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Roles

Why a role model is needed​

Without explicit role allocation, the AI function quickly becomes either a technical team that "builds models," a project office that only collects statuses, or a center of expertise that advises but does not influence outcomes.

For sustainable AI adoption, a company needs a layer that can simultaneously:

  • intake and route AI ideas;

  • choose suitable AI products and tools;

  • launch pilots and adoptions;

  • manage technology infrastructure;

  • ensure security, quality, and support;

  • train the business to use AI;

  • track impact and decide whether to scale or stop initiatives.

The role model defines who owns each of these blocks.

Top level of the model​

Director of the AI function
│
├── Head of Infrastructure
│   └── MLOps / LLMOps / DevOps
│
├── Head of Product Portfolio
│   └── AI products / new product pilots / product adoption
│
├── Head of Project Office
│   └── project managers / delivery tracks / department initiatives
│
└── Head of AI Training and Promotion
    └── training / communications / AI champions / knowledge base / demand generation

Key idea: the AI function manages not only AI solution development, but the entire system of how solutions appear, get adopted, and are used across the company.

1. Director of the AI function​

Role​

The director is the owner of the AI function in the company. They ensure AI is not a set of scattered experiments, but a managed direction with clear goals, priorities, resources, rules, and impact.

Scope of responsibility​

The director owns:

  • AI adoption strategy;

  • linking AI adoption to company goals;

  • budget and resource management for the AI function;

  • aligning rules with business, IT, security, architecture, and leadership;

  • decisions on disputed or critical initiatives;

  • presenting AI function outcomes to leadership;

  • evolving the AI operating model.

Primary focus​

The director's main question:

Which AI directions should the company develop to achieve managed business impact, not a chaotic set of pilots?

Typical artifacts​

  • AI strategy / AI roadmap;

  • AI initiative portfolio;

  • prioritization rules;

  • AI function operating model;

  • leadership reporting;

  • decisions to launch, scale, or stop initiatives;

  • AI product map and capability map.

2. Head of Infrastructure​

The head of infrastructure owns the technology foundation of the AI function: platforms, environments, integrations, deployment, operations, performance, security, and technical readiness of solutions for production use.

If the head of project office owns initiative flow and the head of product portfolio owns AI product value, the head of infrastructure ensures all of this can run stably in the company's real environment.

Scope of responsibility​

The head of infrastructure owns:

  • AI/ML/LLM infrastructure;

  • development, testing, and production environments;

  • MLOps, LLMOps, DevOps;

  • deployment of models, agents, and agent orchestrators;

  • monitoring, logging, and support;

  • integrations with corporate systems;

  • access management, environments, and technical constraints;

  • technical scalability of solutions;

  • compatibility with IT architecture and security requirements.

Primary focus​

The infrastructure lead's main question:

Can this AI solution run safely, stably, and at scale inside the company?

Direct reports / adjacent roles​

Under the infrastructure lead:

  • MLOps engineers;

  • LLMOps engineers;

  • DevOps engineers;

  • backend engineers;

  • monitoring and operations specialists;

  • AI platform technical administrators.

Typical artifacts​

  • AI platform architecture;

  • integration diagrams;

  • deployment rules;

  • production readiness checklists;

  • quality and performance monitoring;

  • SLA/SLO for AI services;

  • technical constraints and requirements;

  • operations documentation.

3. Head of Product Portfolio​

Role​

The head of product portfolio owns the company's AI product portfolio: which products are needed, which scenarios they fit, how they are piloted, evolved, adopted, and become functional "building blocks" for business initiatives.

AI products may include:

  • corporate LLM;

  • RAG platform;

  • ML platform;

  • agents (opencode, hermes);

  • agent orchestrators (multica, paperclip);

  • code agents (cursor, kilocode);

  • other AI products.

Scope of responsibility​

The head of product portfolio owns:

  • the company's AI product map;

  • target users and application scenarios;

  • launching and running product pilots;

  • collecting user feedback;

  • evolving product functionality;

  • packaging products for internal users;

  • rules for onboarding new departments;

  • defining which initiatives can be implemented through existing products;

  • identifying need for new AI products;

  • aligning product value with business goals.

Primary focus​

The product lead's main question:

Which AI products should exist in the company so typical business needs are solved faster, cheaper, and better?

Direct reports / adjacent roles​

Under the product lead:

  • heads of internal AI products;

  • AI platform product owners;

  • business analysts;

  • UX researchers;

  • product adoption specialists;

  • owners of individual capability blocks.

Typical artifacts​

  • AI product catalog;

  • product canvas / product brief;

  • target scenario descriptions;

  • user map;

  • product backlog;

  • product roadmap;

  • piloting rules;

  • adoption and product value metrics;

  • product use case library.

4. Head of Project Office​

Role​

The head of project office owns the movement of AI initiatives from idea to adoption, impact expectation, and closure. Their scope is not one specific product, but the full stream of demand from the business.

They ensure an initiative does not get lost between business, products, development, infrastructure, security, architecture, and operations.

Scope of responsibility​

The head of project office owns:

  • business funnel management for initiatives;

  • initiative portfolio prioritization;

  • initiative detailing and routing;

  • preparing initiatives for stage-gate decisions;

  • choosing the delivery track together with product and infrastructure leads;

  • coordinating business sponsors, IT, security, architecture, and operations;

  • tracking timelines, statuses, blockers, and dependencies;

  • project risk management;

  • preparing initiatives for adoption;

  • recording expected and confirmed impact;

  • closing, rejecting, or handing initiatives over to support.

Primary focus​

The project lead's main question:

How do we guide an initiative through all required decisions, checks, and work so it reaches adoption and measurable outcome?

Direct reports / adjacent roles​

Under the project lead:

  • project managers;

  • business analysts;

  • project administrators.

Typical artifacts​

  • initiative card;

  • business hypothesis;

  • initiative passport;

  • initiative roadmap;

  • delivery plan;

  • stage-gate materials;

  • status reports;

  • risk and blocker register;

  • impact hypothesis;

  • adoption and impact report.

5. Head of AI Training and Promotion​

Role​

The head of AI training and promotion ensures employees do not merely know AI products exist, but can apply them at work, formulate quality ideas, and use AI tools safely.

This role is critical because even a strong AI platform does not create impact on its own. Impact appears only when the business starts using AI regularly in real processes.

Scope of responsibility​

The head of AI training and promotion owns:

  • training employees to work with AI tools;

  • building AI literacy across the company;

  • growing a network of AI champions in departments;

  • communicating successful cases;

  • creating knowledge bases, instructions, and guides;

  • running workshops, demos, and internal events;

  • helping the business formulate quality AI ideas;

  • reducing resistance and fear of AI;

  • promoting safe and deliberate AI use;

  • collecting feedback from users and departments.

Primary focus​

The training and promotion lead's main question:

How do we help employees understand AI capabilities, apply the tools, and bring quality demand to the AI function?

Direct reports / adjacent roles​

Under the training and promotion lead:

  • AI trainers;

  • learning methodologists;

  • internal communications managers;

  • change management specialists;

  • knowledge base owners;

  • AI champions in business departments.

Typical artifacts​

  • AI training program;

  • knowledge base;

  • instructions and playbooks;

  • prompt guides;

  • case library;

  • workshop materials;

  • communications plan;

  • AI champions map;

  • engagement metrics.

How roles interact​

Example initiative route​

Head of training runs workshops in business departments
    ↓
Business formulates a need
    ↓
Head of project office registers the initiative in the business funnel
    ↓
Head of product portfolio determines which AI product best fits
    ↓
Head of infrastructure checks technical feasibility and constraints
    ↓
Head of project office leads the initiative through the delivery track
    ↓
Head of product portfolio collects feedback and evolves the product
    ↓
Head of infrastructure ensures deployment, support, and scaling
    ↓
Director of the AI function decides on priorities, resources, and impact

Responsibility matrix​

  • A — accountable, owns the final decision.

  • R — responsible, executes or leads the work.

  • C — consulted, participates in alignment.

  • I — informed, kept up to date on progress.

Important principle: roles must not blur​

A typical AI function failure mode is role blur.

For example:

  • the infrastructure team starts choosing business priorities;

  • project managers start owning product strategy;

  • product leads own adoption but lack delivery resources;

  • training becomes one-off lectures instead of systematic adoption;

  • the AI director manually runs every initiative instead of managing the system.

The right model works differently:

  • the director manages the system and priorities;

  • infrastructure owns technical feasibility;

  • product owns capability and applicability;

  • projects own initiative flow;

  • training owns demand, adoption, and usage culture.

Linking roles to the AI conveyor​

In the AI conveyor, each role gets its own working view.

Director of the AI function sees:

  • the full initiative portfolio;

  • priorities;

  • business funnel status;

  • bottlenecks;

  • expected and confirmed impact;

  • team load;

  • decisions requiring escalation.

Head of Infrastructure sees:

  • technical requirements of initiatives;

  • production readiness;

  • integration dependencies;

  • deployment statuses;

  • operational risks;

  • load on platforms and teams.

Head of Product Portfolio sees:

  • AI product catalog;

  • initiatives using each product;

  • adoption and feedback;

  • product backlog;

  • need for new capabilities;

  • product metrics.

Head of Project Office sees:

  • initiatives in the business funnel and delivery tracks;

  • statuses, blockers, and dependencies;

  • stage-gate readiness;

  • responsible participants;

  • adoption plans;

  • impact hypotheses.

Head of Training and Promotion sees:

  • departments with high and low adoption;

  • training topics;

  • frequent user questions;

  • ideas arising after training;

  • AI champion network;

  • case and materials library.

Final logic​

The AI function should be built as a management system, not as a group of enthusiasts or a technical lab.

The minimum stable role model includes:

  • Director of the AI function — function and outcome management;

  • Infrastructure lead — technology foundation and operations;

  • Product lead — AI products and capabilities;

  • Project lead — initiative delivery and funnel management;

  • Training and promotion lead — adoption, training, and quality demand.

Together these roles cover the full AI adoption cycle: from strategy and idea to product, adoption, usage, and confirmed impact.