Whether you’re looking to tackle operational inefficiencies by automating workflows, ease budget constraints, or ease the burden on your team so they can focus on high-value strategic work, generative AI can help. Strategic implementation can contribute positively to a business.
South Africa currently does not have a national artificial intelligence policy. However, you need to abide by certain laws when using AI responsibly. This does not mean businesses should wait for a dedicated AI law before they start using the technology. South Africa has already developed a National AI Policy Framework, and the government has been working towards a broader national policy. A draft policy was approved for public comment in 2026, but it was later withdrawn so that it could be reworked. Existing laws still apply to how businesses collect, process, store and share information.
For businesses, this creates an important distinction. There may not be one law that tells you exactly how to use generative AI, but that does not mean there are no rules. If an employee enters customer information into an AI platform, for example, the business still has responsibilities under data protection law like the Protection of Personal Information Act 4 of 2013 (POPIA).
The real question is therefore not whether a business should use generative AI. It is where the technology can create useful value, how it should be controlled and how it can be introduced without creating new problems.
What Is Generative AI and How Can Businesses Use It?
Generative AI refers to artificial intelligence systems that can produce new content based on instructions from a user. This can include written content, images, computer code, summaries and other forms of digital output.
For a business, the value is not simply that AI can produce something quickly. The bigger opportunity is that it can reduce the amount of time employees spend on repetitive tasks. For instance, it can be used to simplify tasks like planning content for the marketing team, organising notes after a meeting, or giving you a summary of a long document.
The benefit of generative AI is not to replace your team but to help in the mundane areas that slow them down. Those areas must be identified before buying an AI tool. PwC’s 2026 research found that 82% of organisations across Africa were running AI pilots, while few have scaled AI across the enterprise. For a South African business, the first step should be to identify a process where AI can solve a measurable problem.
Consider a company that spends several hours every week responding to similar customer questions. An AI assistant could help staff prepare responses more quickly. The goal is not to remove the customer service team. The goal is to reduce repetitive work so employees can spend more time dealing with complicated customer needs.
The same thinking can be applied to finance, sales, marketing, procurement, operations and administration.
Identify the Right AI Use Cases
Not every business task is suitable for generative AI. This is why the use of AI must be personalised to your business.
Consider looking at the following factors as a starting point:
Repetitive: Identify tasks that are performed frequently and don’t change much day-to-day.
- Time-consuming: Focus on manual workflows that currently take up significant employee time.
- Based on large amounts of text: Prioritise tasks that require processing or synthesising dense documents or datasets.
- Easy for a person to review: Ensure the AI’s output can be quickly verified for accuracy by a human before it is used.
- Guided by clear rules: Select processes that follow established procedures or logic which the AI can easily learn and replicate.
- Currently creating delays: Target operational bottlenecks that are slowing down your team’s ability to move to higher-value work.
This could include preparing reports, summarising documents, creating first drafts, sorting information, generating ideas or answering routine internal questions. The strongest generative AI use cases are often not the most impressive ones. They are the tasks that quietly consume hours every week.
A company may gain more from reducing two hours of unnecessary administrative work for ten employees than creating an elaborate AI project that looks impressive but has little effect on daily operations.
Measure the Business Value of AI
Businesses should decide what success looks like before implementing AI. If you aim to save time, you can measure the time employees spend on a particular task before and after introducing an AI tool. Other measures could include customer response times, production costs, sales conversion rates or the number of hours spent preparing reports. This creates a clearer picture of the return on investment you’re getting from generative AI.
Build an AI Governance Structure
Building a governance structure around company AI use is not reserved for large corporations. Small businesses need it too and may have more to lose in the event of AI being used irresponsibly. The irresponsible use of AI can have security and legal implications.
An internal AI policy can establish simple rules for employees. It can explain which tools are approved, what information may be entered into them, when human review is required and who is responsible for checking AI-generated work.
A practical policy could answer five basic questions:
- Which AI tools can employees use?
- What business information can they enter?
- What information must never be entered?
- Which AI-generated outputs require human review?
- Who is responsible when AI produces an incorrect result?
How POPIA Affects Generative AI Use
The Protection of Personal Information Act, commonly known as POPIA, is particularly relevant when businesses use generative AI. POPIA establishes requirements for the lawful processing of personal information by public and private bodies.
It also deals with matters including data subject rights, automated decision-making, and the transfer of personal information across borders.
This matters because many generative AI tools operate through online platforms and may process information outside the business’s own systems.
Before entering personal information into an AI platform, businesses should consider what information is being shared, why it is needed, whether the processing is lawful and what happens to that information. A safer approach is to remove unnecessary personal information before using an AI tool.
Create an AI Implementation Roadmap
Once a business has identified suitable use cases and established basic governance, it can move towards strategic AI implementation.
The best approach is usually to start small. Choose one process, define the expected result and run a controlled pilot. Give employees enough time to learn the tool and identify problems before introducing it across the business.
For example, your team can start by using generative AI to help its marketing department prepare content ideas and first drafts. During the pilot, management could measure:
- Time spent on content preparation
- Number of revisions required
- Employee adoption
- Quality of final content
- Cost of the AI tool
- Time saved each month
The results can then determine whether the project should continue. This approach allows you to measure the value of AI use and scale over time, instead of introducing AI across every department at once.
Train Employees to Use AI Properly
AI training is a critical aspect of ensuring you effectively govern the use of AI. You should never assume that employees know how to use it effectively. Workers need to understand how to write useful instructions, check outputs, protect sensitive information and recognise when AI should not be used. This is where AI literacy becomes valuable. Employees need to understand the limits of the tools they use.
For example, an employee should know about the dangers of using artificial intelligence without human oversight. Issues such as AI hallucination can lead to your company releasing incorrect information. Your team should not only know how to prompt effectively, but they should also know when a task requires an expert rather than an AI-generated response.