanism to record and govern them, their impact remains invisible until flexibility is lost and change becomes expensive. Architecture debt, in this sense, is not a technical failure. It is a governance outcome. When decision trade-offs remain unmanaged, architecture is blamed for consequences it was never empowered to influence.

Daily Tech Digest - February 16, 2026

Quote for the day:

"People respect leaders who share power and despise those who hoard it." -- Gordon Tredgold

TheCUBE Research 2026 predictions: The year of enterprise ROI

Fourteen years into the modern AI era, our research indicates AI is maturing rapidly. The data suggests we are entering the enterprise productivity phase, where we move beyond the novelty of retrieval-augmented-generation-based chatbots and agentic experimentation. In our view, 2026 will be remembered as the year that kicked off decades of enterprise AI value creation. ... Bob Laliberte agreed the prediction is plausible and argued OpenAI is clearly pushing into the enterprise developer segment. He said the consumerization pattern is repeating – consumer adoption often drives faster enterprise adoption – and he viewed OpenAI’s Super Bowl presence as a flag in the ground, with Codex ads and meaningful spend behind them. He said he is hearing from enterprises using Codex in meaningful ways, including cases where as much as three quarters of programming is done with Codex, and discussions of a first 100% Codex-developed product. He emphasized that driving broader adoption requires leaning on early adopters, surfacing use cases, and showing productivity gains so they can be replicated across environments. ... Paul Nashawaty said application development is bifurcating. Lines of business and citizen developers are taking on more responsibility for work that historically sat with professional developers. He said professional developers don’t go away – their work shifts toward “true professional development,” while line of business developers focus on immediate outcomes.

Snowflake CEO: Software risks becoming a “dumb data pipe” for AI

Ramaswamy argues that his company lives with the fear that organizations will stop using AI agents built by software vendors. There must certainly be added value for these specialized agents, for example, that they are more accurate, operate more securely, and are easier to use. For experienced users of existing platforms, this is already the case. A solution such as NetSuite or Salesforce offers AI functionality as an extension of familiar systems, whereby adoption of these features almost always takes place without migration. Ramaswamy believes that customers have the final say on this. If they want to consult a central AI and ignore traditional enterprise apps, then they should be given that option, according to the Snowflake CEO. ... However, the tug-of-war around the center of AI is in full swing. It is not without reason that vendors claim that their solution should be the central AI system, for example because they contain enormous amounts of data or because they are the most critical application for certain departments. So far, AI trends among these vendors have revolved around the adoption of AI chatbots, easy-to-set-up or ready-made agentic workflows, and automatic document generation. During several IT events over the past year, attendees toyed with the idea that old interfaces may disappear because every employee will be talking to the data via AI.

Will LLMs Become Obsolete?

“We are at a unique time in history,” write Ashu Garg and Jaya Gupta at Foundation Capital, citing multimodal systems, multiagent systems, and more. “Every layer in the AI stack is improving exponentially, with no signs of a slowdown in sight. As a result, many founders feel that they are building on quicksand. On the flip side, this flywheel also presents a generational opportunity. Founders who focus on large and enduring problems have the opportunity to craft solutions so revolutionary that they border on magic.” ... “When we think about the future of how we can use agentic systems of AI to help scientific discovery,” Matias said, “what I envision is this: I think about the fact that every researcher, even grad students or postdocs, could have a virtual lab at their disposal ...” ... In closing, Matias described what makes him enthusiastic about the future. “I'm really excited about the opportunity to actually take problems that make a difference, that if we solve them, we can actually have new scientific discovery or have societal impact,” he said. “The ability to then do the research, and apply it back to solve those problems, what I call the ‘magic cycle’ of research, is accelerating with AI tools. We can actually accelerate the scientific side itself, and then we can accelerate the deployment of that, and what would take years before can now take months, and the ability to actually open it up for many more people, I think, is amazing.”

Deepfake business risks are growing – here's what leaders need to know

The risk of deepfake attacks appears to be growing as the technology becomes more accessible. The threat from deepfakes has escalated from a “niche concern” to a “mainstream cybersecurity priority” at “remarkable speed”, says Cooper. “The barrier to entry has lowered dramatically thanks to open source software and automated creation tools. Even low-skilled threat actors can launch highly convincing attacks.” The target pool is also expanding, says Cooper. “As larger corporations invest in advanced mitigation strategies, threat actors are turning their attention to small and medium-sized businesses, which often lack the resources and dedicated cybersecurity teams to combat these threats effectively.” The technology itself is also improving. Deepfakes have already improved “a staggering amount” – even in the past six months, says McClain. “The tech is internalising human mannerisms all the time. It is already widely accessible at a consumer level, even used as a form of entertainment via face swap apps.” ... Meanwhile, technology can be helpful in mitigating deepfake attack risks. Cooper recommends deepfake detection tools that use AI to analyse facial movements, voice patterns and metadata in emails, calls and video conferences. “While not foolproof, these tools can flag suspicious content for human review.” With the risks in mind, it also makes sense to implement multi-factor authentication for sensitive requests.

The Big Shift: From “More Qubits” to Better Qubits

As quantum systems grew, it became clear that more qubits do not always mean more computing power. Most physical qubits are too noisy, unstable, and short-lived to run useful algorithms. Errors pile up faster than useful results, and after a while, the output stops making sense. Adding more fragile qubits now often makes things worse, not better. This realization has led to a shift in thinking across the field. Instead of asking how many qubits fit on a chip, researchers and engineers now ask a tougher question: how many of those qubits can actually be trusted? ... For businesses watching from the outside, this change matters. It is easier to judge claims when vendors talk about error rates, runtimes, and reliability instead of vague promises. It also helps set realistic expectations. Logical qubits show that early useful systems will be small but stable, solving specific problems well instead of trying to do everything. This new way of thinking also changes how we look at risk. The main risk is not that quantum computing will fail completely. Instead, the risk is that organizations will misunderstand early progress and either invest too much because of hype or too little because of old ideas. Knowing how important error correction is helps clear up this confusion. One of the clearest signs of maturity is how failure is handled. In early science, failure can be unclear.

Reimagining digital value creation at Inventia Healthcare

“The business strategy and IT strategy cannot be two different strategies altogether,” he explains. “Here at Inventia, IT strategy is absolutely coupled with the core mission of value-added oral solid formulations. The focus is not on deploying systems, it is on creating measurable business value.” Historically, the pharmaceutical industry has been perceived as a laggard in technology adoption, largely due to stringent regulatory requirements. However, this narrative has shifted significantly over the last five to six years. “Regulators and organisations realised that without digitalisation, it is impossible to reach the levels of efficiency and agility that other industries have achieved,” notes Nandavadekar. “Compliance is no longer a barrier, it is an enabler when implemented correctly.” ... “Digitalisation mandates streamlined and harmonised operations. Once all processes are digital, we can correlate data across functions and even correlate how different operations impact each other,” points out Nandavadekar. ... With expanding digital footprints across cloud, IoT, and global operations, cybersecurity has become a mission-critical priority for Inventia. Nandavadekar describes cybersecurity as an “iceberg,” where visible threats represent only a fraction of the risk landscape. “In the pharmaceutical world, cybersecurity is not just about hackers, it is often a national-level activity. India is emerging as a global pharma hub, and that makes us a strategic target.”

Scaling Agentic AI: When AI Takes Action, the Real Challenge Begins

Organizations often underestimate tool risk. The model is only one part of the decision chain. The real exposure comes from the tools and APIs the agent can call. If those are loosely governed, the agent becomes privileged automation moving faster than human oversight can keep up. “Agentic AI does not just stress models. It stress-tests the enterprise control plane.” ... Agentic AI requires reliable data, secure access, and strong observability. If data quality is inconsistent and telemetry is incomplete, autonomy turns into uncertainty. Leaders need a clear method to select use cases based on business value, feasibility, risk class, and time-to-impact. The operating model should enforce stage gates and stop low-value projects early. Governance should be built into delivery through reusable patterns, reference architectures, and pre-approved controls. When guardrails are standardized, teams move faster because they no longer have to debate the same risk questions repeatedly. ... Observability must cover the full chain, not just model performance. Teams should be able to trace prompts, context, tool calls, policy decisions, approvals, and downstream outcomes. ... Agentic AI introduces failure modes that can appear plausible on the surface. Without traceability and real-time signals, organizations are forced to guess, and guessing is not an operating strategy.

Security at AI speed: The new CISO reality

The biggest shift isn’t tooling, we’ve always had to choose our platforms carefully, it’s accountability. When an AI agent acts at scale, the CISO remains accountable for the outcome. That governance and operating model simply didn’t exist a decade ago. Equally, CISOs now carry accountability for inaction. Failing to adopt and govern AI-driven capabilities doesn’t preserve safety, it increases exposure by leaving the organization structurally behind. The CISO role will need to adopt a fresh mindset and the skills to go with it to meet this challenge. ... While quantification has value, seeking precision based on historical data before ensuring strong controls, ownership, and response capability creates a false sense of confidence. It anchors discussion in technical debt and past trends, rather than aligning leadership around emerging risks and sponsoring a bolder strategic leap through innovation. That forward-looking lens drives better strategy, faster decisions, and real organizational resilience. ... When a large incumbent experiences an outage, breach, model drift, or regulatory intervention, the business doesn’t degrade gracefully, it fails hard. The illusion of safety disappears quickly when you realise you don’t own the kill switches, can’t constrain behaviour in real time, and don’t control the recovery path. Vendor scale does not equal operational resilience.

Why Borderless AI Is Coming to an End

Most countries are still wrestling with questions related to "sovereign AI" - the technical ambition to develop domestic compute, models and data capabilities - and "AI sovereignty" - the political and legal right to govern how AI operates within national boundaries, said Gaurav Gupta, vice president analyst at Gartner. Most national strategies today combine both. "There is no AI journey without thinking geopolitics in today's world," said Akhilesh Tuteja, partner, advisory services and former head of cybersecurity at KPMG. ... Smaller nations, Gupta said, are increasing their investment in domestic AI stacks as they look for alternatives to the closed U.S. model, including computing power, data centers, infrastructure and models aligned with local laws, culture and region. "Organizations outside the U.S. and China are investing more in sovereign cloud IaaS to gain digital and technological independence," said Rene Buest, senior director analyst at Gartner. "The goal is to keep wealth generation within their own borders to strengthen the local economy." ... The practical barriers to AI sovereignty start with infrastructure. The level of investment is beyond the reach of most countries, creating a fundamental asymmetry in the global AI landscape. "One gigawatt new data centers cost north of $50 billion," Gupta said. "The biggest constraint today is availability of power … You are now competing for electricity with residential and other industrial use cases."

Why Data Governance Fails in Many Organizations: The IT-Business Divide

The problem extends beyond missing stewardship roles to a deeper documentation chaos. Organizations often have multiple documents addressing the same concepts, but the language varies depending on which unit you ask, when you ask, and to whom you’re speaking. Some teams call these documents “policies,” while others use terms like “guidelines,” “standards,” or “procedures.” With no clarity on which term means what or whether these documents represent the same authority level. More critically, no one has the responsibility or authority to define which version is the “appropriate” one. Documents get written – often as part of project deliverables or compliance exercises – but no governance process ensures they’re actually embedded into operations, kept current, or reconciled with other documents covering similar ground. ... Without proper governance, a problematic pattern emerges: Technical teams impose technical obligations on business people, requiring them to validate data formats, approve schema changes, or participate in narrow technical reviews, while the real governance questions go unaddressed. Business stakeholders are involved only in a few steps of the data lifecycle, without understanding the whole picture or having authority over business-critical decisions. ... The governance challenges become even more insidious when organizations produce reports that appear identical in format while concealing fundamental differences in their underlying methodology.

Daily Tech Digest - February 03, 2026

Quote for the day:

"In my whole life, I have known no wise people who didn't read all the time, none, zero." -- Charlie Munger

How risk culture turns cyber teams predictive

Reactive teams don’t choose chaos. Chaos chooses them, one small compromise at a time. A rushed change goes in late Friday. A privileged account sticks around “temporarily” for months. A patch slips because the product has a deadline, and security feels like the polite guest at the table. A supplier gets fast-tracked, and nobody circles back. Each event seems manageable. Together, they create a pattern. The pattern is what burns you. Most teams drown in noise because they treat every alert as equal and security’s job. You never develop direction. You develop reflexes. ... We’ve seen teams with expensive tooling and miserable outcomes because engineers learned one lesson. “If I raise a risk, I’ll get punished, slowed down or ignored.” So they keep quiet, and you get surprised. We’ve also seen teams with average tooling but strong habits. They didn’t pretend risk was comfortable. They made it speakable. Speakable risk is the start of foresight. Foresight enables the right action or inaction to achieve the best result! ... Top teams collect near misses like pilots collect flight data. Not for blame. For pattern. A near miss is the attacker who almost got in. The bad change that almost made it into production. The vendor who nearly exposed a secret. The credential that nearly shipped in code. Most organizations throw these away. “No harm done.” Ticket closed. Then harm arrives later, wearing the same outfit.

Why CIOs are turning to digital twins to future-proof the supply chain

The ways in which digital twin models differ from traditional models are that they can be run as what-if scenarios and simulated by creating models based on cause-and-effect. Examples of this would include a demand increase in volume of supply chain product in a short time frame, or changes involving a facility shutting down because of severe weather conditions. The model will look at how this will affect a supply chain’s inventory levels, shipping schedule and delivery date, and even worker availability if any. All of this allows companies to move their decision-making process away from reactive firefighting to the more proactive planning process. For a CIO, using a digital twin model eliminates the historical siloing of enterprise architecture of supply chain-related data. ... Although the value of the digital twin technology is evident, scaling digital twins remains a significant challenge. Integration of data from multiple sources including ERP, WMS, IoT, and partner systems is a primary challenge for all. High fidelity simulation requires high computational capacity, which in turn requires trade-offs between realism, performance, and cost. There are also governance issues associated with digital twins. As digital twin models drift or are modified due to the physical state of the model changing, potential security vulnerabilities also increase as continuing data is streamed from cloud and edge environments.

Quantum computing is getting closer, but quantum-proof encryption remains elusive

“Everybody’s well into the belief that we’re within five years of this cryptocalypse,” says Blair Canavan, director of alliances for the PKI and PQC portfolio at Thales, a French multinational company that develops technologies for aerospace, defense, and digital security. “I see it and hear it in almost every circle.” Fortunately, we already have new, quantum-safe encryption technology. NIST released its fifth quantum-safe encryption algorithm in early 2025. The recommended strategy is to build encryption systems that make it easy to swap out algorithms if they become obsolete and new algorithms are invented. And there’s also regulatory pressure to act. ... CISA is due to release its PQC category list, which will establish PQC standards for data management, networking, and endpoint security. And early this year, the Trump administration is expected to release a six-pillar cybersecurity strategy document that includes post-quantum cryptography. But, according to the Post Quantum Cryptography Coalition’s state of quantum migration report, when it comes to public standards, there’s only one area in which we have broad adoption of post-quantum encryption, and that’s with TLS 1.3, and only with hybrid encryption — not pre or post quantum encryption or signatures. ... The single biggest driver for PQC adoption is contractual agreements with customers and partners, cited by 22% of respondents.

From compliance to competitive edge: How tech leaders can turn data sovereignty into a business advantage

Data sovereignty - where data is subject to the laws and governing structures of the nation in which it is collected, processed, or held - means that now more than ever, it’s incredibly important that you understand where your organization’s data comes from, and how and where it’s being stored. Understandably, that effort is often seen through the lens of regulation and penalties. If you don’t comply with GDPR, for example, you risk fines, reputational damage, and operational disruption. But the real conversation should be about the opportunities it could bring, and that involves looking beyond ticking boxes, towards infrastructure and strategy. ... Complementing the hybrid hub-and-spoke model, distributed file systems synchronize data across multiple locations, either globally or only within the boundaries of jurisdictions. Instead of maintaining separate, siloed copies, these systems provide a consistent view of data wherever it is needed and help teams collaborate while keeping sensitive information within compliant zones. This reduces delays and duplication, so organizations can meet data sovereignty obligations without sacrificing agility or teamwork. Architecture and technology like this, built for agility and collaboration, are perfectly placed to transform data sovereignty from a barrier into a strategic enabler. They support organizations in staying compliant while preserving the speed and flexibility needed to adapt, compete, and grow.

Why digital transformation fails without an upskilled workforce

“Capability” isn’t simply knowing which buttons to click. It’s being able to troubleshoot when data doesn’t reconcile. It’s understanding how actions in the system cascade through downstream processes. It’s recognizing when something that’s technically possible in the system violates a business control. It’s making judgment calls when the system presents options that the training scenarios never covered. These capabilities can’t be developed through a three-day training session two weeks before go-live. They’re built through repeated practice, pattern recognition, feedback loops and reinforcement over time. ... When upskilling is delayed or treated superficially, specific operational risks emerge quickly. In fact, in the implementations I’ve supported, I’ve found that organizations routinely experience productivity declines of as much as 30-40% within the first 90 days of go-live if workforce capability hasn’t been adequately addressed. ... Start by asking your transformation team this question: “Show me the behavioral performance standards that define readiness for the roles, and show me the evidence that we’re meeting them.” If the answer is training completion dashboards, course evaluation scores or “we have a really good training vendor,” you have a problem. Next, spend time with actual end users not power users, not super users, but the people who will do this work day in and day out.

How Infrastructure Is Reshaping the U.S.–China AI Race

Most of the early chapters of the global AI race were written in model releases. As LLMs became more widely adopted, labs in the U.S. moved fast. They had support from big cloud companies and investors. They trained larger models and chased better results. For a while, progress meant one thing. Build bigger models, and get stronger output. That approach helped the U.S. move ahead at the frontier. However, China had other plans. Their progress may not have been as visible or flashy, but they quietly expanded AI research across universities and domestic companies. They steadily introduced machine learning into various industries and public sector systems. ... At the same time, something happened in China that sent shockwaves through the world, including tech companies in the West. DeepSeek burst out of nowhere to show how AI model performance may not be as contrained by hardware as many of us thought. This completely reshaped assumptions about what it takes to compete in the AI race. So, instead of being dependent on scale, Chinese teams increasingly focused on efficiency and practical deployment. Did powerful AI really need powerful hardware? Well, some experts thought DeepSeek developers were not being completely transparent on the methods used to develop it. However, there is no doubt that the emergence of DeepSeek created immense hype. ... There was no single turning point for the emergence of the infrastructure problem. Many things happened over time.

Why AI adoption keeps outrunning governance — and what to do about it

The first problem is structural. Governance was designed for centralized, slow-moving decisions. AI adoption is neither. Ericka Watson, CEO of consultancy Data Strategy Advisors and former chief privacy officer at Regeneron Pharmaceuticals, sees the same pattern across industries. “Companies still design governance as if decisions moved slowly and centrally,” she said. “But that’s not how AI is being adopted. Businesses are making decisions daily — using vendors, copilots, embedded AI features — while governance assumes someone will stop, fill out a form, and wait for approval.” That mismatch guarantees bypass. Even teams with good intentions route around governance because it doesn’t appear where work actually happens. ... “Classic governance was built for systems of record and known analytics pipelines,” he said. “That world is gone. Now you have systems creating systems — new data, new outputs, and much is done on the fly.” In that environment, point-in-time audits create false confidence. Output-focused controls miss where the real risk lives. ... Technology controls alone do not close the responsible-AI gap. Behavior matters more. Asha Palmer, SVP of Compliance Solutions at Skillsoft and a former US federal prosecutor, is often called in after AI incidents. She says the first uncomfortable truth leaders confront is that the outcome was predictable. “We knew this could happen,” she said. “The real question is: why didn’t we equip people to deal with it before it did?”

How AI Will ‘Surpass The Boldest Expectations’ Over The Next Decade And Why Partners Need To ‘Start Early’

The key to success in the AI era is delivering fast ROI and measurable productivity gains for clients. But integrating AI into enterprise workflows isn’t simple; it requires deep understanding of how work gets done and seamless connection to existing systems of record. That’s where IBM and our partners excel: embedding intelligence into processes like procurement, HR, and operations, with the right guardrails for trust and compliance. We’re already seeing signs of progress. A telecom client using AI in customer service achieved a 25-point Net Promoter Score (NPS) increase. In software development, AI tools are boosting developer productivity by 45 percent. And across finance and HR, AI is making processes more efficient, error-free, and fraud-resistant. ... Patience is key. We’re still in the early innings of enterprise AI adoption — the players are on the field, but the game is just beginning. If you’re not playing now, you’ll miss it entirely. The real risk isn’t underestimating AI; it’s failing to deploy it effectively. That means starting with low-risk, scalable use cases that deliver measurable results. We’re already seeing AI investments translate into real enterprise value, and that will accelerate in 2026. Over the next decade, AI will surpass today’s boldest expectations, driving a tenfold productivity revolution and long-term transformation. But the advantage will go to those who start early.

Five AI agent predictions for 2026: The year enterprises stop waiting and start winning

By mid-2026, the question won't be whether enterprises should embed AI agents in business processes—it will be what they're waiting for if they haven't already. DIY pilot projects will increasingly be viewed as a risker alternative to embedded pre-built capabilities that support day-to-day work. We're seeing the first wave of natively embedded agents in leading business applications across finance, HR, supply chain, and customer experience functions. ... Today's enterprise AI landscape is dominated by horizontal AI approaches: broad use cases that can be applied to common business processes and best practices. The next layer of intelligence - vertical AI - will help to solve complex industry-specific problems, delivering additional P&L impact. This shift fundamentally changes how enterprises deploy AI. Vertical AI requires deep integration with workflows, business data, and domain knowledge—but the transformative power is undeniable. ... Advanced enterprises in 2026 will orchestrate agent teams that automatically apply business rules, maintain a tight control on compliance, integrate seamlessly across their technology stack, and scale human expertise rather than replace it. This orchestration preserves institutional knowledge while dramatically multiplying its impact. Organizations that master multi-agent workflows will operate with fundamentally different economics than those managing point automation solutions.

How should AI agents consume external data?

Agents benefit from real-time information ranging from publicly accessible web data to integrated partner data. Useful external data might include product and inventory data, shipping status, customer behavior and history, job postings, scientific publications, news and opinions, competitive analysis, industry signals, or compliance updates, say the experts. With high-quality external data in hand, agents become far more actionable, more capable of complex decision-making and of engaging in complex, multi-party flows. ... According to Lenchner, the advantages of scraping are breadth, freshness, and independence. “You can reach the long tail of the public web, update continuously, and avoid single‑vendor dependencies,” he says. Today’s scraping tools grant agents impressive control, too. “Agents connected to the live web can navigate dynamic sites, render JavaScript, scroll, click, paginate, and complete multi-step tasks with human‑like behavior,” adds Lenchner. Scraping enables fast access to public data without negotiating partnership agreements or waiting for API approvals. It avoids the high per-call pricing models that often come with API integration, and sometimes it’s the only option, when formal integration points don’t exist. ... “Relying on official integrations can be positive because it offers high-quality, reliable data that is clean, structured, and predictable data through a stable API contract,” says Informatica’s Pathak. “There is also legal protection, as they operate under clear terms of service, providing legal clarity and mitigating risk.”