From the boardroom to the engineering floor, one question now eclipses all others: Are we modernizing with artificial intelligence (AI) in a way that truly changes outcomes? In my years working on large-scale transformations at PwC Consulting, IBM, and KPMG, I have watched companies pursue modernization with both zeal and confusion. Today, as AI indisputably becomes part of every company's roadmap, the critical challenge is no longer whether to adopt AI. It is whether that adoption can deliver real impact when it intersects with people, process, and the work of modernization.
In 2025, the corporate world reached a moment both exciting and sobering. McKinsey reports that 88% of organizations now use AI in at least one business function. Yet emerging research on GenAI adoption paints a very different picture of value creation. An MIT study finds that only 5% of GenAI initiatives are generating measurable return on investment, despite tens of billions in enterprise spending.
That contrast reveals why modernization remains stuck. AI, in and of itself, does not reorganize workflows, reform organizational habits, or unlock buried knowledge in legacy systems. What it can do, when implemented with discipline, is amplify disciplined work. What it cannot do is substitute for that discipline.
To understand why, we need to revisit how modernization became a linchpin of corporate strategy. In the past two decades, companies accumulated layers of technology like geological strata: old code mixed with new frameworks, undocumented decisions embedded in brittle logic, and systems that function only because a handful of long-tenured engineers hold the tacit memory. Modernization is organizational archeology, uncovering causal layers of business logic and human practice that lie beneath every button, screen, and integration.
During my years leading enterprise architecture and data initiatives, I watched modernization programs consume massive budgets and stall under their own weight. These efforts often underestimate the complexity of the business, information/data, and technology related to legacy systems. The result was predictable. Timelines slipped, morale eroded, and organizations became more risk-averse than before. That history matters because AI now enters the same environment, carrying enormous promise and equally enormous expectations.
AI changes the odds, although not in the way many assume. AI excels at synthesizing information across codebases, documentation, data models, and operational artifacts. Tasks that once required months of manual analysis now become achievable at scale. In my current work through the International Center of AI (ICAI), I see this daily. We help organizations modernize by pairing AI-driven discovery with human expertise, extracting institutional knowledge embedded across systems and teams. This approach transforms modernization from a disruptive "big bang" into a staged, intelligible evolution.
Yet most organizations treat AI as an add-on, a set of tools to "install" rather than a transformation to ingrain. In doing so, they often overlook the most important ingredient in modernization: people. Without structured change management, proper methodology, comprehensive training, and aligned incentives, AI becomes a shiny object. It generates automated outputs and removes human involvement, resulting in little to no change in underlying work habits or decision frameworks.
This is why even successful deployments tend to share a trait. They focus relentlessly on solving specific business problems and integrate human expertise directly into the process.
Challenges beyond technology are also slowing progress. Scaling AI beyond pilot projects remains difficult for many companies. According to Gartner, continuous alignment between business and AI strategy is necessary for effective AI execution and sustained impact. Only when organizations align people, processes, and technology can AI become a strategic multiplier, not a point solution.
This perspective reframes what modernization really demands. It is reconfiguring how work happens, redesigning workflows so that AI augments human insight rather than autonomy, embedding structured learning so engineers evolve into "super-developers," and honoring the organizational memory that lives in longstanding systems.
When modernization is approached this way, its impact becomes visible in everyday reality. Cycle times shrink, systems become safer to touch, onboarding accelerates, and employee satisfaction rises because people see tangible improvements in how they contribute. Modern systems are not just newer technically but better aligned operationally.
The irony is profound. AI can reveal opportunities for modernization that were previously unreachable precisely because of the effort required to manually extract organizational knowledge. In this sense, modernization and AI adoption are mutually reinforcing. But this harmony only emerges when organizations treat modernization as a people-centered transformation, not a checklist of technologies to adopt.
So where does that leave leadership? First, with a clearer definition of success. Modernization is not an end state achieved by ticking off an implementation of a large language model or an analytics platform. Modernization is a continuous evolution that pairs technology with purpose and places human judgment at its center. Second, it demands investment in people, and by that I mean training, learning environments, and frameworks that elevate teams to leverage AI with confidence and rigor. Finally, it calls for patience and discipline, moving incrementally, validating each step, and ensuring changes reinforce organizational goals rather than create confusion.
At this inflection point, companies that genuinely modernize will be those that see AI as an enabler of disciplined work. The future of business will be defined not by whether AI exists in code, but by how deeply it is woven into the fabric of how people solve problems, build products, and deliver value.
I, for one, am incredibly excited for the future. The possibilities are endless, but it is up to each and every one of us to pave the correct path. Afterall, modernization with purpose changes everything, and that is the imperative before us.
About the Author
David Bar, founder of the International Center for AI (ICAI), is an entrepreneur and technology leader with over three decades of experience working with global enterprises across finance, healthcare, Government, pharmaceutical, and telecommunications. A former partner at KPMG, he has also held senior roles at IBM and PwC Consulting, leading large-scale digital and AI initiatives that reshape how organizations build and deploy technology. His work focuses on aligning strategy, systems, and people to unlock value from emerging technologies, emphasizing disciplined, human-centered modernization. With expertise in solution development, advanced enterprise architecture, and data/AI, he brings a pragmatic, people-first lens to AI-driven transformation.
