Academic certifications

Abilities for professional performance.

The training of these skills is part of the curriculum of some training courses and of the training and assessment platform Skill Arena.

• AI applied to agile working: strategically integrate AI into the agile development cycle, from user stories to testing and code review. Design effective prompts, validate responses, and apply ethical best practices in privacy, data, and security.
• OKRs and product strategy: transform product vision into measurable results by prioritizing impact over deliverables, writing precise Key Results without prescribing solutions. Master predictive metrics, quarterly management, and stakeholder alignment to make evidence-based decisions and avoid the "feature factory".
• Discovery and validation with customers: learn to lead unbiased research processes through interviews and qualitative synthesis, identifying real problems (JTBD) and prioritizing risks. Design learning-oriented MVPs, define success metrics, and develop the judgment to strategically decide whether to iterate, pivot, or persevere.
• Scrum in teams with AI: empowers leaders to drive agile evolution in the age of Generative AI, shifting the focus from technical engineering to strategic product management. It teaches how to prioritize real value through Spec-Driven Development (SDD), transforming traditional roles into AI Product Managers while ensuring technical quality and ethical standards in high-performance workflows.
• SDD in agile teams: qualifies professionals to apply Spec-Driven Development (SDD) as a working method in agile teams using generative AI. Teaches how to produce and manage specs that serve as operational artifacts for orchestrating AI agents, following a structured four-phase flow with approval gates that ensure quality, architectural coherence, and traceability of the generated software.
• Harness Engineering: this credential certifies the ability to apply Harness Engineering as a working method for professionals who delegate tasks to AI agents. The credential holder knows how to measure and refine the guidelines and sensors that govern the model and act as operational controls for the agent—not merely as recommendations that the agent can ignore—ensuring the reliability of the deliverable, the verifiable rigor of the process, and human oversight of the final result. 
• Agile Leadership - Decision Architecture: learn to design the architecture through which an agile team's decisions flow: who decides, with what information, and at what speed. Classify decisions by reversibility, assign explicit decision rights (intent, DACI, decision records), and develop the judgment to integrate AI into the flow without giving up human oversight.
• Communication and conflict management:

  learn to diagnose communication failures in a team and to act at the appropriate level, understand the classification of conflict by type and stage of escalation as a central criterion for intervention, and learn to apply the consolidated frameworks —Watzlawick, Edmondson, Thomas-Kilmann, Fisher-Ury, Rosenberg, Glasl— with the necessary criteria to choose the appropriate tool for each situation.

• Agile product operating model: demonstrates an understanding of the product operating model: the set of decisions regarding structure, funding, governance, metrics, and culture through which an organization builds and operates products continuously, rather than managing them as a succession of projects.
• Agile Inception: Learn to start a product with alignment already settled before the first line of code: what problem is being solved, who decides and what counts as success. Turn vague intentions into verifiable agreements, set scope by exclusion and quantify risk and effort with the uncertainty kept in plain sight, and develop the judgement to bring AI into preparation and stress-testing without handing it the framing decisions.
• Design Thinking: Learn to tackle ill-defined problems starting from people's actual experience: what happens to them, what they have already tried and what would change if the problem went away. Turn observation into understanding, reframe the problem before solving it and alternate divergence and convergence until you reach prototypes that can be put in front of someone, and develop the judgement to bring AI into synthesis and the exploration of alternatives without replacing direct contact with whoever has the problem.
• User Stories: Demonstrates practical proficiency in working with user stories and other elements of the Product Backlog, from the discussion that defines a value-adding portion to the verification of the result. It certifies the ability to decide how much to write and how much to discuss, to assess the quality of the written material and the whole, to articulate the intent in verifiable rules and examples, to break things down without losing value, and to maintain the product context—even when artificial intelligence writes, reviews, or implements. This skill complements Product Owner training and is also aimed at Product Managers, business analysts, Scrum Masters, quality assurance professionals, and developers involved in refinement.