While the potential impact of generative AI (genAI) is widely recognized, implementation challenges persist for organizations. According to Gartner research, approximately 30% of genAI projects stall, or remain stagnant, following development and proof-of-concept phases. Gartner found that the primary challenges are attributed to inadequate data quality and insufficient frameworks to enable seamless integration of AI.
Organizations without comprehensive data strategies that will support genAI now and in the future face significant operational risks, including resource inefficiencies, competitive positioning deterioration, and delayed innovation cycles. Successful genAI implementation requires IT leaders to establish robust data infrastructure that encompasses quality assurance mechanisms, governance protocols, and strategic alignment with organizational objectives.
Addressing Data Quality Assessment Complexities
Data quality is the foundation for effective genAI, and many organizations continue to run into data challenges. This is hindering their execution of strategic AI innovation, resulting in diminished return on investment and extended deployment timelines. For example, IT teams can discover inconsistent data entry protocols, disparate categorization standards across business units, and variations in the languages used to construct AI models.
Quality inconsistencies from any data source can fundamentally undermine AI system effectiveness. Therefore, without a proper assessment of data quality, IT leaders may face complexity when it comes to IT management at-scale. There are several considerations to weigh when evaluating the state of enterprise data:
Tapping Data Trapped in Legacy Systems:
For many organizations, critical enterprise data remains isolated within legacy infrastructure, which has not been built to run AI workflows. In fact, research shows that 40% of enterprise systems are beyond end of life or support. This means systems such as mainframes, proprietary databases, and discontinued platforms have the potential to cause data bottlenecks, making it more difficult for AI solutions to access information needed to produce consistent, accurate outcomes. IT leaders who address this will be positioned to spend more time on strategic initiatives, rather than remediating infrastructure challenges.
Allocating Adequate Resources to Evaluate Existing Systems:
While complete infrastructure overhaul may not be necessary for AI readiness, organizations must conduct a thorough evaluation of all systems contributing to AI data pipelines. This process is in-depth and can include the evaluation of many disparate systems, requiring companies to invest more resources than they may have anticipated. IT leaders who anticipate the resources required to complete this level of assessment will be better positioned to integrate AI efficiently and effectively.
Implementing Data Governance Frameworks for AI Operations
Once the data house is in order, organizations must establish comprehensive governance frameworks that support AI integration and keep teams operating within the proper guardrails. With these protocols in place, IT leaders have greater trust in the data that underpins AI systems, ensuring operational integrity and regulatory compliance.
To accomplish this, IT leaders – working closely with their business leadership – should consider ways to define ownership, label data for consistency, and implement risk mitigation tactics.
Cross-Functional Ownership Structures:
Identifying owners and encouraging accountability is an important first step in building an effective governance structure. Understanding the key stakeholders helps IT leaders eliminate organizational silos that could impede AI success. An example of how this can come to life would be developing specialized teams capable of AI model input validation, privacy and security risk assessment, compliance evaluation, and organizational AI adoption communication. Definition of roles and responsibilities ensures team members are involved and accountable for the seamless operation of AI.
Managing Data Categorization for AI Now and in the Future:
Governance structures should include comprehensive maintenance procedures for data labeling, integration, and storage. For example, a data requirement document that details data sources and formats and refreshes intervals is an effective way to ensure consistency in the process. These tactical elements are critical to support both current AI solutions and the potential scalability requirements of AI in the future.
Establishing Risk Management Protocols:
Undoubtedly, AI introduces new risks to organizations, including those related to privacy, cybersecurity, and regulatory compliance, and addressing those threats is increasingly complex. IT leaders must partner with stakeholders across the C-suite to ensure the proper protocols are in place to combat these risks. For example, organizations can provide training to ensure employees understand the AI safety principles fueling the use of the technology.
Aligning AI Strategy with Business Objectives
With a robust data foundation, IT and executive leadership can then align the AI strategy with their business objectives. While many organizations continue to deploy point-solutions to solve specific operational pain-points, it is critical that IT and business stakeholders identify and prioritize AI applications that deliver maximum organizational value to advance AI momentum and deliver ROI.
Leaders can do this by identifying quantitative success metrics that directly correlate data quality improvements and accessibility enhancements with measurable business outcomes. This step enables streamlined tracking of outcomes and allows teams to assess AI investment over time. By positioning data management as a strategic business enabler rather than purely technical infrastructure, organizations create value that extends beyond traditional IT operational boundaries.
GenAI’s promise depends on a disciplined data strategy – encompassing data quality, management, and governance. By focusing on targeted data improvements, modernizing infrastructure, and uniting business and IT leaders, organizations can maximize AI’s value and minimize project abandonment. The journey starts with a robust assessment and prioritization of data, the building of governance frameworks that drive decision-making, and an approach that aligns AI to the overarching business strategy.
