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Why Standardization Is the Foundation for Successful AI
Historically, custom solutions can slow down innovation. By staying closer to the standard and shifting flexibility to the agent layer, organizations can benefit from AI innovation more quickly.
For years, custom development was often the logical choice. When off-the-shelf software didn’t fully meet an organization’s needs, the application was customized. This resulted in software that aligned with the organization’s way of working.
This approached worked for many organizations over many years. But AI is changing the rules of the game.
The Hidden Downside of Customization
Customization makes processes adaptable, but it also creates dependency. Every modification must be maintained. New platform functionality must be tested. Innovations cannot be quickly or easily adopted. For years, this was acceptable because the innovation cycle for enterprise software was relatively slow.
Today, things are different. New AI functionality is emerging at a rapid pace. Organizations that rely heavily on customization are increasingly finding that they struggle to adopt these innovations quickly, if at all.
A Different Kind of Flexibility
An interesting development is that AI agents offer a new alternative. Where previously organizations relied on custom development to support user processes, agents are increasingly able to provide the same flexibility without customizing the underlying application.
Flexibility is shifting away from the application layer to the agent layer. This has major implications for how organizations view digitization and modernization.
In the past, organizations tried to adapt their processes to software. Then they began adapting software to processes. Now a third model is emerging: the software remains as standard as possible, while AI handles the translation to the user.
From Customization to Orchestration
A second advantage is that organizations become less dependent on traditional system integrations. Historically, a great deal of custom development was required to enable applications to communicate with one another. Every process change led to new adjustments in interfaces, workflows, or integrations.
Agentic AI introduces an alternative approach. Agents can function as an intelligent orchestration layer on top of existing systems. This allows organizations to combine their Microsoft platform with industry-specific solutions or applications from other vendors without having to develop new integrations for every change.
This not only makes organizations more agile; it also reduces the technical complexity of future innovations.
Real-world example: Returning to standards in service delivery
This shift becomes even more apparent when looking at how a real-world service organization has approached it.
Over the years, the organization had built up a highly customized business environment. The solution worked well, but every new innovation required extra effort. New features or capabilities could only be applied in a limited way, because of the heavy customizations.
While modernizing their landscape, the organization took a new strategic approach. Internal business processes were redesigned to align to software standards. Then, AI solutions were deployed to support users with task execution, information provision, and process automation.
In the end, the organization didn't lose any of the flexibility they had from their highly customized legacy environment. On the contrary, the organization is now better able to adopt new functionality and implement changes quickly. Flexibility is no longer tied to custom code, and instead is provided by the combination of an agentic layer and a standardized foundation.
Data Quality Remains Crucial
Standardization alone is not enough. AI agents rely on data to make decisions, execute processes, and provide recommendations. When data is incomplete, out of date, or inconsistent, the quality of the output also suffers.
That is why data quality is an essential prerequisite for any AI strategy. Not because organizations need perfect data before they can get started, but because reliable AI starts with reliable information. This requires clear agreements on data definitions, ownership, governance, and process design.
The New Role of Enterprise Software
The discussion about AI often centers on technology. But for organizations that want to deploy AI at scale, it is primarily a strategic question: how future-proof is our application landscape?
Those who stay close to the standard can benefit more quickly from innovations in the platform. Those who have their data in order can deploy more reliable AI. And those who shift flexibility to the agent layer can adapt more quickly without having to constantly overhaul their core systems.
The question, therefore, is no longer how much customization is possible. The question is how much innovation you’re missing out on if you continue to cling to the status quo. That is precisely where Agentic AI will deliver its greatest value in the coming years.
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