Disconnect between IT and business leaders on AI readiness
There’s a real gap in how business leaders and IT professionals perceive AI progress. Business executives are often quick to say they’ve made solid gains in AI pilots and prototypes. But talk to IT, and the story changes. Less than a third of IT leaders agree with that narrative, based on findings from Unisys.
This gap is about alignment. If one side believes the organization is ahead and the other knows there are obstacles under the surface, it creates friction. Executives expect results, and IT deals with the messy back end. Missed expectations slow progress, confuse priorities, and dilute accountability.
It also reflects a deeper issue, an inconsistent understanding of what AI readiness looks like. Business leaders see outcomes: demos, prototypes, initial models. IT sees scalability, integrations, security, compute infrastructure. These are entirely different benchmarks, and both are essential.
C-suite leaders need a clear and honest view of where the AI stack actually stands. If IT flags capacity issues while the business side is setting launch timelines, something’s going to break. That’s where leadership steps in. Remove the disconnect, build shared goals, and ensure technology and vision move in sync.
Inadequate infrastructure to sustain AI workloads
Enterprises are spending more on AI. The excitement is there. But many systems built ten years ago, or even five, aren’t up for what generative AI demands today. More than 40% of IT leaders told Unisys that their infrastructure simply can’t support the weight of AI at scale.
Modern AI models need high-performance computing, fast data access, and seamless scalability. Legacy infrastructure, rigid systems, scattered data architecture, outdated compute layers, slows all of that down.
Some executives underestimate how fast this changes. What used to be future-proof for five years now has to evolve within 18 months. If your architecture can’t scale as fast as your AI ambitions, then you’re risking more than delay. You’re risking relevance.
You don’t need to rebuild everything overnight. But you need the right foundation. The smart move here is to audit what’s already in place, look at your compute, data storage, and system flexibility. Then get realistic about how to support AI over the next 12 to 24 months.
Infrastructure isn’t just an IT concern anymore. It’s strategic. As AI workloads become central to product development, customer engagement, and competitive advantage, your systems have to be ready, or your competitors will be.
Security frameworks as a barrier to innovation
There’s a visible tension between IT teams and the business side when it comes to security. Nearly two-thirds of executives say outdated, rigid security measures block their ability to share and analyze data. Only about one-third of IT leaders agree. That’s a structural misalignment between those defining the strategy and those implementing it.
From the executive viewpoint, security is often seen as friction, rules and policies that slow down innovation. Cloud restrictions, access protocols, compliance requirements, they’re seen as unnecessary limits. On the other side, IT leaders often view those controls as baseline precautions.
This disconnect matters. If the business side continues to see security as a roadblock, they’ll bypass it. That’s where unauthorized shadow IT and data leaks start. Any executive who ignores the trade-off between innovation and risk is creating exposure, commercial and reputational.
Getting this right means modernizing your security model. Cloud environments today are more dynamic than traditional systems were even five years ago. Security needs to match that pace, still effective, but less rigid. Executives should focus on integrating security into innovation.
A reactive approach dominates cybersecurity strategies
Nearly 90% of enterprise leaders say their organizations respond effectively to incidents, but very few say they prevent them. Reacting to cyber threats after they’ve already entered your system is a dangerous way to run an enterprise in a connected economy.
This is where most strategies fall behind. They rely on detection and response, but not enough on prediction and prevention. And while response plans are important, not investing in preventive capabilities means leaving your front door open and hoping the lock holds.
Every major breach has a cost, in downtime, in customer trust, in investor confidence. If your strategy is only as strong as your reaction time, then you’re not in control of your risk profile.
The argument often comes down to budget and resources. Prevention doesn’t produce flashy headlines or visible wins. But it avoids the kinds of catastrophic failures that take months, or years, to recover from. Executives who understand this shift their language from “How do we respond fast?” to “How do we stop it before it starts?”
Cybersecurity is now an enterprise-level conversation. It’s operational, reputational, and increasingly regulatory. The reactive model is too slow, and attackers are getting faster. Shift direction now, because by the time you need a response plan, the damage might already be done.
Reactive cybersecurity and legacy systems incur high financial risks
Enterprise systems that haven’t been modernized are now a major liability. Outdated infrastructure, paired with a reactive cybersecurity posture, creates a scenario where downtime is inconvenient and expensive. According to Unisys, more than 40% of organizations face losses of up to $500,000 for every hour of unplanned IT outages.
This is real financial risk, realized in real time when systems fail. Older systems are harder to secure, slower to recover, and less flexible to adapt. Combine that with a defense model that only activates after a breach, and the business becomes exposed, technically and financially.
Yet many enterprises delay action. The pressure to minimize short-term capital expenditure often wins. But continued reliance on fragile infrastructure leads to service failures, compliance problems, and lost revenue. Each incident stacks up, not just in balance sheets, but also in customer trust and internal morale.
The cost of inaction needs to be part of every executive discussion. This is about resilience. Organizations that don’t invest in system upgrades and forward-facing security strategies are losing control of risk management. Business continuity isn’t a side issue, it’s central to long-term performance.
Naglapur from Unisys called it out directly: “The next wave of technological disruption is already underway, yet many organizations are still operating on outdated foundations and processes.” That’s not commentary, it’s warning.
C-suite leaders need to view IT resilience as a strategic requirement. Waiting until systems fail will always cost more than building sustainable, secured infrastructure today.
Main highlights
- Misaligned views on AI progress: Business leaders often overestimate AI maturity, while IT sees critical gaps. Aligning expectations internally is essential before scaling initiatives.
- Infrastructure limits AI scalability: Many organizations lack systems capable of supporting modern AI workloads. Leaders should assess and invest in adaptive infrastructure to meet growing AI demands.
- Security policies are stalling innovation: Executives see rigid security and cloud controls as barriers. It’s time to modernize security architectures to enable secure, flexible data use.
- Cyber strategies are reactive: Most organizations respond to cyber incidents but don’t actively prevent them. Leaders should prioritize proactive defense models to reduce risk and downtime.
- Legacy systems increase outage costs: Downtime linked to outdated infrastructure and reactive security can cost up to $500,000 per hour. Executives must modernize core systems to protect business continuity.
