IT leaders don’t lose sleep because they have too few tools. They lose sleep because they have too many.
In every IT leadership role I’ve stepped into, no matter the organization’s size or industry, one thing has been guaranteed: cost will always be a topic of discussion.
“Why is IT so expensive?”
“Why are we paying for this tool?”
“Do we really need that service?”
Those are fair questions. But I believe the first question should be: How did we end up here?
A useful reality check: even mature IT organizations estimate 20–30% waste across desktop software, data center software, IaaS/PaaS and SaaS due to overlap, shelfware and mismanagement.
The path to complexity
In my experience, complexity rarely arrives all at once. It creeps in through a combination of leadership habits, unclear processes, overlapping tools and skill gaps.
This might sound like I’m taking a jab at some technology leaders, but it’s a reality I’ve seen across industries: leaders set in their ways, making purchasing decisions without fully understanding the technical requirements. When leaders approve overlapping solutions, delay decommissioning unused tools or fail to empower technical teams to choose the right platforms, complexity becomes embedded in the organization’s DNA.
Somewhere along the way, the mentorship aspect of leadership disappeared. We’ve replaced coaching and enabling with gatekeeping and sign-off authority. The result? Disconnected teams, poor adoption and wasted resources. And when decision-making is divorced from technical reality, complexity grows unchecked.
Over time, that complexity stops being a background nuisance and starts eroding the very processes meant to keep IT running smoothly. Change management becomes a bottleneck because no one is sure which tools are authoritative. Onboarding slows because new hires have to learn three systems to do one job. Incident response lags because alerts arrive from multiple platforms that don’t align.
At that point, fixing the problem isn’t just about removing a tool; it means untangling workflows, integrations and habits that have formed around it. And the longer it’s left, the more it feels easier to live with the mess than to clean it up.
Security is a good example. Many enterprises now juggle dozens of security tools across vendors, each with its own data model and dashboard, which fragments visibility and slows response. IBM’s Institute for Business Value found organizations use an average of 83 security tools from 29 vendors, a sprawl that strains teams and processes.
The case for consolidation
This is why consolidation remains one of the most underused strategies in IT. It’s not just about reducing licensing costs, though over my career, I’ve helped organizations save more than $2 million through tool and vendor consolidation. The real goal is to build resilience and efficiency.
- Migrating from three separate collaboration tools to a unified platform increased adoption by 40% and saved over $ 100,000 annually.
- Reducing overlapping security products cut annual licensing costs by 25%, simplified compliance audits and reduced incident response times by 30%.
- Streamlining vendor relationships improved SLAs and freed up hundreds of engineering hours each year for innovation and higher-priority projects.
When systems are simpler, teams onboard faster, incidents are resolved more quickly, and strategic projects move forward without friction. Often, the time saved ends up as valuable as the money saved and that’s becoming even more critical as organizations race to adopt AI.
Why AI could finally force consolidation
Consolidation isn’t a new idea; these challenges have been around for decades. AI, however, changes the stakes. It’s the present and the future, and we’re only at the start of its journey. But here’s the catch: AI doesn’t work in isolation. It’s entirely dependent on the quality, consistency and structure of the data it’s fed.
When an organization runs multiple overlapping platforms, each with its own data formats, structures and workflows, AI ends up pulling from messy, inconsistent sources. The result? Expensive, inaccurate and underwhelming outcomes.
This isn’t just intuition. Multiple analyses point out that AI returns stall when models are bolted onto fragmented systems and siloed data. The organizations seeing ROI are the ones that simplified data plumbing and standardized interfaces before scaling AI.
Tool consolidation is no longer just about cost savings or efficiency; it’s about creating a clean, unified ecosystem where AI can actually deliver on its promise. Without that foundation, even the most advanced AI will be bogged down by untangling the mess instead of producing real value.
In other words, consolidation has moved from a “nice-to-have” to a prerequisite for competing in the AI era.
The bottom line
The future of IT operations isn’t about having the most tools; it’s about having the right tools working together, supported by a culture where leaders empower engineers to make smart decisions.
Complexity will always creep in. Great leaders spot it early, address it decisively and build organizations that are leaner, faster and ready for whatever comes next.
So, the real question isn’t “Why is IT so expensive?” It’s “What are we doing to make it simpler?” Because in a world of constant change, simplicity isn’t just a cost-saving measure, it’s a competitive advantage you can’t afford to ignore.
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