Executives see AI as a quick victory, and practitioners know it's a long way. So who is right? The growing disconnect between leadership and IT teams can be the difference between companies thriving with AI and those behind.
With years of leading digital transformation efforts, this kind of inconsistency is not new. Executives often underestimate the complexity of new technology initiatives, but practitioners have a more grounded view of the challenges, but they don't always look at the goals of larger paintings.
What's different now is the scale and impact of the outcomes that arise from this growing disconnect between leadership and IT teams. As non-IT leaders play a bigger role in driving AI investment in 2025, the rapid shift towards decision-making between departments has proven troubling. But given its danger along with global players like the Deepseek Driving Competition, businesses can't afford to leave this old cut. You need to fix it quickly.
So, who has it right when it comes to AI? At the moment, no one has an answer.
Overcoming common fault lines
Bridge the AI division between leadership and IT requires intentional alignment and implementation. and 77% of digital readers With plans to increase AI investment in 2025, the pressure to overcome the typical AI fault line is higher than ever.
To maximize AI innovation, organizations must coordinate leadership decisions with frontline reality, invest in high-class skills in the workforce, and take practitioners to AI strategy discussions from the start.
1. Thoughts of emotions, structures, and silence
If stakeholders don't agree, even the most intended digital initiatives lose traction. It's not surprising The biggest obstacle to digital transformation efforts This is a siloed way of thinking, especially in complex business environments.
For example, executives may think that AI funds alone are enough to drive change, leaving practitioners behind without leading clear expectations, tools or support to those resources. This approach overlooks the more practical reality that practitioners face.
The emotions of organizations surrounding AI will also delay adoption of new AI tools. These challenges can be addressed through both organizational change management (OCM) and emotional change management (ECM) lenses, addressing both the practical and human aspects of change.
To break down the silos, leaders must acknowledge fear and uncertainty and promote inter-departmental collaboration early in the AI decision-making process. By maintaining weed monitoring throughout the repetitive adoption and scale cycle, AI initiatives are guaranteed to be internally integrated and directly engaged with customers.
Continuous conversation, feedback, design, refactoring and refinement can help prevent siloed thinking from derailing the experiences that drive AI. Without it, businesses will risk strategic drifting and further alienate the factors that make AI successful: knowledge sharing and workflow intersecting.
2. Discretionary Goals and Metrics
Employees at different levels of the organization have different expectations for AI, particularly for other leaders. For example, marketing or finance leaders may prioritize high-level goals related to the organization's ROI and growth, while practitioners measure success through operational improvements and increased tactical productivity.
These objectives naturally combine with appropriate executive leadership, but many organizations struggle to coordinate and integrate goals in a complex way with each other.
This cut extends to trust in your investment. 62% of C-Suite Readers The digital transformation investment they say is confident will provide the expected ROI compared to just 45% of line-level managers. moreover, 42% of C-Suite executives We hope that these transformation initiatives will deliver results within six months, but only 19% of line-level managers who share this expectation are line-level managers.
Different goal posts inevitably lead to pressure, unrealistic deadlines and beginnings of falsehood. Executives can be impatient due to slow AI results, but teams can be hesitant to experiment and accelerate the basics. The problem lies in the fact that it operates like two separate groups, rather than a single unified AI team.
When introduced early, KPIs provide leaders and IT teams team up with a shared framework for AI alignment. For example, practitioners can demonstrate to leadership why AI-led success takes time. Conversely, other leaders can express new, more diverse use cases that defend the needs of AI and enhance investment value.
3. The gap between lack of talent and cover
AI investment stalls without the right training, resources and talent. Employee training is The first driver of successful digital transformation. However, nine out of ten organizations report that there is no shortage of the talent needed to effectively implement AI.
Organizations with a lack of robust IT assets and staff struggle to turn AI investments into tangible results. It's when frustration begins – leaders are not seeing progress, and IT practitioners are left without the tools they need to ensure AI innovation thrives.
It's like buying a car without wheels and hoping it will take you where you need to go. You can raise the sound system of your favorite playlist and rev all the engines you need, but it's not going anywhere yet.
Again, a proactive approach to talent management can prevent this disconnect from AI's success. By acknowledging the lapse of organizational knowledge, communicating where those talent gaps exist, responsibly distribute and enabling elevation, leaders can help IT teams invest in resources and prepare them for flexible AI.
From there, both groups can work together on the planning to ensure IT teams evolve and thrive in a rapidly 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 allows stakeholders to regularly improve and improve processes, optimize staffing and L&D, and replicate success.
By leveraging third-party technology partners with deep expertise in employee transformation and talent development, businesses can advocate for cohesive roadmap and drive AI success. Especially in scenarios where stakeholders disagree.
The alignment divides AI into global AI leadership
The AI leadership race is restructuring the industry. AI leaders shape the future of innovation, efficiency and economic growth, but once they get there, they can take practitioners early and prioritize high-skills in the workforce.
Most importantly, AI leadership requires executive decision makers and IT teams to work together more effectively by sharing a shared vision of investment pressures and operational realities.
When it comes to AI, it doesn't matter who's right or who's wrong. What's important in the future is who will be first and who will be able to stay there.
