AI Lead (Hybrid, Espoo)

The AI Lead will drive AI culture, adoption, and practical capability building across the client's Corporate Customer Magement (CCM) organization. The role focuses on helping business teams move from basic AI awareness and prompting skills towards more advanced AI-assisted and agent-based ways of working. The person in this role will be responsible for enabling teams through upskilling, coaching, communication, and hands-on support. The role will also own the GenAI use-case pipeline together with business teams, PMO, and IT, ensuring that ideas are discovered, structured, prioritized, resourced, and moved towards measurable business impact. A key part of the role is to define and track meaningful indicators for AI adoption and value realization, such as time saved, adoption rate, maturity development, and the business impact of AI solutions taken into production.

Start: after summer (August, flexible)
Duration: end of year, extension likely
Language: English, Finnish a plus
Location: Hybrid, Espoo 2-3 days per week
Allocation: 100%
Industry: B2B energy solutions

Key Responsibilities

• Drive AI culture and adoption: The AI Lead is responsible for building confidence, curiosity, and practical adoption of AI across the organization. This includes translating AI opportunities into language and examples that business teams can understand and apply in their daily work. The role will lead cultural adoption by creating clear communications, practical enablement activities, and targeted upskilling initiatives. The aim is not only to increase awareness of AI, but to help teams change how work is done by introducing AI-assisted and agentic workflows where they create measurable value.

• Design and run upskilling and enablement activities: The role will design and run practical learning pathways for different user groups and maturity levels. This includes training, coaching, hands-on workshops, demos, playbooks, guidance material, and peer-learning activities. The AI Lead will identify where AI maturity is low but business potential is high, and direct enablement efforts accordingly. The role is expected to make AI adoption practical, accessible, and relevant for business users, rather than theoretical or technology-led only.

• Own the GenAI use-case pipeline: The AI Lead will own the GenAI use-case pipeline end-to-end. This includes facilitating use-case discovery with business teams, structuring ideas into a manageable backlog, and supporting prioritization together with PMO, IT, and relevant business stakeholders. The role will help teams turn early ideas into clearly described use cases, including expected value, feasibility, dependencies, data and process considerations, and potential route to pilot or production.

• Coordinate pilots and path to production: The role will facilitate and coordinate AI pilots together with business teams, IT, architecture, PMO, and other relevant partners. The AI Lead will help ensure that experimentation is structured and connected to a realistic path towards production where appropriate. This includes clarifying the purpose of pilots, expected outcomes, success criteria, required resources, and decision points. The role will help avoid disconnected experimentation by ensuring that promising initiatives are aligned with business priorities, technology feasibility, and governance requirements.

• Define and measure AI adoption and impact: The AI Lead will define success metrics and measurement methods for AI adoption and impact. Relevant indicators may include adoption rate, active usage, time saved, number and quality of use cases, maturity development, productivity impact, and business value from solutions moved into production. The role will report these metrics forward and use insights to steer further enablement activities.

Role requirements:

• The successful candidate should have some experience in driving digital adoption, digital transformation, or technology-enabled change in a business environment. Experience in people upskilling, enablement, communication, or change management is highly valuable.

• Planning and complexity: The role requires the ability to plan and orchestrate AI adoption across a the organization using structured upskilling, communication, and stakeholder engagement. The work involves a high level of coordination across business teams, PMO, IT, architecture, and other enabling functions.

• The role operates in an area where technology capabilities, user expectations, and governance requirements are evolving quickly. The AI Lead must be able to navigate uncertainty, continuously scan AI capability trends (notably copilot), and translate relevant developments into practical enablement activities and business opportunities.

• Help the organization explore new AI-enabled ways of working while ensuring that use cases are prioritized, resourced, measured, and aligned with responsible AI principles and the operating model.

• Stakeholder management and collaboration: The role succeeds primarily through cross-functional influence. The AI Lead must be able to build buy-in across multiple business areas, align differing expectations, and coordinate joint delivery from discovery to piloting and potential production.

• Strong facilitation and communication skills are essential. The person must be comfortable working with both business and technology stakeholders and be able to explain AI opportunities, limitations, and next steps in a clear and practical way.

• Strong coaching mindset: The AI Lead should help teams shape early ideas into actionable use cases, challenge unclear assumptions, and support people in building confidence with AI tools and new ways of working.

• The consultant should understand how to discover, structure, and prioritize use cases, and how to work with business stakeholders to clarify value, feasibility, dependencies, and expected outcomes. Experience in facilitating pilots together with IT and business teams is important.

• The role requires some familiarity with AI adoption enablers and common barriers, including user adoption, data readiness, governance, security, process integration, and moving from experimentation to scalable use.

Interested? Please contact Lisa_Witted / lisa.sandstrom@witted.com asap.