In the Name of Order: A Seductive Journey Through AI Role Naming within Human Equity Management subsystem - part I

As artificial intelligence becomes a core enabler of enterprise transformation, the need for precise, structured role naming has never been more critical.

This article initiated by Brij kishore Pandey and Monika Pakulska presents a proposed taxonomy of AI-related roles specifically focused on the development of AI tools - those foundational systems, models, and infrastructures that underpin downstream application development for end users.

These roles largely correspond to what was historically known as “development” in software engineering: building reusable libraries, components, and frameworks that others will use to create final products.

The taxonomy covers strategic leadership, scientific and engineering functions, product enablement, ethical oversight, and operational support - each aligned with a distinct segment of the AI value chain.

For instance, roles like AI/ML Engineer (AIM) and AI Architect (AIA) reflect modern equivalents of software library designers or system integrators from earlier IT eras.

Meanwhile, emerging functions such as Prompt Engineer (AIT) or Fairness Auditor (AIFA) highlight the unique demands of AI-specific development cycles, especially for generative models and compliance frameworks like the EU AI Act.

This structured approach supports enterprise-wide alignment, HR role mapping, and workforce planning in an AI-driven economy.

Note: This role taxonomy is developed within the framework of the Human Equity Management (HEM) subsystem, which replaces traditional HR in contemporary post-industrial organizations.

HEM focuses on identifying and developing the hidden value of individuals, transforming it into measurable business value, and aligning talent development directly with strategic business goals. Unlike traditional HR departments, HEM operates as a Competency Center, emphasizing multi-role adaptability and strategic integration. For more see:

 https://www.linkedin.com/pulse/human-equity-management-nie-hr-żelazny-fundament-wioletta-rcsff/?trackingId=ZKolCl3SRciyOStE%2BQ32Zg%3D%3D

Backing to the track. Here is the proposed taxonomy of AI-related roles at the corporate level (minimal set). It’s structured by functional domains in the AI value chain, with suggested role codes, titles, and brief descriptions to support clarity, consistency, and enterprise-wide alignment:

I. Strategic Leadership Roles ("AI Leadership")

AIS – AI Strategy Officer The AI Strategy Officer is responsible for setting the overall direction for how a company uses artificial intelligence. They identify key business areas where AI can bring measurable value and ensure that AI efforts align with long-term business goals. This role requires both technical awareness and business insight to translate AI capabilities into competitive advantage. The officer works closely with executive leadership to prioritize investments and guide AI adoption across departments.

AIG – AI Governance Lead The AI Governance Lead ensures that all AI initiatives follow ethical principles and comply with legal regulations, such as the EU AI Act. This role involves creating company-wide policies for responsible AI use, reviewing risks like bias or privacy violations, and ensuring transparency in AI decision-making. They act as a bridge between legal, technical, and business teams to build trust in AI systems. Their work is crucial for maintaining accountability and public confidence in how AI is used.

AIP – AI Portfolio Manager The AI Portfolio Manager oversees all AI projects within the organization, making sure resources like time, people, and funding are allocated effectively. They prioritize projects based on strategic value, risk, and return on investment. This person tracks progress, manages dependencies, and ensures alignment with broader innovation goals. Their role helps prevent fragmentation and ensures that AI development efforts are coordinated and strategically impactful.

II. Scientific & Engineering Roles ("AI Engineering & Science")

AID – AI/Data Scientist The AI/Data Scientist explores large datasets to find meaningful patterns and insights, using statistical techniques and machine learning algorithms. They design, train, and evaluate predictive models to solve real-world problems—such as forecasting demand, detecting fraud, or personalizing user experiences. This role requires strong analytical skills and a deep understanding of both data and algorithms. Their work often forms the foundation for AI-driven products and decisions across the company.

AIM – AI/ML Engineer The AI/ML Engineer is responsible for turning machine learning models into scalable, reliable systems that can be used in live business environments. They build and maintain the infrastructure needed to run AI models in production, ensuring performance, security, and stability. This role involves close collaboration with data scientists and software developers. Their job is to make sure that AI solutions move beyond prototypes and actually deliver value in real-time applications.

AIR – AI Researcher The AI Researcher focuses on advancing the frontiers of artificial intelligence by developing new algorithms, architectures, or training methods. They often work in academic or innovation-driven settings, publishing findings and contributing to the scientific community. This role is deeply technical and exploratory, aiming to solve fundamental problems in areas like computer vision, natural language processing, or reinforcement learning. Their breakthroughs often pave the way for future AI tools and technologies.

AIO – AI Optimization Engineer The AI Optimization Engineer works to make AI models run faster, use fewer resources, and perform more efficiently—without compromising accuracy. This can involve improving algorithms, reducing model size, or tuning how models interact with hardware and data pipelines. Their work is essential in scaling AI applications and making them cost-effective, especially in real-time systems or resource-constrained environments. They often collaborate with ML engineers, architects, and infrastructure teams.

AIA – AI Architect The AI Architect designs the overall systems that power AI capabilities in the organization. This includes defining how data flows, how models are trained and deployed, and how different AI components integrate with business systems. They create scalable, secure, and flexible AI platforms that support multiple use cases. The architect must balance technical requirements, business goals, and future scalability—ensuring that AI becomes a sustainable part of the company’s digital infrastructure.

III. Product & Business-Oriented Roles ("AI Product & Enablement")

AIPM – AI Product Manager The AI Product Manager acts as the bridge between business needs and AI development teams. They define the vision, roadmap, and features of AI-powered products based on user requirements, market research, and strategic goals. This role involves close coordination with engineers, data scientists, and stakeholders to ensure the product delivers value. They must also balance feasibility, performance, and ethical considerations in AI design. Their success is measured by how effectively AI solutions solve real customer problems.

AIE – AI Experience Designer The AI Experience Designer ensures that interactions between humans and AI systems are clear, effective, and intuitive. They design user interfaces and conversational flows for applications like chatbots, recommendation systems, or virtual assistants. This role blends design thinking with an understanding of AI behavior, aiming to create trust and seamless user engagement. The designer’s work is essential for making complex AI functions accessible and enjoyable for non-technical users.

AIT – AI Trainer / Prompt Engineer The AI Trainer or Prompt Engineer is responsible for preparing and refining the inputs that guide AI behavior—especially in large language models (LLMs). They curate high-quality training data, write and test prompts, and fine-tune outputs to match desired outcomes. This role requires deep familiarity with how AI models interpret instructions and generate responses. It is especially important in generative AI applications, where prompt precision directly influences quality and relevance.

AICS – AI Customer Success Lead The AI Customer Success Lead ensures that clients understand, adopt, and derive value from AI-based products and services. They work closely with users—especially in B2B or SaaS contexts—to provide onboarding, support, and feedback collection. This role combines product knowledge, communication skills, and a service mindset to maximize customer satisfaction and retention. They often relay user insights back to development teams to guide product improvements.

AIQA – AI Quality Assurance Analyst The AI Quality Assurance Analyst tests AI systems to ensure they perform reliably, efficiently, and as intended. They validate model outputs, check for errors or inconsistencies, and run simulations across different use cases. This role helps maintain high standards in AI product deployment, minimizing bugs and unintended behaviors. Their work is crucial for building trust in AI applications—especially in safety-critical or regulated environments.

IV. Risk, Ethics & Security Roles ("AI Risk & Compliance")

AIRM – AI Risk Manager The AI Risk Manager is responsible for identifying and managing risks associated with the development and use of AI systems. These risks may include operational failures, legal non-compliance, ethical issues, or reputational harm. The role involves assessing potential threats, creating mitigation strategies, and establishing internal controls for AI initiatives. They work across departments to ensure AI deployments are both safe and aligned with corporate risk tolerance. This function is critical in maintaining responsible innovation and organizational trust.

AIFA – AI Fairness Auditor The AI Fairness Auditor evaluates AI models to ensure they treat all users and groups fairly, without unintended bias or discrimination. They use both quantitative and qualitative methods to detect imbalances in data, model outputs, and decision logic. This role requires a mix of technical knowledge, ethical reasoning, and regulatory awareness. Fairness auditors help organizations meet legal standards and societal expectations around equity and inclusion. Their work supports the creation of transparent and socially responsible AI systems.

AISD – AI Security & Defense Lead The AI Security & Defense Lead safeguards AI models and data from malicious attacks, manipulation, or misuse. This includes defending against threats like data poisoning, model inversion, and adversarial inputs that can corrupt or mislead AI behavior. They design and implement security protocols specific to AI environments, collaborating with cybersecurity teams. The role is increasingly important as AI becomes a critical part of business infrastructure. Their mission is to ensure resilience and trust in AI technologies.

V. Operational & Support Roles ("AI Operations & Support")

 AIML – MLOps Engineer The MLOps Engineer is responsible for building and maintaining the infrastructure that allows AI models to be deployed, updated, and monitored efficiently. They create automated pipelines for continuous integration and delivery (CI/CD) of machine learning models, ensuring models are versioned, reproducible, and scalable. This role combines software engineering skills with an understanding of machine learning lifecycle management. MLOps Engineers also implement observability tools to track model performance over time and prevent drift. Their work ensures AI solutions remain stable, secure, and production-ready.

AIDS – AI Data Steward The AI Data Steward ensures that the data used for training and running AI systems is accurate, traceable, and properly governed. They monitor data quality, define data standards, and maintain metadata that tracks data lineage—from source to usage. This role plays a key part in preventing issues caused by poor or biased data. Data stewards also enforce policies related to access control and data privacy. Their work forms the backbone of trustworthy and compliant AI development.

AICS – AI Change Specialist The AI Change Specialist helps organizations adapt to the structural and cultural changes that come with adopting AI technologies. They design communication plans, training programs, and stakeholder engagement strategies to ensure smooth transitions. This role requires a strong understanding of both organizational dynamics and AI capabilities. The specialist’s goal is to reduce resistance, increase AI literacy, and align people, processes, and technologies. Their work is crucial to ensuring that AI implementations deliver lasting impact.

AITR – AI Translator / Liaison The AI Translator or Liaison acts as the intermediary between technical AI teams and business leaders. They interpret complex AI concepts, limitations, and capabilities into business-relevant language, enabling informed decision-making. This role ensures mutual understanding, helping businesses set realistic expectations while guiding technical teams to align solutions with strategic goals. Translators are essential in bridging the “language gap” that often exists between engineering and management. Their communication skill drives clarity, trust, and alignment in AI projects.

Additional Guidelines:

Add seniority levels as needed: Lead, Principal, Senior, Associate, etc.

Specializations can be suffixed: e.g., AIPM-GA (Generative AI Product Manager).

Codes and taxonomy support enterprise role mapping, HR systems, and AI workforce planning. This article focuses on roles related directly to the development of AI tools and foundational technologies.

In the next publication, we will present a complementary taxonomy of roles for domain-specific application experts.

These role names will be systematically mapped to the Canonical Business Model - a concept developed by the Polish firm Reinvention Ltd. in response to the growing need for a universal framework capable of describing the structure and functioning of any enterprise, regardless of the industry in which it operates.

This concept provides a coherent foundation for integrating technological roles, business subsystems, and human potential into a unified business (not only IT!) architecture for post-industrial organizations.