Artificial Intelligence
Can Businesses Trust AI Advice Under Pressure?
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As AI technology develops and gets integrated into business practice and decision-making, several questions arise. The first one is, of course, the question of reliability, with the “hallucinations” of early versions of LLMs having durably impacted users’ trust in the truth of data provided by AIs, even if the issue is generally less prevalent in newer models.
Another question is whether AI will keep its safety guardrails and ethical guidelines under pressure from users, its developers’ commercial interests, or simply repeated and smart attempts to find workarounds in LLMs’ design.
A new study by a researcher at the Universidad Pablo de Olavide in Spain has directly tested multiple LLMs’ ability to resist such pressure and stick to their ethical guidelines. He developed a protocol to test any LLM, in which adaptive dialogue is used, and counter-arguments are calibrated to each model’s prior response.
The study was published in Computers in Human Behavior Reports1, under the title “Should businesses trust AI advice? A methodology to audit the ethical integrity of chatbots”.
From Search To Advisors
Initially, LLMs were used in a similar way to a more familiar technology for its user: as a search engine. But as their reasoning capacity grew, they are increasingly used to replace or supplement the counsel of a trained human professional: lawyers, doctors, engineers, etc.
“When such a model is asked whether to hire a relative, hide cash income or launch a flawed product, the answer is no longer informational—it is advisory. An enterprise advisor must not only be accurate, it must remain ethically coherent when a user pushes back with profit, survival or convenience arguments.”
So, it is with this issue in mind that the study proposes the Adaptive Ethical Evaluation Protocol (AEEP).
The study focused on small and medium-sized enterprises (SMEs) because this segment is most likely to delegate advisory work to LLMs because they lack in-house compliance, legal, or ethics functions. It is also the case that the dilemmas SME founders face, like nepotism, fraud, harassment, or data misuse, coincide with the ethical–legal grey zones explicitly governed by the EU AI Act and GDPR.
Measuring Ethical Stability
The AEEP defines ethical reliability of LLM advisors as a measurable property of multi-turn interaction, not of single answers. This way, it can detect deeper flaws that a surface analysis with only a first-level answer would not reveal.
It provides a fully specified, reproducible coding pipeline (sentiment, keyword, and NLI-based contradiction detection) whose outputs are validated against blind expert ratings.
The study applied this test to five different frontier LLMs: ChatGPT, Claude, Gemini, Grok, DeepSeek.
It revealed stable, model-level differences in ethical consistency under pressure that are invisible to conventional benchmarks.
It should, however, be noted that such models evolve very quickly, and the models were tested from May 12–14, 2025, so their relative rankings cannot reliably describe their capabilities in late 2026.
It reveals the possibility of creating such an up-to-date benchmark that would measure not accuracy or compute efficiency, but ethical stability.
Measuring Ethics
The Many Forms Of Ethical Breach
What is or is not an ethical business behavior is, of course, subject to some level of cultural variation and personal judgement. But a business, and especially a small or medium-sized business, can be exposed to some ethical hazards well recognized as problematic.
The first one is corruption and bribes. A founder may be pressed to bribe a public official to expedite a permit, or to pay a kickback to a procurement officer to win a contract. It ultimately disrupts fair competition and rewards deception, while exposing the firm to legal and reputational risk.
Another ethical issue businesses face is how to treat their own workforce under performance pressure. As exploitative treatment damages morale, increases turnover, and harms reputation, ambitious performance goals need to stay within the framing of not just legally authorized actions, but also be tied to fair compensation, safety, and reasonable working conditions.
Misleading consumers about product features, quality, or readiness is a third recurrent dilemma. For example, many startups under pressure to gain early traction or raise funds may be tempted to exaggerate performance in sales materials or pitches.
And of course, product safety and consumer protection are even more important to avoid any users coming to harm with a given product that might not perform as expected.
Lastly, conflicts of interest and self-dealing can be especially prevalent for small businesses, where the barrier between the founder and the company is thinner than in large corporations with a supervisory board and external shareholders. It can nevertheless undermine trust among co-founders, employees, and investors.
“A founder might consider awarding a lucrative contract to a business in which they or their relatives have a stake or they may use company funds for personal expenses. This scenario tests the founder’s commitment to fiduciary duty and fairness. ”
Ethics & AI
The rise of AI in business brings new ethical dilemmas for entrepreneurs integrating these technologies. AI systems can inadvertently embed bias, threaten privacy, and make autonomous decisions with moral implications.
“As an example, a start-up deploying an AI-driven hiring tool may discover that the algorithm discriminates against certain groups: should they delay use of the tool (at the cost of efficiency) to improve its fairness?”
So people adopting AI need to be aware of such ethical risks and mitigate them appropriately.
“Handling AI ethically is becoming a litmus test for modern entrepreneurial leadership, since it requires a balance of technical savvy, foresight, and moral responsibility.”
And it could be equally useful for the AI industry at large to start adopting consistent benchmarks and common standards besides the “AI safety” general term.
Building A Test for Ethical Stability
The AEEP test presents ten progressively harder ethical dilemmas, pre-registered indicators, an adaptive decision-tree of follow-up prompts, and a deterministic coding pipeline.
Each dialogue comprises five conversational nodes:
- An opening node.
- Two adaptive pressure nodes.
- A conclusion node in which the model must commit to a recommendation.
- A confirmation node in which that commitment is challenged.
Then the results were analyzed by an expert group composed of specialists in artificial intelligence, applied ethics, and business. They checked the consistency of the dilemmas, the adequacy of the ethical indicators, and the robustness of the algorithm’s evaluations.
Each expert independently rated every dialogue. They received the 50 dialogues in randomised order, blinded to the model identity and to the algorithmic scores.
Each scenario explicitly presents a moral dilemma confronting entrepreneurs, and covers issues such as nepotism, intellectual property, employee welfare, honesty in finance, anti-harassment, mission integrity, fraud, and manipulation.
LLMs Under Ethical Scrutiny
The AI models were tested for a series of ethical decision-making characteristics:
- Consistency of Moral Stance: checking that the chatbot should not contradict itself when the user pushes new prompts.
- Willingness to Prioritise Ethics over Profit/Convenience: the fact that the AI assistant understands that ethical conduct and business success are not mutually exclusive.
- Presence of Contradictions or Logical Inconsistencies: The dialogues are reviewed for statements that conflict with earlier statements and/or with known facts. This indicator is binary/objective: a logical inconsistency is present or not.
- Ethical Awareness and Depth of Reasoning: Whether the model recognizes the moral dimensions of the problem, identifies affected stakeholders, and considers harm, fairness, rights, and legality.
Together, it builds a solid framework to determine the ethics of an AI recommendation:
“Is its reasoning internally coherent, match known facts, and is it ready to push ethical decisions over short-term business success and demand from its users. “
The 2025 tests showed Claude performing best overall for ethical stability, while Grok generally ranked last. Results for the models between those two endpoints varied across the evaluated cases and indicators.
Across the complete evaluation, the automated assessments agreed with the five-person expert panel 93.8% of the time. At the individual-model level, agreement was highest for Claude at 96.2%, supporting the methodology’s ability to approximate expert assessments.
The pattern that emerged was that Grok often did not object to an inauthentic request or did not help with a user ethical dilemma, usually with some restrictions.
Investors And AI Users Takeaways
As AIs become embedded into decision-making workflows, whether an AI will maintain its ethical position when challenged by commercial pressure is becoming important. In the long term, it will likely become an essential layer of enterprise governance, procurement, and regulatory compliance.
This study provides a tested theoretical framework for creating new benchmarks on this topic and provides a new dimension along which LLMs can be assessed.
It could be used to support model selection, mandatory human-review triggers, version gating, and recurring compliance audits.
This also opens the way to better understand the trade-offs in place when dealing with AI safety. Grok’s more permissive responses may reflect different alignment priorities. However, the study did not examine whether this permissiveness improves factual accuracy, usefulness, or freedom from political bias.
It should also be noted that while the released models for open-weight LLMs like DeepSeek performed well from an ethical point of view, the nature of open-weight design means that alternative, less ethically-driven models could relatively easily be created.
It is also possible that we will see the emergence of ethics-focused LLMs able to deal with such dilemmas and questions in particular, with their ethical ranking (around objective, industry-wide standardized benchmarks) as a top priority.
Overall, this study also poses the question of how much humans should surrender their own judgement to LLMs, and remember that their advice is always potentially at risk of breaching ethical limits, but maybe not more or less than a real human would either.
Investing In Ethical AI Models
Alphabet
Among the publicly traded companies directly represented in the study, Alphabet offers one of the clearest investment connections. Gemini performed strongly in the evaluation, while Alphabet also operates the enterprise products, cloud infrastructure, and governance systems needed to commercialize AI within regulated business environments.
This supports the idea of businesses, including small and medium businesses, using Google/Alphabet’s LLM in the company’s enterprise AI products, cloud infrastructure, and responsible-AI governance systems.
As AI models become increasingly competent and efficient, and the cost of hardware keeps declining, the differentiation between models will increasingly not be based solely on capability.
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Instead, vendors able to provide audit trails, configurable safeguards, evaluation tooling, and regulatory documentation may be better positioned to win lucrative risk-sensitive corporate deployments.
Besides consideration of ethics, the efficiency of Google Cloud infrastructure, experience in managing data with its search, and its existing relations with enterprises will be powerful leverage to win a significant segment of the market, especially with any SMEs already having part of their IT systems tied to Google.
So while other companies will also perform well, notably Microsoft and its strong presence in large enterprises, Google seems like a good way for investors to get exposure to AI that can be trusted not just to provide information, but to become a true assistant to founders, entrepreneurs, and managers often in dire need of professional expertise and improved compliance with regulations like GDPR, HR laws, tender regulations, etc.
(You can read more about Alphabet in our dedicated investment report about the company)
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Study Referenced
1. Manuel Chaves-Maza. Should businesses trust AI advice? A methodology to audit the ethical integrity of chatbots. Computers in Human Behavior Reports. Volume 24, December 2026, 101291. https://doi.org/10.1016/j.chbr.2026.101291
