The pressure is on: 85% of senior enterprise leaders believe they have less than 18 months to develop artificial intelligence (AI) capabilities before falling behind. This race has fueled a surge of new AI tools, experiments and hefty investments. But while organizations race ahead with deployment, one critical question lags behind: Do employees actually know how to use these tools in ways that drive measurable outcomes?

Currently, most enterprises treat employee skills development and AI deployments as separate efforts. IT focuses on tools, while human resources (HR) and learning and development (L&D) focus on training and professional development. Employees are left to bridge the gap on their own, often learning in isolation, applying AI inconsistently and generating uneven results.

The next step in AI transformation isn’t about adding more technology. It’s about aligning the teams rolling out AI with the teams responsible for helping employees build the skills to use it effectively. This alignment depends on real-time data about AI usage, shared measurement and actionable insights.

An AI-ready workforce cannot be developed without understanding employee behaviors. And this is exactly where IT and L&D must work as true partners to close the gap between deployment and real capability.

Why IT and L&D Teams Need Shared AI Adoption Data

Leaders across both IT and L&D must be able to answer these important questions:

1.     What AI tools are employees actually using?

2.     How proficiently are they using these tools?

3.     How do their behaviors translate into productivity and business impact?

For years, corporate learning has emphasized course delivery and completion metrics to support workforce development. While this approach established foundational skills, it often fell short in adapting to rapid technological shifts. L&D teams bring critical insights into how employees absorb new skills, engage with content and respond to change. They understand where learners struggle with AI adoption, whether due to confidence, comprehension or unclear application to workflows. However, in most cases, they lack visibility into the actual tools employees are using or how those tools integrate into daily operations.

IT, on the other hand, has visibility into tools and systems, but often lacks context for how employees use them and how skill gaps block ROI. Without behavioral context, it’s difficult to discern whether low engagement stems from technical limitations, workflow misalignment or insufficient training. Without that understanding, IT cannot determine whether low adoption means a tool is flawed, a workflow is unclear or employees simply do not know how to use it.

Bringing these perspectives together, L&D’s understanding of human capability and IT’s view into digital infrastructure are both essential. Only through shared insights can organizations accurately diagnose barriers, close skill gaps and translate AI investments into measurable business value.

AI Skills Gaps Are a Workforce Development Problem, Not a Tech Problem

When we begin to view employee interaction with AI as a continuously learned behavior, a new “growth mindset,” everything shifts.

Instead of single training events, organizations can understand and measure how employees build proficiency over time: where they struggle and excel, which use cases are worth scaling, where new use cases are emerging, which teams need more support, and where a lack of confidence is holding back AI adoption.

These insights allow L&D to target programs where they matter most and allow IT to understand where adoption challenges are rooted in workflow design versus skills development.

This is the foundation of an AI-enabled workforce: not knowledge of AI in the abstract, but real-world proficiency built and reinforced through continuous learning and measurement.

As AI blends into daily workflows, employees don’t just need to know what a tool does, but also when to trust it, when to override it, how to prompt it properly, how it fits into team processes and where its limitations are. These are not technical questions but ones that pertain to learning and development. And they require the same rigor we apply to any other form of organizational development. The companies most likely to succeed with AI will be the ones that treat skills development as a strategic capability.

Building an AI-Ready Workforce Requires a Cultural Shift

A workforce that is confident using AI looks very different from one that feels intimidated by it. Confidence drives experimentation, proficiency, adoption, innovation and ROI.

Teams that excel often share similar characteristics:

• They talk openly about what’s working and what’s not.
• They have managers who coach them in AI just as they do with other professional development skills.
• They have access to L&D programs that use real-time data.

In this scenario, IT and HR speak a similar language. AI becomes a partner, even a co-worker. Employees don’t fear losing their jobs because they understand that those who know how to use AI will only benefit from it.

However, if enterprises continue to deploy AI without aligning workforce development, a few things will happen: 1) The tools will outpace the skills, leaving people feeling overwhelmed and underprepared; 2) Workforce use of AI be uneven, with early adopters leaving others to fall behind and superstars may become burned out or lured to work for competitors; 3) benefits will flatten, and 4) investments will be questioned or canceled altogether.

This is clearly an organizational issue, not a technical one. Leaders who assume employees will “figure it out” will fall behind.

Practical Steps to Align IT and L&D for AI Success

To overcome siloed operations and strengthen the partnership between IT and L&D, leaders can take several proactive steps:

• Establish Regular Touchpoints: Host frequent meetings between IT and L&D teams to discuss ongoing projects, share insights and align objectives. These touchpoints can facilitate open communication and ensure both teams are on the same page regarding AI initiatives.

• Collaborate on Training Programs: Work together to design training programs that leverage IT’s technical expertise and L&D’s understanding of employee development needs. This collaboration creates comprehensive learning experiences that equip employees with the skills necessary to effectively use AI tools.

• Identify and Address Barriers: Recognize potential barriers to collaboration, such as differing priorities or communication styles. Encouraging cross-departmental training can also help team members appreciate each other’s roles and challenges.
• Create Joint Success Metrics: Develop shared goals to evaluate the success of AI initiatives. By jointly measuring outcomes, both IT and L&D can better understand the impact of their efforts and adjust strategies accordingly.

These collaborative practices lay the groundwork as organizations scale their AI capabilities for the future.

The Future of Enterprise AI Depends on Workforce Readiness

AI adoption and optimization are no longer only a CIO, CFO or CHRO priority. They are a strategic priority, and measurement connects these worlds.

To be successful in this new era of AI, organizations must leverage real-time data on how employees engage with AI to guide training, shape workflows and develop the next generation of skills. As a result, a deeper understanding of AI use in enterprises is critical and will become defining competitive advantages of the next decade for both organizations and professionals.