Enterprises are ramping up AI investments while many projects remain in the pilot phase
AI momentum is growing, fast. Across the board, enterprise leaders are spending more to integrate artificial intelligence into operations, strategy, and products. A 33% average increase in AI investment, as reported in the 2025 Kyndryl Readiness Report, reflects that. Now, 68% of companies are committing serious resources to at least one domain of AI. Good. But here’s the thing, while the dollars are flowing, results are still mostly early stage.
Despite real investment, most of that AI is stuck in pilot mode. You see a few positive outcomes. According to the report, 54% of organizations are seeing returns, a 12-point jump in just a year. Encouraging. But 62% of companies say they’re still holding at experimental phases. There’s a gap here.
The problem is momentum. Over-reliance on legacy systems, poor system integration, and unclear implementation roadmaps are dragging companies down. Many believe they’re innovating, but innovation isn’t a proof of concept. It’s scalability. And in too many cases, the tech stack is acting more like an anchor than fuel.
If you’re running a company, ask yourself, are you getting useful outcomes from AI, or just pilot results? Projects don’t scale themselves. Infrastructure, execution discipline, and integration into actual business processes, that’s where transformation happens.
People readiness is emerging as a key barrier to fully harnessing AI’s transformative potential
You can’t scale AI without people who know what to do with it. And right now, there’s a serious readiness gap in the workforce.
The Kyndryl report is blunt about it: 87% of leaders say AI will fundamentally change jobs in the next 12 months. Only 29% believe their employees are ready for that shift. That’s not a small mismatch, it’s a strategic risk. Companies are betting on a future their people aren’t trained to deliver yet.
This isn’t about fear. It’s about capability. Most teams don’t lack motivation, they lack the tools, support, and knowledge to adapt. Leaders need to fix that. Upskilling, cultural alignment, and speed in internal decision-making have to be frictionless.
Nearly half of CEOs admit their companies are slow movers. Not because they don’t want innovation, but because decisions just don’t happen fast enough. That doesn’t work in AI. The tech is moving too fast. You either keep up, or you stall out.
If you’re an executive, the move is clear: invest in your people as seriously as you invest in infrastructure. AI isn’t plug-and-play, it needs qualified operators. Build a workforce that understands it, uses it, and scales it. That’s the real pivot point.
Cultural and leadership alignment distinguishes companies that successfully scale AI initiatives
Some companies are making real progress with AI. These are the ones cutting through the noise and getting results beyond proof-of-concept. They’re not improvising, they’re aligned. According to the Kyndryl Readiness Report, this high-performing group, called “Pacesetters,” shows what happens when culture, leadership, and skills are calibrated.
These companies aren’t just more optimistic about AI, they’re more capable. They’re 32 percentage points less likely to be blocked by outdated tech stacks. They’re 20 points less likely to suffer cyber-related outages. That’s not coincidence. It’s execution built on coordinated leadership and workforce investment.
You can’t bolt AI onto a business that fights change from the inside. You need leadership pushing in one direction and a workforce trained to follow through, quickly. That clarity across the organization enables faster integration of new tools, faster course-correction when things need to shift, and faster scaling when results start showing.
If you’re running a company where AI feels distant or stuck, stop treating it like a side project. It belongs in the core business strategy. Strong culture makes it easier to pivot. Clear leadership alignment removes conflicting priorities. Progress happens when everyone is on the same page and velocity is baked into how you operate.
Geopolitical and regulatory challenges are driving strategic shifts in cloud management
AI depends on data. Data lives in the cloud. And cloud strategies are now being reshaped by geopolitics and regulation.
Three out of four business leaders are now concerned about the geopolitical risks of global data storage, especially in response to evolving national regulations. And they’re acting on it. According to Kyndryl, 65% of organizations have already modified their cloud approaches. Some are moving data back in-house. Others are switching vendors or shifting to private cloud to retain tighter control.
The cost of getting this wrong isn’t theoretical. It impacts security, compliance, and operational risk every day. Nearly a third of executives pointed to regulatory and compliance pressures as the single biggest barrier to scaling technology investments. That pressure gets stronger as data volumes grow and AI gets deeper into decision-making processes.
The old approach, pushing all data to the cloud without thinking about jurisdiction or compliance risk, doesn’t scale anymore. Today, cloud strategy is a leadership responsibility. You need to know exactly where your data lives, who controls it, and whether your policies can adapt quickly when rules change.
If you’re leading at the C-level, stop treating cloud like IT plumbing. It’s fundamental to your AI roadmap. The faster you align your cloud architecture with regulatory realities, the more resilient your AI initiatives will be.
Cybersecurity remains the foremost AI application, driving significant investment in threat detection and incident response
This part is clear, AI’s most valuable and most adopted use case right now is cybersecurity. Companies are putting money where they see immediate risk. According to the 2025 Kyndryl Readiness Report, 75% of organizations are deploying AI to detect and respond to cyber threats. This isn’t hype. This is urgent need.
In the past year, 82% of companies faced a cyber-related outage. That’s a systemic problem, and leaders are treating it as such. In response, 42% are upgrading IT infrastructure; 39% are ramping up cybersecurity defenses. These aren’t isolated fixes. They’re structural upgrades aimed at making AI an active part of their security strategy.
The logic behind it is strong. Threat landscapes are evolving faster than human teams can keep up with on their own. AI makes it possible to detect anomalies, react in real time, and scale protections across systems that are too complex for manual oversight. That’s not futuristic, it’s already operational in the enterprises that are leading the field.
If you’re a C-suite leader still treating cybersecurity as an IT-only concern, you’re leaving your entire business exposed. AI isn’t about replacing people, it’s about increasing capacity to defend what matters. Security must be embedded into every layer of your tech stack. Strong cybersecurity isn’t a checkbox, it’s an operating condition.
Automation is delivering measurable operational benefits, including cost reductions and enhanced resilience
Automation is one of the strongest levers available right now for improving enterprise performance. The latest Kyndryl data backs this up: 32% of companies reported lower operational costs in the last year thanks to automation and system optimization. And that’s not the only upside. They’re also seeing quicker recovery times, fewer errors, and stronger resilience overall.
What’s working here is density, getting automated processes deeply embedded into everyday operations. That’s how you create impact that scales. Automation is being used to plug visibility gaps, enhance decision consistency, and maintain uptime under pressure. These are the kinds of benefits that stick.
Organizations that apply automation seriously, not just selectively, are building stronger foundations for AI, cybersecurity, and cloud management. Automation strengthens the systems that everything else depends on. It doesn’t just remove inefficiencies, it increases operational clarity, which is critical when systems need to interoperate in real time.
For decision-makers, the insight is direct. This isn’t the time to automate the margins. Focus on operational nodes where real cost or risk lives. Build automation into monitoring, recovery, provisioning, and optimization. When time is saved and variability is reduced, your teams can do higher-value work, with fewer errors and better outcomes. That’s how you create performance that’s repeatable.
Main highlights
- AI investment outpaces results: While AI budgets are up 33% year-over-year and 68% of enterprises are heavily investing, 62% are stuck at pilot stage. Leaders must pair spend with execution strategies to move beyond experimentation.
- Workforce readiness remains a weak link: Despite 87% of leaders expecting AI to reshape jobs this year, only 29% believe their teams are ready. Executives should accelerate upskilling and align workforce plans with AI objectives.
- Culture and leadership accelerate AI success: Top-performing “Pacesetters” show faster AI progress by prioritizing culture shifts and strong leadership alignment. Leaders should treat AI as a company-wide strategy, not just a tech initiative.
- Cloud strategy now requires geopolitical awareness: 75% of business leaders are concerned about global data risks, prompting 65% to adjust their cloud approach. C-suite leaders must integrate data sovereignty and compliance into infrastructure planning.
- Cybersecurity is AI’s most urgent application: With 82% experiencing cyber outages and 75% using AI for threat response, security remains the top use case. Executives should embed AI into cybersecurity ops to drive proactive risk reduction.
- Automation delivers clear operational returns: 32% of companies report lower costs due to automation, with added benefits in resilience and error reduction. Leaders should prioritize automation at scale to drive efficiency and reliability.
