London's mayor praised the beavers, who are already having a noticeable impact.For years, the cloud market has made a simple promise: Move workloads to large-scale platforms, gain better resilience, and worry less about downtime. That promise was never entirely wrong, but it is becoming less complete. The latest findings from Uptime Institute’s seventh Annual Outage Analysis suggest that the outage landscape is changing in ways that should concern both cloud providers and cloud customers. The biggest risks are no longer limited to broken physical infrastructure. They are increasingly tied to the complexity of the systems used to run, coordinate, update, and recover that infrastructure.
The most alarming number in the report is that IT and networking issues accounted for 23% of impactful outages in 2024. Uptime Institute links these increases to growing IT and network complexity; the long-term shift toward colocation, cloud, and third-party digital services; and the resulting increase in change-management failures and misconfigurations. That number is more than a statistical footnote. It points to a structural change in how outages happen and why cloud outages are becoming such a stubborn problem.
Hardware redundancy can protect against component failures, but it doesn’t help much when the outage stems from a bad configuration, an automation error, a faulty network change, or an underappreciated control-plane dependency. In those cases, the infrastructure itself may remain intact while the system that governs it breaks down. The industry is learning that resiliency is less about duplicating equipment and more about managing complexity. Today’s increasingly distributed and software-defined environments cannot operate safely at scale.
Uptime’s findings show that power remains the leading cause of major outages, underscoring that traditional infrastructure engineering still matters a great deal. But even as providers continue to improve physical resilience, outages can still arise from the digital and procedural layers above it. Cloud platforms are now dense stacks of services, APIs, orchestration systems, software-defined networks, identity controls, failover logic, and third-party dependencies. That complexity creates more possible points of interaction and more opportunities for an error in one layer to cascade into several others.
This helps explain why outages can feel more surprising today than they did a decade ago. In older data center models, an outage often had a more apparent root cause, such as a power event, a cooling failure, or a hardware fault. In cloud environments, the trigger may be a small configuration change that propagates across regions, a policy update that unintentionally blocks service communication, or a network control failure that affects seemingly unrelated services. These are not failures of raw infrastructure capacity. They are failures of complexity management.
The report’s language around change management and misconfiguration is especially important because it challenges one of the most common assumptions in the cloud market: that scale automatically produces better operational outcomes. The reality? Scale can magnify both strengths and weaknesses. Large cloud providers have more engineering talent, more sophisticated tools, and more redundancy than almost any enterprise customer. But they also run far more interconnected systems at far greater speeds with far more automation. A single process failure can have a wider blast radius.
Another important lesson from the Uptime analysis is that automation has not removed the human factor. If anything, it has changed its form. Even in highly automated environments, human error remains central to the problem. The report notes that in 2025, the share of outages caused by human failure to follow procedures rose by 10 percentage points compared with 2024. A related industry summary of the report notes that 58% of human error-related outages were caused by staff failing to follow established procedures.
That matters because cloud providers often position automation as the answer to reliability. Automation is essential, but it only works as well as the operational model that surrounds it. If teams deploy changes too quickly, rollback paths are weak, approval chains are bypassed, or procedures are incomplete, automation can accelerate failure rather than prevent it. In a modern cloud environment, a human mistake is rarely just a single keystroke. It is more often a design weakness in process, governance, testing, or accountability.
This is also why customers should resist the comforting notion that outages are somebody else’s problem once workloads move to the cloud. Provider-side mistakes remain real, but customer architectures are increasingly entangled with provider networking, identity, observability, and platform services. When an outage occurs, the customer may not have caused it, but they still bear the business impact. The shared responsibility model does not end with security. It extends to resilience planning as well.
The Uptime data points to a clear conclusion: Cloud providers need to treat operational discipline as a first-class design requirement. That starts with better change management. High-risk changes should be tested more aggressively, staged more gradually, and accompanied by stronger rollback mechanisms. Providers also need better dependency mapping to understand how a change in one control layer can affect services far beyond its immediate scope. If the system is too complex to clearly explain, it is too complex to operate.
Providers also need to improve procedural quality. The rise in outages caused by failing to follow procedures suggests that procedures are being ignored under operational pressure or that they are too cumbersome, outdated, or unclear for real production conditions. Neither explanation is comforting. Stronger runbooks, better training, more realistic failure drills, and tighter operational guardrails are not glamorous investments; they are increasingly central to resilience.
Another pressure point is visibility. Uptime notes that software-based and distributed resiliency tools can improve availability, but they also introduce new risks and complicate root-cause analysis. Cloud providers need more transparent and faster incident diagnosis, not just more layers of abstraction. Customers cannot build trust in resilience if every major incident becomes a long exercise in reconstructing opaque service dependencies after the fact.
What’s the financial impact of more frequent problems? Uptime’s 2024 analysis found that 54% of respondents reported that their most recent significant outage cost more than $100,000, and 20% said it cost more than $1 million. These are not edge-case losses. They show that outages remain costly even if they are less frequent than in earlier years.
Customers need to stop evaluating cloud resilience through uptime promises and start evaluating it through failure behavior. How does a provider isolate faults? How transparent is incident communication? How portable are workloads if a major service degrades? How dependent is the architecture on a single region, network path, identity service, or control plane? These are not just technical questions; they are now critical business questions.
The core lesson from Uptime’s data is simple. Outages are becoming a bigger problem for cloud providers and customers because the cloud’s biggest vulnerabilities are increasingly tied to complexity, process failures, and control-plane mistakes, not just broken infrastructure. In addition to adding redundancy, the next phase of cloud improvement will focus on building systems that are easier to understand, safer to change, and more disciplined to operate.
Under-the-hood AI changes and efficiency improvements at the OS layer across Apple’s platforms are certainly the highlights at WWDC 2026. But there have also been significant changes IT admins will need to prepare for, particularly around Declarative Device Management (DDM).
Apple warned us this was coming, but macOS 27 will not support Intel at all. The company will deliver three more years of security updates for those devices, and you will still be able to use Rosetta to get Intel app binaries to run legacy apps on Apple Silicon. But if you still rely on any Intel apps or Macs, it really is time to plan your upgrade.
It was the future once, but when it comes to Declarative Device Management (DDM) that future is now. Apple is removing all its legacy MDM mechanisms to replace them with DDM. “For IT admins, WWDC 2026 is a migration year,” wrote Fleet. “Apple is removing legacy MDM mechanisms and replacing them with Declarative Device Management (DDM). Some of it is urgent. Some of it just needs a plan.”
It’s a great step, though IT admins will need to ensure they aren’t relying on legacy MDM to handle any of their device fleets. That’s a particular issue around software and security update management. In most cases, your MDM provider has probably already introduced DDM support. But if you aren’t certain, now is the time to find out before your systems fail.
Apple has also added new DDM tools across various systems, apps, identities and more. Some of the highlights include:
“One of the new features I’m most excited about is the ability to set permission defaults for managed apps and websites viewed in Safari,” said Adam Henry, senior product manager at Iru. “While the user is still prompted to allow these permissions, we can now present those requests as a unified prompt immediately upon app launch, along with a custom explanation as to why those permissions are important — think a teleconferencing app or website that always needs access to camera and microphone.
“Overall, I think this is a much more user-friendly solution that will likely increase permission compliance.”
Although, Apple has introduced new management tools for AI, it’s important to remember some advice from Joel Rennich, senior vice president for product management at JumpCloud: “Traditional IAM models assume users directly interact with applications, but agentic systems change that assumption. AI intermediaries can now retrieve data, execute workflows, and make decisions across systems. Enterprises will need identity frameworks that govern both human and non-human actors consistently.”
He also noted: “The separation between where data lives and where it is used becomes increasingly invisible to the user. Intent becomes the primary input, not app selection.”
At the same time, the evolution of AI on Apple’s devices promises a lot for enterprise users. Matt Vlasach, Jamf senior vice president, enterprise products and solutions engineering, told me: “Most notable for me was Siri AI and the push towards on-device and more capable models that can do more with user context. While obvious for consumer use cases as illustrated in the keynote, the opportunity to evolve this to the work context using a more advanced Apple Intelligence framework is an exciting evolution.”
Apple has finally eradicated Apple Filing Protocol (AFP) in macOS. This will be an issue for any business that uses legacy Time Capsule or NAS storage devices, though in most cases those products are already obsolete and should be replaced. This is unlikely to be a huge challenge for most, given that Apple began using SMB as its primary file sharing protocol back in 2013 and support for AFP server disappeared in 2020. (Time Capsule fans might want to take a look at the TimeCapsuleSMB open-source project.)
Apple will introduce a new remote log collection capability that integrates directly with the company’s support infrastructure. So, when AppleCare support engages with an organization’s IT team, they can provide an enhanced logging token which can be shared to get the device to collect diagnostic logs to upload to AppleCare. You just know this will expedite remedy.
As I noted here, there are some significant Single Sign-On (SSO) updates; two that caught my eye include:
Another change affects the system processes used in device management at a network level. Apple now requires that you use TLS 1.2 or later. If you or your MDM systems are not doing so, get ready for things to break. (Apple has published a support article to help IT test their network environments in preparation for this change.)
Apple also announced that IT admins will be able to purchase and manage app subscriptions directly in Apple School Manager and Apple Business Manager. And it introduced a managed migration feature that should help migrate data, while preserving device management enrollment and settings.
I’ve really only offered a flavor of some of the IT improvements introduced at WWDC. To find out more, watch the Apple sessions on “What’s new in managing Apple devices” and take a look at the Apple Platform Deployment guide; it should be updated before the new operating systems ship this fall
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Databricks on Wednesday unveiled OpenSharing, a new open protocol designed to let enterprises share AI models, agent skills, dashboards, and unstructured data across platforms without having to copy or move those assets.
That sharing is made possible by OpenSharing’s zero-copy credential vending model that allows recipients to securely access shared assets directly from a provider’s cloud storage using temporary, scoped credentials rather than requiring the assets themselves to be copied, moved, or replicated, the company wrote on its GitHub page.
The ability to share AI assets without creating duplicate copies could help reduce integration complexity, improve governance, and limit the operational overhead associated with operationalizing AI systems across environments for CIOs, said Ashish Chaturvedi, leader of executive research at HFS Research.
“Every organization building AI, such as multi-agentic systems, is hitting the same wall, i.e., the model, the skill, and the consumer reside on three different platforms. The integration tax is enormous, and it grows exponentially with every new partner, customer, or internal team,” Chaturvedi said.
Echoing Chaturvedi, The Futurum Group’s lead of the CIO practice, Dion Hinchcliffe, pointed out that the reduction in operational overhead could help CIOs cut down on the hidden costs of integration around AI deployments: “Today, hidden costs include more than just model development. It is the endless packaging, translation, sync, and governance effort required to operationalize AI assets across organizational boundaries.”
That cost reduction is becoming even more important because enterprises are beginning to treat AI assets as business assets that need to be shared, said Stephanie Walter, practice lead of the AI stack at HyperFRAME Research.
“Enterprises are quickly realizing that the value is no longer just in the dataset. It is in the governed context, logic, and intelligence built around the dataset. Existing approaches can share datasets well, but they often do not address the broader AI package,” Walter said.
“OpenSharing is directionally aligned with that shift because it extends the sharing model beyond tables and files toward the artifacts that power AI workflows,” Walter added.
For Hinchcliffe, that alignment should work in CIOs’ favor, trying to operationalize AI across their systems: “CIOs increasingly want AI supply chains, not isolated data lakes like before.”
Additionally, Chaturvedi pointed out that the new protocol can help CIOs accelerate the monetization of AI investments.
“For CIOs, the speed at which you can share AI assets across partners, subsidiaries, and customers determines the speed at which you can monetize your AI investments. If sharing an agent skill takes six weeks of integration work, you’ve lost the window. If it takes a protocol call, you’ve turned AI into a distribution business,” he said.
Achieving those benefits, however, will require developers to move AI assets across disparate platforms more efficiently, and analysts pointed out that OpenSharing’s ability to reduce integration complexity could significantly improve productivity.
“Developer productivity depends on reducing platform translation work. Developers do not want to rebuild the same asset for every consuming environment, and enterprises do not want every partner or customer interaction to become a platform migration conversation,” Walter said.
In fact, Chaturvedi sees the new protocol as unique in the industry, in the specific sense that “no other open protocol covers agent skills and AI models as shareable, governed objects”.
Walter, in contrast, sees the openness of the protocol as novel: “What is more interesting is the combination: an open protocol, cross-platform interoperability, Linux Foundation governance, and a broader asset model that extends beyond datasets into AI models, agent skills, dashboards, applications, and unstructured data.”
“The novelty is not that Databricks invented sharing, zero-copy access, or marketplace-style distribution. Those capabilities already exist in various forms across the market,” Walter said, pointing towards Snowflake’s offerings, such as the Zero-Copy integrations.
The difference, though, the analyst noted, is that Snowflake allows data to be copied only if both provider and receiver are on Snowflake.
With Databricks’ OpenSharing, data can be copied across platforms, Walter added.
OpenSharing, which is an evolution of Databricks’ existing Delta Sharing protocol, is currently a sandbox project under the Linux Foundation AI & Data Foundation and is available via GitHub.
Its current list of generally available connectors includes Python, Apache Spark, Tableau, PowerBI, Snowflake, DuckDB, Clojure, Node.js, Java, Arcurate, Rust, Go, C++, and R.
Other connectors that are expected to be made generally available soon include Google Spreadsheet, Excel, Airflow, and Lakehouse Sharing.
While AI is proliferating across the workplace, it is introducing a new productivity paradox: While the technology makes work feel faster, it actually pushes more burden onto employees to provide context, perform quality checks, then rinse and repeat across numerous disparate tools.
This, according to a new survey of 6,000 full-time digital workers by Glean’s Work AI Institute, results in two emerging behaviors: “botsitting,” all the unrecognized work that goes into making AI actually usable; and “botshitting,” shipping AI-generated work that is unverified, not that well understood, or perhaps not even trustworthy. The survey report was co-authored by experts from Work AI Institute, Emory University, Stanford University, UC Berkeley, UC Santa Barbara, UNC Charlotte, University College London, and University of Notre Dame.
“It’s definitely in many ways a vicious cycle that feeds itself,” said Rebecca Hinds, head of Glean’s research center the Work AI Institute, a research collaborative of AI experts. Enterprises need to begin understanding and addressing the “massive, massive human labor that’s at the core of this.”
There’s no doubt that AI is quickly becoming a central teammate in the workplace. Glean’s Work AI Institute found that 87% of digital workers are using AI: It is already automating more than a quarter of their work and saving about 11 hours a week.
Still, only 13% say the use of AI has significantly improved their company’s performance, and their time savings are being eaten up by the same technology that is producing them. Employees lose about one-third of their work week (6.4 hours) botsitting: feeding AI context, supervising outputs, debugging errors, cleaning up AI-generated work, and switching between AI tools.
“We’re seeing high, high rates of multiple tool usage, and often those tools aren’t connected,” said Hinds.
In terms of context-feeding, large language models (LLMs) are trained on the vast corpus of the internet, but not always on enterprise-specific data. Thus, employees often have to provide additional information around their company’s products, customers, services, or other details.
“They’re often feeling frustrated when the tools don’t understand enough about day to day work to be useful,” said Hinds. Also, because employees are using multiple tools, they often have to repeat the same prompt over and over.
“It’s exhausting for workers to not only do this, but to have the work be unrecognized, often unrewarded and unacknowledged within the organization,” she said.
Similarly, workers are having to catch outputs that might look polished and finished on the surface, but could be wrong, incomplete, or missing important context. Debugging is the biggest driver of exhaustion, because it is often conducted by people who didn’t necessarily contribute to the initial output, Hinds noted, so they first have to dig up background information.
However, “not all botsitting is bad,” Hinds emphasized. “Certainly, we want workers to have some level of ownership and oversight.”
But when it is unnecessary, it can lead to botshitting, where users ship AI-generated work they haven’t verified because they’re overwhelmed or time-constrained. Sixty-nine percent of users admit to doing so, and 41% say they sometimes deliver work they could not explain if asked. Another 28% blame AI for mistakes they themselves caused.
“Botshitting is offloading your critical human thinking, judgment, and understanding,” Hinds explained. “You’re offloading that work that absolutely needs to remain with the human.”
Workers using multiple AI agents are significantly more likely to do this, she added, because agents are so scalable, and can spiral out of control if they don’t have the right controls or permissions built around them, causing overwhelmed users to give up on their verification efforts.
“You don’t often see the negative impacts until 3, 4, 5, steps down the line,” said Hinds. “Then it requires all of this cleanup work, detective work, to understand where did the agent go wrong.”
Interestingly, more than half of the workers surveyed said they get more day-to-day help from AI than they get from their managers, and consider it easier to collaborate with than humans.
Still, they seem to be facing a Goldilocks problem when it comes to sharing their use of AI. Among self-identified high AI achievers, 54% are using unapproved tools or using approved tools in noncompliant ways, and 36% are hiding how much AI helps them.
As Hinds explained, depending on the context and the level of psychological safety an organization has provided, it can be “differentially beneficial or harmful” to show you’re using AI, and, on the flip side, to conceal that you’re using it too much, because that might make you less valuable, or perceived as less valuable, she said.
It’s a complicated balance, because, she noted, “there’s massive pressure in so many organizations to demonstrate AI fluency, to demonstrate you’re a power user.”
In fact, the report said, “The companies pulling ahead are doing something different. They aren’t spending a greater share of their AI time using AI. They’re spending a greater share on the work around it: setting context, defining what ‘good’ looks like, building judgment, and deciding what should never have been handed to a model in the first place.”
The most transformative organizations are addressing AI challenges proactively: Providing training and support, treating AI as an opportunity to redesign work, and formally rewarding AI skills. In addition, it noted, the hardest skill to build is knowing when not to use AI.
It is “not just clicks of the tool, not just tokens used, but real skills, real learning,” said Hinds. In addition to investing in workers, these organizations are clearly stating AI strategy and clarifying the “why” behind it. Governance should also be “living and breathing,” with companies continuously re-evaluating policies.
And it needs to happen at every level, top execs included, said Hinds: “It’s being able to see the executives use the technology, sharing both the success stories and the failures.”
Successful companies are also actively using metrics anchored in existing key performance indicators (KPIs). They are measuring quality, efficiency, and employee engagement in different ways, and putting data in the hands of employees so they can assess their own adoption and success.
“It’s less about surveillance and more about feedback in terms of how we work collectively,” said Hinds.
What’s “fascinating but perhaps not surprising,” she said, is that workers are increasingly using AI itself as a teacher, and prefer it over other learning channels. This speaks to the importance of low-code, no-code tools, with low learning curves and organizational context, that are embedded directly into workflows.
“It is starkly different from what we’ve seen with previous technologies,” she said.
This article originally appeared on CIO.com.
For many people, rheumatoid arthritis seems to appear suddenly. One day they begin experiencing painful, swollen joints, overwhelming fatigue, and stiffness that makes everyday activities difficult. But scientists now know that the disease often starts developing long before the first diagnosis is made. New research from King’s College London suggests that treating people before rheumatoid […]
The post A Drug Given Before Arthritis Starts May Delay the Disease for Years appeared first on Knowridge Science Report.
A US federal judge has ruled that the Trump administration’s $100,000 fee on new H-1B visa petitions was unlawful, giving technology companies temporary relief from a policy that threatened to raise the cost of hiring foreign skilled workers.
The decision removes, at least for now, a major cost burden for employers that use the H-1B program to fill roles in domains including software development, cloud computing, data science, and AI.
US District Judge Leo Sorokin in Boston found that the fee functioned as a tax that the administration did not have authority to impose without congressional approval. The ruling came in a lawsuit brought by 20 Democratic state attorneys general challenging the fee.
Standard employer costs for H-1B petitions typically range from about $2,000 to $5,000, making the proposed $100,000 payment a sharp increase for companies seeking foreign talent.
The ruling is unlikely to end uncertainty for employers, with the Trump administration expected to appeal. But it could allow companies that had paused international hiring plans to resume normal recruitment for the upcoming H-1B cycle, said Pareekh Jain, CEO of Pareekh Consulting. Still, he said, employers should remain cautious because the legal and policy concerns are likely to continue.
“This provides breathing room for CIOs, even though it’s temporary,” said Neil Shah, vice president for research and partner at Counterpoint Research. “They should make the necessary contingency plans, whether that means doing more with less by leveraging AI or relying more on local talent.”
If higher H-1B costs return in another form, CIOs will have to be more selective about sponsorship, weighing the added cost against the strategic value of the role and the long-term potential of the employee, Shah said.
“Ultimately, the decision comes down to business unit P&L: whether the unit can absorb the cost of acquiring the talent for that role,” Shah added.
That uncertainty could also lead CIOs to compete for talent from other companies, potentially driving up salaries for skilled workers. Some CIOs may conclude that paying a one-time $100,000 fee, amortized over the employee’s tenure, is still more cost-effective than engaging in a bidding war for scarce local talent.
Danish Faruqui, CEO of Fab Economics, said that CIOs may reserve H-1B sponsorship for a narrower set of mission-critical roles if costs increase.
“If there is such a financial burden, CIOs will justify sponsoring very specific roles,” Faruqui said. “These would be principal enterprise architects, AI, ML, and deep-tech researchers, senior product managers, and regulatory and compliance experts.”
More routine or project-based roles are likely to be treated differently, Faruqui said.
“Junior to mid-level software engineers, entry-level business analysts, and entry-level data scientists would shift from H-1B to domestic hiring,” Faruqui said. “Cloud migration, DevOps, ERP, and CRM implementation could be done through contractors or consulting firms, while QA, product testing, tier-one help desk support, and legacy maintenance are roles that CIOs could prioritize for automation.”
Startups, smaller companies, and enterprise IT departments would have faced the greatest pressure from the fee and stand to benefit most from the ruling, Jain said.
Large technology companies would have been better placed to absorb the $100,000 cost, he said. Meanwhile, companies with mature offshore delivery models may be less likely to increase their reliance on H-1B hiring.
The article originally appeared on CIO.
Model Context Protocol (MCP) has gained considerable momentum as a standard connector between LLM-powered tools and local systems, internal and external APIs, and data sources. From major clouds to devops tools, MCP servers are enabling powerful, AI-powered development and operations capabilities through natural language commands.
Nowhere is this more true than in the world of databases. Most major database platforms now support agentic access through MCP servers. Using an MCP server for databases, you and your AI agent proxies can perform lookups, create and update data, and perform administrative tasks without you having to write SQL by hand.
The MCP server could also guide your LLMs to write new code or build automations that align with your database schema, like its tables, structure, and fields, as well as embeddings, indexes, and metadata. It could also aid debugging by enabling faster queries to surface data issues or misconfigurations, along with plenty of other possible use cases.
Below, we’ll cover official MCP servers from some of the top platform options across major database styles. Though maturity varies, the MCP servers discussed below represent some of the best vendor-backed offerings available today across relational SQL, NoSQL, graph, vector, and data warehouse systems.
These servers can be used by any MCP-compatible tool, IDE, or agent, whether it’s Claude Code, Codex, Cursor, Gemini CLI, Google Antigravity, VS Code, Windsurf, or something else. Adding them is typically simple, often involving a lightweight JSON addition to your MCP configuration file.
MySQL and PostgreSQL are the world’s most widely-used open source databases. However, in both cases, there is no canonical MCP server—what we see are different MCP servers emerging across vendors. One of these vendors is Amazon Web Services (AWS), which offers official MCP servers for Amazon Aurora, its managed relational database service compatible with both MySQL and PostgreSQL.
According to the documentation on GitHub, the Amazon Aurora MySQL MCP Server can be used to convert natural language commands into MySQL-compatible SQL queries, which can then be executed against Aurora MySQL databases. Similarly, the Aurora Postgres MCP Server provides MCP tools for working on PostgreSQL databases. The Aurora DSQL MCP Server does the same for distributed Postgres databases.
AWS provides a growing portfolio of official MCP servers across its product line, including MCP servers for other Amazon database platforms like DynamoDB, ElastiCache, and Redshift. If you’re a heavy AWS shop and you want to enable LLM interactions with your data, these are sensible choices.
BigQuery is Google’s cloud-based data analytics platform, and a popular data source for AI applications. BigQuery users with API access configured can also utilize the BigQuery MCP Server to interact with the platform using MCP-compatible AI clients.
Using the remote BigQuery MCP Server, engineers can generate and execute queries on data sources, or return metadata on datasets, tables, and schema. This can be done with a simple natural language prompt like “List the datasets in project PROJECT_ID.” Results are filterable by region, data set ID, column name, and more.
As part of Google’s fully-managed, remote-hosted MCP portfolio, the BigQuery MCP Server provides some peace of mind regarding security, maintenance, and ease of use for distributed teams. The MCP tools are subject to some limitations, however, in terms of query result size, processing time, and other factors. If you’re using BigQuery and you want more agentic control, you’ll want to check this one out.
Another important database category includes platforms designed for keyword and semantic search. In this space, Elasticsearch is commonly deployed. Instead of providing a single MCP server, Elasticsearch provides the Elastic Agent Builder, which is a more comprehensive framework aimed at agentic workflows.
Using Elastic Agent Builder, you can chat with an agent to retrieve data context from Elasticsearch data and extend it into various environments. The Agent Builder itself includes an MCP server endpoint for programmability and exposing the agent to other clients.
Unlike others on this list, this is not a direct MCP interface to raw Elasticsearch APIs. Instead, it’s an interface that exposes skills from the agent platform. This should also not be confused with the Elasticsearch MCP Server, released in mid-2025, which has since been deprecated.
A possible downside of using this utility is that it includes an additional layer between your IDE or agent and the data you’re searching. The agent setup requires a higher subscription and takes additional steps to configure compared to other MCP servers.
That said, if you want an extensible common layer to interact with both Elasticsearch and external MCP servers, while centralizing responsibilities like permissions, this is an interesting proposition.
Graph databases are another key NoSQL database type these days, specializing in using nodes and edges to accelerate queries of highly interconnected data. Of these, Neo4j is a popular graph database option.
The Official MCP Server for Neo4j works with all kinds of Neo4j deployment (desktop, sandbox, self-managed, and the managed Neo4j Aura cloud service), and allows LLM-based clients to retrieve graph schema, execute read and write statements, execute graph algorithms, and more.
In addition, several other MCP servers for Neo4j are available from Neo4j Labs. These have specialized uses, such as generating Cypher queries from natural language, maintaining an in-memory graph database, modeling and visualizing graph, and interacting with the Neo4j Aura API.
The first MCP server for Neo4j was developed in December 2024. If you’re an avid Neo4j user and want to experiment with interacting with your graph databases in a chat-infused way, these stand as an interesting platform of servers.
Google’s MCP Toolbox for Databases is a notable MCP server because it’s a popular catch-all for various database types. Unlike the other entries on this list, this server connects LLMs not to a single managed database, but unifies LLM access to multiple systems. The open source utility ships with pre-built configurations for nearly 30 databases including PostgreSQL, MySQL, SQL Server, Oracle Database, MongoDB, Redis, Neo4j, and Snowflake, as well as the databases in Google Cloud.
Once you define data sources in a tools.yaml file, you can use MCP Toolbox to perform structured queries or semantic searches against databases directly from within an IDE or agentic client using plain English. MCP tools translate commands into actions like list_tables and execute_sql.
MCP Toolbox for Databases is mature, originally built as a generative AI utility and later re-worked for MCP-style workflows. It offers numerous download, configuration, and interaction methods.
If you’re using a variety of databases on Google Cloud and elsewhere and you want an “all-in-one” MCP server, MCP Toolbox for Databases is a great place to start.
MongoDB is the popular NoSQL document-oriented database. The creators of MongoDB have released an official MCP server that works with the open-source database as well as the company’s cloud-hosted MongoDB Atlas database platform.
The MongoDB MCP Server provides a number of tools to interact with MongoDB. You can query the database, return information on collections, create or remove collections or indexes, gather statistics on database usage, and more. Other tools enable MongoDB Atlas operations, like creating users or clusters, returning cluster data, and other functions.
The server’s tools are read-only by default but can be switched to allow write capabilities. It can be used locally, but also supports Streamable HTTP transport for remote servers, although that comes with greater security concerns.
For those using MongoDB and wanting to hook their AI-enabled IDE or CLI up with more automated powers, the official MongoDB MCP Server is worth checking out.
Among vector-native databases, Pinecone stands as a strong, widely used option with a well-designed API and comprehensive SDKs. The Pinecone MCP server extends this experience, allowing users to query its documentation and execute functionality via AI agents and AI-enabled IDEs.
To date, the Pinecone MCP server consists of nine MCP tools. These cover read-only actions, like knowledge gathering via the Pinecone official documentation and querying vector records, index metadata, configurations, and statistics. It also allows for write operations like updating records and creating new indexes.
Released in mid-2025, the Pinecone MCP server is one of the more complete early implementations, with easy configuration and installation.
For those using Pinecone who want to test new LLM-assisted workflows for creating indexes with embeddings, performing reranking, or testing results using natural language commands, the Pinecone MCP server is worth trying out.
An ultra-fast in-memory database, Redis is commonly used for caching, real-time analytics, and other latency-sensitive use cases. And as you might have guessed, the company behind the Redis database provides an official MCP server. The server allows read, query, and write capabilities.
Developers can use Redis MCP from an LLM client to perform high-level actions to analyze, reference, or embed Redis data and interact with the Redis server within their prompts. The documentation suggests some example prompts for common use cases, such as “Cache this item,” “How many keys does my database have?,” and “What is user:1’s email?”
Unlike other MCP servers, which only allow a slice of platform capabilities, Redis MCP offers full Redis support. This enables working with Redis constructs such as hashes, lists, sets, sorted sets, streams, and more, according to the GitHub repository.
One possible drawback is that Redis MCP has yet to support Streamable HTTP transport. Until this is developed, the server is constrained to local deployment. But for those seeking a local MCP server to work with Redis data, this is the best choice.
Snowflake is a cloud-hosted, AI-enabled data platform widely used in enterprise contexts for data warehousing, data analytics, and data engineering purposes. Compared to other data storage systems, Snowflake is unique in that it’s more fully managed and combines structured and non-structured data types.
The Snowflake MCP Server, available on GitHub, can be used to perform many of the standard Snowflake platform operations. This includes a “fuzzy” search of all records via Snowflake’s Cortex Search and structured data semantic lookups using Cortex Analyst.
Other abilities include object management operations like creating, updating, and deleting records. The server also can invoke other agentic-designed capabilities, like the ability to generate and execute SQL statements against back-end databases.
Snowflake MCP Server is well-thought-out and well-documented, with walkthroughs for various agent and deployment patterns. Those already building with Snowflake should find it complements the mechanics they already employ.
A longtime open-source favorite, PostgreSQL is one of the most popular and trusted object-relational SQL-based database systems. With an active open source community, Postgres has been maturing for decades. Given its open source nature, there isn’t a single “official” MCP server for the platform. Anthropic built an original reference implementation, but it’s now archived.
Instead, database platforms built on PostgreSQL provide different flavors of MCP servers, with a range of vendor neutrality and specificity. One notable option is the Supabase MCP Server, provided by Supabase, a cloud-based “back end as a service” and Postgres development platform.
Supabase MCP Server connects AI agents with Supabase projects, allowing engineers to issue natural language commands to manage tables, query data, get logs, fetch configuration information, and more. The Supabase MCP Server is pre-1.0 release and some features are still experimental.
If you’re an engineer using Supabase and looking for an MCP server to connect your AI assistant with your Postgres databases, this is a good tool to test out.
So far, we’ve reviewed official, vendor-backed MCP servers from some of the most-adopted managed databases. However, numerous MCP servers exist across other database platforms and types.
One MCP server that aggregates LLM access across various database types is DBHub, which works with MySQL, PostgreSQL, SQL Server, MariaDB, and SQLite. Developed by Bytebase, DBHub is described as a zero-dependency, token-efficient MCP server.
For SQL, the options are nearly endless. Official servers exist for Microsoft Azure SQL and DuckDB. PulseMCP catalogs more than 100 MCP servers for MySQL, although most are unofficial, solo-creator open source projects. Of these, one of the most starred is MCP Server for MySQL, developed by full-stack developer Ben Borla and optimized for Claude Code.
For Postgres, notable alternatives to Supabase include pgEdge Postgres MCP, Neon MCP server, and Postgres MCP Pro. For vector databases, others beyond Pinecone have been quick to adopt MCP as well, including Weaviate and Milvus.
Before diving into MCP servers for enterprise databases, it’s important to understand the security risks. For instance, prompt injection remains an unsolved problem, so it’s recommended to limit permissions for SQL statements.
To mitigate this, Supabase recommends enabling AI client settings that require manual approval for each tool call before execution. Experts also recommend assigning only the minimum permissions required and avoiding exposure of sensitive data like API credentials. Due diligence around authentication and authorization is especially important when hosting remote servers.
Lastly, to avoid shadow IT, it’s becoming common practice to catalog the internal MCP servers you use, even for experimental projects. For this, experts recommend an MCP registry that documents approved servers. An MCP registry improves both MCP server discovery and security awareness.
Licensing can be complicated, particularly when enterprises are forced to double-pay because the software they already own is only licensed for a specific environment, and moving it requires a whole different licensing model. Without proper portability rights, they need to make additional financial investments to run the same workloads in a different home.
AWS says its new Bring Your Own Media (BYOM) service eliminates this duplication. Customers can now reuse their existing Microsoft SQL Server media and licenses on Amazon Relational Database Service (RDS) with no additional licensing fees.
This means enterprises no longer have to justify workload migrations to the cloud against existing licensing commitments and infrastructure investments, AWS said.
“What used to be a licensing barrier between operational SQL Server data and agentic AI is now gone,” AWS data engineer Srikanth Katakam and product marketing manager Colleen Betik wrote in a blog post announcing the service.
To drive true value, agentic AI apps must have access to elastic GPU capacity as well as direct, low-latency access to the data they need to reason and make decisions. But, AWS argued, that can be difficult in self-managed data centers and on premises infrastructure with limited access to agentic AI cloud-native services. But a lot of enterprise data lives in Microsoft SQL Server, and until now customers have had to pay for a second license to use a fully managed cloud service like RDS.
Now, with BYOM on Amazon RDS for SQL Server, enterprises can reuse their existing SQL Server Enterprise Edition or Standard Edition licenses through a “lift-and-shift” model. This brings their data into a fully-managed environment.
According to the licensing distribution terms, enterprises must provide their licensed SQL Server Release to Manufacturing (RTM) media to Amazon RDS. They can then upload that media to Amazon S3 and launch BYOM instances. Enterprises must configure AWS License Manager to perform automatic instance tracking.
From there, RDS automates patching, backups, high availability, and monitoring, while providing direct access to agentic AI and analytics services native to AWS. The platform also reports vCPU usage to provide visibility into SQL Server license usage, Katakam and Betik wrote.
“You do not need to choose between protecting your SQL Server licensing investments and giving your data a path to the AWS analytics and agentic AI services redefining what’s possible in the cloud,” they said.
It’s important to note that this service is only available to enterprises with Microsoft Software Assurance (SA), an add-on which supports mobility and rehosting of existing licenses. Enterprises must verify that their SQL Server licenses comply with Microsoft’s licensing agreement, and that they have a SQL Server License (Standard or Enterprise) with SA. They also must submit a License Mobility Verification Form to Microsoft; it, in turn, will notify AWS once verification is complete.
AWS emphasized that enterprises remain responsible for compliance; it does not block operations if license limits are exceeded.
The advantage of this service, explained Mike Leone, principal analyst at Moor Insights & Strategy, is that enterprises can get a Microsoft SQL Server workload onto a managed AWS service without rewriting it for Aurora or paying for the license twice. This means they can modernize on their own schedule rather than being forced into rewrites before they’re ready.
“For a lot of shops, that control over timing is worth more than the license saving itself,” Leone said.
This also loosens Microsoft’s grip on enterprise workloads, because organizations finally have somewhere else to run SQL Server using licenses they already own, he noted. Realistically, their dependency shifts to AWS rather than disappearing entirely, as their data ends up living next to AWS’s AI services.
“For a lot of teams, that’s a trade they’re glad to make for the managed infrastructure and the AI tooling they get in return,” Leone said.
Yaz Palanichamy, a senior advisory analyst at Info-Tech Research Group, also pointed to significant improvements stemming from the migration. Notably, it allows organizations to transition away from static, linear, or reactive storage capabilities towards more dynamically intelligent environments.
“The key is to balance the unification of transactional data towards managed AI and machine learning pipelines,” he said.
The catch? Leone noted that enterprises now own the task of licensing compliance. Staying current on SA, the Microsoft maintenance contract that makes any of this legal, and the license mobility rules, “become your headache instead of something baked into the bill,” he said.
Furthermore, he added, lift-and-shift has a way of turning into “lift-and-forget” when enterprises move SQL Server as-is and never actually modernize, “so you end up carrying all the old baggage onto a shinier platform.”
Palanichamy also pointed to skyrocketing AWS costs, since operationalizing AI agents requires a considerable amount of data ingestion and querying. This can potentially be cost prohibitive on RDS for SQL Server.
“One aspect to consider would be the relative time to production value, so that enterprises can better handle the management of volatile AI workloads,” he said.
Although AWS frames licensing complexity as a roadblock to agentic AI, analysts say it’s really just one piece of the puzzle.
Organizations are not in an AI-ready state due to a number of factors, Palanichamy noted. This could be total cost of ownership (TCO)-related, a lack of appropriate acceptable use policies (AUPs), or concerns around security and compliance.
If and when enterprises are ready to adopt AI, they must perform thorough needs analysis/process optimization benchmarking exercises to determine what workflows could benefit from the technology, he said, and, conversely, flag workflows where AI could prove “an impediment or potential disservice.”
Leone also pointed out that moving SQL Server onto RDS doesn’t “magically make your data agent-ready,” nor does it make it smarter. The data proximity and managed plumbing are what actually support agents. Sitting data next to Bedrock and the rest of AWS’s AI services means builders “stop wasting time shipping the data around or building one-off integrations every time an agent needs it.”
Ultimately, though, licensing isn’t the real issue when it comes to AI; it’s more of a migration problem. The real roadblocks are messy data, questionable governance, and teams that aren’t ready to run it in production, and, Leone said, “no change to a license bill touches a single one of them.”
I’ve seen a lot of promising AI prototypes fall apart after launch. And it’s rarely because the model was bad. More often, the problem starts much earlier; teams treat the data layer like something they can figure out later.
They’ll spend weeks fine-tuning prompts, testing models and debating evaluation scores, then throw together the retrieval pipeline over a weekend and move on. At first, everything looks great in demos. But a few months later, the system gives outdated answers; the embeddings no longer match the source documents, and nobody fully understands what changed.
What started as an impressive prototype slowly becomes difficult to trust in production. The teams that avoid this tend to realize one thing early: Embedding pipelines are fundamentally a data engineering problem, not an entirely new AI discipline. It’s still ETL (Extract, Load, Transform) at its core, but with embeddings and vector stores as the destination instead of a warehouse.
Once you start looking at it that way, a lot of things become clearer. Problems like versioning, data freshness, lineage and retries stop feeling “AI-specific.” They’re data infrastructure problems we’ve already spent years learning how to solve.
Large language models are extraordinary reasoners trapped inside a time capsule. When training ends, the model’s knowledge is sealed. It does not know what your team decided in last quarter’s strategy review. It has never read the support ticket that came in this morning. It cannot find the clause buried on page 47 of your master service agreement. It’s brilliant, but blind to anything specific to your organization.
Layer on top of that a hard context window limit, a ceiling on how much text the model can process in a single interaction, and you have a clear problem: you cannot just hand it everything you own.
The answer the industry converged on is retrieval-augmented generation, or RAG. Instead of stuffing everything into the context window, you build a retrieval layer that fetches only the most relevant pieces of information at the moment a question is asked and passes just those to the model. That retrieval layer is powered by a vector database, and the process that populates it, which is taking raw documents and transforming them into searchable semantic representations, is what I mean when I say embedding pipeline.
Every team building an internal AI assistant, a smarter enterprise search tool, an automated customer support agent or a document Q&A system needs one. The question is not whether to build it. The question is whether you build it like a prototype or like infrastructure.
An embedding pipeline has three stages: ingestion, chunking and indexing. Here is what each one means and how I relate them to a typical ETL process.
Getting your raw content, PDFs, wiki pages, Word documents, database records, transcripts, out of wherever it lives and into the pipeline. This is ETL’s extract stage, almost verbatim.
I see teams cut corners here more than anywhere else, and it’s often where production systems first start to fail. A document gets updated, but the pipeline doesn’t pick it up. A file gets deleted, but its chunks remain in the index, still returning outdated answers months later. And because there’s no obvious error, no one reports it.
The fix is Change Data Capture (CDC). This maintains a manifest of every document you have ingested, a content hash and a timestamp. On each run, we compare sources against that manifest, re-ingest what changed, delete what is gone and treat your document the way you would treat any source table you are syncing incrementally.
Once your documents are in the pipeline, you cannot embed them whole. A 30-page technical report is too long to represent meaningfully as a single vector, and even if it were not, returning the entire report in response to a narrow question would bury the model in irrelevant context.
Chunking is the process of breaking each document into smaller pieces that are focused enough to embed accurately and retrieve precisely. This is ETL’s transform stage, and it deserves the same level of design discipline.
The most common mistake I see is treating chunk size as a default configuration option rather than a product decision. It is not. The right chunk size depends entirely on the nature of your content and the nature of your queries. Dense technical documentation needs finer granularity than a collection of FAQs. A legal contract with clause-level logic needs different treatment than a set of onboarding emails. What works for one document will actively degrade retrieval quality on another.
My strong preference is to treat your chunking configuration as a versioned pipeline parameter, not hardcoded logic. When you change it, and trust me, you will. You need to re-chunk in a controlled, observable way, compare retrieval quality before and after, and roll back if it degrades. That is just good transform-layer hygiene. It is no different from versioning a data cleaning rule or a field mapping.
The final stage is where chunked text gets converted into vectors and stored in a vector database where it can be searched by semantic similarity rather than keyword match.
In the conversion step, embedding is handled by a model specifically trained to turn text into dense numerical representations that encode meaning. Two chunks expressing the same idea in different words will produce vectors that cluster close together in that mathematical space. Two chunks discussing entirely different topics will sit far apart. When a user asks a question, the system embeds that question the same way, finds the chunks whose vectors are nearest, and returns them as context for the model to reason over.
That is a genuinely new capability. But the discipline around indexing is not. One data engineering principle I keep coming back to is versioning.
In embedding pipelines, every chunk in your index should be tagged with the embedding model name and version used to generate it, this is non-negotiable. Embedding models evolve, and vectors produced by different versions are not comparable in a reliable way. You cannot safely search across them as if they are interchangeable.
This exact problem shows up when teams upgrade embedding models mid-pipeline without a proper migration plan. You end up mixing vectors from different generations in the same index, and retrieval starts to degrade in ways that are hard to detect. The system just quietly begins returning subtly wrong answers.
I treat an embedding model upgrade the same way I treat a schema migration: Plan it explicitly, execute it in full and validate retrieval quality on a representative query set. The stakes are the same as any breaking change to your data model.
Once an embedding pipeline is running in production, the question shifts from “did it run” to “did it run correctly.” That distinction matters more here than in most pipelines, because failures are rarely loud because the index looks fine, queries return without errors and the system quietly surfaces wrong answers until someone notices the AI has stopped being useful.
The same observability discipline that makes any data pipeline trustworthy applies directly here. Once you treat embedding pipelines as production systems, you stop thinking in isolated steps and start thinking in signals. For example, chunk counts per document become a simple but powerful health check, a sudden drop is usually not a model issue, but a sign of broken ingestion or upstream parsing failures.
You also need a “golden set” of queries with known-good outputs. This runs after every pipeline change, much like data quality checks after a transformation. This is how you catch regressions that don’t show up as explicit failures.
On top of that, you can track lineage: Which embedding model version produced which chunks, and when each document was last ingested. That makes it possible to trace retrieval issues back to specific changes instead of guessing.
And finally, freshness becomes a first-class signal. If documents start going stale beyond an acceptable threshold, that should surface in monitoring long before users experience degraded results.
The metric that ties it all together is retrieval quality over time. Treat it like any other pipeline SLA, measured, tracked and owned.
Embedding pipelines definitely come with a lot of new language, new tools and a genuinely different capability in the semantic layer. But the funny thing is, the principles that actually make them reliable in production are not new at all.
We have versioning, freshness, quality checks and monitoring. These are problems data engineering has already spent years solving.
The real work is taking that same discipline and applying it to a pipeline that just happens to output vectors instead of rows in a table. Once you start seeing it that way, a lot of the chaos around AI systems becomes much easier to reason about.
That’s the difference between building a cool AI demo and building something people can actually depend on. One is a prototype, whereas the other is infrastructure.
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Artificial intelligence does not advance at the same pace across industries. It presses forward in some directions while lagging behind in others.
Spend time with today’s most advanced AI applications, and this contrast becomes obvious. In software development, AI is quickly becoming ubiquitous. It writes production-ready code, explains obscure libraries, and iterates at a pace human teams have difficulty matching.
But place that same AI model inside a complex customer support workflow or ask it to reason through a nuanced clinical scenario, and the cracks begin to show. Multi-step reasoning falters. Context gets lost. Performance drops in ways that can seem inconsistent with the model’s strengths elsewhere.
These AI models are often similar. They run on similar hardware and are often trained in similar ways. So why the mismatch in performance across tasks? The simplest explanation is also the most overlooked: data.
Software engineering benefits from an immense, structured, and highly visible digital record. Code is written in standardized languages, benefits from robust documentation, is reviewed in public forums, and is discussed at scale. That ecosystem has generated a robust and massively useful pool of training material.
Other fields often do not. For example, healthcare data is scattered across institutions, wrapped in privacy constraints, expressed in multiple modalities, and rarely ready out-of-the-box for AI training. Enterprise workflows are captured in internal systems that were never designed for training AI. Multilingual speech data varies widely in quality and representation.
This imbalance creates what I describe as “the data gap.” This is the distance between what models are capable of in theory and what they can achieve in practice, because the right data does not yet exist in usable form. Closing this data gap may be the most important—and least glamorous—challenge in AI today.
Three forces are driving the recent advances in AI: the models, the chips, and the data.
On the AI models, the field has invested heavily. Major research organizations employ thousands of researchers and scientists who are actively refining architectures, training techniques, and evaluation methods. Breakthroughs are measured in benchmark scores, conference papers, and model performance on human tasks. On the computing chips, the investment has been equally intense. Hardware manufacturers and infrastructure providers are pouring billions of dollars into building and supporting data centers that deliver faster results via large-scale training.
Yet data has not received the same institutional focus for AI development. Conversations with researchers at frontier AI labs share a similar frustration that today’s model capabilities in key use cases, such as healthcare, are limited less by architectural imagination and more by the availability of high-quality, domain-specific data. The bottleneck is not always a lack of ideas, but a lack of reliable inputs.
We are long past scraping the internet for useful data, and this path does not scale. Progress depends on building and curating datasets that reflect the complexity of true lived experience and organizational processes. That work requires both scientific rigor and research specialization in the field of data for AI.
The history of AI reinforces a consistent lesson: major leaps in model capability follow major leaps in the availability of quality data. From early vision systems that relied on clearly labeled images to today’s language models trained on massive text collections, each major leap has depended on access to more high-quality data.
Architectural innovation alone is rarely enough. The value of these new approaches only emerges when paired with large, structured, and representative datasets that reveal what the models can actually do in practice. Whether in vision or language, progress has depended on the painstaking work of collecting, organizing, and validating the underlying data.
Large language models illustrate this clearly. Their emergence was not just the result of better training techniques, but of access to an unprecedented volume of data. The models did not generate that data. They relied on it. That pattern raises a pressing question for the present: who is building the next generation of foundational datasets?
Across domains ranging from healthcare to audio to agentic task performance, there is no widely accepted blueprint. What constitutes a gold-standard dataset for training an AI agent to handle complex enterprise tasks? What does a clinically meaningful evaluation look like for a model that will assist in medical decision-making? How should multilingual speech data be curated to ensure broad representation and reliable performance?
These are not simple sourcing problems. They are fundamental research challenges that need to be solved.
Too often, consequential data decisions are handled like procurement exercises. An organization requests “medical conversations” or “wildlife scenes,” and the request is routed to internal procurement or data sourcing teams, or to external data vendors, who assemble data that appears to match the description. The implicit assumption is that data is interchangeable, that one dataset is as good as another so long as it meets a basic specification.
Actual application suggests otherwise. Seemingly small choices about factors such as inclusion criteria, annotation standards, filtering rules, and validation protocols can dramatically alter downstream performance. Data design shapes model behavior as much as architecture does.
Three structural issues compound the problem:
The rise of annotation providers and reinforcement learning services has addressed part of the need. Rating model outputs, labeling text, and evaluating structured information are essential for many optimization tasks. But these activities generate data that is carefully constructed for specific, bounded purposes.
The frontier challenges in AI require more. They demand datasets derived from real human activity and organic organizational processes. Such data is complex, multimodal, and sensitive. It is rarely AI-ready by default. And converting it into reliable training and evaluation material is a scientific undertaking.
If high-quality data is a central bottleneck, then scientific rigor is part of the solution. Just as leading model-builders have dedicated research labs and hardware has dedicated development ecosystems, the data layer for AI requires focused, scientifically-grounded institutions.
This means engaging directly with core questions like dataset design, evaluation methodology, and quality control. The conversation cannot end at volume; it must address data structure, representativeness, and expert validation.
Dataset construction must be approached as experimental design. Protocols must be documented and validated. Evaluation frameworks must test whether the dataset truly reflects the intended applications.
The field also requires standards and benchmarks that reflect real-world complexity, not simplified proxies. In healthcare, for instance, evaluating a system intended for clinical assistance with generic question-and-answer tests is insufficient. Real-world clinical environments involve multimodal inputs and contextual judgment. Benchmarks must reflect that reality if they are to function as meaningful gates before deployment.
Quality measurement is another crucial frontier. Finance relies on standardized metrics such as credit scores to assess risk. AI lacks an equivalent for datasets and benchmarks. Developing clear methodology to quantify dataset quality and evaluation reliability brings clarity to model assessment.
The criteria for evaluating a multilingual audio library will differ from those of a multimodal oncology dataset. Yet the underlying principle remains constant. Better models require better-defined, better-measured data.
As AI systems move closer to high-stakes deployment, weak data practices carry tangible risks.
Benchmarks cannot be created with the same data that is used for training—that’s giving the test answers to the model ahead of time. Scaling data volume without prioritizing data quality and selection diminishes model performance gains, and can even bias against or omit underrepresented populations. These are methodological challenges, and ones that must be solved.
The rigor required at the data layer may not attract headlines. It does not typically lend itself to dramatic product launches. Yet the data layer for AI is foundational to trust, safety, and sustained progress for all AI progress.
No single organization can resolve the data gap alone. What is needed is an ecosystem of AI data labs and research groups, each focused on different domains and challenges but united by a commitment to scientific discipline. These institutions would collaborate with model researchers and domain experts who would tackle challenges such as dataset contamination, factuality, groundedness, de-identification, international representation, and bias. They would design benchmarks that mirror real-world complexity rather than simplified abstractions.
AI’s trajectory will not be determined solely by larger models or faster chips. It will be shaped by the datasets we construct, the standards we adopt, and the rigor we apply at the foundation. The uneven frontier we see today reflects an uneven data landscape. Bridging the gap requires deliberate, research-driven dataset design.
If we want AI systems capable of operating reliably in clinical contexts, navigating enterprise workflows, and functioning responsibly across languages and cultures, we must treat data for AI as a first-class scientific endeavor.
AI models have their research labs. AI chip-builders have their fabrication plants. AI data needs institutions of equal seriousness and ambition.
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New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.
AI governance is an ongoing game of catch-up for enterprises. Model updates and iterations are rolling out at a rapid clip, often making governance frameworks obsolete before they’re battle-tested.
To evolve beyond this paradigm, OpenAI is introducing Active sessions. This new ChatGPT security feature allows users to review and log out of one or more sessions through a simple interface. The feature is now available across all ChatGPT accounts and workspace types, including personal and managed workspaces. Experts call it an important development for the model provider, which currently has 1 billion monthly active users.
Previously, organizations often had limited visibility into where users were logged in, and simply relied on password resets or broad account actions to force re-authentication, noted Ensar Seker, CISO at SOCRadar. “Granular session control is a more efficient and less disruptive approach. From a governance perspective, session transparency improves accountability and supports investigations,” he explained.
Active sessions allows admins to see known browser and app sessions across ChatGPT, Codex, and API Platform. Specifically, they are able to view device and browser information, approximate location, sign-in date and time, whether a device is trusted, and whether the session is current.
To access the feature, users can go to ‘Settings’ > ‘Security’ > ‘Active sessions.’ They can then log out of specific sessions and remove devices from trusted services. They also have the ability to log out of all sessions (thus ending sessions across devices), however, this action can take up to 30 minutes to complete.
However, OpenAI emphasizes that session details may be “approximate or incomplete,” and that the feature has limits. It does not show or manage connected apps or third-party app sessions, sign-ins through third-party services, Codex CLI sessions, or recently signed-out sessions.
Further, Active sessions cannot be used with accounts linked to an enterprise’s single sign-on (SSO), including security assertion markup language (SAML) and OpenID Connect (OIDC).
While Active sessions is an important security and governance development, experts note that the feature is basic, and was a long time coming.
“The reality of OpenAI offering the ability to end active sessions on ChatGPT by administrators is that it’s something that exists in lots of platforms,” said David Shipley of Beauceron Security. “They should’ve had it sooner, but better late than never.”
From a security standpoint, he noted, OpenAI could do a better job policing ChatGPT to prevent it being used by threat actors to host malware, which is the latest threat to enterprises.
SOCRadar’s Seker also pointed out that this type of visibility and oversight is something that enterprises have expected from SaaS platforms for years. “It allows administrators and users to quickly identify unauthorized access, terminate stale sessions, and reduce the risk of account compromise persisting undetected.”
Last week, OpenAI updated GPT-5.5 Instant in both the ChatGPT app and API to “improve response style and quality,” the company said. It had rolled out GPT-5.5 Instant earlier in May as a successor to GPT‑5.3 Instant, calling it “generally smarter” and prone to fewer hallucinations.
According to OpenAI, the update makes GPT-5.5 Instant “easier to read, more natural in everyday conversations, and better paced in practical help tasks, with fewer overly long or bullet-heavy responses.”
But even with tools like Active sessions, enterprises continue to struggle with governance amidst seemingly continuous iterative model updates. It’s simply not sustainable, said Beauceron’s Shipley: “How do you build an appropriate testing plan with a nondeterministic system?”
SOCRadar’s Seker pointed out that many organizations perform security, compliance, and business validation testing before approving a model for production use. But, “when model behavior changes under the same version family, previously documented assumptions may no longer fully reflect actual performance,” he noted.
“The biggest governance challenge in AI is not model adoption, it’s model change,” Seker said. “Most organizations can evaluate a model once. Far fewer are prepared to continuously evaluate how that model evolves over time.”
This particularly creates challenges for regulated industries where auditability, repeatability, and change management are critical, he said. Even beneficial improvements can introduce governance concerns if organizations are not clearly informed about what changed and when.
Valence Howden, advisory fellow at Info-Tech Research Group, noted that organizations often can’t assess the implications of model iterations against their boundaries, and, worse, are often unaware of them.
While the biggest enterprise challenge was initially tied to which AI model was being used, what that model did, and who owned it, iterative updates can muddy those waters and increase reliance on third party practices and tools that organizations often don’t have the resources for, he noted.
“Without the ability to opt out [of an update] before it’s incorporated, [enterprises] are basically red-teaming the updates with their clients,” said Howden.
Security teams today are pushed to their limits because they are expected to manage rapidly evolving models, new features, and changing behaviors, while maintaining compliance, risk management, and business continuity, said SOCRadar’s Seker.
“Governance is difficult because organizations are no longer evaluating a static product,” he said. Rather, they are managing a “continuously evolving service” where capabilities, integrations, and user behaviors can change far faster than can traditional security review cycles.
Info-Tech’s Howden agreed, saying that enterprises’ existing governance practices, especially accountability, are poor, as are their risk practices.
“It’s hard to suddenly become good at things they’re already poor at doing,” he said. “They are also incentivized for speed and innovation, so they ignore governance as a constraint, or don’t want to do it at all.”
Seker advised that, ultimately, organizations should treat AI models as living systems rather than fixed software releases.
Security and governance programs should include continuous validation, monitoring, and periodic re-assessment instead of sole reliance on one-time approval processes, he said. Enterprises should also establish clear vendor change management expectations, including requiring transparency around model updates, behavioral changes, and potential impacts to existing workflows.
“Effective AI governance increasingly depends on visibility into change, not just visibility into risk,” Seker said.
As enterprises move from AI experimentation to production deployments, one challenge is becoming increasingly apparent: AI systems are only as reliable as the business context they operate in.
Snowflake is attempting to address that problem with Horizon Context, a new set of semantic and metadata-management capabilities, currently in preview, that it unveiled Tuesday at its annual Snowflake Summit conference.
Artin Avanes, head of core data platform at Snowflake, said that the offering, launched as part of Horizon Catalog, the company’s existing data discovery, management and governance suite, collects metadata from across an enterprise’s data estate, enriches it with business definitions, relationships, lineage, and governance information, and makes that context available across AI and analytics systems.
These capabilities, according to Avanes, build on Snowflake’s acquisition last year of Select Star, a metadata management startup known for its integrations with database systems such as PostgreSQL and MySQL, business intelligence tools like Tableau and Power BI, and data pipeline/orchestration tools such as dbt and Airflow.
For CIOs, Horizon Context should reduce operational complexity because it will provide a governed map of their organization’s data estate, said Stephanie Walter, practice lead of the AI Stack at HyperFRAME Research.
“The value is not simply cataloging where data lives; it is giving AI systems the metadata, lineage, permissions, and business context needed to retrieve the right data safely,” Walter said.
In fact, according to Robert Kramer, managing partner at KramerERP, the challenge of missing context for analytics and reporting is not new for enterprises.
“Most enterprises were stitching together catalogs, BI semantic layers, governance tools, lineage, access controls, dbt models, security policies, and custom integrations. The issue was not that these pieces did not exist. The issue was that they were fragmented,” he said.
That duct-taping together of different pieces, said Mike Leone, principal analyst at Moor Insights & Strategy, is the actual reason why most teams in an enterprise would end up with slightly different versions of a business metric, such as monthly active users, resulting in AI agents also being inconsistent downstream.
But, said Walter, the stitching together of different catalogs and semantic layers only works for semi-autonomous workflows. “What changed with AI agents is that those systems increasingly need access to context at runtime rather than through documentation and human interpretation,” she said. “Snowflake is trying to pull those pieces closer to the data platform, so context, semantics, access control, and execution are part of the same operating environment”
To supplement Horizon Context, Snowflake is also adding Semantic Studio, currently in private preview, to help enterprises reduce the effort required to build and maintain business context for agents and agentic workflows.
“Semantic Studio is a core part of the Enrich layer of Horizon Context, providing the AI-assisted workspace where teams define, test, and publish that business logic,” Avanes said.
The Enrich layer itself, which consists of companion capabilities like the Semantic View Autopilot, automatically layers intelligence on data assets, providing insights such as which assets are most trusted, how they connect, what they mean, and how to correctly calculate metrics, Avanes added.
According to Leone, Semantic Studio solves a critical challenge: “It will reduce the burden of SQL-savvy data engineers and let business owners author the shared definitions. That’s where most semantic-layer projects die today.”
For Walter though, there is still a caveat to these offerings.
“While these offerings reduce complexity, they do not remove the work. Someone still has to decide which metrics are authoritative, which data products are trusted, and who owns the business definitions,” she said.
Alongside these updates, Snowflake is also trying to address security concerns around agentic workflows with new capabilities inside Horizon Catalog’s Trust Center.
These capabilities, which are focused on AI Security Posture Management (SPM), include a new agent identity capability that enables enterprises to distinguish between human activity within a session and actions taken by an AI agent operating on a user’s behalf, Avanes said.
“This should provide greater transparency and auditability for agents, while also enabling enterprises to apply existing data access controls, such as dynamic masking and row access policies, on an agent-by-agent basis,” he said.
Another new capability, data exfiltration policies, currently in private preview, will help enterprises prevent unauthorized movement of sensitive data. These policies are part of Horizon Catalog’s broader governance framework, and will allow enterprises to define controls around how data can be accessed, shared, and moved across systems, Avanes said.
For enterprises, Walter noted, these capabilities could help address one of the biggest barriers to deploying AI agents in production: governance.
“Data exfiltration controls, AI Security Posture Management, and other centralized AI controls help CIOs move from experimentation to production by giving security teams a way to see and govern how AI workloads interact with enterprise data,” she said.
Echoing Walter, Leone pointed out that the “security story” is the gating issue for almost every agent deployment that Moor Insights & Strategy is currently tracking.
“Features such as data exfiltration policies and AI SPM are what convert a CISO from a ‘no’ to a ‘yes’ on moving an agent deployment into production,” he added.
This article originally appeared on CIO.com.
In May and June of most years, NASA satellites typically begin to detect large numbers of wildland fires throughout the Top End and Arnhem Land regions of Australia’s Northern Territory. On some days, especially in the afternoon, the blazes can resemble sizable wildfires in satellite imagery, spreading widely and producing expansive smoke plumes.
That was the case when NASA’s Aqua satellite acquired this image of smoke and fires on the afternoon of May 28, 2026. Often, however, fires burning in this area look smaller and less imposing. In the mornings just a few days earlier and later, for instance, NASA satellites detected little smoke despite observing many thermal anomalies, or hotspots, that indicated fire activity.
The pattern of burning, location, and timing are consistent with prescribed fires lit intentionally to manage the landscape. Land managers tend to light fires in the morning, and smoke builds over the course of the day. The process sometimes creates sizable plumes when there are updrafts and winds of moderate strength that carry smoke away from the fires, as happened on May 28 and again on June 2. The fires typically burn through the fire-adapted grasses, underbrush, and scattered trees in the region’s tropical savanna ecosystems.
Over the past few decades, the region’s land managers have combined deep-rooted Indigenous land management practices and modern technologies to establish large-scale landscape management programs such as the West Arnhem Land Fire Abatement project and Arnhem Land Fire Abatement. The goal of such efforts is to intentionally burn some of the savanna underbrush to create firebreaks and reduce fuel loads early in the dry season, reducing more destructive and emissions-intensive fires later in the season. The dry season generally begins in May and extends through September, according to Australia’s Bureau of Meteorology.
While research is ongoing, there are signs that the prescribed burning efforts are having the intended effect. Analysis of satellite observations of the fires suggests that prescribed burning efforts have shifted fire activity from late to early in the dry season, leading to a reduction in high-intensity fires and emissions.
NASA Earth Observatory image by Michala Garrison, using MODIS data from NASA EOSDIS LANCE and GIBS/Worldview. Story by Adam Voiland.
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Dry, warm, and windy conditions across the U.S. Great Plains led to extreme fire activity in March 2026.
Firefighters are battling two destructive blazes in the southern part of the state as drought grips the U.S. Southeast.
A wildland fire charred grassland, coastal sage scrub, and chaparral across one-third of the island, the second largest of the…
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A report released Wednesday by Boston Consulting Group (BCG) indicates that many organizations are having difficulty converting efficiency gains that are AI-driven into any sort of measurable value.
The fourth edition of the consultancy’s annual Global AI at Work Survey reveals 42% of frontline employees who use AI on a regular basis save upwards of a full day each week; however, 66% are not given guidance on what to do with time they save, and “more than half don’t redirect it to strategic work.”
The report, AI at Work: Strategy Matters More Than Tools, is based on a global survey of 11,749 employees in 14 markets, from industries ranging from financial services to the healthcare sector.
David Martin, global leader of people and organization work at BCG, and the report’s lead author, said via email that the number of employees lacking the required guidance is surprising, “but it also tracks with what we see in many AI transformations. Companies have moved quickly to give people tools, but many have not yet redesigned the work around those tools.”
Saved time, he added, does not automatically become value. If a frontline employee saves a few hours a week, but has no direction on whether to use that time for customer service, quality improvement, innovation, or faster execution, “that value can simply leak out of the organization”
The fix is for leaders to change the scoreboard, Martin said: “Don’t just measure AI adoption or hours saved. Decide where that time should go, measure whether it is being reinvested, and give managers clear guidance on how to help teams use it. This is where AI transformation becomes a management challenge, not just a technology rollout.”
In fact, said Vinciane Beauchene, a managing director and partner at BCG and one of the report’s five co-authors, “the first wave of AI focused on individual productivity. The coming wave will need to transform collective work.”
“Everyone is talking about AI replacing work,” she said, “but it is in fact really about rethinking the human value-add inside.”
According to Beauchene, “this is the role of leaders. Our survey reveals a true managerial revolution in the age of AI; 65% of managers and leaders now believe agents will take over at least half of their job in the next three years, and frontline workers see their jobs evolving towards more managing and directing AI.”
A BCG release stated that the survey also highlights the continued emergence and maturity of AI agents, with 30% of respondents saying that agents are already integrated into workflows, more than double the number from last year’s report (13%).
Other key findings revealed that AI adoption among frontline workers has surged, with 74% saying they now use it daily or a few times a week, which is up 23 percentage points from a year ago. In addition, six out of 10 people believe that, within the next three years, AI agents could do at least half of their jobs.
And a survey slideshow released by the company pointed out, “the AI ‘honeymoon’ won’t last unless leaders bring strategic clarity driving sustained impact AI’s novelty and cognitive stretch fuel enjoyment early on. But sustained joy comes from strategic clarity. Employees thrive when the direction is real and the message reaches them with strong CEO involvement.”
The report suggests that CEOs take a holistic approach to AI transformations by focusing on business outcomes as opposed to AI usage, investing in “redesigning work end-to-end, not in more tools,” placing people at the heart of that redesign, and governing AI not as a one-off program, but as a moving target.
Overall, BCG says that strategic clarity, “more broadly emerges from the survey as the most crucial differentiator in sustaining AI’s impact over time as organizations are moving past simply implementing AI tools in use case deployment initiatives.”
Increasingly, it adds, “the focus is shifting to redesigning end-to-end workflows and processes to reimagine functions, as well as to building and innovating new business models and products to drive growth, which have nearly doubled year-over-year.”
Global leader of BCG’s tech build and design unit BCG X Sylvain Duranton, also a report co-author, added, “employees don’t push back on AI intensity; they thrive when the strategy is clear, the direction is real, and the message reaches them.”
He added, “Business value and employee enjoyment aren’t trade-offs. The organizations capturing the greatest business value are the same ones where employees enjoy work the most.”
Despite the opportunity, the report notes that only one-third of frontline employees say that leadership’s communications about AI are clear, and only 28% “see a strong connection between what leaders say and what the organization actually does.”
However, Martin said, management can’t deal with this situation on its own. “CIOs have a critical role, but this is not a problem they can solve alone, and I would not frame it as something IT created by itself,” he noted.
Many organizations, he said, “started with the natural first step of getting tools into people’s hands safely and at scale. That was necessary, but it is not sufficient.”
The next phase “has to be much more cross-functional,” he said. “CIOs should help set the technology foundation, governance, data model, and measurement systems, but they also have an important role in creating strategic clarity. Employees need to understand why the organization is using AI, where it is meant to create value, and how it should change the work.”
Martin pointed out that CIOs should also pay close attention to cognitive load, especially on technology teams, as those teams are often the heaviest AI users.
“This means they may be among the most exposed to the mental strain that can come with reviewing outputs, managing AI tools, and keeping up with constant change,” he observed. The biggest gains come when technology strategy, workforce strategy, and employee experience move together. If AI remains only an IT program, companies will “undercapture” the value.
The abundance of AI activity is also having another effect, in that 60% of respondents say the bar for work that counts as ‘good enough’ is now higher.
That, said Martin, is because AI is changing expectations. “If a tool can produce a first draft, summarize research, generate options, or automate a routine task, then ‘good enough’ moves up the value chain,” he said. “People are being asked to spend less time producing basic output and more time exercising judgment like checking quality, improving the answer, making decisions, and applying context.”
While that can be a good thing, he said, because it can make work more interesting and more valuable, “it also explains why employees are feeling more mental strain. The work that remains is often more complex. Leaders need to recognize that AI does not just make people faster, it changes what excellence looks like. That means companies need to update training, performance expectations, and management support accordingly.”
This article originally appeared on CIO.com.
Cloud computing has reached a crossroads. The high cost and data sensitivity of AI workloads are raising the appeal of private clouds, even as neoclouds and sovereign clouds shake up the cloud provider landscape. New cyberthreats, shifting compute requirements, and management complexity are adding to cloud complications.
Download the June 2026 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World, and learn how to navigate the latest cloud strategy developments.
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