Understanding high-risk AI through a commercial lens

AI can improve decision-making, increase productivity, strengthen customer experience, support operational resilience, improve M&A, investor readiness, and open new routes to market. For organisations selling into, operating in, or scaling across Europe, the challenge is how to capture that value in a way that customers, supply chains, investors, regulators and boards can trust. That is where the EU AI Act should be viewed through a commercial lens.

Too often, regulation is framed as a barrier. For AI, the better way to look at the EU AI Act is as a ticket through the door. Europe is not one isolated jurisdiction. The EU single market brings together 27 Member States, around 450 million people, 26 million businesses and an economy valued at approximately €18 trillion. For AI providers and deployers with European ambitions, this is a market worth preparing for properly.

If your AI system, product, platform or service can be assessed, explained, governed and trusted for the EU market, you are not simply reducing regulatory risk. You are improving your ability to sell, procure, partner, invest, scale and compete.

The commercial winners will be those who understand high-risk AI through a commercial lens and treat AI strategy, risk, literacy and governance as part of market access, not as an afterthought.

The EU AI Act as a commercial moat

As the AI domain matures, customers will increasingly ask harder questions before buying, adopting or integrating AI systems. Procurement teams will want evidence. Boards will want assurance. Investors will want to understand scalability and exposure. Partners will want to know whether the system can operate across markets without triggering avoidable legal, operational or reputational problems.

Organisations that can evidence responsible AI governance will be better placed to answer due diligence questions, pass procurement reviews, win trust, reduce sales friction and protect their reputation. Organisations that cannot explain what their AI system does, how it is controlled, or what impact it may have will face delay, challenge and potentially costly redesign.

That creates a new form of commercial advantage and in that sense, the EU AI Act creates a moat. It rewards organisations that can show discipline, evidence and control. It makes it harder for weaker competitors to sell AI systems that rely on vague claims, generic policies or untested vendor assurances. It gives serious organisations a route to differentiate themselves through trust.

For AI providers, this can support product-market fit and customer confidence. For deployers, it can reduce implementation risk and help ensure AI adoption produces business value rather than internal resistance. For boards, it can create a more reliable basis for investment decisions.

Why inference matters commercially

One of the most important issues for AI market access is understanding and managing how AI is different from regular deterministic software. AI systems do more than process data. They identify patterns, classify information, make predictions, recommend actions and prioritise outcomes. In doing so, they may make unexpected inferences about people, groups, assets, operations or environments.

Those inferences may be valuable. They may support better services, faster decisions, stronger safety outcomes and improved efficiency. They may also create risk if the organisation does not anticipate what the system is learning to infer. An AI system does not need to be given someone’s age, disability, ethnicity, income, health status or personal circumstances to produce an outcome that correlates with those characteristics. Behavioural data, location, device type, transaction history, education history, work pattern, facial image, response time, application behaviour or operational data may all operate as proxies.

The commercial advantage is leveraged through recognising the regulatory position early, understanding inference and designing in trust from the outset.

Success with AI is not just technical

Many AI initiatives are still approached solely as technical projects. However, a technically impressive AI system may still fail commercially if it lacks a clear business case, executive ownership, governance, user confidence, human oversight, procurement readiness or regulatory evidence. AI success depends on the connection between strategy, risk, literacy and execution.

This is especially true in EU-facing markets. Leaders need to know what the AI system is intended to achieve, how it aligns with business objectives, what decisions it supports, which risks it creates, who owns those risks, and what evidence is available to show that the system is being used responsibly.

AI literacy is part of this and is a legal requirement in the EU. In practice, senior leaders, product owners, procurement teams, operational users and governance teams all need AI literacy to ask the right questions, understand system limits, challenge over-claims and make informed decisions. Without that, organisations may buy tools they do not understand, deploy systems they cannot oversee, and fall behind the competition who were ahead of them in their planning and foresight.

Commercially, the point is broader. Organisations cannot lead AI strategy effectively if decision-makers do not understand the relationship between AI opportunity, AI risk and business value.

What does high-risk AI actually mean?

High-risk AI is not a label attached to an entire industry. It is a classification attached to specific AI systems and use cases.

Under the EU AI Act, an AI system may be high-risk because it is used as a safety component in a regulated product, or because it is used in areas where poor design, weak oversight or unreliable outputs could affect health, safety, fundamental rights or access to important services.

That is why the classification matters commercially.

If an AI system is high-risk, customers, procurement teams, investors and regulators will expect more than a technical demonstration. They will expect evidence that the system has been assessed, documented, governed, monitored and placed under meaningful human oversight.

For CEOs and senior leaders, this is not simply a legal question. It is a market-access question. Can the organisation prove that its AI system is safe enough, explainable enough and well governed enough to be trusted in the European market?

The following areas show where AI opportunity, commercial value and regulatory exposure often meet. They are not blanket sector classifications. They are strategic prompts for boards and senior leaders to examine the actual AI use case, the decision pathway, the inference risks, the operational impact and the evidence needed to scale with confidence.

Critical infrastructure: AI for resilience, safety and operational confidence

Critical infrastructure deserves particular attention because it sits at the intersection of AI opportunity, operational resilience and public trust.

AI has significant potential in energy, water, transport, communications, security, smart buildings and other essential infrastructure environments. It may be used to monitor assets, detect anomalies, support maintenance, manage traffic, improve service continuity, prioritise alerts, identify operational risk or support incident response.

These are valuable uses of AI. They can strengthen resilience, improve efficiency and support better operational decision-making.

The governance question is how the AI system will perform in the real world.

A critical infrastructure AI system may infer operational risk, asset vulnerability, incident severity, safety priority, threat likelihood or service disruption. Those inferences may then influence escalation, resource allocation, operator response, access control or service continuity.

For CEOs and senior leaders in critical infrastructure, the business case for AI should include resilience, safety, accountability and trust. The organisation needs to understand how AI outputs will be interpreted by operators, whether automation bias may occur, whether escalation routes are clear, whether system performance is monitored, and whether human oversight remains meaningful under pressure.

AI should strengthen critical infrastructure, not introduce hidden fragility.

Recruitment and workforce AI: better decisions, stronger assurance

Recruitment and workforce AI is one of the clearest examples of where AI can create value and regulatory exposure at the same time.

AI systems may help organisations manage high application volumes, improve consistency, identify skills, support workforce planning, allocate tasks or monitor performance. Used well, they can reduce administrative burden and support better decisions.

The issue is that these systems may also infer suitability, reliability, motivation, productivity, personality or “fit”. Those inferences may be influenced by indirect signals such as career gaps, education history, address, language patterns, response time, work history or behavioural data.

The system may never ask for protected characteristics. It may still produce outcomes that correlate with them.

For CEOs and HR leaders, this is not only a legal issue. There are talent, brand and trust issues. A recruitment system that cannot be explained or challenged may damage candidate trust, weaken diversity objectives, create internal concern and expose the organisation to challenge.

Successful workforce AI needs clear governance, outcome monitoring, human oversight and evidence that the system supports better decisions rather than embedding hidden assumptions.

Credit, scoring and automated assessment

Credit scoring and automated assessment provide some of the strongest signals for the future of AI governance.

The Schufa case showed how automated decision bias can have real consequences for individuals where another organisation relies heavily on it to decide whether to enter, implement or terminate a contractual relationship. The later CK v Dun & Bradstreet Austria judgement reinforced the importance of meaningful information about the explainability of automated decision-making, in the context of creditworthiness assessment.

These developments are relevant to lenders, telecoms providers, retail finance providers, insurers and any organisation using automated scoring, eligibility or risk assessment. A score may look like an internal efficiency tool, but it may affect whether a person receives access to finance, services, insurance, support or commercial opportunity.

The commercial lesson is clear. If an automated assessment impacts an individual’s rights, the organisation needs to understand the logic, the inference pathway, the data quality, the proxy variables and the explanation that can be provided if the outcome is challenged.

Good governance supports trust. It also supports defensibility and retention of commercial value.

Insurance and essential services

AI can support better underwriting, claims handling, fraud detection, service prioritisation and customer support. In insurance and essential services, this can improve consistency, reduce delay and help organisations manage complex information.

The question is how the system reaches its conclusions.

An AI system may infer health, frailty, lifestyle, financial stress, vulnerability, employment instability or socioeconomic status through indirect variables. Postcode, transaction history, device behaviour, application behaviour, purchasing patterns and online activity may all influence outcomes.

For insurers and essential service providers, model performance is not enough. The organisation needs to understand fairness, explainability, customer impact and human review. A statistically accurate model may still create opaque or disproportionate outcomes if it has not been assessed in context.

The emerging DZI preliminary reference is worth watching because it raises questions about AI-assisted expert evidence, human verification, traceability and explainability. Although the case arises in a dispute involving insurers, the wider business point is that organisations using AI to support assessment or evidence need to understand how the result was generated and whether human review is meaningful.

Retail systems and consumer trust

Retail systems offer another useful example. AI may be used to personalise recommendations, rank search results, target promotions, determine offer eligibility, prioritise customer journeys, segment consumers, detect fraud, optimise loyalty programmes or decide which product, message or incentive a person receives.

These systems can create real commercial value. They can improve relevance, increase engagement, reduce friction and strengthen customer relationships.

The governance issue is whether the system is personalising the experience in a transparent and fair way, or whether it is making hidden inferences that materially affect consumer treatment.

A retail system may infer loyalty, preferences, eligibility, urgency, sensitivity to prompts, financial pressure or likely response to a particular offer. It may rely on indirect signals such as postcode, device type, browsing behaviour, purchase history, time of interaction, payment method, basket behaviour or loyalty status.

For consumer-facing organisations, trust is part of the commercial asset. Retail AI should strengthen the customer relationship, not undermine confidence. Organisations that understand and govern these systems properly will be better placed to scale personalisation while protecting reputation.

Biometrics and facial recognition: trust as a route to adoption

Biometrics remains one of the clearest examples of inference in high-risk AI. Facial recognition is often described as identification or verification, but the risk profile changes when biometric data is used to categorise, predict, rank or infer characteristics about a person. This may include sensitive or protected characteristics, emotional state, behavioural risk, group membership or perceived suspiciousness.

For the security market, this is a major strategic issue. The market will catch up with it because customers, regulators and the public will increasingly expect stronger assurance around biometric AI.

That should not be seen only as a constraint. It is also an opportunity for serious providers, integrators and deployers to differentiate themselves.

Organisations that understand biometric risk properly will be better placed to select appropriate technologies, define permitted use cases, set operational limits, evidence proportionality, manage false positives and false negatives, and maintain public trust.

A biometric system may be sold as operationally useful, but the real risk and value depend on how it is configured, what it infers, what action follows and who is affected.

Public services, education and emergency response

AI can also support education, public services and emergency response. In education, AI may help assess learning outcomes, identify support needs, monitor assessments or guide future learning pathways. In public services, it may help assess eligibility, prioritise support or manage complex case information. In emergency response, it may help classify calls, prioritise dispatch or support healthcare triage.These are areas where AI could deliver genuine public value.

They are also areas where affected people may have limited visibility, limited choice and limited ability to challenge a decision. A system that infers ability, urgency, credibility, entitlement or priority may affect access to life chances, support or urgent intervention.

The Dutch childcare benefits scandal remains a powerful warning to CEOs about what can happen when automated systems, weak governance and poor human oversight combine. It is a “how not to do it” case for any organisation using AI or automated assessment in sensitive contexts. The lesson for leaders is not to avoid AI. The lesson is to build governance, accountability and human judgment into the system before harm occurs.

Justice, evidence and democratic processes

Justice and democratic processes may sound remote from day-to-day commercial AI adoption. They are not.

AI is already moving into legal research, evidence review, expert reports, claims assessment, fraud investigation, complaint handling, contract enforcement, public administration, alternative dispute resolution and decision support. In democratic contexts, AI may also be used to target messages, influence behaviour, segment voters or shape public engagement.

The commercial issue is straightforward. If AI-generated analysis is used to support a decision, defend a claim, challenge a customer, assess liability, influence a public process or inform an expert opinion, the organisation needs to understand how that result was produced.

This is where trust becomes practical.

A business may use AI to accelerate review, identify patterns, compare evidence, support case strategy or produce an expert input. Those uses may create value. They may also create exposure if the organisation cannot show what data was used, what assumptions were made, what the system produced, what a human reviewed, and whether the result can be explained.

The emerging DZI preliminary reference is worth watching for this reason. It raises questions about AI-assisted expert evidence, human verification, traceability and explainability. For commercial leaders, the signal is clear: AI-supported evidence and decision-making must be capable of scrutiny.

This matters beyond the courtroom. It affects insurers, lenders, public-sector suppliers, regulated businesses, legal technology providers, professional services firms and any organisation using AI to support decisions that may later be challenged.

The opportunity is to use AI to improve speed, consistency and insight while preserving defensibility. That means maintaining audit trails, documenting human review, understanding system limits and ensuring that AI-assisted conclusions can be explained to customers, counterparties, regulators, courts or boards.

AI can strengthen decision-making, but only where the reasoning pathway remains visible enough to be trusted.

The CEO question: can we scale this AI system and access the EU market?

For organisations with European ambitions, the EU AI Act is more than a compliance exercise. It is a market-access framework for one of the world’s largest economic regions.

Throughout this article, we stress that organisations which understand their AI use cases, identify potential high-risk applications, manage inference risks, invest in AI literacy, and implement effective governance will be better positioned to access the EU market and compete within it.

The EU AI Act rewards organisations that can demonstrate trustworthiness, accountability and control. Customers, procurement teams, investors and partners increasingly want evidence that AI systems can be explained, governed and deployed responsibly. Those organisations that can provide that evidence will face fewer barriers to adoption, procurement and growth.

The commercial opportunity is significant. The same governance disciplines that support compliance with the EU AI Act also help organisations reduce implementation risk, strengthen customer confidence, improve procurement outcomes and create a stronger foundation for scaling AI across the business.

The question for CEOs and senior leaders is simply:

“Can we demonstrate that our AI systems are trustworthy enough to win business, attract investment and operate confidently across the European market?”

The organisations that answer that question early will be better placed to build competitive advantage inside the EU AI Act’s emerging trust framework.

How Anekanta® supports AI success

Anekanta® helps organisations turn AI ambition into responsible, commercially viable adoption. Our work sits at the intersection of AI strategy, risk, literacy and governance. We help boards, CEOs and senior leaders understand how AI can create value, where it may create exposure, and what needs to be in place for successful adoption.

That includes:

AI strategy and use case evaluation
EU AI Act risk assessment and classification
AI literacy for boards and senior leaders
AI governance frameworks and workshops
ISO/IEC 42001 preparation
AI risk and impact assessment
Biometrics and high-risk AI evaluation
Practical governance controls for providers and deployers.

Whether you are developing AI products, procuring AI solutions, deploying AI internally, or preparing to enter EU markets, we help organisations understand what the EU AI Act means in practice and how to turn regulatory expectations into commercial advantage.

The organisations that succeed in Europe will be those that build trust, evidence and accountability into AI from the outset.

If your organisation is assessing AI opportunities, preparing for the EU AI Act, or looking to strengthen its route to market in Europe, contact Anekanta® to discuss how we can help you align AI strategy, governance and market access objectives.

A diverse group of six professionals walking together in a modern urban setting, featuring green buildings and wind turbines. A train passes through behind them, and the scene is under a blue sky with a few clouds.

Learn How to Access AI Opportunities in the EU with the Right Planning and Foresight

Intellectual Property: © 2016–2026 Anekanta®. All rights reserved. Unless otherwise expressly stated, all materials published on this website, including the Anekanta® AI Governance Framework for Boards, the 12 Principles, and all AI risk and impact evaluation methodologies, software, models, diagrams, text and materials, are proprietary intellectual property of Anekanta®. No reproduction, adaptation, distribution, or commercial exploitation is permitted without prior written authorisation. No rights are granted other than those expressly stated. The Anekanta® AI Governance Framework and 12 Principles are developed, maintained and continuously enhanced as part of Anekanta®’s proprietary governance architecture.
Professional Disclaimer: The information provided on this website is for general informational purposes only and does not constitute legal, regulatory, financial or professional advice. Any reliance placed on the information is strictly at the user’s own risk. Professional advice should be sought in relation to specific circumstances through a formal engagement with Anekanta®.
Use of Generative AI: Generative AI tools may be utilised in research and drafting processes. All published materials are subject to substantive human review, professional judgment and oversight prior to release.