Closing the AI Readiness Gap: Evolving People and Infrastructure Together

The AI revolution is well underway. Enterprises across industries are investing heavily in generative AI tools and platforms, eager to automate workflows, enhance productivity, and deliver new kinds of customer value.  

But unfortunately, investment doesn’t guarantee impact or outcomes.  

What’s becoming increasingly clear in my conversations with CIOs and COOs is that two forces are holding back enterprise AI adoption: legacy technology and legacy thinking. Companies are trying to implement next-generation systems on outdated foundations while relying on teams that haven’t been trained or empowered to use them effectively. Both problems must be solved together. 

Where does AI progress break down in the enterprise? 

Too many enterprises remain trapped between AI experimentation and execution, and legacy infrastructure is a major culprit. 

Most organizations run on core systems that were built for stability, not the speed or scale AI demands. These monolithic systems struggle to integrate with generative models that thrive on flexible, data-rich environments, leading to data silos and performance issues. In many transformation reviews I sit in, the first blockers we hit are not the models themselves, but the limitations of these underlying cores and the data they expose. 

More than 86% of enterprise systems still operate on outdated infrastructure and often lack the necessary APIs and processing power required for modern AI. As a result, organizations spend more on building middleware and connectors than on the AI tools themselves, slowing both system performance and employee productivity. 

But even when companies successfully upgrade their tech stack, progress frequently slows again, this time on the human side. Employees introduced to AI tools without sufficient training or context don’t understand how systems fit into their daily workflows. And frequently, leaders lack the necessary expertise to guide responsible adoption. 

In fact, 69% of CEOs believe generative AI will require most of their workforce to reskill. Yet only 38% of CHROs prioritize employee training in AI and data skills, exposing a critical disconnect between executive expectations and actual investment in talent. This gap is a clear example of legacy thinking: leaders expect AI-driven change, but the organization is still relying on outdated approaches to developing people. 

Modernizing infrastructure without upskilling people, or training people without improving systems, creates the same outcomes: stalled execution and limited progress. 

Four ways to bridge the gap  

Bridging the AI readiness gap demands a parallel investment in systems and skills. Organizations that modernize technology and talent together scale more confidently and sustain results. These four actions can help enterprises build real AI readiness. 

1. Modernize infrastructure to empower people 

Legacy systems don’t just slow innovation, they slow learning. Employees cannot experiment or scale new ideas when data is trapped in silos or processes rely on outdated platforms. Upgrading to modular, cloud-based architectures gives teams the freedom to test, integrate, and deploy AI responsibly. 

Consider a financial services organization where customer banking data resides in a legacy system and AI tools for credit decisions operate in another. Without integration, employees spend more time reconciling data than improving outcomes. Modernizing infrastructure bridges these gaps, providing teams with the access and confidence they need to utilize AI effectively. 

It also ensures that models are trained on clean, consistent data — the foundation for accurate and reliable outputs. When infrastructure is modern and accessible, talent development accelerates because employees can apply what they learn in real time. 

2. Set KPIs for infrastructure and upskilling milestones 

AI transformation does not happen overnight. Organizations need a tech roadmap that measures progress across both technology and talent. Setting KPIs for infrastructure upgrades ensures teams modernize in phases, starting small, validating success, and scaling deliberately.  

The same logic should guide workforce enablement. Upskilling goals must align with technical milestones, so employees advance alongside the systems they use.  

One practical way to do this is with paired KPIs. For example, if the organization plans to integrate a majority of customer-facing workflows into a new AI platform within six months, it can track the share of customer-facing employees certified to use that platform within the same period. 

In my view, leaders should track two curves together: how quickly the stack is modernizing and how quickly people are becoming confident users of it. If a company launches a new AI platform, its leaders should also track the percentage of employees trained to use it within a defined period, ensuring both progress and proficiency move in sync. Leadership’s role is to maintain that alignment and prioritize that capability grows in step with innovation. 

3. Embed learning into the flow of work 

Employees learn best through doing. Training should be hands-on, embedded in real workflows, and tied to specific business outcomes. The most successful implementations start small: automating one underwriting process, reimagining one customer service workflow, or enhancing one development cycle.  

For example, if a claims team experiments with an AI tool that summarizes case files or flags inconsistencies, they see immediate gains while learning how to apply the technology in context. 

These targeted pilots build familiarity and encourage experimentation, helping employees understand how AI can support, rather than replace, their expertise. When employees are trained and mentally stimulated, they stay engaged and accelerate transformation. 

4. Build internal ecosystems that reward curiosity and collaboration  

AI evolves too quickly for training to be a one-time event. Organizations need internal ecosystems that keep skills fresh and encourage experimentation.  

Some global enterprises are achieving this by rolling out AI certification programs that reach every employee, including leadership. Others are launching idea platforms that invite teams to propose and test new use cases, turning innovation into a collective effort. 

​​​​I’ve personally seen this approach deliver meaningful results in my role leading AI at Hexaware. Our employees shared more than 9,000 suggestions for AI use cases through our internal submission platform, Brainbox, and leadership chose to implement over 6,000 of them. This level of engagement showed me that empowering employees to experiment transforms innovation from a project into a habit. 

These approaches create a self-reinforcing cycle. As people learn and experiment, they generate new ideas, uncover new efficiencies, and build confidence in the technology. Over time, that culture of learning becomes a competitive differentiator and momentum for innovation. 

Build trust and talent into your AI strategy 

Organizations moving from AI experimentation to execution must rethink how people and infrastructure work together. 

When companies approach innovation as both a technological and human effort, the execution gap that slows AI’s potential begins to close. Modern platforms provide the flexibility to build and scale, and skilled, curious people turn that capability into real progress. 

Aligning architecture with talent and reshaping culture around learning creates an environment where people can understand, trust in, and influence the technology shaping the future. From my perspective, that alignment is what separates AI programs that stay on slides from those that change how work actually gets done.