SAP: Data Quality is Key to AI Success & Transformation

German software giant tells IT leaders to blend internal expertise with external talent as McKinsey research highlights implementation challenges

SAP has identified lack of in-house expertise as a primary barrier facing IT leaders implementing AI systems.

The company argues that organisations must combine internal knowledge with external talent to achieve successful digital transformation outcomes.

Jared Coyle, Chief AI Officer at SAP America, explains that organisations should prioritise blending their in-house experts with external talent – as internal experts can identify suitable AI use cases whilst external consultants bring insights from implementations at other organisations.

“The in-house knowledge is critical to make sure you integrate with existing systems and processes, and the external talent better helps you fully leverage newer AI capabilities to keep AI systems running smoothly,” Jared says.

McKinsey highlights C-suite commitment requirements

This mixed approach of internal and external talent becomes increasingly important as organisations work to keep pace with AI developments, particularly Gen AI systems that can create text, images and code.

Especially because these systems require integration with existing enterprise software and databases to deliver business value.

Research from McKinsey & Company supports SAP's position on implementation challenges through its global survey on AI adoption that finds organisations are actively redesigning workflows, elevating governance structures and addressing new risks associated with AI deployment.

Alexander Sukharevsky, Senior Partner and Global Co-Leader of QuantumBlack, McKinsey's AI division says: “The more we see organisations using AI, the more we recognise that it takes a top-down process to really move the needle.

“Effective AI implementation starts with a fully committed C-suite and, ideally, an engaged board.”

Many companies delegate AI implementation to IT or digital departments, but this approach frequently fails according to McKinsey's research – as it finds that successful AI programmes require executive sponsorship and board engagement rather than purely technical leadership.

“Many companies' instinct is to delegate implementation to the IT or digital department, but over and over again, this turns out to be a recipe for failure,” Alexander says.

SAP identifies data quality as transformation measurement

SAP has also identified data quality as the primary determinant of digital transformation success.

The company warns that without high-quality data, core technologies including AI, process automation and predictive analytics cannot deliver benefits to organisations.

“Despite the immense attention and investment paid to digital transformation, most businesses still miss the most critical part of their evolution: data,” SAP says. 

“It doesn't matter if AI, process automation, bots or predictive analytics is adopted. Without high-quality data, these core technologies can never truly benefit any company.”

The software company additionally references research from Gartner which warns of “dirty data” that is inaccurate, incomplete and contains duplicates. Such data can impact customer retention, expense management, sales opportunities and back-office operations.

Gartner’s principles for better data quality management 

Gartner has established five core principles for data quality management:

• Consistency
• Accuracy
• Validity
• Integrity
• Relevance

These principles provide frameworks for organisations to assess and improve their data management practices.

SAP additionally warns that improving data quality requires sustained effort and consistent maintenance – that organisations must develop focused approaches and maintain consistent practices to leverage data for competitive advantage and financial stability.

“Improving data quality to the point where any digital transformation gains a beneficial edge takes special effort to attain it and consistency to maintain it,” SAP says.

“With a focused mindset and healthy habits, companies can leverage their data to stay relevant and financially stable with room for future growth and new business models.”

Now, SAP encourages business leaders to focus on data quality improvements, modernise legacy systems and create organisational cultures that support exploration and adaptability. 

The company argues that such approaches transform implementation obstacles into opportunities for operational efficiency, innovation and business growth.

For Jared, turning barriers into opportunities with AI requires a strategic approach focused on business value, trust and continuous improvement: “Combining internal expertise with external AI talent is a powerful strategy to ensure seamless integration with legacy systems while unlocking the full value of emerging capabilities.”

Explore the latest edition of AI Magazine and be part of the conversation at our global conference series, Tech & AI LIVE. 

Discover all our upcoming events and secure your tickets today.

Also sign up to our free weekly newsletter for the latest insights and stories straight into your inbox.