Venolan Naidoo: Hi everyone, I'm Venolan Naidoo and this is the next podcast episode on AI and the world of work.

This episode tackles when AI goes wrong at work, discipline,

data and defensibility. In the previous episode, if you recall,

I spoke about AI workforce literacy or AI workforce fluency as I like to call it,

as a board management level risk.

In this episode, I want to focus on what happens when organisations don't address it and how AI issues surface as

real workplace disputes. Because most organisations won't realise they have an AI problem until it lands on someone's

desk as a disciplinary issue. A data breach, an AI output relied upon that went wrong because it was not

properly checked or a legal challenge.

Discipline and procedural fairness.

The stuff that us Labour lawyers love to deal with.

One of the most difficult questions organisations will face is this can disciplinary outcomes be defended when AI played

a role? We're already seeing situations where AI influences performance assessments.

We see it where AI assists in investigations, and we see it where employees rely on AI without clear

guidance. If an employee is disciplined in those circumstances,

the question becomes whether the process was fair, especially if the organisation never set clear rules around

AI use. AI complicates accountability, and decision makers,

especially a presiding officer in a hearing, will look closely at whether the employer created an

environment where AI use was properly governed and if it was underlyingly responsible.

Data confidentiality and POPIA risk is something else that many organisations do not realise is also important.

It is a major risk with data protection.

Employees using AI may unintentionally upload confidential information.

They may expose personal data or trigger a cross-border data transfer,

which has its own rules. When that happens, many organisations focus heavily on international AI

frameworks while overlooking South African specific workforce risks.

Under POPIA people, organisations don't necessarily know this,

but POPIA applies to the use of AI systems, especially in workplace environments involving individuals.

That gap creates exposure, particularly when employees were never properly guarded on,

and it isn't acceptable. Sector specific realities, AI doesn't affect every sector in the same way.

For example, industries like insurance, financial services,

Professional services. Ai already influences outcomes there,

from claims processing, assessments, recommendations and decision making.

But the common thread is this AI is already being used in ways that affect people and outcomes.

Without AI workforce literacy, these sector specific risks multiply exponentially.

From training to diagnosable risk.

And this isn't about AI training, per se.

Organisations don't pay lawyers to teach people how to use tools.

They pay for answers to hard questions.

Are our AI influence decisions defensible from a legal standpoint?

Are they workforce risk gaps that exist?

Have we identified those and are we exposed from an employment or data protection perspective?

These are the hard questions that organisations need to address.

And that's why AI literacy needs to be treated as a diagnosable legal risk,

not only an education exercise, which is, of course, important.

This would innately require AI risk management exercises being conducted to identify the nature and level of risk.

And then with this very critical information to begin with, education edifying a workforce for understanding the nuances

of AI use and ultimately for better adaptation.

Yes, it's about adaptation. In conclusion, AI won't wait for organisations to catch up.

The choice is whether you address workforce issues with readiness proactiveness or manage the fallout

reactively. In my view, proactive is the prevailing position to pursue,

and I look forward to catching up on the next episode where we'll be talking on another AI topic and the world of work.

See you then.