In the Name of Order: A Seductive Journey Through AI Role Naming – Shaping the Soul of the Postindustrial Firm (Part II)

The first part of this article focused on roles responsible for developing foundational AI software components - those working under the supervision of senior engineering leaders to create reusable models, tools, and infrastructure.

See here: https://www.linkedin.com/posts/tadeusz-lis-467705292_as-artificial-intelligence-becomes-central-activity-7330834744944889856-8nFU?utm_source=social_share_send&utm_medium=member_desktop_web&rcm=ACoAAEbwmE8B46L_QSi5V2FCtJTzr3oOnrgydq4

This one focuses on people who build AI apps under supervision by Human Equity Management Competence Center. These apps are used directly in day-to-day business tasks. They are made by combining advanced pieces built earlier by core AI teams – see mentioned article.

These AI applications (specialised Agents) represent robust, industrial-grade software - often suitable even for mission-critical environments. They mix AI flexibility with solid engineering rules. The result is stable, reliable software that works well in company systems.

Their development and integration are by no means tasks for newly trained amateurs fresh from a "Become an AI Agent Builder in 5 Minutes" YouTube course. These jobs require strong tech knowledge, structured thinking, and the ability to handle complex company rules - far beyond superficial tool usage.

It’s essential not to confuse the design of professional LoB AI Agents with low-code/no-code platforms. While useful for prototyping, such platforms are limited to MVP stages due to critical concerns around security, consistency, and alignment with enterprise IT architecture.

To understand where these roles belong and how they interconnect within a modern enterprise, we now turn to the Canonical Business Model.

The Canonical Business Model helps companies see themselves clearly. It breaks the company into seven connected management areas. The model was created by Polish firm Reinvention Ltd.

It helps businesses that work in a fast-changing world rethink how they are built and stay competitive - in the form of postindustrial company.

The seven functional subsystems defined by the Canonical Business Model

1.  Production Subsystem - governs the creation and delivery of value (products, services, operations).
2.  Market Subsystem - addresses customer needs through marketing, sales, and value communication.
3. Financial-Accounting Subsystem - ensures control over monetary value flows and financial sustainability.
4. Organizational-Managerial Subsystem - covers organizational structure, governance mechanisms, and process design.
5. Human Equity Management Subsystem - manages human potential and capabilities in alignment with long-term strategy.
6. Corporate Governance Subsystem - sets internal rules, legal norms, and compliance frameworks.
7.  Political-Business Relattionship Management Subsystem – shapes relationships with government bodies, regulators, and institutional partners.

The main applications ?

• Comprehensive organizational transformation (e.g., digitalization, culture shift, new business models)
• Strategic and operational coherence audits
• Role, process, and structure design based on complete management logic • Enhancing adaptability and resilience in dynamic environments
• Building post-industrial organizations – agile, contextual, and value-driven

The Canonical Model enables leaders to holistically understand their enterprise, reduce the chaos of change, and deliberately craft sustainable competitive advantage in an era of discontinuity.

Below is an expanded version of the corporate AI roles taxonomy, now aligned with the Business Canonical Model of a Reinvention-Based Enterprise. Each management subsystem is reflected in its own category, and AI-related roles are logically assigned based on their contribution to that domain.

I. PRODUCTION SUBSYSTEM

AID – AI/Data Scientist

Not just data analysis – they build predictive models that directly influence operations (shutdowns, rerouting, anomaly alerts). Effective ones collaborate with engineers and ensure models are actually used.

Great models die in silence when built in isolation. The ones that survive? They’re co-created with the people who feel the consequences of a false positive at 3 a.m. In other words: I’ve seen AI teams pop champagne after model validation  - only to realize six months later that no one even knows it exists. Real impact only begins once the code is being used.

AIM – AI/ML Engineer

Turns models into reliable, scalable systems. Ensures they run efficiently, stay online, and are monitorable. Bridges the gap between prototype and production.

Having spent years wrestling with brittle data pipelines and flaky model deployments, I’ve developed a deep respect (and perhaps a bit of bias) for ML Engineers. But without them, AI remains a whiteboard fantasy

AIO – AI Optimization Engineer

Makes models faster and more efficient. Key in embedded, mobile, or power-constrained environments. Often overlooked, but crucial for margin impact.

In my view, optimization is where smart math creates real savings. And if you want this role to sit close to the emperor’s ear, staff it wisely  - follow HEM recommendations and choose someone whose technical brilliance comes with strategic awareness.

AIML – MLOps Engineer (Industrial)

Maintains ML pipelines, infrastructure, and monitoring. Ensures long-term operational health of deployed AI, especially in industrial settings.

I’ve witnessed brilliant models die quiet deaths simply because no one owned their long-term health. This role isn’t glamorous, but without it, AI systems decay  - often silently, until it's too late. As managers, take care to budget for these unsung heroes  - or your deployments will blow up in your face. I’ve seen it happen more than once.

AIRM – AI Reliability Modeler

Models degradation, failure rates, and risk patterns in intelligent systems. Especially critical in aerospace, defense, and safety-critical environments.

What fascinates me about this role is its focus on failure as a system property. It’s not just about predicting when things break - it’s about anticipating when intelligence itself goes wrong. In high-stakes environments, that distinction can mean lives. Piotr Brzyski , this is exactly the curse (or blessing)  of attractors we discussed in the context of catastrophe theory and the risks embedded in the systems we implement for our clients.

Once the whole model or single Agent enters a trajectory of error, it's hard to escape without external intervention  - and that’s precisely where this role proves essential.

II. MARKET SUBSYSTEM

AIPM – AI Product Manager

Connects customer needs with AI capabilities. Goes beyond market insight to translate model behavior into business value.

An AI Product Manager bridges customer needs and algorithmic capability. It’s not enough to “understand the market” - they must understand what’s technically feasible. Many confuse this with traditional product roles; in reality, it demands an ability to translate neural network behavior into business value. The good ones speak Python and P&L.

AIE – AI Experience Designer

Designs human-AI interactions that are natural and trustworthy. Ensures UX does not alienate or confuse users.

Their job is to prevent AI from becoming alienating. They craft human-AI interactions - whether chatbots, recommendation engines, or adaptive UX - that feel intuitive, responsive, and non-patronizing. I’ve seen brilliant models fail in adoption because users didn’t trust or understand the interface.

Let me be blunt: if you neglect this role, your AI will be rejected  - not because it doesn’t work, but because people won’t want to use it. I’ve seen technically flawless systems die on arrival simply because the interface felt alien, manipulative, or dehumanizing.

AICS – AI Customer Success Lead

Drives post-deployment adoption. Ensures AI features deliver ongoing value and are used meaningfully.

Where this role shines is post-deployment. They ensure that AI-powered features are not just “turned on,” but used meaningfully. Especially in SaaS or B2B, this role is the key to retention. It combines empathy, product fluency, and the ability to troubleshoot both human and machine errors. From experience, the best people in this role often have a background in psychology or behavioral science  - because unlocking adoption isn’t just about functionality, it’s about trust, perception, and habit formation. Without that, even brilliant AI gets quietly ignored.

AIT – Prompt Engineer / AI Trainer

Crafts prompts and behaviors of generative AI. Shapes model outputs through creative, linguistic, and technical design.

One of the newest, and most misunderstood roles. Prompt Engineers don’t just “write better prompts” - they design the very behaviors of generative AI models. Think of them as psychological linguists for machines.

It’s a craft that blends intuition, logic, and relentless experimentation. Having worked with early-stage LLM implementations across industries, I’ve seen firsthand how the best Prompt Engineers operate more like behavioral designers than coders  - shaping not just outputs, but expectations.

AIFA – AI Fairness Auditor (Market)

Audits personalization and marketing algorithms for bias. Ensures AI aligns with public trust and legal expectations.

They run simulations and audits to ensure that marketing or personalization algorithms don’t create discriminatory patterns - explicit or implicit. In the age of algorithmic profiling, this role is not about political correctness, but risk mitigation and public legitimacy.

AIRM – AI Pricing Strategy Modeler

Designs self-adjusting pricing models. Balances algorithmic precision with market sensitivity and fairness.

Develops dynamic, sometimes self-learning pricing algorithms. In my view, this is one of the most underregulated frontiers - yet it has outsized impact on margin, equity, and trust. You want someone here who understands economics, not just math. Tariff and pricing policy is one of the few remaining levers that directly shapes both competitiveness and trust. In low-margin environments, even a 0.5% optimization can differentiate survival from stagnation.

And only those who understand economic nuance  - not just algorithms  - can tune the system with the necessary finesse. In my experience, too many companies hand this role to data wizards without grounding in market dynamics - and then wonder why their pricing erodes brand trust or fuels volatility. This role needs economic judgment as much as technical skill.

III. FINANCIAL-ACCOUNTING SUBSYSTEM

AID-F – AI Financial Analyst

Develops forecasting models for liquidity, portfolio risk, and macroeconomic scenarios. Supports CFOs and treasury in planning under uncertainty.

In postindustrial firms, this isn’t just a technical role  - it’s a strategic one. The future liquidity of the company can hinge on how well this person understands both volatility and vision. Marek Opowicz – I am very impressed by your implementation art during last 8 years!

AIQA-F – AI Audit Quality Analyst

Validates that financial AI systems are explainable, traceable, and auditable. Key for regulatory compliance and auditor trust.

Ensures that AI systems used in accounting or trading are explainable, traceable, and testable. Often interfaces with external auditors. Without this role, algorithmic finance would not pass a regulatory smell test.

AIFA – Ethics & Bias Officer (Finance)

Assesses credit and fraud models for bias. Ensures fairness in AI-driven decisions, protecting both reputation and compliance.

Evaluates models used for credit scoring, underwriting, or fraud detection to eliminate bias. This role protects both reputation and compliance - especially in ESG-sensitive institutions.

AIRM – AI Risk Manager (Finance)

Monitors financial AI systems for drift, rare event risks, and systemic exposure. Advises on resilience and stress testing.

This is the role that watches the horizon. In my experience, it’s not the average case that breaks you  - it’s the edge case you didn’t model. The best in this role think like adversaries and guardians at once: always asking, “What if everything goes wrong?”

AIA – Finance AI Architect

Designs AI systems for internal audit, anomaly detection, and advisory tools. Focuses on performance, reliability, and explainability.

Designs AI platforms for internal audit, anomaly detection, or robo-advisory functions. Must balance performance with absolute traceability. In highly regulated environments, speed is meaningless if you can’t explain the outcome. The best here think like forensic engineers  - every output must be auditable, defensible, and reproducible under scrutiny.

IV. ORGANIZATIONAL-GOVERNANCE SUBSYSTEM

AIS – AI Strategy Officer

Shapes the long-term AI vision and investment roadmap. Balances strategic ambition with today’s data, infrastructure, and skills gaps. Requires both business foresight and technical fluency.

The best in this role, sent by HEM Competence Center,  combine a ten-step-ahead vision with intimate knowledge of today’s technical and organizational constraints. I’ve learned to listen closely to them: they see the mountain and the loose rocks under your boots. Both.

AIG – AI Governance Lead

Formalizes policies, ethical standards, and documentation for AI usage. Often fills the AI compliance function, even unofficially. Ensures consistent, auditable practices.

Formalizes AI use policies, ethics charters, and documentation standards. In many firms, they are the de facto 'chief compliance officer for AI' - even if no one gave them that title.

AIP – AI Portfolio Manager

Oversees the organization’s entire AI project landscape. Ensures that innovation aligns with strategy and resources are used effectively. Prevents redundancy, resource clashes, and misaligned pilots.

Too many AI initiatives run in parallel, burning talent and budget with no shared outcome. This role is the antidote  - part strategist, part referee  - making sure that innovation scales without turning into organizational chaos. Staff this role in consultation with HEM experts  - you’ll need mature, seasoned professionals with strong psychological resilience to withstand internal pressures. Without that, they’ll either burn out or become complicit in the very chaos they’re meant to prevent.

AICS – AI Change Specialist

Translates strategy into action. Crafts adoption programs, training paths, and feedback loops. Helps employees engage with AI practically and confidently.

This is the human glue between strategy and adoption. The brilliant AI strategies fall flat because no one translated them into lived, daily practice. This role reduces fear, builds trust, and turns abstract vision into something people actually use. Usually…

AITR – AI Translator

Bridges technical and business worlds. Helps avoid misalignment, rework, and failed expectations. Trust and clarity are their main tools.

This role thrives on translation  - not just of language, but of intent. I’ve often seen that the natural empathy many women bring to the table makes this role more easily trusted by the business side. But don’t take that as dogma  - what matters most is clarity, credibility, and the ability to bridge worlds.

AIQA-O – AI Organizational Assurance

Audits AI implementations for compliance with internal governance structures. Prevents systems from drifting from intended use or violating internal norms.

In postindustrial firms built according to the Reinvention methodology, internal governance poses less of a challenge  - most normative acts are generated by semi-automated IT systems, ensuring structural coherence by default. Adopt this best practice in your own architectures: build systems anchored in rule frameworks derived from legal theory and principles of statecraft. It’s a quiet revolution in how governance becomes both scalable and trustworthy.

AISD – AI Security & Defense Lead

Protects AI systems from intrusion, poisoning, and exfiltration. Combines deep model knowledge with cybersecurity practices. One of the most sensitive and under-resourced roles.

In my personal view, this is one of the most strategically exposed roles today  - too many firms still treat AI security as an afterthought. But once a model is poisoned or exfiltrated, the damage is deep, silent, and often irreversible. You don’t need paranoia here  - you need foresight and top-tier talent. Grzegorz (Greg) K. - your vision of the future of teh professional preparation people for playing this role?

AIRM-L – AI Risk Officer (Compliance)

Manages model risk registers, third-party audits, and failure contingencies. Ensures transparency and accountability.

Owns the registry of model risk exposures. Coordinates third-party audits and stress tests. Ensures that if a model fails, the blame lands on process - not on ignorance. Andrzej Głogowski - comment from expert?

AIQA-L – AI Model Compliance Auditor

Checks that deployed AI aligns with governance documentation and regulatory expectations. This is the final internal gate before public accountability begins.

Checks that deployed models align with documented governance frameworks. This role provides the last mile of accountability before regulators come knocking. Do not underestimate this role  - it’s the final checkpoint before accountability becomes liability. Violating certain regulations, like GDPR, isn’t just a legal misstep; it can be financially and reputationally devastating. Better to catch the flaw internally than explain it to a regulator. It may not sound groundbreaking, but no one ever choked from being too cautious. When it comes to regulatory alignment, better to overprepare than to overpay.

V. HUMAN EQUITY MANAGEMENT SUBSYSTEM

AICH – AI Capability & Hiring Strategist

Identifies, recruits, and develops AI talent aligned with long-term organizational needs. Balances short-term project demand with long-term skill portfolios. Must understand tech trends and workforce evolution.

This role isn’t just about hiring fast coders. It’s about curating a sustainable edge in talent – one that evolves with the company. In organizations following Human Equity Management principles, this role ensures human capital is not a cost center but a long-term asset under intelligent development. Wioletta Koper-Staszowska EMBA - please share your long and exciting live experience.

AIDV – AI Development Coach

Mentors AI professionals and teams. Fosters growth in technical mastery, leadership, and ethical reflection. Helps prevent stagnation and burnout.

Human potential does not scale linearly with experience alone. Great coaches unlock nonlinear growth – often by creating space for reflection, challenge, and courageous exploration. If your AI team feels stuck or cynical, this is the role you’re probably missing. Maciej Wiśniewski your deep insight?

AIPC – AI Performance & Compensation Analyst

Aligns performance indicators with project value and reward structures. Tracks contributions in complex, multi-role environments. Ensures fairness and strategic incentives.

In postindustrial companies, performance can’t be judged by code output alone. This role helps quantify unseen impact – mentorship, ethical escalation, architectural foresight – and links it to transparent reward systems that don’t break trust.

AISC – AI Skills Curator

Designs, maintains, and evolves AI learning pathways. Identifies obsolete knowledge and emerging critical skills. Works closely with HR and team leads.

You don’t need everyone to be a genius – you need your people to grow in the right direction. This role curates not just content, but timing, context, and motivation. Great skills curators reduce the noise and amplify what matters.

AICX – AI Collaboration Experience Lead

Improves the daily collaboration dynamics within and between AI teams. Focuses on psychological safety, communication patterns, and decision clarity.

AI teams fail not only because of weak models, but because of fractured communication. This role studies the social layer – the trust, roles, conflict, and clarity – that allows technical excellence to become business value.

VI. CORPORATE GOVERNANCE SUBSYSTEM

AIGV – AI Governance Strategist

Defines AI-related governance principles and frameworks. Aligns model usage with internal norms, legal obligations, and industry standards. Supports both policy drafting and strategic oversight.

In mature enterprises, this role ensures AI development doesn’t outpace accountability. They define the 'why and how' of AI adoption – ensuring traceability, documentation, and alignment with evolving norms. Without this role, models may succeed technically, but fail institutionally.

AIQA-G – AI Governance Auditor

Verifies adherence to internal governance protocols. Conducts AI policy audits and compliance reviews. Works closely with general counsel and audit committees.

Most companies overestimate their AI governance maturity. This role provides a necessary mirror – often uncomfortable, always valuable. It helps prevent inconsistencies between AI claims and actual practice, which can otherwise undermine credibility or create legal risk.

AILE – AI Legal & Ethics Counsel

Advises on legal exposure related to AI systems. Translates abstract ethical standards into enforceable internal policy. Ensures lawful deployment in sensitive domains.

Good lawyers protect the company. Great ones future-proof it. This role bridges emerging AI regulation and internal risk appetite. It’s especially vital where AI touches personal data, decisions with human consequences, or cross-border data flows.

VII. POLITICAL-BUSINESS ENVIRONMENT SUBSYSTEM

AIPB – AI Public Affairs Strategist

Shapes the company’s AI narrative across media, public forums, and stakeholder engagements. Builds public legitimacy for the technology and its use. A strategic communicator with deep understanding of AI implications.

Crafts the narrative around AI in the public sphere - media, conferences, and stakeholder events. This isn’t PR fluff; it’s strategic narrative shaping to secure operating legitimacy. If you ever doubt the importance of this role, just look at how many AI “missteps” exploded during heated election campaigns. This isn’t about spin  - it’s about securing the social license to operate. Get the story wrong, and even the best tech won’t survive public scrutiny.

AIRG – AI Regulation Analyst

Tracks emerging regulations globally and forecasts their impact. Advises on compliance and risk mitigation strategies. Crucial in fast-changing legal landscapes.

Tracks global AI regulations and forecasts their impact on products, partnerships, and compliance. Acts as the radar system for policy turbulence. In times of legal flux, this role is invaluable.

AICS-P – AI Stakeholder Engagement Lead

Engages civil society, NGOs, academia, and public institutions. Builds trust outside the corporate walls. Translates social expectations into meaningful design input.

Builds trust bridges with civil society, academia, NGOs, and institutions. Not a lobbyist, but a listener and convener. Ensures the company’s AI development reflects not just internal logic, but societal expectations. Think of this role as an AI-CSR expert, shaped by the Human Equity Management ethos  - not a lobbyist, but a deeply informed listener who builds trust bridges beyond the corporate bubble. Their job is to ensure that what the company builds with AI doesn’t just work, but belongs in the society it’s meant to serve.

AIG-P – AI Policy & Diplomacy Officer

Represents the organization in global AI policy circles and standard-setting bodies. Requires both diplomatic skill and engineering literacy. Shapes the rules by being present early.

Represents the company in international consortia, standards bodies, or cross-sector dialogues. A role that blends diplomacy, engineering insight, and strategic foresight. This role sits at the intersection of influence and alignment  - a geopolitical function in technical disguise. The best people here don’t just follow emerging standards; they help shape them. In an AI-driven world, if you’re not at the table, you’re on the menu.

I’ve seen companies lose entire product lines because they weren’t represented early in standard-setting discussions. This isn’t a ceremonial post  - it’s where your long-term license to operate is negotiated, standard by standard, clause by clause. It demands a rare blend: the patience of a diplomat, the clarity of an engineer, and the foresight of a strategist. Underinvest here, and you’ll feel the cost five years too late.

AIRM-P – AI Risk Signal Analyst

Monitors global technological and geopolitical shifts. Detects threats early - like export controls, supply bottlenecks, or AI-specific sanctions. Strategic foresight role that supports executive awareness.

Monitors geopolitical and tech macro-trends - such as semiconductor chokepoints or AI sanctions - to anticipate threats. A sentinel at the edge of enterprise awareness. Worth every dollar  - but only if tightly constrained within OSINT boundaries. Human intelligence sources are not only costly and hard to manage, they carry extreme reputational risk if exposed. This role is your early warning system, not a spy ring. Treat it like strategic radar, not covert ops.

Final words

In this article, I focused on the business role model of AI implementers, particularly within large enterprises. The taxonomy was guided by three key criteria:

1. model completeness,
2. role complementarity
3. cost efficiency.

But is it possible to build a professional AI implementation team in much smaller companies — those with only a few dozen employees?

Absolutely. Viola Wioletta Koper-Staszowska EMBA will address this in the next article, highlighting the value of systematically building lean competency models under superviosin of the HEM Competence Center.

These are models that promote multi-skilled employees — one of the most distinctive features of post-industrial companies.

Stay tuned!