Executives see AI as a quick win, while practitioners know it’s a long road. So, who’s right? The growing disconnect between leadership and IT teams could be the difference between companies that thrive with AI and those that fall behind.
Having led digital transformation efforts for many years, I can say this kind of misalignment is nothing new. Executives often underestimate the complexity of new technology initiatives, while practitioners have a more grounded view of the challenges — though they may not always see the big-picture goals.
What’s different now is the scale and impact of the consequences arising from this growing disconnect between leadership and IT teams. As non-IT leaders take a bigger role in driving AI investments in 2025, the rapid shift to cross-departmental decision-making has proven messy. But given what’s at stake — with global players like DeepSeek driving competition — companies can’t afford to let this old disconnect linger. It needs to be fixed, fast.
So, who has it right when it comes to AI? Right now, the answer is no one.
Overcoming Common Fault Lines
Bridging the AI divide between leadership and IT requires intentional alignment and execution. And with 77% of digital leaders planning to ramp up AI investments in 2025, the pressure to overcome common AI fault lines is higher than ever.
To maximize AI innovation, organizations must align leadership decisions with frontline realities, invest in workforce upskilling and bring practitioners into AI strategy discussions from the start.
1. Emotions, structure and siloed mindsets
Even the best-intentioned digital initiatives lose traction when stakeholders disagree. It’s no surprise the biggest obstacles to digital transformation efforts are siloed mindsets, particularly in complex business environments.
For example, executives may believe AI funding alone is enough to drive change, leaving practitioners without clear expectations, tools or support to make good on those resources. This approach overlooks the more practical realities practitioners face, e.g., fragmented workflows, legacy dependencies and cross-team misalignment.
Organizational emotions surrounding AI also slow the adoption of new AI tools. We can tackle these challenges through both an organizational change management (OCM) and emotional change management (ECM) lens, making sure we address both the practical and human sides of change.
To break down silos, leaders must acknowledge fear and uncertainty and foster interdepartmental collaboration early during AI decision-making processes. Maintaining in-the-weeds oversight throughout iterative adoption and scale cycles ensures AI initiatives remain integrated internally and in direct engagement with customers.
Continuous conversation, feedback, design, refactoring and refinement help prevent siloed thinking from derailing AI-powered experiences. Without it, companies risk strategic drift and move further away from the factors that make AI successful: knowledge sharing and intersecting workflows.
2. Mismatched goals and metrics
Employees at different levels of the organization have different expectations for AI — especially leaders outside of IT. For example, leaders in marketing or finance may prioritize higher-level objectives tied to organizational ROI and growth, while IT practitioners measure success through operational improvements and tactical productivity gains.
Although those objectives naturally coalesce with the right executive leadership, many organizations struggle to align and integrate goals in a mutually compounding fashion.
This disconnect extends to confidence in investments, with 62% of C-suite leaders saying they’re confident digital transformation investments will deliver the expected ROI, compared to just 45% of line-level managers. Moreover, 42% of C-suite executives expect these transformation initiatives to deliver results within six months, yet only 19% of line-level managers share this expectation.
Differing goalposts inevitably lead to pressure, unrealistic deadlines and false starts. Executives may grow impatient with slow AI results, while IT teams may hesitate to experiment and accelerate groundwork. The problem lies in operating like two separate groups rather than a single, unified AI team.
When introduced early, KPIs give leaders and IT teams a shared framework for AI alignment. For example, practitioners can show leadership why AI-driven success takes time, phasing deliverables for increased visibility while still advancing bottom-line goals. Conversely, leaders outside of IT can champion AI needs and surface new, more diverse use cases that reinforce investment value.
3. Talent shortages and upskilling gaps
AI investments stall without proper training, resources and talent. Training employees is the No. 1 driver of digital transformation success. Yet, nine out of 10 organizations report a lack of the necessary talent to implement AI effectively.
Organizations that lack robust IT assets and staff struggle to turn AI investments into tangible results. That’s when frustration kicks in — leaders see no progress, and IT practitioners are left without the tools AI innovations require to thrive.
It’s like buying a car without wheels and expecting it to take you where you need to go. You can turn up the sound system on your favorite playlist and rev the engine all you want, but you’re still going nowhere.
Again, a proactive approach to talent management can prevent this disconnect from derailing AI success. By acknowledging lapses in organizational knowledge, communicating where those talent gaps exist, and responsibly distributing and enabling upskilling, leaders can help IT teams invest in the resources to build a flexible, AI-ready workforce.
From there, both groups can collaborate on a plan to ensure IT teams evolve and thrive in a fast-changing AI landscape. For IT practitioners and leaders, this means integrating feedback loops driven by user insights and real-time AI performance data. Shared ownership enables stakeholders to regularly improve and refine processes, optimize staffing and L&D, and replicate successes.
By tapping into a third-party technology partner with deep expertise in workforce transformation and talent development, companies can champion a cohesive roadmap to drive AI success – especially in scenarios where stakeholders disagree.
Alignment Turns AI Divides into Global AI Leadership
The race for AI leadership is reshaping industries. AI leaders will shape the future of innovation, efficiency and economic growth — but getting there means bringing practitioners in early and prioritizing workforce upskilling.
Most importantly, AI leadership will require executive decision-makers and IT teams to work together more effectively, with a shared vision for investment pressures and operational realities.
When it comes to AI, it doesn’t matter who’s right and who’s wrong. Going forward, what will matter is who’s ahead — and who can stay there.