Outpacing the AI hype: What top enterprises are doing differently

Enterprises we meet with face numerous challenges, including fragmented technology environments, deeply entrenched data silos, legacy system constraints, and often insufficient governance structures. Each of these factors represents a significant obstacle, complicating efforts to fully realize the benefits of AI.

Organizations frequently encounter difficulties in integrating multiple AI technologies into a cohesive architecture. This complexity is exacerbated by a proliferation of vendor solutions, each with different standards and protocols, making interoperability a substantial challenge.  

Interoperability—the ability of different systems and AI agents to communicate seamlessly—is often underestimated despite its critical importance. Enterprises need cohesive, interconnected systems capable of facilitating real-time communication and collaboration across disparate technology stacks.

Data silos pose another formidable barrier to AI maturity. Historically, organizations have accumulated data in isolated repositories, with limited integration or accessibility across business units. These data silos are frequently reinforced rather than resolved by system of record transitions to cloud-based software as a service (SaaS) solutions. Consequently, valuable insights remain fragmented, reducing the potential effectiveness of AI systems. Enterprises must prioritize initiatives aimed at breaking down these silos to ensure a unified, actionable data view. 

A significant challenge identified by organizations exploring AI adoption is achieving tangible, measurable outcomes. Despite enthusiasm for AI’s potential, executives frequently express frustration with AI initiatives that fail to deliver clear, quantifiable results. This issue often arises from inadequate planning, ambiguous objectives, and insufficient alignment between AI deployments and overarching business goals. Enterprises need to clearly define measurable success metrics at the outset of any AI initiative, ensuring consistent alignment between AI efforts and strategic organizational objectives.

Further compounding these challenges is the growing prominence of agentic AI—autonomous AI agents capable of performing sophisticated tasks independently. The agentic AI market is experiencing rapid growth, underscoring its importance and potential impact on enterprise operations. 

According to recent market forecasts, the global agentic AI market is projected to grow dramatically, from $5.1 billion in 2024 to over $41 billion by 2030, reflecting a robust compound annual growth rate of 44.8%. This exponential growth highlights the increasing reliance on AI agents to drive efficiencies, automate complex processes, and support strategic decision-making.

To effectively navigate these challenges and accelerate their AI maturity, enterprises must adopt a strategic, structured approach. This approach involves several key considerations:

1. Establish robust AI governance: Centralized governance frameworks are crucial to standardizing practices, ensuring ethical compliance, and aligning AI initiatives with strategic business objectives. Effective governance clearly defines roles, accountability structures, and consistent performance metrics to sustain ongoing AI maturity and success.

2. Eliminate data silos: Enterprises must prioritize initiatives that consolidate fragmented data into integrated, accessible repositories. This unified view facilitates more informed decision-making, enhances AI-driven insights, and ensures real-time responsiveness to changing business conditions.

3. Enhance interoperability: Ensuring interoperability among diverse AI technologies and existing systems is critical. Enterprises should select technologies that easily integrate with their current ecosystem, enabling flexibility in adopting future innovations without significant re-engineering.

4. Foster an AI-ready culture: Organizations must actively cultivate a culture that supports AI readiness. This involves investing significantly in employee training to ensure a comprehensive understanding of AI capabilities, limitations, and ethical considerations. A workforce well equipped to engage with and leverage AI innovations will drive sustainable business transformation.

5. Focus on measurable outcomes: Clearly defining measurable success criteria for AI initiatives is essential. Continuous monitoring, assessment, and iterative adjustments ensure that AI deployments remain aligned with organizational goals and deliver meaningful business value.

While addressing these challenges may seem daunting, businesses equipped with the right strategic frameworks and a clear vision can successfully leverage AI to drive substantial competitive advantages. Companies should approach AI adoption not merely as a technological initiative, but as a strategic imperative that reshapes their organizational culture, governance structures, and operational workflows.

As enterprises deepen their understanding of AI maturity, they must remain agile and responsive to ongoing technological advancements. By confronting the structural and cultural barriers to AI adoption, businesses can unlock AI’s transformative potential, driving innovation and creating enduring competitive advantage. This strategic approach positions organizations not only to adapt to the rapidly evolving technology landscape, but also to proactively shape their future through intelligent, data-driven decisions.

In this context, it’s little surprise that two-thirds of AI Pacesetters identified in our research employ a platform approach with built-in AI capabilities across the enterprise, compared with less than half of less-mature organizations. That’s because comprehensive platforms, such as ServiceNow’s, provide robust solutions capable of addressing these critical challenges. ServiceNow’s integrated approach helps enterprises achieve greater visibility, streamline governance, and enhance interoperability across their technology ecosystems, ultimately empowering them to harness AI’s full potential effectively and efficiently.

However, achieving true AI maturity will ultimately depend on each enterprise’s commitment to addressing the foundational challenges outlined above, ensuring sustainable and impactful AI-driven transformation.