AI Impact Lead at Konecranes

Hyvinkää, Uusimaa, Finland -

Full Time

Start Date

Immediate

Expiry Date

19 Jul, 26

Salary

0.0

Posted On

20 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI strategy, Business value realization, Project management, Rapid experimentation, Stakeholder management, Data literacy, Change management, Low-code development, No-code development, GenAI, Machine learning, Process redesign, Cross-functional leadership, Value framing, Feasibility analysis, Technical communication

Industry

Machinery Manufacturing

Description

Company Description At Konecranes, we believe that great customer experience is built on the people behind the Konecranes name. Everything we do, we do with passion and drive. We believe diversity drives business success and is the foundation for our growth. We welcome different backgrounds and skills that enrich our community and we promote a place where we can ALL be ourselves. This is what makes Konecranes a unique place to work. Job Description AI Enablement team in our Digital Center of Excellence is now on the lookout for an AI Impact Lead. In this role, your mission is to turn AI from scattered experiments into real, repeatable business value. You will scout for high–impact opportunities, validate them fast, and co-drive delivery from concept to production – while making sure the organization learns and scales as it goes. This is a hands-on strategic role: you will work across business units, IT, data, AI and corporate functions to connect AI capabilities to actual process changes and measurable outcomes. Your key responsibilities Scout, test, and shape high-impact AI initiatives Identify and refine opportunities across functions and business units Translate business opportunities into executable AI initiatives: scope, value hypothesis, success metrics, feasibility constraints, and ownership Run quick tests, lightweight pilots, and early proofs of value to validate opportunities – using low/no-code and modern vibe coding tools such as MS 365 Copilot, Claude Code etc. where appropriate Define what a “serious MVP” means: what must be proven, integrated, secured, and measurable to justify scaling Co-drive delivery: From concepts to MVP and production readiness Co-drive selected use case deliveries with business owners, IT, data/AI teams, vendors/partners, and other relevant stakeholders Remove blockers (data readiness, integration decisions, operating model gaps, unclear ownership, approval friction) Drive AI adoption and practical change Support AI adoption at individual, team, and process level across the organization. Help teams translate AI into everyday ways of working, from personal productivity and team collaboration to selected workflow and process improvements. Facilitate practical change so AI becomes part of daily operations, while also supporting future-state workflow and process redesign where relevant Help stakeholders validate whether the intended adoption, behavior change, and value are actually being realized in practice Make AI repeatable across the organization Build/iterate lightweight toolkits, templates and trainings (e.g. training concepts, feasibility checklist, value framing etc.) Capture/codify learnings and promote shared components and delivery patterns across domains Provide pragmatic everyday AI advisory Offer guidance/training on everyday AI usage (genAI/agentic AI in knowledge work, and practical low/no-code automation/vibe coding) Help teams understand what can be explored quickly with lightweight prototypes and approved tools, and when stronger engineering/development or approval is needed Qualifications What you should bring You’re likely a strong fit if you communicate fluently in English and you recognize yourself in all or most of the following: AI + business delivery 5-10 years of experience in leading AI/ML initiatives from idea to MVP and/or production in complex, matrixed organizations; strong business framing (value, feasibility, ownership, system thinking) Rapid experimentation and piloting Comfortable with quick testing and piloting using low-code and/or modern AI vibe coding tools; able to learn fast and validate ideas before overcommitting Cross-functional communication and orchestration Strong communication and delivery skills; Workshop and decision facilitation, prioritization, scope control, dependency management. Comfortable with business, AI/SW teams, data, IT, security/legal/compliance, and vendors Practical AI understanding Solid literacy in AI (both genAI and traditional ML), assistants/agents and common pitfalls; able to reason build/buy/compose (you don’t need to be an AI engineer) Data understanding for AI Good understanding of the role of data in successful AI initiatives, including data availability, quality, structure, ownership, and readiness for different AI use cases. Able to assess why data matters, where weak data can limit outcomes, and when stronger data foundations are needed before scaling Pragmatic governance mindset Understanding that some AI experiments can move fast, while others require stronger guardrails, review, and production discipline Nice to have: Manufacturing/industrial context, AI intake funnels, AI trainings for various target groups, evidence of measurable business outcomes (e.g. cycle time, cost-to-serve, quality, OEE, service improvements), fluent Finnish. Additional Information Why join us? Innovative Environment: Be part of a forward-thinking company that embraces technological transformation and the digitalization of industry. Global Impact: Contribute to projects that have a significant impact on our global operations and customer experiences. Professional Growth: Opportunities for continuous learning and development in the rapidly evolving field of AI. Collaborative Culture: Work in a dynamic and inclusive environment that values teamwork and innovation. Interested? If you want to help turn AI into tangible business value through smart experimentation, strong collaboration, and practical adoption – we’d love to hear from you. Please submit your application in English via Konecranes careers page*, including your resume and a cover letter detailing: Examples of larger AI projects/initiatives you have led; what was “shipped”, and what were the measured outcomes Examples of what kind of AI experiments/piloting you have conducted personally & which tools you have used for that. Describe how you drove adoption of an AI solution (individual/team/process). What changed in day-to-day work, how did you support behaviour change, and how did you verify adoption and realized value in practice? We will be reviewing applications on an ongoing basis and the position will be filled once we find the right match. *Due to GDPR regulations, we cannot accept applications via email. For additional information about the role, please contact Niina Hagman, Director AI Enablement via email: niina.hagman[ at ]konecranes.com. For any recruitment process-related questions, please reach out to Marion Soon, Global Talent Acquisition Specialist, via email: marion.soon[ at ]konecranes.com Konecranes moves what matters. We are a global leader in material handling solutions, serving a broad range of customers across multiple industries. We consistently set the industry benchmark, from everyday improvements to the breakthroughs at moments that matter most, because we know we can always find a safer, more productive and sustainable way. That's why, with 16 000+ professionals in over 50 countries, we are trusted every day to lift, handle and move what the world needs. Konecranes is committed to ensuring that all employees and job applicants are treated fairly in an environment which is free from any form of discrimination. Job Category: IT Employee Group: Employee Employment Type: Undefined term Full-time/part-time position: Full-time Workplace Type: Hybrid

Responsibilities

The AI Impact Lead will scout, validate, and scale high-impact AI initiatives to drive measurable business value across the organization. They will co-drive delivery from concept to production while facilitating practical change and AI adoption at the team and process levels.