Data Quality Is the Cornerstone of AI Success and Digital Transformation, SAP Says

SAP: Data Quality as the Cornerstone of AI Success and Digital Transformation

SAP argues that data quality sits at the heart of any meaningful AI-driven transformation. The German software giant contends that organizations must blend deep internal expertise with external AI talent to overcome implementation challenges and realize measurable business value. In a stance that aligns with recent McKinsey research on AI adoption, SAP emphasizes that success hinges on integrating strong governance, top-down leadership, and sustained data-quality efforts with the practical use of cutting-edge AI capabilities. The company also underscores that high-quality data is the critical fuel for AI, automation, and predictive analytics, without which even the most advanced technologies cannot deliver their promised benefits.

SAP’s stance on AI transformation: blending internal know-how with external AI talent

SAP identifies a notable gap in in-house expertise as a primary barrier faced by IT leaders when deploying AI systems. The core of SAP’s argument is simple: organizations must combine their existing domain knowledge and systems experience with the fresh capabilities and implementation insights that external AI specialists bring. Internal experts are indispensable for identifying relevant AI use cases and ensuring seamless integration with current processes and tech stacks. External consultants, meanwhile, bring a breadth of real-world experience across multiple organizations and industries, applying learnings from other AI deployments to help accelerate value realization.

Jared Coyle, who serves as Chief AI Officer for SAP America, emphasizes the synergy between internal and external talent. He explains that in-house experts are essential to help identify viable AI use cases and to ensure that AI initiatives align with legacy systems and operational realities. External talent then complements this foundation by providing broader implementation insights and the latest capabilities, enabling organizations to extract maximum value. The combined approach, according to Coyle, helps organizations integrate AI into existing environments more smoothly and maintain ongoing AI operations more effectively.

This blended talent model is framed as increasingly critical as enterprises strive to keep pace with rapid AI advancements, especially with the rise of Generative AI systems capable of producing text, images, and code. SAP notes that these capabilities must be thoughtfully integrated with existing enterprise software and data ecosystems to deliver tangible business outcomes. The message is clear: AI success is not solely about adopting new technologies; it requires careful alignment with current processes and a balanced mix of internal and external expertise.

The stance also reflects a broader industry reality highlighted by McKinsey, which points to the need for a top-down, organization-wide commitment to AI adoption. SAP uses this alignment to argue that effective AI deployment is not purely a technical project—it’s a strategic organizational transformation that demands executive engagement and cross-functional collaboration.

The role of executive sponsorship and governance in AI deployment

A central tenet of SAP’s perspective—and one echoed by McKinsey—is that AI initiatives must be governed from the top. The idea is that AI adoption cannot rely solely on IT departments or digital teams; it requires active involvement from the C-suite and, ideally, an engaged board. This top-down approach helps ensure that AI projects receive the necessary resources, strategic alignment, and accountability to move beyond pilot programs into scalable, enterprise-wide transformations.

McKinsey’s research reinforces this view. It suggests that many organizations delegate AI implementation responsibilities to IT or digital units, a pattern that often undermines long-term success. Instead, McKinsey’s findings indicate that sustainable AI programs typically require sustained executive sponsorship and board engagement in addition to technical leadership. Alexander Sukharevsky, a senior partner at McKinsey and Global Co-Leader of QuantumBlack, AI by McKinsey, has stated that the more organizations engage in AI, the more evident it becomes that a top-down process is essential to truly move the needle. The implication is that without high-level commitment, AI projects risk stagnation, misalignment with business goals, or underutilization of the technology’s potential.

SAP’s framing of governance also aligns with the broader risk management context surrounding AI in the enterprise. As AI systems become more integrated with critical business processes, governance mechanisms—covering strategy, risk, compliance, and performance measurement—become essential. The idea is that governance structures should ensure that AI investments are not isolated experiments but coordinated, accountable programs linked to strategic outcomes. This governance lens complements the emphasis on data quality, as governance bodies oversee data practices, data stewardship, and the ethical and compliant use of AI across the organization.

Data quality as the core determinant of transformation success

SAP makes a bold assertion: data quality is the primary determinant of digital transformation success. The company argues that without high-quality data, core technologies—such as AI, process automation, predictive analytics, and even robotic process automation—cannot deliver meaningful benefits. This prioritization of data quality is presented as a prerequisite for any digital initiative. Even if an organization adopts advanced AI tools, the absence of clean, reliable data will impede performance, reduce accuracy, and limit the potential gains from automation and analytics.

SAP cites Gartner research to bolster its position, highlighting the pernicious effects of “dirty data”—data that is inaccurate, incomplete, or contains duplicates. Such data problems can ripple through business operations, impacting customer retention, expense management, sales opportunities, and back-office efficiency. The implication is that data quality is not a back-office concern but a strategic asset that underpins customer experience, cost control, and growth trajectories.

In articulating its stance, SAP underscores that data quality is a prerequisite that determines whether AI, automation, or advanced analytics can actually deliver value. This perspective reframes data quality as a strategic investment rather than a technical housekeeping task. The broader lesson is that organizations must embed data quality into their transformation programs, ensuring that data governance, data cleansing, data stewardship, and data quality monitoring are integral components of any AI initiative.

To translate this into practice, SAP references Gartner’s data quality principles and framework (discussed in detail in the next section). The company emphasizes that improving data quality requires sustained, deliberate effort—consistent maintenance and disciplined practices. Transformations can stall or regress if data quality efforts lapse, so a focused program with ongoing governance and measurement becomes essential for sustaining competitive advantage and financial stability.

Gartner’s five principles for data quality management

Gartner’s framework for data quality management identifies five core principles that organizations can apply to assess and improve their data practices. These principles areConsistency, Accuracy, Validity, Integrity, and Relevance. Each principle offers a lens for evaluating data quality and guiding improvement initiatives across data sources, processes, and systems.

• Consistency: Data should be uniform across sources and over time, with standardized definitions, formats, and rules that prevent contradictory values from existing within the data landscape.
• Accuracy: Data must reflect the true state of the real world, free from errors and misrepresentations that could distort decision-making and automated processes.
• Validity: Data should conform to defined constraints and business rules, ensuring that values are permissible within the expected ranges, types, and relationships.
• Integrity: Data relationships and dependencies must be preserved, with referential integrity maintained across datasets, tables, and systems.
• Relevance: Data collected and stored should be appropriate for the business purpose, supporting the needs of processes, analytics, and AI models without unnecessary bloat.

SAP highlights these principles as practical tools that organizations can use to assess data-management maturity and identify priority areas for improvement. Implementing Gartner’s principles is presented as a continuous journey rather than a one-off project. SAP emphasizes that achieving durable data quality requires sustained effort, disciplined practices, and a culture that values data as a strategic asset. By adhering to these principles, organizations can create a foundation that supports scalable AI, reliable automation, and robust analytics capable of driving sustained competitive advantage.

The emphasis on data quality also ties into SAP’s broader message about modernization and cultural change. SAP argues that improving data quality must go hand in hand with modernizing legacy systems and building organizational cultures that support experimentation, learning, and adaptability. The readiness of an organization to explore new AI capabilities—while preserving data integrity and governance—determines how effectively it can turn AI investments into tangible outcomes.

Turning barriers into opportunities: a practical path for data quality and AI

With data quality and governance as central pillars, SAP outlines a practical pathway for organizations to convert AI implementation obstacles into improvements in operational efficiency, innovation, and growth. The core idea is to treat data quality as a strategic enabler of transformation, rather than a mere compliance or cleanup activity. By elevating data quality to a strategic capability, organizations can unlock sustained business value across multiple domains, including customer experience, supply chain, finance, and product development.

The recommended approach begins with a clear focus on data quality improvements integrated into the broader transformation program. This involves modernizing legacy systems where feasible and feasible, and fostering an organizational culture that supports disciplined data practices, continuous improvement, and experimentation. SAP argues that a disciplined, data-centric approach helps ensure that AI initiatives deliver their intended benefits, including more accurate insights, faster decision-making, and improved operational efficiency.

Jared Coyle frames the strategic takeaway as a call to blend internal expertise with external AI talent in a way that prioritizes business value, trust, and ongoing optimization. The combined team should emphasize seamless integration with legacy systems while enabling the exploration of emerging capabilities. In practice, this means aligning AI pilots with business processes, establishing governance that spans the enterprise, and maintaining an emphasis on data quality throughout the transformation journey.

This approach has broader implications for organizations across industries. It suggests that AI adoption is not simply a technology project but a strategic program that requires careful coordination, governance, and data stewardship. It also implies that the success of AI initiatives depends on the organization’s ability to maintain high data quality as data sources evolve, systems are upgraded, and new use cases are added. The result is a transformation that is not only technically sound but also resilient, scalable, and capable of delivering sustained value over time.

The blended talent approach in practice: internal depth meets external breadth

The practical implementation of SAP’s blended talent philosophy involves two complementary roles: internal experts who understand the business, processes, and legacy systems, and external AI specialists who bring cutting-edge techniques and broader industry experience. This combination helps ensure that AI initiatives are grounded in real business needs and are technically sound, scalable, and adaptable as technology and business requirements evolve.

Internal experts contribute critical domain knowledge, process maps, and contextual understanding of how data flows through the organization. They can help identify high-potential use cases, validate AI hypotheses, and ensure that AI outputs align with business objectives. They also play a pivotal role in integrating AI systems with existing IT infrastructure, data pipelines, and enterprise software—areas where domain expertise is essential for minimizing disruption and maximizing value.

External AI talent, by contrast, provides a breadth of experience across different environments and best practices from previous deployments. They bring advanced modeling techniques, tooling, and orchestration capabilities that can accelerate deployment, optimize AI performance, and help scale AI initiatives across functions and geographies. By applying lessons learned from other organizations, external talent helps reduce risk and shorten time-to-value, enabling enterprises to move beyond isolated pilot projects to enterprise-wide adoption.

The synergy created by this blended approach fosters more effective identification of AI use cases, better governance structures, and a more comprehensive strategy for data quality. It also supports a more robust risk management posture, as external advisors can introduce fresh perspectives on data governance, model risk management, and compliance considerations, while internal teams retain the critical business context.

Governance, risk management, and responsible AI considerations

As organizations expand their AI efforts, the governance and risk management dimensions become more prominent. The combination of top-down leadership, robust data governance, and ongoing risk assessment helps ensure that AI deployments remain aligned with strategic objectives and regulatory expectations. The emphasis on executive sponsorship and board engagement highlights the need for governance mechanisms that can oversee AI programs at scale, including decisions about investments, performance metrics, and risk tolerance.

The idea is to create a governance framework that not only monitors technical progress but also tracks strategic impact, data quality trends, and ethical considerations associated with AI use. A strong governance model supports accountability, ensuring that AI initiatives deliver on promised outcomes and that data assets are protected and managed responsibly. This approach reduces the likelihood of scope creep, misaligned priorities, and data quality degradation as AI programs mature.

In this context, the blended talent model gains additional significance. Governance responsibilities can be distributed across internal and external teams, with clear roles and responsibilities, decision rights, and collaboration protocols. Shared governance helps ensure that AI projects remain focused on business value while benefiting from external expertise and industry best practices. Ultimately, a well-designed governance framework creates an environment where AI can thrive without compromising data integrity, security, or compliance.

Practical implications for enterprises across industries

The SAP perspective is broadly applicable across sectors that rely on data-driven decision-making, automation, and AI to improve efficiency and competitiveness. The emphasis on data quality, combined with executive sponsorship and a blended talent approach, has several practical implications:

• Use-case prioritization: Organizations should identify high-impact use cases that can demonstrate rapid value while staying aligned with strategic objectives. Internal expertise helps prioritize use cases that fit existing processes and data structures, while external talent can bring broader perspectives on how similar problems have been solved elsewhere.

• Data quality program design: A comprehensive data quality program should be established, anchored in the Gartner principles—Consistency, Accuracy, Validity, Integrity, and Relevance. This program should include data profiling, cleansing, governance, metadata management, and ongoing quality monitoring, integrated into the broader transformation roadmap.

• Modernization and integration: Modernizing legacy systems and ensuring seamless integration with AI platforms and enterprise software are critical. This includes establishing robust data pipelines, scalable data architectures, and secure interfaces that can support AI workloads, analytics, and automation initiatives.

• Governance and risk controls: Establishing governance structures that involve senior leadership and the board can ensure sustained focus, accountability, and alignment with risk management objectives. This helps in addressing potential biases, model drift, and regulatory considerations associated with AI.

• Change management and culture: Building a culture that values data quality, experimentation, and continuous improvement is essential. This includes training, awareness campaigns, and incentives that reward data stewardship and cross-functional collaboration.

• Measuring impact: Clear metrics and KPIs that tie data quality improvements to business outcomes—such as improved customer retention, reduced operational costs, higher process automation yields, and faster time-to-insight—are necessary to demonstrate value and sustain investment.

The transformative potential: turning data quality into competitive advantage

A central takeaway from SAP’s position is that high-quality data is not merely a prerequisite for AI—it is a strategic differentiator. By prioritizing data quality, organizations can unlock more accurate analytics, more reliable automation, and more effective AI systems that translate into tangible business results. The combination of rigorous data quality practices, strong governance, and a blended talent model enables enterprises to realize the full value of AI while maintaining control over risk and compliance.

The broader implication is that organizations that invest in data quality as a strategic priority will be better positioned to adapt to evolving AI capabilities, scale AI initiatives across functions, and sustain long-term growth. As AI technologies continue to evolve, the data foundation behind them becomes increasingly critical. SAP’s framework suggests that companies that invest in data quality, governance, and cross-functional collaboration will be well-equipped to navigate the complexities and opportunities of AI-driven transformation.

Conclusion

In a landscape where AI technologies are rapidly transforming how businesses operate, SAP reinforces a clear, data-centered view of transformation. The company contends that data quality is the pivotal determinant of success, while advocating a blended talent approach that unites internal expertise with external AI specialists to navigate implementation challenges and accelerate value realization. This strategy is further supported by McKinsey’s emphasis on top-down governance, executive sponsorship, and board engagement as essential drivers of durable AI programs.

The practical path outlined by SAP emphasizes modernization of legacy systems, sustained data quality efforts, and the cultivation of organizational cultures that support exploration, adaptability, and disciplined data management. By adhering to Gartner’s five data quality principles—Consistency, Accuracy, Validity, Integrity, and Relevance—organizations can build a robust foundation for AI, automation, and predictive analytics that delivers measurable outcomes.

Ultimately, SAP presents a comprehensive framework for enterprise transformation that integrates strategic leadership, rigorous data governance, and the deliberate deployment of AI capabilities. The outcome is not merely the successful implementation of new technologies but the creation of a resilient, data-driven organization capable of sustained competitive advantage in the AI era.