Implementing AI transformation effectively: Strategy, organization and enablement

Artificial intelligence is changing not only individual tools and processes, but also strategy, the operating model, leadership, collaboration and the workforce. undconsorten supports organizations from their initial ambition and concrete use cases through to building the necessary capabilities and embedding AI for the long term.

AI transformation: Where are you today?

Many companies have launched their first AI applications. The next step will determine their impact: how can individual pilots be turned into a measurable contribution to performance, quality and effectiveness?

Typical starting points include:

• Initial tools and pilots are in place, but priorities and a vision for the future are missing.
• The AI agenda is driven by the business or IT. HR is brought in only once roles, skills or ways of working are already changing.
• HR is testing individual use cases but has not yet found a path to scale.
• The implications for the workforce, leadership, collaboration and employee co-determination have not been sufficiently clarified.
• The existing HR organization is structured around services and functions rather than the specific needs of employees, leaders and the business.

Our work begins with an assessment of where the organization stands: What is its ambition? Where can the greatest value be created? What changes will its people, structures and processes need to navigate?

The right starting point for AI transformation

Not every organization needs the same support at the same time. We distinguish four typical starting points:

• Get oriented: The organization wants to understand the opportunities, risks and areas for action associated with AI in HR.
• Set priorities: Initial ideas and tools are in place. The next step is to define a clear AI ambition and prioritize a portfolio of use cases.
• Implement: One or more pilots need to be developed, tested and embedded in day-to-day work quickly.
• Redesign: AI is changing processes, roles and services so significantly that the HR operating model and workforce need to be rethought.

These starting points are not fixed maturity stages. They help tailor the intervention to the organization’s specific needs. A more detailed explanation of the underlying four-wave model can be found in our assessment of where companies actually stand in their AI transformation today.

HR’s dual role

1. HR supports AI transformation across the business

AI is changing tasks, teams and skill requirements across business functions. HR creates the conditions for these changes to deliver results:

• Equip leaders and employees for new ways of working.
• Make changes to roles and skill requirements visible.
• Help teams clarify how they collaborate, make decisions and assign responsibilities.
• Develop a clear and understandable change narrative.
• Involve employee representatives early and address co-determination requirements.
• Model the implications for the workforce and workforce planning.

2. HR drives its own AI transformation

HR cannot credibly embed AI across the company if its own function remains focused on isolated tools. It needs an integrated perspective on:

• HR strategy and service portfolio
• Specific use cases and end-to-end processes
• Data quality, technology and governance
• Roles, skills and collaboration
• Adoption, impact and continuous improvement

3. HR evolves into an AI-centric organization

An AI-first HR operating model starts by asking three questions for every process: What can AI execute? What does HR need to orchestrate? And where does human judgment remain indispensable?

We explore the specific design principles, the shift from the traditional three-pillar model to a platform organization and the future HR value chain in detail in our perspective on the AI-centric HR organization.

For our consulting work, this means translating the vision into decisions for your organization, processes and employees. AI is not just another tool; it is an actor with clearly defined tasks, interfaces and performance requirements. HR oversees, decides, escalates and remains accountable. Final decisions rest with people—in HR, leadership or the workforce.

Our AI transformation consulting services

AI strategy and ambition

We clarify what AI should achieve across the organization and specifically within HR. From there, we develop a vision for the future, strategic priorities and a sound basis for investment decisions.

Typical outcomes: An AI ambition, strategic guardrails, prioritized areas for action, and an investment and implementation roadmap.

Selecting and implementing use cases

We identify use cases based not on technological availability, but on actual needs, potential value and feasibility. We prioritize both individual solutions and AI-enabled end-to-end processes, supporting their development from minimum viable product (MVP) through to everyday use.

Typical outcomes: A use-case shortlist, business case, pilot design, user testing, feedback loops and a decision on scaling.

AI-first HR operating model and process design

We design HR with the future in mind: driven by needs rather than functions, adaptive rather than static, and with AI as part of the operating model. This includes HR processes, services, roles, interfaces, decision-making paths and collaboration with IT and the business.

Typical outcomes: An AI-first vision, HR operating model, redesigned roles, process design and implementation plan.

Implications for the workforce and skills

We model how AI changes tasks, roles and functions. Together with leadership and HR, we translate scenarios into workforce planning, competency models, reskilling and concrete people-related measures.

Typical outcomes: Workforce scenarios, identification of affected roles and functions, skill requirements, a reskilling roadmap and a decision-making document for management.

AI enablement and adoption

Foundational knowledge alone is not enough. We develop learning journeys built around specific roles and work situations. These include AI training, practical application, day-to-day support and knowledge sharing within HR and across the organization.

Typical outcomes: A framework for target groups and roles, a training and learning journey, practical learning formats, a change champion concept and measures to support sustained use.

Leadership, collaboration and change

AI changes how leaders act, how teams collaborate and what the organization expects from HR. We shape the change so that clear direction, participation and implementation work together. Throughout, we work with leaders and the organization as equals and in genuine co-production.

Typical outcomes: A change narrative, leadership formats, team interventions, a communication architecture and implementation support.

Governance, guardrails and employee co-determination

We translate strategic ambition into clear rules for data, technology, responsibilities and quality. When changes affect employees, we help prepare for their involvement and develop workable agreements.

Typical outcomes: A governance model, defined roles and responsibilities, quality controls for AI solutions, guardrails and an approach to working with employee representatives.

Measuring impact and managing the portfolio

AI initiatives need more than activity metrics. We connect customer needs, usage, quality and impact so that HR can develop its portfolio based on evidence.

Typical outcomes: An impact framework, adoption and outcome measurement, portfolio reviews and a regular decision-making cadence for further investment.

Your next step

We start with your specific challenge and use an initial conversation to identify where the greatest opportunity for impact lies. Together, we decide whether the right starting point is an assessment of your current position, a concrete pilot or the further development of your operating model, workforce and capabilities.