In historically siloed IT departments, the impact of the enterprise architect (EA) was often diluted. Though intended to bolster business optimization initiatives, EAs lacked visibility and influence at the board level, limiting their ability to provide visibility of data, people, processes, and technology to those tasked with enterprise-wide decision-making.
More than in the past, IT investments are now understood by business leaders as key drivers of competitive value for organizations across industries, “but the ones that could put that in a real scenario were not talking to each other,” said Guillem Oña Vizcaino, enterprise architecture expert advisor at SAP, who spoke at the Next Generation SAP Enterprise Architect Learning Forum 2025.
Discussing how enterprise architecture can bring a true competitive advantage to the businesses of the future, Viscaino noted that investment in IT infrastructure and technology overall continues to grow nearly 10% year over year. Taking enterprise-grade technology to the next level and into the future will increasingly become a business differentiator.
Vizcaino argued that greater business value will come from organizations with leaders who think like enterprise architects, seeing their company’s AI strategy, IT assets, and data as areas they can optimize.
Executive Decisions Will Fall into Enterprise Architecture Domain
Decisions around digital transformation can have a big impact on an organization’s overall success — in both positive and negative directions.
Vizcaino noted as an example of this the BBC’s digital media initiative, which launched around 2007. Its goal was to digitize its archive and all production processes in order to better manage its existing assets and provide stronger customer service. This seemed to make sense from a business standpoint, but, by 2012, the project was scrapped. Competing priorities, misaligned teams, and mismanaged governance ultimately led to the project’s downfall, costing millions and presenting legal risks.
Vizcaino also discussed The Co-Operative Bank, which had aimed to move to a more modern landscape, streamline banking operations, and enhance digital services. Management’s failure to create a structured framework from the start led this business astray, he recounted. Along with delays due to poor project governance, conflicting IT priorities, and integration issues causing disruptions to core banking solutions, the estimated spend of the failed project was nearly $500 million. The company had to seek external investors to stabilize operations. This amount of shortfall could be detrimental to many organizations, leading to market share loss or even bankruptcy.
“You can all think of other examples that you have lived, that you have seen,” Vizcaino told the audience. “The bottom line here is not having a proper alignment between the vision of the business and the enablers of that vision to become true, to become relevant, to become real, is really dangerous.”
Competitive Advantage Will Come from Enterprise Architecture
Enterprise architecture provides the structure and governance for effective digital transformation, laying the groundwork for innovation such as AI models.
Vizcaino said that SAP will soon have successfully established an AI copilot mode to provide expansive process automation, helping workers make decisions more quickly and effectively. Even as companies enhance automation in this way, leaders’ core responsibilities will largely remain the same: creating business, sustaining business, and creating competitive advantage. It’s how they go about doing this in the age of AI that will look a bit different.
Many different types of assets and techniques optimized by data and AI — as enabled by enterprise architecture — will become more valuable moving forward. Vizcaino listed the following ways that enterprise architecture can create value:
Complex decision-making in ambiguous, high-stakes scenarios
Orchestrating AI investments
Innovation: breaking conventional thinking
Ethical AI governance and human oversight
Ensuring fairness, transparency, and accountability in AI
Managing human-AI workforce integration
Disinformation security and fraud management
Innovation Will Come from Orchestrating AI Investments
While leveraging AI to automate processes is a key area of current innovation, future innovation will involve optimizing the ways in which those AI investments are orchestrated.
Enterprise architecture is not a skill, but rather a discipline that should be infused across all departments of an organization, Vizcaino added. The silos of the past should be avoided in the ways that organizations restructure their operations; instead, inculcating an enterprise-architecture mindset within all business units can serve to bring stakeholders from across an organization together in service of shared technology goals.
With stakeholders aligned, they’ll be able to better understand where to integrate AI into various aspects of the business. As AI becomes more readily available, the race is on to find business applications for this emerging capability and achieve a competitive advantage.
Data Will Be the Most Valuable Asset
As companies seek to enable AI, ensuring the quality of enterprise data through data cleansing, validation, and governance efforts will only become more important.
Similarly, semantics are important; it’s through understanding data objects, structures, and relationships between objects that AI can actually drive process improvements. Creating these relationships also helps reduce the risk of hallucinations and other issues with AI outputs.
Also essential to optimizing the business’s use of AI is a widespread understanding of data strategy, how to organize data, where to put up guardrails, and where AI can be used in the first place. This level of understanding from every leader across an enterprise will bring value, but it can be led by those who strive to attain an enterprise-architecture mindset.
Ethical Oversight of AI
Of course, not every decision will be up to AI. There will have to be human leaders ensuring fairness and accountability in a competitive market, achieving goals while aligning to ethical standards.
Vizcaino said that AI cannot process everything that a business does. In hiring, for example, AI can look at a candidate’s CV to find any red flags, but it can’t intuit the soft skills of a person or why they might have taken a few years off from work. Cultural fit will have to be determined by hiring managers, too. Trying to leave everything up to AI simply won’t work.
Training the workers of the future — especially leaders — will also have to involve education around moral and ethical challenges that AI can present, he said. These leaders will learn to think, as opposed to memorize. In fact, Vizcaino said that being able to solve ethical challenges as they relate to AI in business will set the leaders of tomorrow apart from others, becoming a highly valued skill within enterprises embracing the future of innovation.