Understanding Enterprise AI Readiness: It’s All About Data

AI is becoming a critical component of business operations, but it’s not an easy road for many IT teams to navigate. The best way to understand these challenges is often to simply ask the practitioners tasked with bringing AI into the enterprise — precisely what NetApp did with its recently released “2024 Cloud Complexity Report,” which provides insights into how organizations are managing the challenges, showing that AI requires thinking differently about data.

As artificial intelligence becomes integral to modern enterprises, understanding how organizations manage their data has never been more critical. NetApp’s report, focusing on the complexities of data management and AI readiness, provides valuable insights for companies navigating their AI journey.

The Role of Data in AI Success

At its core, AI relies on data. The success of AI initiatives depends on how well organizations can prepare, manage, and leverage their data. NetApp’s report reveals that the challenges of deploying AI often stem from underlying data issues, particularly in hybrid and multi-cloud environments. Enterprises that approach AI readiness through the lens of data strategy are better equipped to unlock value and achieve their goals.

This shift in focus reflects a broader recognition that data strategy is not static. Companies increasingly view data as a strategic asset, requiring a framework that supports dynamic environments, from on-premises systems to cloud-based operations. Establishing a unified approach to data management is emerging as a foundational step for AI integration.

Challenges in Achieving AI Readiness

NetApp’s 2024 Data Complexity Report identifies several key challenges enterprises face in becoming AI-ready:

• Data silos remain a significant barrier, limiting the accessibility and flow of information across organizations.
• Security concerns have also grown, particularly as AI expands the volume and sensitivity of the data used. Organizations realize that integrating robust security and governance measures into their data strategies is critical to mitigating risks and ensuring compliance.
• Sustainability has surfaced as another consideration. As AI adoption accelerates, so too does its energy footprint. Enterprises are beginning to address these environmental concerns by exploring new technologies and frameworks that reduce the carbon impact of AI operations.

Steps Toward Effective AI Integration

Despite the challenges, there is promising progress among enterprises that prioritize their data strategies. Companies that adopt a unified approach to managing data—connecting information across diverse environments without consolidating it into a single location—are better positioned to support AI operations. This approach allows enterprises to align their data infrastructure with AI goals while maintaining flexibility and scalability.

Organizations are also investing in strategic frameworks that incorporate sustainability, security, and adaptability. By embedding these considerations into their planning, businesses can address immediate AI needs while preparing for future demands.

Analyst’s Take: A Moment for Strategic Reflection

It’s hard to disagree with the report’s conclusion that 2025 will be a pivotal year for AI in the enterprise. Organizations that prioritize unifying their data, addressing operational complexities, and integrating sustainability practices will be better positioned to unlock AI’s full potential and gain a competitive edge. Enterprises are at a critical juncture in their AI journeys.

While many organizations have made strides in optimizing their data environments, significant work remains. Businesses that take the time to assess their data strategies and invest in long-term solutions are better positioned to succeed as AI becomes more deeply embedded in enterprise operations.

To drive AI success, organizations must adopt a strategic framework for data management. This approach should include:

• Unified Data Infrastructure: Providing coherent access to data across diverse locations without relying on outdated monolithic data lakes.
• Strategic Investments: Focusing on tools and practices that enhance data governance, security, and sustainability.
• Proactive Planning: Future-proofing AI strategies by addressing challenges like cybersecurity threats and environmental impact.

Undertaking research that feeds reports like its 2024 Data Complexity Report provides the basis for how data infrastructure must evolve. NetApp takes these results to heart. Earlier this year, the company laid out its vision for an intelligent data infrastructure for AI that promises to address many of the needs identified in the report, moving beyond traditional storage towards an enterprise-ready AI data management engine.

NetApp leads its traditional competitors in this space with a more extensive and integrated vision for managing data throughout the AI lifecycle than we’ve publicly heard from companies like Dell Technologies or Lenovo – though I fully expect these companies will follow NetApp down this road. As the report indicates, enterprises will demand it.

The key takeaway for enterprises navigating this space is clear: understanding and addressing data complexity is essential to achieving AI readiness. AI requires thinking differently about data. By focusing on the foundational elements of data management, security, and sustainability, organizations can set the stage for meaningful AI integration and innovation, positioning themselves for long-term success.

Disclosure: Steve McDowell is an industry analyst, and NAND Research is an industry analyst firm, that engages in, or has engaged in, research, analysis and advisory services with many technology companies; the author has provided paid services to every company named in this article in the past and may again in the future. No company mentioned in this article contributed to its creation. Mr. McDowell does not hold any equity positions with any company mentioned.