Skip to main content

AI's Inflection Point Will Redefine Enterprise Readiness in 2026

Dennis Perpetua
Kyndryl

Across industries, enterprises are facing an uncomfortable truth: years of rapid AI adoption have created fragmented IT infrastructures, accidental cloud environments, and workforces that struggle to keep pace with constant innovation.

At the same time, Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026. As companies finalize their budgets and plans, we'll see a clear shift from ambition to execution. The priority will move toward building resilient foundations, aligning technology and employee readiness, and scaling AI initiatives with greater confidence.

Enterprises that adapt will be positioned for sustainable growth. Those that don't will fall behind in the next wave of disruption.

External Adoption Takes a Step Back

After several years of AI-first investments, 2026 will mark a return to fundamentals. Organizations are recognizing that lasting innovation depends on the strength of the infrastructure supporting it. With 25% of mission-critical systems near end-of-service, leaders will shift their focus to reinforcing their IT core — sometimes at the expense of experimental AI pilots.

Infrastructure modernization will move to the forefront, claiming a larger share of IT budgets as companies solidify the systems that enable AI. This isn't a retreat from innovation; it's a recalibration. The next wave of competitive advantage will come from combining advanced AI capabilities with secure, high-performing, and compliant foundations.

Workforce Readiness Will be a Leading Indicator of AI ROI

Simultaneously, the AI employee readiness crisis will evolve from a technical challenge to a cultural one. While 87% of business leaders believe AI will completely transform jobs within a year, only 31% say their workforce is ready to leverage it. This growing gap creates a "readiness paradox": one in which organizations are scaling technology faster than their people can absorb it.

As this paradox deepens, leaders will begin to recognize that the real determinant of transformation success isn't tooling, it's trust. In fact, 42% of leaders cite building employee trust as a major obstacle to AI adoption. Companies can no longer rely solely on hiring or deploying new technology to stay ahead.

Investment in change management, upskilling, and re-skilling will surge as leaders prioritize workforce readiness as the key lever for realizing AI ROI. The organizations that close the cultural gap, empowering employees to engage confidently with AI, will be the ones that capture its full potential.

Geo-Aligned Cloud Ecosystems Have Arrived

Cloud strategy will become as much about sovereignty as it is about scale. Enterprise leaders must navigate a complex web of regional data laws and compliance standards that shape where and how they deploy AI infrastructure.

Already, a majority of enterprises have adapted their cloud strategies in response to geopolitical pressures. In 2026, that number is expected to climb. Localized compliance frameworks will no longer be an exception; they'll be a core KPI measured against AI and cloud implementation strategies.

In this new era, enterprises will prioritize trusted geography over pure cost efficiency, and multinational organizations will shift toward multi-cloud-by-design architectures, striking a balance between performance and resilience. This evolution has direct implications for the nearly 70% of business leaders who feel unprepared to manage external risks such as regulatory uncertainty and market volatility. The same proportion reports that their current cloud environments evolved "by accident, not by design," and nearly all (95%) say they would redesign their cloud strategies if given the opportunity.

The difference between enterprises that use AI and those truly prepared for it will become clear in 2026. Readiness won't be measured by how fast organizations adopt new technologies, but by how strategically they align their infrastructure, governance, and people to use them effectively. Building resilience — technological, cultural, and operational — will define the next wave of transformation.

Dennis Perpetua is Global CTO of Digital Workplace Services & Experience Officer, VP, and Distinguished Engineer at Kyndryl

The Latest

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...

AI's Inflection Point Will Redefine Enterprise Readiness in 2026

Dennis Perpetua
Kyndryl

Across industries, enterprises are facing an uncomfortable truth: years of rapid AI adoption have created fragmented IT infrastructures, accidental cloud environments, and workforces that struggle to keep pace with constant innovation.

At the same time, Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026. As companies finalize their budgets and plans, we'll see a clear shift from ambition to execution. The priority will move toward building resilient foundations, aligning technology and employee readiness, and scaling AI initiatives with greater confidence.

Enterprises that adapt will be positioned for sustainable growth. Those that don't will fall behind in the next wave of disruption.

External Adoption Takes a Step Back

After several years of AI-first investments, 2026 will mark a return to fundamentals. Organizations are recognizing that lasting innovation depends on the strength of the infrastructure supporting it. With 25% of mission-critical systems near end-of-service, leaders will shift their focus to reinforcing their IT core — sometimes at the expense of experimental AI pilots.

Infrastructure modernization will move to the forefront, claiming a larger share of IT budgets as companies solidify the systems that enable AI. This isn't a retreat from innovation; it's a recalibration. The next wave of competitive advantage will come from combining advanced AI capabilities with secure, high-performing, and compliant foundations.

Workforce Readiness Will be a Leading Indicator of AI ROI

Simultaneously, the AI employee readiness crisis will evolve from a technical challenge to a cultural one. While 87% of business leaders believe AI will completely transform jobs within a year, only 31% say their workforce is ready to leverage it. This growing gap creates a "readiness paradox": one in which organizations are scaling technology faster than their people can absorb it.

As this paradox deepens, leaders will begin to recognize that the real determinant of transformation success isn't tooling, it's trust. In fact, 42% of leaders cite building employee trust as a major obstacle to AI adoption. Companies can no longer rely solely on hiring or deploying new technology to stay ahead.

Investment in change management, upskilling, and re-skilling will surge as leaders prioritize workforce readiness as the key lever for realizing AI ROI. The organizations that close the cultural gap, empowering employees to engage confidently with AI, will be the ones that capture its full potential.

Geo-Aligned Cloud Ecosystems Have Arrived

Cloud strategy will become as much about sovereignty as it is about scale. Enterprise leaders must navigate a complex web of regional data laws and compliance standards that shape where and how they deploy AI infrastructure.

Already, a majority of enterprises have adapted their cloud strategies in response to geopolitical pressures. In 2026, that number is expected to climb. Localized compliance frameworks will no longer be an exception; they'll be a core KPI measured against AI and cloud implementation strategies.

In this new era, enterprises will prioritize trusted geography over pure cost efficiency, and multinational organizations will shift toward multi-cloud-by-design architectures, striking a balance between performance and resilience. This evolution has direct implications for the nearly 70% of business leaders who feel unprepared to manage external risks such as regulatory uncertainty and market volatility. The same proportion reports that their current cloud environments evolved "by accident, not by design," and nearly all (95%) say they would redesign their cloud strategies if given the opportunity.

The difference between enterprises that use AI and those truly prepared for it will become clear in 2026. Readiness won't be measured by how fast organizations adopt new technologies, but by how strategically they align their infrastructure, governance, and people to use them effectively. Building resilience — technological, cultural, and operational — will define the next wave of transformation.

Dennis Perpetua is Global CTO of Digital Workplace Services & Experience Officer, VP, and Distinguished Engineer at Kyndryl

The Latest

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...