Designing digital resilience in the agentic AI era

Digital resilience—the ability to prevent, withstand, and recover from digital disruptions—has long been a strategic priority for enterprises. With the rise of agentic AI, the urgency for robust resilience is greater than ever.

Agentic AI represents a new generation of autonomous systems capable of proactive planning, reasoning, and executing tasks with minimal human intervention. As these systems shift from experimental pilots to core elements of business operations, they offer new opportunities but also introduce new challenges when it comes to ensuring digital resilience. That’s because the autonomy, speed, and scale at which agentic AI operates can amplify the impact of even minor data inconsistencies, fragmentation, or security gaps.

While global investment in AI is projected to reach $1.5 trillion in 2025, fewer than half of business leaders are confident in their organization’s ability to maintain service continuity, security, and cost control during unexpected events. This lack of confidence, coupled with the profound complexity introduced by agentic AI’s autonomous decision-making and interaction with critical infrastructure, requires a reimagining of digital resilience.

Organizations are turning to the concept of a data fabric—an integrated architecture that connects and governs information across all business layers. By breaking down silos and enabling real-time access to enterprise-wide data, a data fabric can empower both human teams and agentic AI systems to sense risks, prevent problems before they occur, recover quickly when they do, and sustain operations.

Machine data: A cornerstone of agentic AI and digital resilience

Earlier AI models relied heavily on human-generated data such as text, audio, and video, but agentic AI demands deep insight into an organization’s machine data: the logs, metrics, and other telemetry generated by devices, servers, systems, and applications.

To put agentic AI to use in driving digital resilience, it must have seamless, real-time access to this data flow. Without comprehensive integration of machine data, organizations risk limiting AI capabilities, missing critical anomalies, or introducing errors. As Kamal Hathi, senior vice president and general manager of Splunk, a Cisco company, emphasizes, agentic AI systems rely on machine data to understand context, simulate outcomes, and adapt continuously. This makes machine data oversight a cornerstone of digital resilience.

“We often describe machine data as the heartbeat of the modern enterprise,” says Hathi. “Agentic AI systems are powered by this vital pulse, requiring real-time access to information. It’s essential that these intelligent agents operate directly on the intricate flow of machine data and that AI itself is trained using the very same data stream.” 

Few organizations are currently achieving the level of machine data integration required to fully enable agentic systems. This not only narrows the scope of possible use cases for agentic AI, but, worse, it can also result in data anomalies and errors in outputs or actions. Natural language processing (NLP) models designed prior to the development of generative pre-trained transformers (GPTs) were plagued by linguistic ambiguities, biases, and inconsistencies. Similar misfires could occur with agentic AI if organizations rush ahead without providing models with a foundational fluency in machine data. 

For many companies, keeping up with the dizzying pace at which AI is progressing has been a major challenge. “In some ways, the speed of this innovation is starting to hurt us, because it creates risks we’re not ready for,” says Hathi. “The trouble is that with agentic AI’s evolution, relying on traditional LLMs trained on human text, audio, video, or print data doesn’t work when you need your system to be secure, resilient, and always available.”

Designing a data fabric for resilience

To address these shortcomings and build digital resilience, technology leaders should pivot to what Hathi describes as a data fabric design, better suited to the demands of agentic AI. This involves weaving together fragmented assets from across security, IT, business operations, and the network to create an integrated architecture that connects disparate data sources, breaks down silos, and enables real-time analysis and risk management. 

“Once you have a single view, you can do all these things that are autonomous and agentic,” says Hathi. “You have far fewer blind spots. Decision-making goes much faster. And the unknown is no longer a source of fear because you have a holistic system that’s able to absorb these shocks and disruption without losing continuity,” he adds.

To create this unified system, data teams must first break down departmental silos in how data is shared, says Hathi. Then, they must implement a federated data architecture—a decentralized system where autonomous data sources work together as a single unit without physically merging—to create a unified data source while maintaining governance and security. And finally, teams must upgrade data platforms to ensure this newly unified view is actionable for agentic AI. 

During this transition, teams may face technical limitations if they rely on traditional platforms modeled on structured data—that is, mostly quantitative information such as customer records or financial transactions that can be organized in a predefined format (often in tables) that is easy to query. Instead, companies need a platform that can also manage streams of unstructured data such as system logs, security events, and application traces, which lack uniformity and are often qualitative rather than quantitative. Analyzing, organizing, and extracting insights from these kinds of data requires more advanced methods enabled by AI.

Harnessing AI as a collaborator

AI itself can be a powerful tool in creating the data fabric that enables AI systems. AI-powered tools can, for example, quickly identify relationships between disparate data—both structured and unstructured—automatically merging them into one source of truth. They can detect and correct errors and employ NLP to tag and categorize data to make it easier to find and use. 

Agentic AI systems can also be used to augment human capabilities in detecting and deciphering anomalies in an enterprise’s unstructured data streams. These are often beyond human capacity to spot or interpret at speed, leading to missed threats or delays. But agentic AI systems, designed to perceive, reason, and act autonomously, can plug the gap, delivering higher levels of digital resilience to an enterprise.

“Digital resilience is about more than withstanding disruptions,” says Hathi. “It’s about evolving and growing over time. AI agents can work with massive amounts of data and continuously learn from humans who provide safety and oversight. This is a true self-optimizing system.”

Humans in the loop

Despite its potential, agentic AI should be positioned as assistive intelligence. Without proper oversight, AI agents could introduce application failures or security risks.

Clearly defined guardrails and maintaining humans in the loop is “key to trustworthy and practical use of AI,” Hathi says. “AI can enhance human decision-making, but ultimately, humans are in the driver’s seat.”

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Scaling innovation in manufacturing with AI

Manufacturing is getting a major system upgrade. As AI amplifies existing technologies—like digital twins, the cloud, edge computing, and the industrial internet of things (IIoT)—it is enabling factory operations teams to shift from reactive, isolated problem-solving to proactive, systemwide optimization.

Digital twins—physically accurate virtual representations of a piece of equipment, a production line, a process, or even an entire factory—allow workers to test, optimize, and contextualize complex, real-world environments. Manufacturers are using digital twins to simulate factory environments with pinpoint detail.

“AI-powered digital twins mark a major evolution in the future of manufacturing, enabling real-time visualization of the entire production line, not just individual machines,” says Indranil Sircar, global chief technology officer for the manufacturing and mobility industry at Microsoft. “This is allowing manufacturers to move beyond isolated monitoring toward much wider insights.”

A digital twin of a bottling line, for example, can integrate one-dimensional shop-floor telemetry, two-dimensional enterprise data, and three-dimensional immersive modeling into a single operational view of the entire production line to improve efficiency and reduce costly downtime. Many high-speed industries face downtime rates as high as 40%, estimates Jon Sobel, co-founder and chief executive officer of Sight Machine, an industrial AI company that partners with Microsoft and NVIDIA to transform complex data into actionable insights. By tracking micro-stops and quality metrics via digital twins, companies can target improvements and adjustments with greater precision, saving millions in once-lost productivity without disrupting ongoing operations.

AI offers the next opportunity. Sircar estimates that up to 50% of manufacturers are currently deploying AI in production. This is up from 35% of manufacturers surveyed in a 2024 MIT Technology Review Insights report who said they have begun to put AI use cases into production. Larger manufacturers with more than $10 billion in revenue were significantly ahead, with 77% already deploying AI use cases, according to the report.

“Manufacturing has a lot of data and is a perfect use case for AI,” says Sobel. “An industry that has been seen by some as lagging when it comes to digital technology and AI may be in the best position to lead. It’s very unexpected.”

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Realizing value with AI inference at scale and in production

Training an AI model to predict equipment failures is an engineering achievement. But it’s not until prediction meets action—the moment that model successfully flags a malfunctioning machine—that true business transformation occurs. One technical milestone lives in a proof-of-concept deck; the other meaningfully contributes to the bottom line.

Craig Partridge, senior director worldwide of Digital Next Advisory at HPE, believes “the true value of AI lies in inference”. Inference is where AI earns its keep. It’s the operational layer that puts all that training to use in real-world workflows. Partridge elaborates, “The phrase we use for this is ‘trusted AI inferencing at scale and in production,’” he says. “That’s where we think the biggest return on AI investments will come from.”

Getting to that point is difficult. Christian Reichenbach, worldwide digital advisor at HPE, points to findings from the company’s recent survey of 1,775 IT leaders: While nearly a quarter (22%) of organizations have now operationalized AI—up from 15% the previous year—the majority remain stuck in experimentation.

Reaching the next stage requires a three-part approach: establishing trust as an operating principle, ensuring data-centric execution, and cultivating IT leadership capable of scaling AI successfully.

Trust as a prerequisite for scalable, high-stakes AI

Trusted inference means users can actually rely on the answers they’re getting from AI systems. This is important for applications like generating marketing copy and deploying customer service chatbots, but it’s absolutely critical for higher-stakes scenarios—say, a robot assisting during surgeries or an autonomous vehicle navigating crowded streets.

Whatever the use case, establishing trust will require doubling down on data quality; first and foremost, inferencing outcomes must be built on reliable foundations. This reality informs one of Partridge’s go-to mantras: “Bad data in equals bad inferencing out.”

Reichenbach cites a real-world example of what happens when data quality falls short—the rise of unreliable AI-generated content, including hallucinations, that clogs workflows and forces employees to spend significant time fact-checking. “When things go wrong, trust goes down, productivity gains are not reached, and the outcome we’re  looking for is not achieved,” he says.

On the other hand, when trust is properly engineered into inference systems, efficiency and productivity gains can increase. Take a network operations team tasked with troubleshooting configurations. With a trusted inferencing engine, that unit gains a reliable copilot that can deliver faster, more accurate, custom-tailored recommendations—”a 24/7 member of the team they didn’t have before,” says Partridge.

The shift to data-centric thinking and rise of the AI factory

In the first AI wave, companies rushed to hire data scientists and many viewed sophisticated, trillion-parameter models as the primary goal. But today, as organizations move to turn early pilots into real, measurable outcomes, the focus has shifted toward data engineering and architecture.

“Over the past five years, what’s become more meaningful is breaking down data silos, accessing data streams, and quickly unlocking value,” says Reichenbach. It’s an evolution happening alongside the rise of the AI factory—the always-on production line where data moves through pipelines and feedback loops to generate continuous intelligence.

This shift reflects an evolution from model-centric to data-centric thinking, and with it comes a new set of strategic considerations. “It comes down to two things: How much of the intelligence–the model itself–is truly yours? And how much of the input–the data–is uniquely yours, from your customers, operations, or market?” says Reichenbach.

These two central questions inform everything from platform direction and operating models to engineering roles and trust and security considerations. To help clients map their answers—and translate them into actionable strategies—Partridge breaks down HPE’s four-quadrant AI factory implication matrix (see figure):

Source: HPE, 2025

• Run: Accessing an external, pretrained model via an interface or API; organizations don’t own the model or the data. Implementation requires strong security and governance. It also requires establishing a center of excellence that makes and communicates decisions about AI usage.
• RAG (retrieval augmented generation): Using external, pre-trained models combined with a company’s proprietary data to create unique insights. Implementation focuses on connecting data streams to inferencing capabilities that provide rapid, integrated access to full-stack AI platforms.
• Riches: Training custom models on data that resides in the enterprise for unique differentiation opportunities and insights. Implementation requires scalable, energy-efficient environments, and often high-performance systems.
• Regulate: Leveraging custom models trained on external data, requiring the same scalable setup as Riches, but with added focus on legal and regulatory compliance for handling sensitive, non-owned data with extreme caution.

Importantly, these quadrants are not mutually exclusive. Partridge notes that most organizations—including HPE itself—operate across many of the quadrants. “We build our own models to help understand how networks operate,” he says. “We then deploy that intelligence into our products, so that our end customer gets the chance to deliver in what we call the ‘Run’ quadrant. So for them, it’s not their data; it’s not their model. They’re just adding that capability inside their organization.”

IT’s moment to scale—and lead

The second part of Partridge’s catchphrase about inferencing—”at scale”— speaks to a primary tension in enterprise AI: what works for a handful of use cases often breaks when applied across an entire organization.

“There’s value in experimentation and kicking ideas around,” he says. “But if you want to really see the benefits of AI, it needs to be something that everybody can engage in and that solves for many different use cases.”

In Partridge’s view, the challenge of turning boutique pilots into organization-wide systems is uniquely suited to the IT function’s core competencies—and it’s a leadership opportunity the function can’t afford to sit out. “IT takes things that are small-scale and implements the discipline required to run them at scale,” he says. “So, IT organizations really need to lean into this debate.”

For IT teams content to linger on the sidelines, history offers a cautionary tale from the last major infrastructure shift: enterprise migration to the cloud. Many IT departments sat out decision-making during the early cloud adoption wave a decade ago, while business units independently deployed cloud services. This led to fragmented systems, redundant spending, and security gaps that took years to untangle.

The same dynamic threatens to repeat with AI, as different teams experiment with tools and models outside IT’s purview. This phenomenon—sometimes called shadow AI—describes environments where pilots proliferate without oversight or governance. Partridge believes that most organizations are already operating in the “Run” quadrant in some capacity, as employees will use AI tools whether or not they’re officially authorized to.

Rather than shut down experimentation, it is now IT’s mandate to bring structure to it. And enterprises must architect a data platform strategy that brings together enterprise data with guardrails, governance framework, and accessibility to feed AI. Also, it’s critical to keep standardizing infrastructure (such as private cloud AI platforms), protecting data integrity, and safeguarding brand trust, all while enabling the speed and flexibility that AI applications demand. These are the requirements for reaching the final milestone: AI that’s truly in production.

For teams on the path to that goal, Reichenbach distills what success requires. “It comes down to knowing where you play: When to Run external models smarter, when to apply RAG to make them more informed, where to invest to unlock Riches from your own data and models, and when to Regulate what you don’t control,” says Reichenbach. “The winners will be those who bring clarity to all quadrants and align technology ambition with governance and value creation.”

For more, register to watch MIT Technology Review’s EmTech AI Salon, featuring HPE.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Networking for AI: Building the foundation for real-time intelligence

The Ryder Cup is an almost-century-old tournament pitting Europe against the United States in an elite showcase of golf skill and strategy. At the 2025 event, nearly a quarter of a million spectators gathered to watch three days of fierce competition on the fairways.

From a technology and logistics perspective, pulling off an event of this scale is no easy feat. The Ryder Cup’s infrastructure must accommodate the tens of thousands of network users who flood the venue (this year, at Bethpage Black in Farmingdale, New York) every day.

To manage this IT complexity, Ryder Cup engaged technology partner HPE to create a central hub for its operations. The solution centered around a platform where tournament staff could access data visualization supporting operational decision-making. This dashboard, which leveraged a high-performance network and private-cloud environment, aggregated and distilled insights from diverse real-time data feeds.

It was a glimpse into what AI-ready networking looks like at scale—a real-world stress test with implications for everything from event management to enterprise operations. While models and data readiness get the lion’s share of boardroom attention and media hype, networking is a critical third leg of successful AI implementation, explains Jon Green, CTO of HPE Networking. “Disconnected AI doesn’t get you very much; you need a way to get data into it and out of it for both training and inference,” he says.

As businesses move toward distributed, real-time AI applications, tomorrow’s networks will need to parse even more massive volumes of information at ever more lightning-fast speeds. What played out on the greens at Bethpage Black represents a lesson being learned across industries: Inference-ready networks are a make-or-break factor for turning AI’s promise into real-world performance.

Making a network AI inference-ready

More than half of organizations are still struggling to operationalize their data pipelines. In a recent HPE cross-industry survey of 1,775  IT leaders, 45% said they could run real-time data pushes and pulls for innovation. It’s a noticeable change over last year’s numbers (just 7% reported having such capabilities in 2024), but there’s still work to be done to connect data collection with real-time decision-making.

The network may hold the key to further narrowing that gap. Part of the solution will likely come down to infrastructure design. While traditional enterprise networks are engineered to handle the predictable flow of business applications—email, browsers, file sharing, etc.—they’re not designed to field the dynamic, high-volume data movement required by AI workloads. Inferencing in particular depends on shuttling vast datasets between multiple GPUs with supercomputer-like precision.

“There’s an ability to play fast and loose with a standard, off-the-shelf enterprise network,” says Green. “Few will notice if an email platform is half a second slower than it might’ve been. But with AI transaction processing, the entire job is gated by the last calculation taking place. So it becomes really noticeable if you’ve got any loss or congestion.”

Networks built for AI, therefore, must operate with a different set of performance characteristics, including ultra-low latency, lossless throughput, specialized equipment, and adaptability at scale. One of these differences is AI’s distributed nature, which affects the seamless flow of data.

The Ryder Cup was a vivid demonstration of this new class of networking in action. During the event, a Connected Intelligence Center was put in place to ingest data from ticket scans, weather reports, GPS-tracked golf carts, concession and merchandise sales, spectator and consumer queues, and network performance. Additionally, 67 AI-enabled cameras were positioned throughout the course. Inputs were analyzed through an operational intelligence dashboard and provided staff with an instantaneous view of activity across the grounds.

“The tournament is really complex from a networking perspective, because you have many big open areas that aren’t uniformly packed with people,” explains Green. “People tend to follow the action. So in certain areas, it’s really dense with lots of people and devices, while other areas are completely empty.”

To handle that variability, engineers built out a two-tiered architecture. Across the sprawling venue, more than 650 WiFi 6E access points, 170 network switches, and 25 user experience sensors worked together to maintain continuous connectivity and feed a private cloud AI cluster for live analytics. The front-end layer connected cameras, sensors, and access points to capture live video and movement data, while a back-end layer—located within a temporary on-site data center—linked GPUs and servers in a high-speed, low-latency configuration that effectively served as the system’s brain. Together, the setup enabled both rapid on-the-ground responses and data collection that could inform future operational planning. “AI models also were available to the team which could process video of the shots taken and help determine, from the footage, which ones were the most interesting,” says Green.

Physical AI and the return of on-prem intelligence

If time is of the essence for event management, it’s even more critical in contexts where safety is on the line—for instance a self-driving car making a split-second decision to accelerate or brake.

In planning for the rise of physical AI, where applications move off screens and onto factory floors and city streets, a growing number of enterprises are rethinking their architectures. Instead of sending the data to centralized clouds for inference, some are deploying edge-based AI clusters that process information closer to where it is generated. Data-intensive training may still occur in the cloud, but inferencing happens on-site.

This hybrid approach is fueling a wave of operational repatriation, as workloads once relegated to the cloud return to on-premises infrastructure for enhanced speed, security, sovereignty, and cost reasons. “We’ve had an out-migration of IT into the cloud in recent years, but physical AI is one of the use cases that we believe will bring a lot of that back on-prem,” predicts Green, giving the example of an AI-infused factory floor, where a round-trip of sensor data to the cloud would be too slow to safely control automated machinery. “By the time processing happens in the cloud, the machine has already moved,” he explains.

There’s data to back up Green’s projection: research from Enterprise Research Group shows that 84% of respondents are reevaluating application deployment strategies due to the growth of AI. Market forecasts also reflect this shift. According to IDC, the AI market for infrastructure is expected to reach $758 billion by 2029.

AI for networking and the future of self-driving infrastructure

The relationship between networking and AI is circular: Modern networks make AI at scale possible, but AI is also helping make networks smarter and more capable.

“Networks are some of the most data-rich systems in any organization,” says Green. “That makes them a perfect use case for AI. We can analyze millions of configuration states across thousands of customer environments and learn what actually improves performance or stability.”

At HPE for example, which has one of the largest network telemetry repositories in the world, AI models analyze anonymized data collected from billions of connected devices to identify trends and refine behavior over time. The platform processes more than a trillion telemetry points each day, which means it can continuously learn from real-world conditions.

The concept broadly known as AIOps (or AI-driven IT operations) is changing how enterprise networks are managed across industries. Today, AI surfaces insights as recommendations that administrators can choose to apply with a single click. Tomorrow, those same systems might automatically test and deploy low-risk changes themselves.

That long-term vision, Green notes, is referred to as a “self-driving network”—one that handles the repetitive, error-prone tasks that have historically plagued IT teams. “AI isn’t coming for the network engineer’s job, but it will eliminate the tedious stuff that slows them down,” he says. “You’ll be able to say, ‘Please go configure 130 switches to solve this issue,’ and the system will handle it. When a port gets stuck or someone plugs a connector in the wrong direction, AI can detect it—and in many cases, fix it automatically.”

Digital initiatives now depend on how effectively information moves. Whether coordinating a live event or streamlining a supply chain, the performance of the network increasingly defines the performance of the business. Building that foundation today will separate those who pilot from those who scale AI.

For more, register to watch MIT Technology Review’s EmTech AI Salon, featuring HPE.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Improving VMware migration workflows with agentic AI

For years, many chief information officers (CIOs) looked at VMware-to-cloud migrations with a wary pragmatism. Manually mapping dependencies and rewriting legacy apps mid-flight was not an enticing, low-lift proposition for enterprise IT teams.

But the calculus for such decisions has changed dramatically in a short period of time. Following recent VMware licensing changes, organizations are seeing greater uncertainty around the platform’s future. At the same time, cloud-native innovation is accelerating. According to the CNCF’s 2024 Annual Survey, 89% of organizations have already adopted at least some cloud-native techniques, and the share of companies reporting nearly all development and deployment as cloud-native grew sharply from 2023 to 2024 (20% to 24%). And market research firm IDC reports that cloud providers have become top strategic partners for generative AI initiatives.

This is all happening amid escalating pressure to innovate faster and more cost-effectively to meet the demands of an AI-first future. As enterprises prepare for that inevitability, they are facing compute demands that are difficult, if not prohibitively expensive, to maintain exclusively on-premises.

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.