SAP: AI Fundamental to Future of Digital Transformation

What are the top barriers facing IT leaders today?

Lack of in-house expertise, SAP says, is one of them.

According to SAP America's Chief AI Officer Jared Coyle, organisations should prioritise blending their in-house experts — who can identify the most suitable AI use cases — with external talent who bring valuable insights from AI implementations at other organisations. 

He says: “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.”

Why is in-house expertise important for digital transformation?

“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,” Jared adds in a LinkedIn post.

This is becoming increasingly important, McKinsey & Co emphasises in its global survey on the state of AI, as it finds organisations are actively working to keep up with AI, especially Gen AI.

It says this can be done by redesigning workflows, elevating governance and addressing new risks.

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

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

“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.”

This shines a spotlight on another teething problem IT leaders encounter: data.

In a blog, SAP emphasises that data quality is the true measurement of digital transformation.

“Despite the immense attention and investment paid to digital transformation, most businesses still miss the most critical part of their evolution: data,” the German multinational software leader 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 importance of data quality 

Advising off the back of Gartner studies that warn of “dirty data” — information that is inaccurate, incomplete and pervaded by duplicates — SAP says companies should tackle this challenge by implementing practices aligned with Gartner’s core principles of data quality management. 

This type of data runs the risk of impacting customer turnover, expense management, sales opportunities and back-office functions, SAP warns. 

SAP says: “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. 

“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.”

Turning barriers into opportunities

The message from industry experts is clear: success hinges not just on adopting the latest technologies, but on building the right foundations — by investing in both people and data.

For Jared, turning barriers into opportunities with AI requires a strategic, grounded approach focused on practical business value, trust and continuous improvement.

SAP itself is a prime example of the benefit of following these principles.

By embedding AI into core workflows and focusing on orchestration across complex tech stacks, SAP is enabling end-to-end automation and collaboration. 

It encourages leaders to focus on data quality, modernise legacy systems and create a culture of exploration and adaptability — transforming obstacles into opportunities for efficiency, innovation and growth.

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