Summary
Key results
Board-ready AI vision and prioritized use case portfolio in line with business goals
Standardized framework to govern AI initiatives and measure their business value
Scaling AI value in a safety- and audit-driven engineering environment
Automotive software is developed under demanding conditions. Functional safety expectations, process maturity audits, traceability requirements, and strict information security controls shape how engineering teams work. In this environment, every new tool and every process change must fit a regulated, audit-ready delivery model.
The client had already become an active early adopter of AI. Teams across the software development lifecycle had explored assistants, code generation, requirements analysis, retrieval-based search, and internal platform components. This experience showed organizational readiness and technical capability, but it also created a new challenge: AI value was difficult to scale.
Initiatives were spread across teams and regions, with no consistent way to compare them, prioritize investment, or demonstrate return. Heavy governance and approval cycles slowed safe experimentation. Without a shared method to measure business impact, leadership could not easily decide which initiatives to expand, consolidate, or retire.
The ambition was clear: move from broad, fragmented experimentation to a focused, governed, and measurable AI program tied directly to business goals. To support that shift and secure executive sponsorship, the company engaged Sii Poland as its AI transformation partner, bringing a structured methodology and practical understanding of regulated software engineering.
A structured AI strategy engagement built on proven frameworks
Sii Poland designed and ran a structured AI strategy engagement based on Gartner’s AI maturity approach and the ISO 42001 standard. The work focused on 4 areas: strategic alignment and AI vision, data and governance foundations, AI portfolio prioritization and value realization, and change enablement and ethical AI adoption. The first stage assessed AI maturity and readiness, then translated the findings into a tailored transformation roadmap.
The scope of work included:
- Facilitating executive workshops to define an AI vision, set ambition levels, and agree on measurable success metrics
- Conducting an AI maturity and readiness assessment across data, technology, people, processes, and governance
- Reviewing existing AI initiatives and mapping them to the software development lifecycle to reveal reuse opportunities and gaps
- Running cross-functional discovery across key engineering domains, including requirements engineering, architecture reuse, testing and quality, DevOps, compliance, and secure AI enablement
- Building a catalogue of AI use cases and prioritizing them by strategic value, technical feasibility, and time to value
- Designing a value-tracking framework, a governance model, and a repeatable intake-and-gating process for new AI initiatives
To keep the engagement focused on business outcomes, Sii Poland applied 2 guiding models. The first defined 3 levels of AI ambition: augmenting individual work, automating processes, and redesigning how value is created. The second showed that most AI value comes from people and processes, not from models and tools alone. Together, these models helped leadership direct effort toward areas with the clearest measurable impact.
The engagement intentionally stopped before technical deployment. Its purpose was to give the client a confident, evidence-based basis for deciding the next steps in its AI transformation.
From scattered pilots to a governed, measurable AI program
The engagement gave leadership a single, business-aligned view of where AI should create value. Instead of managing disconnected pilots, the organization gained one strategic direction.
With a prioritized portfolio in place, the client can now direct AI investment by business impact and feasibility rather than by individual initiative. A standardized value-tracking and governance model also gave the organization a clearer way to measure return and scale only what demonstrably works.
The consolidated, reuse-oriented view of existing initiatives reduced duplicated effort across teams and regions. At the same time, the new intake-and-gating process shortens the path from idea to approved initiative without weakening compliance.
Together, these foundations prepare the client to accelerate audit-ready software delivery while maintaining control over risk, governance, and quality.
Key results
- Board-ready AI vision and ambition aligned with business goals
- Prioritized portfolio of high-value AI use cases across the software development lifecycle
- Standardized framework to measure AI value and return on investment
- Repeatable governance and intake-and-gating process for new AI initiatives
- Transformation roadmap and recommendations to guide the next phase of adoption
- Reduced duplication through a consolidated, reuse-oriented view of existing AI initiatives