TELUS Digital Research Reveals a Hidden Risk in AI Model Behavior

Rhea-AI Summary

TELUS Digital (TU) published a study, The Robustness Paradox, showing that persona prompting can shift LLM moral judgments and produce inconsistent responses. The study evaluated 16 model families and found moral robustness is driven by model family while moral susceptibility rises with model size within a family.

Findings highlight enterprise risk for high-impact uses and recommend careful model selection, ongoing testing, guardrails, and continuous red-teaming with tools like Fuel iX Fortify.

Positive

• Moral robustness varies by model family
• Study evaluated 16 leading model families
• Identifies predictable persona-driven judgement shifts
• Fuel iX Fortify enables continuous automated red-teaming

Negative

• Moral susceptibility increases with model size within families
• Larger models may produce larger judgment shifts across personas
• Inconsistent moral judgments create enterprise risk in high-impact uses

News Market Reaction – TU

-0.74%

1 alert

-0.74% News Effect

On the day this news was published, TU declined 0.74%, reflecting a mild negative market reaction.

Data tracked by StockTitan Argus on the day of publication.

Key Figures

AI model families: 16 model families

1 metrics

AI model families 16 model families TELUS Digital robustness study sample

Market Reality Check

Price: $13.45 Vol: Volume 3,895,650 is at 0....

low vol

$13.45 Last Close

Volume Volume 3,895,650 is at 0.64x its 20-day average of 6,061,595 shares. low

Technical Shares at $13.55, trading below the 200-day MA of $15.04 and 19.03% under the 52-week high.

Peers on Argus

TU slipped 0.22% while peers were mixed: BCE and SATS declined (-0.46%, -0.25%) ...

TU slipped 0.22% while peers were mixed: BCE and SATS declined (-0.46%, -0.25%) but RCI, VIV and CHTR rose (0.57%, 2.73%, 1.9%). This points to stock-specific trading rather than a unified telecom move.

Previous AI Reports

5 past events · Latest: Feb 11 (Neutral)

Same Type Pattern 5 events

Date	Event	Sentiment	Move	Catalyst
Feb 11	AI research findings	Neutral	-0.5%	Poll and benchmark showed follow-up prompts rarely improve LLM accuracy.
Jan 28	AI trust survey	Positive	+0.7%	Cross-border survey highlighted strong AI adoption and support for regulation.
Jan 27	AI product expansion	Positive	+0.0%	RingCentral partnership added AI-powered automation to TELUS Business Connect.
Nov 04	AI platform expansion	Positive	-0.3%	Fuel iX rollout enabled 70,000 employees to build no‑code AI copilots.
Jul 17	AI ethics initiative	Positive	-0.7%	Participation in UN AI for Good Summit showcased responsible and ethical AI efforts.

Pattern Detected

Recent AI-tagged news has produced modest, often mixed price reactions, with an average move of -0.14% and more instances of divergence than alignment with generally positive AI narratives.

Recent Company History

Over the past year, TELUS has released multiple AI-focused updates, from expanding its Fuel iX platform to surveys on AI trust and product enhancements like AI-powered Business Connect features. Price reactions around these AI announcements have been relatively muted, averaging -0.14%. The current TELUS Digital study on persona prompting continues this pattern of emphasizing AI robustness, governance, and responsible deployment rather than near-term financial impacts.

Historical Comparison

-0.1% avg move · In the past year, TELUS issued 5 AI-tagged updates with an average move of -0.14%. This persona-robu...

AI

-0.1%

Average Historical Move AI

In the past year, TELUS issued 5 AI-tagged updates with an average move of -0.14%. This persona-robustness study fits the pattern of governance- and research-driven AI news with typically modest stock reactions.

AI news has progressed from ethics and global initiatives to platform rollouts, business AI features, and increasingly technical robustness and trust research.

Market Pulse Summary

This announcement spotlights TELUS Digital’s research into how persona prompting affects AI moral ju...

Analysis

This announcement spotlights TELUS Digital’s research into how persona prompting affects AI moral judgments, emphasizing governance, robustness, and continuous testing through tools like Fuel iX Fortify. It builds on a series of AI-focused initiatives and studies over the last year. Investors may watch how these efforts translate into commercial traction for Fuel iX, integration with TELUS’s broader platforms, and ongoing execution in the core telecom business.

Key Terms

persona prompting, role prompting, large language models, LLMs, +4 more

8 terms

persona prompting technical

"Persona prompting, also known as role prompting, refers to instructing an AI model"

Persona prompting is the practice of instructing an AI or automated writer to adopt a specific identity, tone and set of assumptions when producing text—like telling a spokesperson to sound formal, a teacher to be explanatory, or an analyst to be cautious. For investors, it matters because the chosen persona shapes how company updates, risks and forecasts are framed and understood, affecting clarity, trust and the chance of misleading or non‑compliant language; it's like picking a narrator who guides reader interpretation.

role prompting technical

"Persona prompting, also known as role prompting, refers to instructing an AI model"

Role prompting is the practice of telling an automated writing or analysis tool which persona or perspective to take before asking it to produce text or answers — for example, instructing it to act like a financial analyst, compliance officer, or customer service rep. For investors, this matters because it shapes how information is framed, the level of detail and risk emphasis, and the tone of automated summaries or disclosures; like a director giving an actor direction, it helps ensure the output fits the intended audience and reduces misunderstanding.

large language models technical

"asks large language models (LLMs) to "role-play" as part of a query"

Large language models are advanced AI systems trained on vast amounts of text to understand and generate human-like writing, like a very fast reader and writer that learns patterns in words and sentences. They matter to investors because they can change how companies operate—automating customer service, speeding analysis, cutting costs, creating new products—and they introduce risks around accuracy, security and regulation that can affect a firm’s revenue and reputation.

LLMs technical

"asks large language models (LLMs) to "role-play" as part of a query"

Large language models are advanced computer programs that read and generate human-like text by learning patterns from huge amounts of written material; think of them as digital employees that can draft reports, answer questions, summarize documents, or generate code. They matter to investors because they can change a company’s costs, speed of product development, customer service, and competitive edge — and they also create new risks and regulatory questions that can affect profits and valuation.

Moral Foundations Questionnaire technical

"researchers used the Moral Foundations Questionnaire, a tool used in social psychology"

A short survey that measures a person’s moral priorities across a handful of basic categories—such as care, fairness, loyalty, authority and purity—by asking how strongly they agree or disagree with simple statements. For investors it can signal how customers, employees or communities are likely to react to a company’s products, policies or controversies; think of it as a personality quiz for groups that helps assess reputation, consumer preferences and social risk.

moral robustness technical

"The study identified two properties:Moral robustness describes how consistent a model's"

Moral robustness is an organization’s ability to act consistently according to clear ethical principles when faced with difficult choices or pressure, supported by strong policies, leadership, and culture. Investors care because companies that withstand ethical shocks tend to avoid costly scandals, fines and customer loss, preserving value over time—much like a building with a solid foundation is less likely to collapse during a storm.

moral susceptibility technical

"Moral susceptibility captures how much a model's judgments shift when it moves"

Moral susceptibility is a company’s or individual’s tendency to be swayed by ethical pressures, conflicts of interest, or temptations that can lead to poor or questionable decisions. Like a ship with a weak rudder that is easily pushed off course by storms, high moral susceptibility raises the risk of scandals, regulatory fines, and loss of customer or investor trust, all of which can reduce earnings and harm a stock’s value over time.

automated red-teaming technical

"Fuel iX Fortify to enable continuous automated red-teaming, including stress-testing"

Automated red-teaming is the use of software tools to simulate attacks on a company’s systems, networks, or AI models to find weaknesses before real criminals do. Like hiring a mechanical burglar that runs repeated break-in drills, it helps reveal security gaps, operational vulnerabilities, and compliance shortfalls quickly and at scale. Investors care because frequent, automated testing can reduce the chance of costly breaches, regulatory fines, and reputational damage, and can signal stronger risk management and lower unexpected liabilities.

AI-generated analysis. Not financial advice.

Study shows the use of persona prompting can cause shifts in LLMs' moral judgements,
leading to unexpected and inconsistent responses

For enterprises, this means careful model selection, rigorous testing and ongoing evaluation are essential to ensure consistent,
reliable AI behavior in production

VANCOUVER, BC, Feb. 25, 2026 /PRNewswire/ - A new study published by TELUS Digital, The Robustness Paradox: Why Better Actors Make Riskier Agents, finds that the use of persona prompting, a technique that asks large language models (LLMs) to "role-play" as part of a query or conversation, can cause shifts in moral judgements that lead to unexpected and inconsistent responses. In addition, the research demonstrates that moral consistency across repeated tests is primarily driven by the model family (i.e., models made by a single vendor) while susceptibility to moral variance rises with LLM size within a model family. These findings highlight a hidden enterprise risk that requires attention during AI model selection, solution design, and in ongoing testing and monitoring.

"When AI models adopt different personas, they don't just change how they speak, they can fundamentally alter their reasoning and decision-making," said Renato Vicente, Director, TELUS Digital Research Hub. "In enterprise settings, this matters because these systems are increasingly being used to support important decisions and affect outcomes that may impact customers, employees and businesses at scale. Knowing that an AI model's judgment may shift based on the persona it's prompted to adopt by a user, companies need to assess when that variance is acceptable or creates too much risk, and select an AI model vendor and model size accordingly. Builders should also design appropriate guardrails as well as test and evaluate AI model behavior under different persona prompting conditions on an ongoing basis, especially when relying on them in high-impact use cases."

What is persona prompting?

Persona prompting, also known as role prompting, refers to instructing an AI model to respond as if it were a specific type of person or role with specific expertise or knowledge, such as a business leader, teacher, or customer support agent, rather than responding as a neutral system. For example: "You are a certified financial planner, tell me where to invest my retirement savings."

Persona prompting is also commonly used by model builders in system design and production to hardcode personas and assign fixed roles that will define the AI's behavior. For instance, building an AI-powered customer service bot that's configured to act as a helpful support agent with deep knowledge of product features and return policies. In practice, personas make AI outputs feel more consistent, helpful and context-aware without changing the underlying model.

How was TELUS Digital's research done?

The study, conducted by researchers working at the TELUS Digital Research Hub in the University of São Paulo's Center for Artificial Intelligence and Machine Learning (CIAAM), evaluated 16 leading AI model families, including Open AI GPT, Anthropic Claude, Google Gemini and X.ai Grok. Researchers prompted the models to adopt a range of personas, including contrasting pairs like a "traditionalist grandmother" and a "radical libertarian," and then observed how each model responded as each persona.

To assess the responses, researchers used the Moral Foundations Questionnaire, a tool used in social psychology to measure how judgments are made across dimensions such as harm, fairness, authority and loyalty. Rather than analyzing individual answers, the researchers examined patterns across tens of thousands of responses to measure how consistently each model reasoned across different personas.

The study identified two properties:

1. Moral robustness describes how consistent a model's judgments remain while it stays within a single persona.
2. Moral susceptibility captures how much a model's judgments shift when it moves from one persona to another.

When evaluated together, moral robustness and moral susceptibility reveal whether an AI model maintains consistent moral reasoning or produces contradictory judgments based on an assigned persona.

TELUS Digital's key findings on how personas affect AI model behavior

While it's well understood that LLM outputs can shift when personas are added to prompts, TELUS Digital's study highlights a more specific pattern. Moral robustness is driven mainly by model family, while moral susceptibility tends to increase with model size within the same family when the persona changes. This becomes a higher risk when those shifts show up in business decisions where consistency and oversight matter most, such as compliance, finance, healthcare or human resources.

The study identified additional patterns in how AI models respond when prompted to adopt different personas. Researchers described the findings as a "robustness paradox" because the models that were better at staying in character also showed larger shifts in moral judgments when the persona changed.

• Persona based prompts can systematically influence moral reasoning in AI: Changes in the models' judgment is not random, it shifts in predictable ways that are aligned to the assigned roles.
• Judgment stability is primarily driven by model family: A subset of the findings indicated that:
  • Claude demonstrated the highest overall moral robustness
  • Gemini and GPT demonstrated moderate moral robustness
  • Grok demonstrated comparatively low moral robustness

What are the real world impacts of persona prompting when building AI?

TELUS Digital's research findings highlight the importance of conducting ongoing testing and oversight of AI models as part of a robust governance framework. This is particularly important when AI models are employed in scenarios where decisions affect people's lives, safety, or rights, and in regulated environments, such as banking and finance, insurance and healthcare. Understanding how different AI models behave under different persona prompts is key information to help model builders and enterprises identify where variability is acceptable and where it can introduce risk.

"Our research findings underscore why enterprise AI deployment requires more than just picking the most advanced or largest model. Organizations must evaluate how individual models respond to variables such as persona prompting and choose options that deliver consistent, reliable outputs without introducing unexpected risk," said Bret Kinsella, General Manager and Senior Vice President of Fuel iX^™ at TELUS Digital. "Every time a system prompt is modified within the model, or the model is changed, it needs to be tested again to validate its judgment, consistency, and safety. The scale and frequency of this testing, monitoring and validation is significant. TELUS Digital developed Fuel iX Fortify to enable continuous automated red-teaming, including stress-testing how AI systems behave under different persona prompts."

Are you ready to uncover the vulnerabilities in your GenAI applications? Learn more at: https://www.fuelix.ai/products/fuel-fortify

The TELUS Digital Research Hub brings together academic researchers and industry practitioners to study how advanced AI models behave in real-world, human-facing contexts. For more information visit: https://www.telusdigital.com/research-hub

View related press releases:
TELUS Digital Launches Fuel iX^™ Fortify for Automated Red-Teaming
TELUS Digital Research Hub Unveiled at USP

Frequently asked questions:

What is persona prompting in AI models?

Persona prompting, also known as role prompting, refers to instructing an AI model to respond as if it were a specific type of person or role, such as a compliance officer, business leader, teacher, doctor, or customer support agent, rather than responding as a neutral system. This technique is commonly used to make AI outputs feel more relevant, context-aware, and aligned with organizational tone or expectations.

What does TELUS Digital's study say about persona prompting?

TELUS Digital's study, The Robustness Paradox: Why Better Actors Make Riskier Agents, found that when the same AI model is prompted to adopt different personas by users, it can make different judgment calls even when the underlying question does not change. These shifts followed consistent patterns aligned with the persona being used, showing that persona-based prompting can influence how models make decisions, not just their tone and how they communicate.

What is moral robustness in AI models?

Moral robustness describes how stable an AI model's moral judgments remain while it stays in the same persona. For example, if a model is role-prompted to respond as a compliance officer, moral robustness measures whether its judgment stays consistent across many questions while it remains in that compliance role.

What is moral susceptibility in AI models?

Moral susceptibility describes how much an AI model's moral judgments change when the persona changes. For example, a model might respond one way when role-prompted as a compliance reviewer, but shift its judgment when role-prompted as a business leader focused on efficiency, when asked the same question.

Why do TELUS Digital's study findings in the paper: The Robustness Paradox: Why Better Actors Make Riskier Agents, matter for enterprises deploying AI?

TELUS Digital's study reveals a risk for enterprises deploying AI. It found that when leading open-source and proprietary LLMs are asked by users to "role-play" as part of a query or conversation (a technique known as persona prompting), the degree to which their moral judgments shift is driven primarily by model family and model size within a given family. These findings highlight a hidden risk for enterprise AI builders that should be proactively addressed during model selection and design, and through ongoing testing and monitoring once in production.

How does TELUS Digital's study inform AI governance and risk management frameworks?

TELUS Digital emphasizes the importance of ongoing testing and oversight as part of an enterprise AI risk management framework. Understanding how models behave under different persona prompts is a key input to responsible AI governance, helping organizations identify where variability is acceptable and where it introduces risk, particularly in higher-impact or regulated environments.

How does Fuel iX^™ Fortify support continuous automated red-teaming, and policy-aligned persona testing?

Fuel iX Fortify supports AI testing with automated red-teaming and ongoing monitoring as part of enterprise AI governance. It helps teams evaluate how AI models respond under a wide range of real-world conditions, including adversarial prompts, high-risk scenarios and persona prompting.

About TELUS Digital

TELUS Digital, a wholly-owned subsidiary of TELUS Corporation (TSX: T) (NYSE: TU), crafts unique and enduring experiences for customers and employees, and creates future-focused digital transformations that deliver value for our clients. We are the brand behind the brands. Our global team members are both passionate ambassadors of our clients' products and services, and technology experts resolute in our pursuit to elevate their end customer journeys, solve business challenges, mitigate risks, and drive continuous innovation. Our portfolio of end-to-end, integrated capabilities include customer experience management, digital solutions, such as cloud solutions, AI-fueled automation, front-end digital design and consulting services, AI & data solutions, including computer vision, and trust, safety and security services. Fuel iX^TM is TELUS Digital's proprietary platform and suite of products for clients to manage, monitor, and maintain generative AI across the enterprise, offering both standardized AI capabilities and custom application development tools for creating tailored enterprise solutions.

Powered by purpose, TELUS Digital leverages technology, human ingenuity and compassion to serve customers and create inclusive, thriving communities in the regions where we operate around the world. Guided by our Humanity-in-the-Loop principles, we take a responsible approach to the transformational technologies we develop and deploy by proactively considering and addressing the broader impacts of our work. Learn more at: telusdigital.com

Contacts:

TELUS Digital Media Relations 
Ali Wilson 
media.relations@telusdigital.com 

TELUS Investor Relations 
Olena Lobach 
ir@telus.com

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SOURCE TELUS Digital

FAQ

What did TELUS Digital announce on February 25, 2026 about persona prompting and TU?

They reported that persona prompting can change LLM moral judgments and cause inconsistent outputs. According to TELUS Digital, the study of 16 model families found moral robustness linked to model family and susceptibility rising with model size, creating enterprise risks for high-impact applications.

How does TELUS Digital say model family affects moral robustness for TU-related models?

Model family primarily determines moral robustness and consistency within a persona. According to TELUS Digital, some families (Claude) showed higher robustness, while others (Grok) showed lower robustness, affecting reliability when systems stay in a fixed role.

What does the study say about model size and moral susceptibility for companies using TU models?

Larger models within the same family tend to be more morally susceptible when personas change. According to TELUS Digital, susceptibility rose with model size, meaning bigger models may shift judgments more when role-prompted, increasing oversight needs.

Which model families did TELUS Digital find most and least morally robust on Feb 25, 2026?

TELUS Digital found Claude highest in moral robustness and Grok comparatively low. According to TELUS Digital, Gemini and GPT showed moderate robustness, based on tests across contrasting personas and tens of thousands of responses.

How should enterprises change AI governance after the TELUS Digital study for TU deployments?

Enterprises should add persona-focused testing, guardrails, and continuous monitoring before deployment. According to TELUS Digital, organizations need model-specific validation, frequent re-testing after prompt or model changes, and tools like Fuel iX Fortify for automated red-teaming.

What practical risk does persona prompting pose for TU users in regulated industries?

Persona prompting can produce inconsistent judgments that risk compliance and decision quality in regulated settings. According to TELUS Digital, this unpredictability matters for finance, healthcare, and HR, requiring stricter validation and oversight before production use.