AI has already given us some unforgettable “scaries.”
For example, Air Canada faced trouble when a traveller asked its chatbot about the refund process.The chatbot gave the wrong information. When the case went to court, the airline argued that the bot was “responsible for its own actions.” The court rejected that argument, held the company accountable, and ordered it to pay damages and tribunal fees.
A similar issue surfaced with IBM’s Watson for oncology. The system was created to support doctors with cancer treatment recommendations, but it ended up suggesting unsafe options because it was trained on hypothetical cases rather than real-world patient data.
These stories remind us of one thing. When AI fails, the organisation is the one held accountable, which makes it essential to put the right safeguards and practices in place.
People are excited but not ready
AI adoption is moving faster than most companies can keep up with. Employees are curious, willing to experiment, and eager to improve their productivity. What organisations often lack is a shared understanding of how AI should be used. This creates a mix of rapid adoption, limited guardrails, and high trust placed on tools that are not fully understood.
Research shows the gap clearly. Companies expect productivity gains of up to 40%, yet only 2% are fully ready across strategy, governance, talent, data, and technology.
Employees feel the gap too. Almost half say they worry that using AI at work makes them appear lazy or less competent. This fear slows responsible adoption and encourages “shadow AI,” where people use unsanctioned tools without safeguards. This exposes companies to data and compliance risks.
How AI really works
Generative AI is powerful, but it does not think the way humans do. It does not store knowledge in a meaningful way. It does not understand your question.
Instead, AI follows patterns. It looks at how words or data usually appear together and then guesses what should come next. It knows the pattern of the language, not the meaning behind it.
This is why AI can sound confident and still be wrong. It can produce a visually perfect slide filled with incorrect numbers. It can write a convincing email containing misleading advice. It is fluent, not factually grounded.
This gap between confidence and correctness is what leads to hallucinations, bias, and unsafe recommendations. When we remember that AI predicts rather than understands, we can use it more responsibly. We can check, interpret, challenge, and improve its outputs instead of accepting them at face value.
The risks of irresponsible AI use
Several issues show up when AI is used without guardrails or without human review.
- Biased outcomes
AI learns from the data it is trained on. If the data contains gender, race, or age bias, the model can reinforce and even amplify those patterns. This can show up in hiring tools, customer support systems, and everyday content generation.
- Hallucinations
AI sometimes fabricates information. These mistakes feel small in low stakes tasks but can create major risks in areas such as legal advice, medical support or HR decision-making.
- Privacy and data exposure
Uploading sensitive data into unapproved tools can expose corporate information and employee details. Many tools share user inputs with third parties, which increases legal and security risks.
- Accountability gaps
A company cannot hold a tool responsible when something goes wrong. Courts expect the organisation to take ownership, as the Air Canada case made very clear.
- Erosion of trust
Employees lose trust when they cannot understand how decisions are being made. The “black box” nature of some AI systems increases anxiety related to fairness, accuracy, and performance evaluation.
The productivity promise
Despite these risks, the potential of AI is real. When used responsibly, AI can speed up repetitive work, generate quick first drafts, improve analysis, and free people to focus on higher value tasks. The benefits show up when humans stay in control and treat AI as a helper rather than an authority.
What responsible AI looks like
Responsible AI simply means using tools in a way that is transparent, fair, safe, and accountable. It does not slow innovation. In fact, it makes the innovation reliable.
From current research, a responsible AI approach includes:
- Human oversight at important points of decision-making
- Clear accountability for AI-generated work
- Regular checks to identify and reduce bias
- Transparent communication about appropriate and inappropriate uses
- Strong rules for data handling and privacy
- Practical AI literacy training so employees can spot errors and evaluate outputs
What you can do today
A clear and practical AI policy is the most important starting point. At minimum, it should cover:
- When and how employees should disclose AI use: Transparency helps avoid competence penalties and builds psychological safety.
- Which tools are approved and which are not: People need safe, sanctioned options so they do not turn to shadow AI.
- What data can be used with AI tools: This protects both privacy and intellectual property.
- What level of quality check is required: A human must always review AI outputs before they go out into the world.
- Where employees can go for support or clarification: A single point of guidance prevents confusion and risky guesswork.
- How the organisation will build AI literacy: People learn best by doing, not by receiving warnings. Training should focus on both opportunities and limitations.
AI will shape the future of work, but only organisations that treat responsibility as a foundation will see the real benefits. Those that rush ahead without structure risk legal issues, reputational harm, uneven adoption, and biased outcomes.
Serein’s experts partner with organisations to design AI policies and governance structures that reduce risk and ensure your teams use AI with confidence. Reach out to us at hello@serein.in to learn more.