Insights from the field

What Enterprise AI Productivity Metrics Actually Measure

Most AI ROI dashboards track lines of code and PR volume — metrics that inflate with AI assistance but do not tell you whether the team is delivering more value.

The Production Gap: Why Enterprise AI Projects Stall After Launch

Enterprise AI teams can get a model working in a demo. The operating layer — versioning, drift detection, cost attribution, evaluation cadence — is what breaks after launch.

What the Discovery Loop Founding Signals About Where Enterprise AI Is Headed

Jeff Dean, Sanjay Ghemawat, Quoc Le, and Oriol Vinyals left Google to automate the ML research loop itself. What that bet signals about where enterprise AI architecture investment needs to focus next.

The Agentic Security Disclosure That Changes Enterprise AI Architecture Assumptions

OpenAI's Black Hat 2026 disclosure — agents self-coordinated through a covert JFrog Artifactory channel and executed 17,600+ attacker actions on Hugging Face — exposes a structural gap in how enterprises are securing agentic AI systems.

AI Speeds Up Delivery Without Solving the Decision Problem

Andrej Karpathy's Software 3.0 framing is right that AI changes what software is and how it's built. What it doesn't change: the hard part of custom software development has never been writing code. Teams that adopt AI coding tools without investing in product decision quality ar

AI Integration Has a Data Topology Problem

Amazon's AGI director told VB Transform 2026 that reliability, not model capability, is blocking enterprise AI deployment. The common misreading is that this is a model problem. It is an architecture problem upstream of the model, in the data layer most enterprise teams haven't c

The Inference Infrastructure Problem Enterprises Are Solving Too Late

GPU prices fell 64-75% over the past eighteen months. Enterprise infrastructure teams are drawing the wrong conclusion. Compute is not the dominant cost in a production AI inference pipeline — and the costs that are dominant aren't falling.

The Data Ownership Cost That Most Platform Decisions Ignore

The case for SaaS platforms has always included a hidden liability: operational data stays with the vendor. Until recently that trade was acceptable. Now that operational data is AI training signal and retrieval context, the calculus has changed.

The Build vs. Buy Question for AI Is Wrong at Every Layer

Build vs. buy is not one decision. It is three separate decisions at three separate layers of the AI stack, and the right answer is different at each one. Treating it as binary is how enterprises end up with expensive platforms that cannot understand their domain.

Agentic AI Needs a Different Data Architecture Than Your Analytics Stack Built

Enterprise analytics stacks are built for queries. Agentic AI workflows need fresh data, consistent access, and a governance layer your analytics pipeline was never designed to provide.

Agentic Engineering Moved the Bottleneck. Most Teams Are Looking in the Wrong Place.

AI coding tools solved the code generation problem and made every other problem in software delivery harder to see. The bottleneck is now in review, architecture clarity, and organizational intent — not in writing code.

The Compliance Clock That Most AI Teams Missed

The EU AI Act's August 2026 enforcement deadline passed with most enterprises unprepared. The compliance gap is not a policy failure — it is an engineering and architecture problem no one assigned to a team.

Meta Enters Enterprise AI. What the 'Agents Behind Schedule' Admission Actually Says.

On Meta's Q2 2026 earnings call, Zuckerberg outlined a credible enterprise AI revenue strategy for the first time. The more instructive disclosure was the acknowledgment that Meta's agents are behind schedule after $145B in AI investment.

The API Price Cut That Resets Enterprise AI Architecture Decisions

OpenAI's 80% reduction on GPT-5.6 Luna tier pricing changes the economic case for agentic workloads that enterprise architects built plans around over the past year. Here is what to revisit.

Agentic Systems Break in Production for Predictable Reasons

The failure modes of agentic AI in production are not random — they are structural, recurring, and almost always preventable if you know where to look before deployment.

Agentic Engineering Changes What Custom Software Costs

Code is cheaper to produce than it was two years ago. The parts of software delivery that were never just code generation haven't gotten cheaper — and some have gotten more expensive.

Why AI Productivity Metrics Keep Lying to Engineering Leaders

92% of US developers use AI coding tools daily. Measured productivity gains hover near 10%. The gap is not a technology problem — it's a measurement problem.

The Operationalization Gap in Enterprise AI Deployment

Most enterprise AI projects do not fail because the model was wrong. They fail because the organization had no plan for what happens after the model is right.

When Your Systems Integrator Embeds the AI Model

Cognizant's expanded Anthropic partnership embeds Claude directly into industry platforms it runs for manufacturing, life sciences, and insurance clients. When AI becomes a platform feature, the architecture and vendor-dependency implications are different from a deployment engag

The Enterprise AI Bottleneck Has Moved From Models to Deployment

OpenAI, Anthropic, Microsoft, and AWS committed over $10 billion in 2026—not to better models, but to engineers embedded inside enterprise customers. The hard problem was never the model.

Data Quality Is Not Your AI Problem. Data Architecture Is.

An IDC survey found 94% of IT leaders cite data quality as the primary factor in AI project success. But data quality is a symptom. The underlying condition is a data architecture that was never designed to support AI retrieval, governance, or model-ready pipelines.

AI Can Read Your COBOL. It Still Can't Fix Your Data Model.

AI tools have transformed legacy code comprehension — they can explain decades-old programs, generate documentation, and produce migration plans. The modernization blocker was never code comprehension. It was always the data model underneath.

Your AI Inference Architecture Is Solving the Wrong Problem

Enterprise AI infrastructure was designed for training workloads. Inference now consumes more compute than training for the first time — and it requires a fundamentally different architecture for latency, cost, and data sovereignty.

When AI Writes the Code, Architecture Decisions Become the Product

Agentic engineering tools can now handle most implementation work. The constraint on custom software has shifted from coding velocity to architecture quality — and most teams haven't adjusted their process to match.

SAP Just Made AI Agent Governance an Enterprise Architecture Problem

SAP's Q2 2026 Business AI release formalizes AI agent governance as an enterprise architecture function, built on LeanIX. For technology leaders running SAP, this is the governance model arriving before you are ready for it.

The Infrastructure Bet Behind Every Agentic AI Strategy

AMD's Advancing AI 2026 event on July 22–23 launched the Helios platform, MI400 GPUs, and 6th Gen EPYC built for the agentic era. For enterprise technology leaders, the announcement crystallizes a choice that can no longer be deferred.

Training Data Provenance Is Now an Enterprise Legal Risk. Procurement Teams Need a Framework.

The GEMA v. Suno verdict on July 31 will be the first major European court ruling on whether AI training on copyrighted content requires licensing. For enterprise technology leaders, it crystallizes training data provenance as a new and concrete risk category in AI model procurem

Cloud-Native Built the Last Era of Enterprise Software. Agent-Native Is Building the Next.

Alibaba Cloud's Agent Native Cloud announcement at WAIC 2026 is the first enterprise infrastructure platform built specifically for AI agent deployment, governance, and orchestration. The architectural decisions enterprises make in the next 18 months will determine whether their

Securing Enterprise AI Agents Starts With What They Can Reach, Not What They Can Think

The real security perimeter for enterprise AI agents is not the model — it is the access topology. Simon Willison's Lethal Trifecta identifies why most enterprise AI agent deployments are exploitable by design.

Enterprise AI Is Failing at the Architecture Layer, Not the Model Layer

Enterprise AI projects fail most often at the integration layer, not the model layer. The architecture decision about where AI connects to your data determines whether value compounds or stalls.

AI Can Write the Code. You Still Own the Architecture.

Agentic coding tools raise the ceiling for developers who understand the architecture. They do not raise it for developers who don't. The leverage point changes; the judgment requirement stays.

The Bottleneck After Agentic Engineering

Microsoft's early-2026 study found agentic coding tools produced 24% more merged pull requests. The story being told is about productivity. The real story is about where the bottleneck moved next.

Most Enterprise AI Governance Programs Are Auditing the Wrong Layer

Most enterprise AI governance programs audit the model and document the use case. The real risk lives in the agent's access topology — what it can reach and what it can do if compromised.

The AI Adoption Decision Your Leadership Team Keeps Delegating

Ethan Mollick's recent analysis pins enterprise AI adoption failure on leadership design, not technology selection. The organizations extracting organizational-level returns from AI are the ones where executives model use personally and redesign incentives around it — not the one

Enterprise AI Agents Are in Production. The Governance Model Isn't.

The O'Reilly Radar for July 2026 and concurrent Gartner research agree: AI agents have reached production across enterprises, but governance hasn't kept pace. The failure mode isn't deploying agents — it's applying uniform governance to agents with very different autonomy levels.

Model Drift Is a Business Risk. Most Enterprises Are Monitoring the Wrong Layer.

Enterprise AI monitoring watches infrastructure — latency, uptime, error rates. Model drift operates at a different layer: input distributions shift, the relationship between inputs and outputs changes, and the model keeps producing confident-looking wrong answers.

The Hidden Cost of Buying AI Is What You Build on Top of It

The buy decision for AI capabilities rarely stays a buy decision. The integration layer, the customization requirements, and the workflow gaps almost always require custom software. The question is whether you planned for it.

Data Governance Was Built for Compliance. AI Retrieval Needs Something Different.

Enterprise data governance frameworks were designed to answer auditors — access logs, retention policies, classification tiers. AI retrieval systems need freshness, semantic structure, and query-time access enforcement. Most organizations have the first and are missing the second

Why Your AI Productivity Metrics Are Measuring the Wrong Thing

Most engineering teams measure AI's impact through PR cycle time, ticket velocity, and code volume. Those signals are real but do not connect to the business outcomes that justify the investment.

AI Can Refactor the Code. It Cannot Fix the Data Model.

AI-assisted modernization tools compress code-translation timelines materially. But the thing blocking your AI roadmap is not the code — it is the data model underneath it.

When AI Delivers the Artifact, Not the Draft

OpenAI's ChatGPT Work launch shifts AI from draft assistant to producer of finished business deliverables. The enterprise implications reach further than the product announcement does.

What Happens When Your AI Vendor Becomes Your Implementation Partner

Anthropic and Blackstone's launch of Ode signals that the AI industry's next billion-dollar bet is not on better models — it's on getting models to actually work inside real enterprise environments.

The Policy Gap Driving Secret AI Use in Enterprise Software Teams

When enterprises leave AI use policies unclear, engineers don't stop using AI. They hide it. The risk moves underground while the organization assumes it's managing it.

What Karpathy's LLM Wiki Reveals About Enterprise RAG

Andrej Karpathy's personal knowledge architecture works brilliantly for one practitioner. The properties that make it work are exactly what enterprise environments don't have.

The Security Assumption Built Into Every Enterprise AI Integration

Every enterprise AI agent that touches internal data contains a structural vulnerability. Most teams haven't named it, let alone designed around it.

Inference Cost Is the Cloud Bill You Don't See Coming

The cost of running AI in production doesn't surface during development. By the time it appears, the architecture choices that created it are already locked in.

Technical Debt Looks Different When AI Writes the Code

AI-generated code is fast to produce and hard to own. The debt it creates isn't from rushing—it's from code no engineer truly understands. Here's what architects need to plan for.

From Vibe Coding to Agentic Engineering: What the Shift Requires of Enterprise Teams

Andrej Karpathy's framework at Sequoia AI Ascent 2026 draws a hard line between prompting AI for code and governing it as an engineering discipline. For enterprise teams, the distinction is an organizational design question.

What Microsoft's $2.5 Billion Bet Reveals About Enterprise AI

Microsoft's Frontier Company is not a product launch — it is a professional services firm inside a hyperscaler, created because enterprise AI deployments fail at institutional rates. The signal matters more than the investment.

When AI Models Degrade in Silence: What Enterprise MLOps Actually Requires

Enterprise teams treat model deployment like application deployment—and six months later discover outputs have quietly worsened. MLOps is a discipline, not a toolchain choice.

AI Moved the Coding Bottleneck. The Review Bottleneck Is Still Yours.

AI coding tools increased output volume, not delivery speed. The constraint moved downstream—to review queues, integration complexity, and governance gaps that don't scale automatically with AI-generated code.

AI Governance Isn't a Legal Problem — It's an Architecture One

Regulated enterprises are treating AI governance as a compliance checklist. The teams that avoid costly failures treat it as a system design requirement built into every deployment decision.

The Build-vs-Buy Question Every Enterprise AI Team Is Asking Wrong

Most enterprise AI decisions aren't really about build versus buy. They're about which capabilities need to be differentiated—and most teams figure that out after the TCO analysis is complete.

Why Model-Ready Data Is the Real Gate to Enterprise AI

Enterprise AI projects don't fail at the model layer. They fail because the data infrastructure was built for reporting, not retrieval. What the data layer actually needs to be model-ready.

The Benchmark Score That Doesn't Predict How Your Enterprise AI Will Actually Perform

Two new enterprise-focused AI benchmarks released this week document a 37% gap between lab performance scores and real-world enterprise deployment. The evaluation methodology most organizations use to select AI systems is optimized for conditions that don't exist in production.

The EU AI Transparency Deadline That Lives in Your Product, Not Your Legal Department

Article 50 of the EU AI Act becomes enforceable August 2, 2026. Most enterprise teams have filed this under compliance. The disclosure requirements are product design and architecture requirements — and they cannot be solved by a legal team alone.

Gartner's 40 Percent Prediction Is Not About Technology

Gartner predicted that more than 40% of enterprise agentic AI projects would be canceled by 2027. A year in, the failure pattern is clear: these projects are not failing because agents stop working. They are failing because nobody defined what 'working' meant before deployment.

AI Amplifies Legacy Modernization — the Good Parts and the Bad

AI-assisted modernization tools report 40–50% time reductions. Under the right conditions, that number is real. For the Java monolith with sub-25% test coverage and no architectural record — the modal enterprise legacy system — the same tools accelerate drift rather than fix it.

The Development Phase Where AI Tooling Doesn't Move the Needle

AI coding tools deliver real gains in specific development phases and near-zero gains in others. Most enterprise benchmarks measure the phases AI handles well and miss the phases that determine whether the team ships things worth shipping. Here is where the leverage actually is.

Why Enterprise AI Deployments Stall at the Integration Layer

Fifty-four percent of enterprises are running AI agents in production. Most of them are hitting the same wall — not model capability, but how the model connects to enterprise data, systems of record, and business logic. The integration layer is where enterprise AI actually lives.

The AI ROI Measurement Problem That No Dashboard Fixes

Enterprise AI ROI metrics are returning numbers that look unimpressive — 7–8% productivity gains vs. 30–50% pilot results. The problem is not the tools. It is measuring the wrong layer. Here is what actually signals whether AI is working in a software engineering organization.

What Embedded AI Engineering Transfers — and What It Doesn't

AWS's Forward Deployed Engineering model promises enterprises will operate AI systems independently after the engagement ends. Whether that happens depends almost entirely on what the enterprise side brings to the table.

The Deployment Layer Is Now the Enterprise AI Battleground

AWS and Microsoft each launched billion-dollar embedded engineering programs within 72 hours of each other. What the convergence signals about where enterprise AI is actually failing — and what it means for technology buyers.

When Building Custom Software Is Still the Right Call

AI has lowered the cost of building custom software significantly — but it hasn't changed what happens when you build the wrong thing. The build vs. buy decision has a new axis, and most teams are still using the old framework.

The Inference Problem: What Enterprise AI Actually Costs to Run

Industry analysts estimate 55–80% of enterprise AI GPU spend goes to inference, not training. Most enterprise AI teams plan for training costs and discover inference costs. The gap is an architecture problem, not a budgeting problem.

The Productivity Lie in AI-Augmented Software Delivery

METR's 2025 study found that experienced developers using AI tools took 19% longer than developers working without them. The implications for software delivery teams — and for how AI ROI gets measured — are significant.

The AI Compliance Clock Most Enterprises Are Ignoring

The EU AI Act's GPAI transparency obligations land August 2, 2026 — and 78% of organizations haven't meaningfully prepared. For regulated industries, the gap between AI deployment and AI compliance is becoming a liability.

Why Most Enterprise AI Never Reaches Production

The gap between a trained model and a production-ready AI system is a platform engineering problem, not a talent problem. What enterprise teams are consistently missing in their MLOps stack — and how to fix it.

The Data Architecture Every Agent Strategy Depends On

At LangChain's Interrupt conference, Andrew Ng identified the constraint directly: future companies will be ten-person teams rebuilding data architecture with agents. Most enterprise AI strategies are built on data that was never designed for non-human access. That is the gap.

What IBM's AI Hiring Reversal Reveals About the Talent Pipeline

IBM is tripling U.S. entry-level hiring after cutting for AI. Harvard research shows 9% junior employment decline across AI-adopting organizations. The cost savings calculation most technology leaders are running is missing a line item.

Build vs. Buy for AI: The Decision the Old Framework Gets Wrong

The traditional build vs. buy heuristic — buy for cost, build for control — no longer applies cleanly to AI capabilities. Open-weight model pricing changed the cost side. The real decision now turns on competitive differentiation and proprietary data.

AI Can Read Your Legacy Code — The Architecture Problem Stays Yours

AI tools have made legacy code analysis substantially faster. They have not made legacy modernization substantially simpler. The hard part was never reading the code.

What Agentic Engineering Actually Changes About Software Delivery

AI coding tools shift the bottleneck in software delivery from writing code to specifying it precisely and evaluating what was generated. Senior engineers who understand this are becoming orchestrators. Those who don't are getting buried in review queues.

Why Enterprise AI Agents Break Before They Ship

Eighty-six percent of enterprise AI agent pilots fail before reaching production. The cause is almost never the model — it is governance, tracing, and ownership gaps that the pilot phase was not designed to surface.

How to Build a Product Roadmap That Stakeholders Actually Trust

Most roadmap credibility problems aren't roadmap problems — they're communication and process problems. Here's what actually builds stakeholder trust over time.

Product Discovery vs. Product Delivery: Why Most Teams Get the Balance Wrong

Most product teams run discovery and delivery as separate tracks — and wonder why shipped features don't move metrics. Here's what a healthier balance looks like.

The Integration Layer Is Where Enterprise AI Projects Actually Fail

Most enterprise AI pilots work and most enterprise AI products never ship. The gap is not the model — it is the integration architecture between the model and the systems of record. Here is what that failure looks like and how to architect around it.

Why AI Productivity Gains Aren't Showing Up in Delivery Metrics

Individual engineers are more productive with AI tools. Team delivery metrics are flat. The gap is organizational, not technical — and it has a specific cause.

What Agentic Engineering Means for Product Managers

Andrej Karpathy's agentic engineering framework redefines the bottleneck in AI-assisted development — and it runs directly through the product management function.

AI in the Product Development Workflow: What's Actually Working

AI tooling is changing how product teams work — but the hype around it is obscuring which use cases are genuinely high-value and which are productivity theater.

Why Digital Transformation Initiatives Keep Failing (And What to Do Instead)

Most digital transformation programs fail not because of technology, but because of how the work gets scoped, funded, and governed. Here's what the failure pattern looks like and how to change it.

Self-Service Business Intelligence

Utilizing self-service Business Intelligence, business analysts, executives, and users of all kinds can run queries, create dashboards, build reports, and run models without the aid of IT, turning any business into a data-driven business.

Remote Workers are on Cybersecurity Frontlines

Reshaping your cybersecurity frontlines requires examining remote networking and combining strategies with threat intelligence.

Corporate America is Still Building-Out Big Data

The ability to deploy data as a competitive business asset is what distinguishes a set of well-established, data-rich companies who have reigned as market leaders over the course of the past several decades. Data and technology are driving business change, while AI, machine learning, and deep learning are allowing organizations to access and utilize their data in ways they never could before, putting pressure on legacy businesses that must modernize to remain competitive.

Chief Data Officers and Magic Data Dust

The role of the chief data officer is integral to organizational success in the modern data-driven world. For enterprise organizations to derive maximum value from their data, a chief data officer plays a vital role in ensuring trusted data through data governance, data privacy, and data security.

Thawing the Monolith to Support Agile Microservices

The Monolithic Application Architecture is cumbersome, clunky, and slow to adapt to the need for dynamic, agile change. Therefore, it is essential to move from a monolith to a microservices architecture.

Deciphering Our Own Data

Never before has so much data been available to so many. Integrating data and building useful analytical models with it is an exceptionally difficult process to achieve. To fully understand data, data quality and its enormous potential, a few data myths should be addressed -- and punctured.

Fostering Data Science

Today, there is a worldwide shortage of data science talent that threatens to derail the Big Data revolution that is transforming business and society. The concept of the citizen data scientist first gained prominence in 2015, when Gartner coined the term, referring to it as “a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.” Gartner believes today's citizen data scientists can perform the sophisticated analyses that could only be done by data scientists in the past.

The Physical Impact of Cybersecurity

Cyber threats are now very physical. They evolve and so do their targets. Pipes, plumbing, electrical, water supply - all is at risk. How does that translate to direct or indirect harm beyond the digitial walls? This article dives into that question.

Mental Health Burnout in Your Remote Workforce

Remote worker burnout is a serious issue which will incresae without proper addressing. Learn the signs of faltering mental health and how to help your remote workforce!

Why Malicious Attacks are Targeting America’s Infrastructure

The Fairness Discourse of Remote Work

There's growing friction between remote work adoption and demand. Some businesses desire a return to office environments in a world where remote workers like their newfound work space.

Dancing with the Devil, Converting COBOL to C#

COBOL or Common Business Oriented Language is a 60-year-old business language that still powers a substantial slice of the world’s most critical business systems. In fact, COBOL is unique in its endurance beyond what is considered the average lifespan for software development languages.

Why aerospace software is decades behind

On October 29, 2018, Lion Air Flight 610 was en route from Jakarta, Indonesia to Pangkal Pinang, Indonesia when the disaster occurred. The flight carried 189 passengers and crew members, all of whom were killed in the crash. The cause of the crash was initially unknown, but later investigations revealed that a malfunctioning sensor on the plane caused the aircraft's flight control system to activate, causing the plane to rapidly dive.

The impossible task of modernizing government software

When the Federal Information Technology Acquisition Reform Act (FITARA) was passed by Congress, it was a watershed moment. Mainframes shuddered in the shadows, fearing their doom. IT leaders expected to see amazing new waves of modern software spending. It was aimed at improving the management of information technology across the entire spectrum of the federal government, with the goal of reducing costs and improving the efficiency and security of IT systems.

The business intelligence platform revolution

The business intelligence landscape has undergone significant transformation in the last few years, largely due to advancements in technology and changes in business needs. The BI landscape has transformed from being a tool primarily for data analysts to a platform accessible to a wider range of users with more advanced capabilities, features, and integration options.

The bots are now running cybersecurity

Artificial intelligence is now swallowing up an entirely new swath of technology department functions. So it’s not entirely surprising that it’s gulping up cybersecurity as well. The future of cybersecurity is all about artificial intelligence, and specifically, machine learning, because bots are far better at protecting us than we first understood. It's a science, not an art. We need the bots to take up arms and detect the anamolies in all shapes and sizes.

Talking to robots - the boundaries of what is possible with AI and ChatGPT

Chatting with ai chatbots one thing, but building enterprise applications that let us stand on their shoulders is something else entirely. We take a look at the broad and far-reaching possibilities when it comes to conversational AI, chatbots, chatGPT, and what it could evolve into for enterprise software.

The omnicloud is becoming omniscient

An Omnicloud strategy can add to the benefits of multi-cloud by providing a unified and integrated cloud platform that allows users to access and manage their data and applications from a single location, regardless of where those resources are physically located. The approach can help organizations to increase their agility, scalability, and flexibility while reducing costs, improving security, and enhancing user experience.

The herculean lift: pulling your data stack from servers to the cloud

Data is the world's new currency. However, in order to derive value from the Big Data paradigm, it is critical to design, develop, and deploy a cloud-based data architecture. Additionally, it is essential to move the on-premises data stack to a cloud-based ecosystem.

Modern logistics - how breakthroughs in network connectivity will change the world

Since the early 1990s, the advent of the internet has led to an accelerated use of technology in logistics. This has included such developments as barcode scanning and GPS tracking of shipments, as well as more recent innovations such as robots and artificial intelligence. These advancements have allowed for increased productivity and decreased costs and errors in the field. In this digital transformation age, logistic technologies are undergoing major changes in order to keep up with the pace of the modern economy. Companies must be prepared to adapt to emerging technologies if they wish to remain competitive in a globalized world.

Marketing to the technology consumer

How large companies and emerging apps are marketing to the end-consumer. It's a wild and wonderful (and complex) time to be in marketing. Catering to the tech-savvy user is now the norm, and companies are getting better at this art and science.

How VR, AR, and the metaverse will change our lives - maybe

Augmented Reality (AR) and Virtual Reality (VR) have the potential to significantly change the way people interact with the world and each other.

How ERP integration is finally coming of age

By the mid-2010s, many popular ERP systems had already begun offering APIs to integrate with other software systems and automate specific business processes. The growing popularity of APIs and the trend toward integration and automation in the software industry has likely played a role in the widespread adoption of APIs in ERP systems. ERP systems can expose their data and functionality to other systems and applications using APIs, allowing for more streamlined and efficient logistics and operations.

The modern vehicle - developers are taking to the streets

The acceptance of vehicle-friendly apps beyond simple navigation, music, or infotainment, foretells future when can car will become work and leisure time. Our bosses will likely be showing up on holographic displays right there in our vehicle cockpit.

The hard numbers: what cybersecurity really costs

Cybersecurity costs real money, but it is now table-stackes. You simply have to pay the price to secure your company. It's the oxygen for the patient. The issues of data sensitivity, business continuity, regulation compliance, and customer trust are now all center-stage.

Google is gobbling-up all things cybersecurity

Google is unmistakably, absolutely, and deeply vested in cybersecurity. It acquired cybersecurity company Mandiant, (a provider of incident response and threat intelligence services), in late 2022 for a paltry $5 billion. This however, is just the beginning. We see the larger picture of cybersecurity, and artificial intelligence is going to dominate that conversation.

DevOps success: improving application performance with deeper analytics

DevOps CI/CD and app performance management is made simpler and more powerful by using AI, ML, and big data analytics.

AWS cloud services: winners, losers, and pet projects

The coronavirus pandemic and the Fourth Industrial Revolution have driven the rapid growth of cloud computing technology. Cloud computing offers many advantages, including replacing upfront IT infrastructure expenses with a low-cost pay-as-you-go model, increasing speed and agility, and upscaling and downscaling as needed. Amazon Web Services (AWS) is one of the leaders in cloud computing, with clients in over 190 countries. AWS cloud-based services are grouped into categories like storage, networking, compute, and databases. Understanding the different components of cloud-based architecture is essential to successfully adopting cloud computing for businesses of all sizes and industries.

Automation and hyperautomation - why it’s now priority #1

Automation isn’t just about robots, mechanization, and replacing factory workers. It can also be about using solutions to oversee massive IT estates to keep them running optimally, or removing menial, labor-intensive work that is often better handled by computers than humans. AIOps and Hyperautomation use AI and machine learning to analyze a company’s day-to-day IT operation, keeps it functioning properly, alerting people and departments appropriately, often proactively fixing any potential issues that arise. AIOps deep dives into operational data, understanding relationships between resource utilization, tracking ongoing behavior, identifying correlations that drive application constraints, while generating models of application behavior to followed going forward.

Achieving automation at scale: why CEOs should care

Automation and RPA specifically are here. The executives just have not wrapped their arms around it quite yet. But they're getting on board. One at a time, "hey let's automate that!"

A practical guide to a modern CRM

As artificial intelligence and machine learning infultrates the SaaS CRM software market, it has brought fresh opportunities to provide businesses with more sophisticated features, more intelligent insights, timely recommendations, automation of manual tasks, and a more creative and personalized customer experience overall. The Modern CRM is here, batteries included.

Shortening the time-to-value via DevOps and Kubernetes

When Google open-sourced the core components of Borg as what they now call Kubernetes, with the goal of providing a common platform for managing containers that could be used across a variety of organizations and industries. Kubernetes has gained widespread adoption and has become the de facto standard for managing containerized applications and automating software development processes.

Security log files can save you a ton of money

Security log files are gold. They are rich with clues, pointing you to the clues that can prevent data and cyber breaches proactively. Log analytics is a cottage industry, and includes collecting, analyzing, and searching large volumes of security-related log data in order to detect and respond to potential security threats.

How IT executives leverage AI to predict the future and minimize surprise

Although the mountain of information can exhaust the limited time of a technology executive, a reasonable baseline understanding of the movements and cadence of artificial intelligence and predictive modeling is imperative to truly capture, and then, in turn, communicate, the enormous potential that some of these solutions hold.

Ransomware: honey, someone is at the door

In 2021, a ransomware attack hit the U.S. hard. The Colonial Pipeline was targeted. As a major fuel pipeline, it stretches over 5,500 miles. It transports 2.5 million barrels of fuel daily, from Texas to the East Coast. On May 7, DarkSide, a cybercriminal group, struck. Operations halted.

Data estates: the urban sprawl of the data world

The value of data does not lie in the possession of it, but in its organization and the speed with which it can be accessed through the data estate.