It is working alongside technology and business leaders to decide how the organization’s technology gets built, operated and evolved: software factory model, architecture, sourcing, delivery and artificial intelligence governance. The output is not a theoretical diagnosis, but a model you can implement, with roles, processes, metrics and a roadmap.

When the software factory has to be redefined; when models need standardizing across countries or units; when artificial intelligence is coming in and governance and control are missing; when the sourcing or supplier model is not delivering the results expected; when responsibilities, metrics and follow-up mechanisms are missing; or when a technology transformation is not being taken up by the teams.

It is the set of principles, policies, roles, controls and metrics that define how an organization uses artificial intelligence: which use cases get approved, which data may be used, who answers for the results and how they are audited. Without governance, AI amplifies what works and what fails alike. 

It is the international standard for artificial intelligence management systems. It sets the requirements for an organization to manage AI responsibly: governance, risk management, data control, traceability and continuous improvement. Asesoftware supports this with training, assessment gap analysis and an implementation plan.

It is defined from real demand, the criticality of the systems and the internal capacity available. It covers team structure, roles, governance, the sourcingmodel, engineering processes, tooling and metrics for quality, productivity and service. The goal is an operation that is predictable and measurable.

It depends on three variables: how strategic the system is, how stable the demand is and what internal capacity exists. What is strategic and highly variable usually stays in-house; what is specialized or has variable demand is usually outsourced under clear service agreements. The decision is backed by metrics, not intuition.

An assessment focused on one area takes 4 to 8 weeks. The full design of an operating model with its roadmap usually takes 8 to 16 weeks, depending on the number of units, countries and systems involved. Support during implementation is scoped separately, in phases.

With indicators agreed from the start: cost per unit delivered, cycle time, defects in production, availability of critical systems, level of rework and uptake of the new practices by the teams. Without indicators there is no way to know whether the model worked.

Because we do not advise on engineering from theory alone: we have been running it for over three decades in banking, insurance, government and large enterprises, certified CMMI-DEV Level 5. The models we design are models we have had to execute ourselves.