Leveraging AI in business
The adoption of AI should be viewed not as a technology project but as the incremental development of organizational capability: building competence, operating models, knowledge, and technical solutions.
In practice, leveraging AI in organizations manifests at three levels and in three waves:
1.
Individuals use AI to enhance their work efficiency. AI accelerates information retrieval, serves as a thinking partner, and simplifies routine tasks, freeing experts to focus on what matters most.
2.
Organization-level processes are redesigned with AI. AI should not be applied to old processes as-is. A better question is: what would this process look like if it were built from scratch today with AI in mind? The best results emerge where volume is high and humans remain involved in evaluating AI recommendations (human in the loop).
3.
New types of business ecosystems emerge through AI. This is the most ambitious level. AI is no longer just an internal tool—it is an opportunity to build new ways of collaborating across the entire value chain. Organizations that understand their role in an AI-driven ecosystem early can influence the development of the entire operating environment.
We help companies at all these levels—from assessing AI maturity to building practices and redesigning processes.
Get in touch, and we will map out the right starting point for you.
Guiding AI development
Regulation and the accelerating pace of AI development require a more disciplined approach to development governance.
Regulation prohibits certain AI applications entirely. High-risk AI applications require stricter testing, documentation, transparency, and human oversight before deployment. Lower-risk applications are subject to fewer requirements.
AI governance must account for standalone AI applications, but also for AI functionalities embedded in off-the-shelf packaged systems and software platforms. It is precisely these embedded AI functionalities and the risks they pose that often remain in the shadows.
At the heart of AI risk management is a well-defined and implemented AI Governance model. AI is part of development, so its governance should be part of overall governance. If AI demand management—the process of channeling business-driven ideas and needs into the development pipeline—is properly established, it often corrects demand management as a whole, including non-AI-related needs.
Data quality and governance are the cornerstones of success
Data is the lifeblood of AI. If data does not exist or is of poor quality, AI cannot be built on top of it. Realizing an AI application idea often requires a data project first.
“Getting data in order” can feel like a daunting goal. How do you get started with concrete action when data is everywhere, expectations and requirements for data are numerous, and data ownership is often unclear? Does IT own the data because it resides in systems, or does the business own it because the business is responsible for what data is entered into systems?
Getting data in order is ultimately quite concrete work. You need to understand what data you have, clarify its governance, identify problem areas, and begin addressing issues in order of priority. It is also essential to address the root causes of problems rather than just fixing the end result. Often, solving problems requires launching IT projects.
We help companies with the full scope of data governance, starting from mapping the current state of data and clarifying governance models to prioritizing concrete corrective actions. We help resolve data ownership questions and direct development efforts where they deliver the most value for AI adoption. We bring both business and technical perspectives. Get in touch, and we will determine together where you should start.
Leveraging AI in development teams—beyond coding
Among development processes, coding is the furthest along in leveraging AI: AI-assisted tools have already enabled process-level changes.
Other development roles and processes are not yet as advanced in AI adoption and currently form a bottleneck at both the beginning and end of the development process. Service designers and requirements analysts, project and program managers, as well as architects and test managers can significantly and broadly enhance their work with AI tools. Process-level transformations comparable to those in coding have not yet arrived for other roles. However, it is already becoming clear what opportunities future technologies could bring to the entire development pipeline.
Development is moving fast. We help your team stay ahead. You will find support for AI-assisted requirements definition, architecture, quality assurance, and general project management at both individual and process levels.
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