How modern requirement engineering drives better business outcomes.
In this blog, Sirpa Korhonen together with Dr. Edmary Altamiranda and Tania Savitri from Aker BP ASA reveal how forward-thinking organizations are unlocking greater results, using governance not as a constraint, but as a strategic catalyst for clarity and control. Discover how you can transform governance into your organization’s secret weapon for thriving in the era of data and AI.
The nature of governance has fundamentally transformed with digitalization and the rise of AI. Today, governance is no longer a static, after-the-fact process. It now starts from physical infrastructure and is embedded into planning and coding, spreading seamlessly across the organization in real time. Success now depends on clear and rigorous governance and business requirements. Establishing this from the outset is essential. This is far more than a procedural formality, it serves as a decisive benchmark for board members and business leaders, representing a core responsibility that must be owned directly and cannot be delegated.
“Governance is a strategic foundation for requirements.”
Real-world cases reveal that gaps between pilot and production requirements can quickly lead to losing control over both processes and outcomes, highlighting the need for governance that is proactive, integrated, and strategic.
Modern governance is no longer just about compliance or documentation. It embeds requirements development and management directly into the organization’s operational and technical fabric.
Business, IT, and other requirements are the “rules of the game”. They define what needs to be achieved, how, and under which constraints. Governance ensures these requirements are clear, traceable, and enforced throughout the lifecycle of data and AI initiatives.
In other words, governance and data quality are no longer background tasks. They drive real business impact, powered by AI. Modern governance is proactive, fast, and built for trust, not just for ticking compliance boxes. Yet, many organizations still rely on outdated, manual processes. As result they miss out on real-time oversight and expose themselves to hidden risks.
To fully realize the potential of AI at scale, governance must evolve. Policies on paper are not enough. Systems need to monitor data and AI in real time, especially as AI agents move beyond observation to action. Critical safeguards, like security and compliance with laws such as the EU AI Act must be built in from the start to ensure trust throughout deployment.
“Governance becomes a strategic asset not a bottleneck.”
Resilience and trust should be designed in. By integrating requirement development and management directly into data architecture, organizations can set clear rules and quality standards before deployment. This ensures that governance becomes a strategic asset rather than a bottleneck.
Business impact begins when stakeholders get hands-on with governance, moving beyond documents to dynamic requirement engineering. By investing in requirement development and management set-up, organizations gain control over complex dependencies and keep pace with rapid AI-driven change.
Modern frameworks turn requirements from static paperwork into structured, living model-driven approaches. By formalizing requirements using standard modelling language and frameworks (such as SysML and UAF), organizations strengthen alignment between strategy and execution. This allows operational strategies, functional requirements, and performance standards to link directly to design and verification activities. This shift boosts clarity, traceability, and integration, linking business goals and security directly to design and verification. The result? Less ambiguity, faster implementation, better interoperability, and technical deliverables that align with strategy.
Strong data standards and robust metadata infrastructure reduce AI hallucinations and put you in charge of your contextualised business outcomes. Embedding data quality and governance from the ground, from sensor systems to the boardroom, creates trust and resilience.
Bottom line: When your organization takes full ownership of business requirements, signoffs, and lifecycle management, ensuring these responsibilities are managed seamlessly from start to finish rather than delegated or fragmented across silos, you unlock unprecedented value and competitive advantage in the data and AI era.
Misalignment between pilots and production environment can create hidden risks and inefficiencies. Skipping dependency checks or robust reviews, like the “four-eyes principle”, may lead to costly oversights.
A real-life example illustrates this: A project that had built an automation tool rushed into execution without mature requirements, clear roles, or a solid master data foundation, which led to misaligned expectations, costly workarounds, and ultimately increased long-term expenses. This is typical in POCs and pilots, where pressure to deliver quick wins leads teams to avoid delays or the additional costs of building foundational components. Without solid governance and foundational elements in place, quick wins can turn into long-term technical debt.
The successful solution and impact? Shift from static documents to dynamic, traceable models that support interoperability and lifecycle assurance. For instance, by adopting Model-Based Systems Engineering (MBSE) approach and integrating requirement development, management, and visualization tools, organizations strengthen requirement traceability. This also enables digital twin capabilities such as simulation and orchestration, laying the groundwork for AI-assisted modeling. This approach reduces reliance on outdated processes and ensures scalable, resilient outcomes.
The current political climate and new regulations are driving greater data sharing. The EU AI Act, for example, sets strict requirements for agent-based systems in high-risk areas like credit scoring. Organizations must now trace, audit, and justify automated decisions, not only to regulators, but also to impacted individuals and stakeholders.
Concrete actions for board members and business leaders:
In today’s data and AI-driven landscape, governance is no longer a passive safeguard. It’s a dynamic engine that unites business, IT, and operational requirements, transforming them into measurable outcomes. Modern governance is proactive, real-time, and purpose-built to drive business value, not just to satisfy regulatory checklists.
Governments worldwide are shifting from voluntary compliance to mandatory cyber requirements. As organizations scale AI, data governance and security must be foundational. With cyber threats escalating and quantum computing on the horizon, treat cyber risk as business risk.
But here’s the opportunity: governance isn’t a barrier; it’s your strategic advantage. When you embed governance and security from the ground up, you empower your organization to innovate confidently, adapt swiftly, and build trust with customers and stakeholders. Make securing organizational data a top priority.
So, the next time you hear “governance,” don’t freeze, lead the way. Embrace governance as the catalyst for sustainable growth, resilience, and competitive edge in the era of intelligent business.
Sirpa is dedicated to creating business value for customers and enabling growth through data from a strategic perspective. She has more than twenty years of experience in banking and finance in various roles within investment banking (M&A), corporate and industry analysis, rating and large data sourcing and platform integration projects.