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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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...