AI Business Partner

AIBP (AI Business Partner) - a unifying leader of AI transformation. They define tools, set up processes, and guide people to measurable business results.

Mission

To make AI a source of strategic advantage for the company.

Success Criteria

AIBP is successful when:

• the company benefits from any changes and breakthroughs in AI;
• AI transformation delivers measurable economic impact;
• company employees share a common understanding of AI value;
• new practices are embedded in operational processes and company culture.

Competencies and Experience

Mindset

• Business-centricity.
• Focus on measurability: clear metrics, "before/after", hypothesis testing.
• Leadership without formal authority.
• Facilitation and empathy.
• Focus and rejection of unnecessary: concentrate efforts on what matters, cut initiatives without value.

Business and Product

• Strategy, operational management, governance, P&L.
• Projects and processes: benefit/cost and risk assessment, Go/No-Go decisions, WIP limitation.
• Financial impact calculation: revenue, OPEX, TCO, payback/ROI.
• Change implementation: roles, processes, training, communications when implementing solutions.

Technology Literacy

• LLM and agents; RAG; integrations and APIs.
• Low/No-code and process automation (e.g., scenarios on n8n/Make).
• Data and BI for measuring results: quality, availability, simple pipelines and dashboards.
• General understanding of software/infrastructure architecture and limitations: integrations, security, scalability.

Communications

• Facilitating meetings and collaborative decisions.
• Negotiations and expectation management.
• Mentoring and training.
• Conflict management.

Experience

• 8–12+ years in management, entrepreneurship, product development and launch; experience in IT environment is welcome.
• Projects affecting multiple departments (sales, operations, finance, IT) — from alignment to implementation.
• Interaction with process owners and IT at the decision level (priorities, budgets, risks).
• Cases of "proved value → scaled → embedded in processes".

Responsibilities

Strategy

• Strategic adaptation. Capturing signals, assessing impact, bringing to AI Council; reflecting decisions in roadmap and execution control.
• Synchronization of strategy and operations. The loop "signals → decisions → backlog → actions → strategy"; unified direction for departments.

People

• Working with leadership. Aligning goals and priorities; AI Council agenda; maintaining overall focus.
• Training and mentoring teams. Training programs, practical guides, support for first launches.
• Creating and maintaining AI application culture. The principle "AI is a tool"; reducing fear/skepticism; preventing "blind reliance".
• Creating and developing a champion network. Selection, training, experience sharing, and internal support.

Processes

• Identifying processes and areas for change. Prioritization by expected ROI; formulating and filtering value hypotheses;
• Designing target processes. Roles, steps, checkpoints; where AI is needed/not needed.
• Implementing changes. The cycle "hypothesis → pilot → solution → operations"; embedding in regulations and metrics.
• Measuring results and management decisions. "Before/after" measurement, short reports; decisions "continue/stop/scale".

Technologies

• Selecting technical solutions. Comparing options; justifying with risks and costs; quality/support criteria.
• Data and quality management. Data/quality requirements; roles and responsibilities; availability of checks and monitoring.
• Knowledge of IT solutions market. Product classes and use cases; matching tasks with available solutions and company constraints.
• Ensuring security and compliance. Policies on personal data and information security; risk assessment; access control and logging.
• Working with integrators. Selecting partners; interaction model and KPIs; result acceptance.

KPIs

AIBP KPIs reflect the success of the entire AI transformation, not just personal performance. AIBP itself does not have isolated performance metrics — their result is measured through the impact of changes they help launch and embed. Indicators are selected situationally from the list below, depending on the company context and transformation priorities.

Economic Indicators

• Δ profit / OPEX reduction on selected processes.
• Achieving planned payback and ROI.
• Revenue growth in AI solution implementation areas.
• Labor productivity increase.

Implementation and Scaling Indicators

• Share of pilots that ended with confirmed value.
• Number of solutions scaled to key departments.
• Average cycle "hypothesis → pilot → solution".
• Solution sustainability in operations.

Quality and Perception Indicators

• Reduction of errors, rework, SLA deviations.
• User satisfaction (CSAT).
• Employee activity when using AI tools.
• Trust level in AI-based solutions.

People and Culture Development Indicators

• Number of trained employees and "AI champions".
• Share of departments where a unified mindset "AI as a tool" is formed.
• Reduction of skepticism and growth of engagement in transformation.

Transparency and Governance Indicators

• Availability of up-to-date impact dashboards.
• Compliance with security and compliance requirements.
• Absence of critical incidents.
• Regular model and process reviews.