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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →In 2023, generative AI (GenAI) had moved from a newly public technology into early business workflows. It could generate text, images, music, video and code, but its outputs were not automatically accurate or safe. “GenAI Report 2023” is best understood as a 2023 overview, not the established title of one globally scoped report; Malaysia-specific figures below are attributed to MyDIGITAL Corporation’s 2023 report as reproduced in a 2025 journal article.
What is generative AI?
Generative AI refers to machine-learning models trained on large volumes of data to generate new content. A large language model such as ChatGPT responds to text prompts with language; other systems can produce or transform images, music, video and computer code. The Congressional Research Service (CRS), in its August 4, 2023 overview, traces the recent acceleration to the transformer architecture introduced in 2017, later advances in GPT models, and the public availability of generative AI tools in 2022.
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These systems produce plausible outputs by learning patterns in their training data. That ability makes them useful for drafting or transforming content, but it does not guarantee that a response is true, complete or appropriate for its context.
How broad was public interest in 2023?
A SYZYGY AG survey of people in Germany, published March 23, 2023, found that nearly two-thirds of respondents knew technology could behave creatively, a similar proportion had heard of ChatGPT, and about half were interested in trying GenAI to assist or improve their creativity. One in four said they were interested in more controversial applications that were already available in the United States.
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Those results describe awareness and stated interest among survey respondents in Germany; they are not measures of global adoption or proof that respondents had used the tools. SYZYGY’s Dr. Paul Marsden said, “Brands in Germany have the ‘green light’ to embrace and leverage GenAI to unlock business creativity.” The company also emphasized transparency and safeguards against misuse and false or harmful content.
What were businesses doing with GenAI?
By 2023, business discussion was moving beyond general experimentation toward particular workflows. Thomson Reuters described possible professional uses including legal research, document classification, suspicious-activity reporting and IT support. These examples show the range of tasks being explored, not that every organization had deployed them at scale.
Professional workflows and product development
Thomson Reuters highlighted AI-Assisted Research in Westlaw Precision and a GenAI platform intended to support reusable skills across its products. The company said it planned to invest more than $100 million per year over the following years in integrating GenAI into flagship products; that was a stated investment plan, not a reported measure of results.
The company also described outputs from a 2023 hackathon: a chat-based legal assistant, an automated suspicious-activity reporting tool, a natural-language model for classifying court-filed documents and an IT-support bot. These examples illustrate prototypes or concepts created in that event, rather than evidence that each tool was a production service.
Why professional tools need more than a capable model
Thomson Reuters surveyed 1,200 professionals in the United States, Canada, the United Kingdom and Latin America for its Future of Professionals report. It argued that useful professional GenAI depends on comprehensive, authoritative reference data and subject-matter experts who understand context and nuance. A fluent answer without reliable grounding or knowledgeable review can still be wrong in consequential ways.
Which industries were potentially exposed in Malaysia?
The following figures are estimates of the share of Malaysian work activities that could potentially be transformed by generative AI, not percentages of jobs already automated, companies already using AI, or observed productivity gains. The 2025 Journal of Business and Social Sciences article reproduces these figures from MyDIGITAL Corporation’s GenAI Report 2023:
| Industry grouping | Share of potentially transformed work activities |
|---|---|
| Other industries, including financial services | 26% |
| Wholesale and retail trade | 21% |
| Manufacturing | 19% |
| Hotels and restaurants | 13% |
| Education, health and social work | 13% |
| Construction | 8% |
The “other industries” category is broad, so its 26% should not be read as a financial-services-only figure. The same journal article reports that MyDIGITAL anticipated 15–20% of enterprises would operationalise AI capabilities in the following three to five years. This was a forecast made in the 2023 report, not a later measurement of how many firms actually did so. Neither statistic should be generalized beyond Malaysia.
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GenAI can generate and revise creative material, which can change how people draft, prototype or explore ideas. The 2023 Germany survey indicates interest in using it to assist creativity, but it does not establish how many creative jobs would be eliminated or how the technology would affect employment. The evidence here supports treating GenAI as a capability that can contribute to parts of creative workflows—not as proof that it can replace creative workers wholesale.
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For organizations, the more useful question is which parts of a task can be delegated safely and where human judgment remains essential. In creative work, that includes deciding what is original, suitable for the audience, factually sound and consistent with a brand or brief.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What risks should organizations account for?
GenAI’s ability to produce convincing content creates risks that need to be managed alongside its usefulness. CRS identifies opacity, bias, limited explainability, substantial data requirements and uncertain or untested capabilities. It also warns that the resources needed to develop these systems may be concentrated among a small number of technology companies.
- Incorrect or harmful output: Generated text can be false or damaging while sounding confident. SYZYGY called for transparency and safeguards to protect consumers from such content.
- Bias and limited explainability: Models can reflect bias in their training data, and it may be difficult to explain why a particular output was produced.
- Privacy and security: Organizations need to understand what information users enter, how it is handled, and what security protections apply before using a service with sensitive data.
- Overreliance: Treating a model’s answer as authoritative without checking it can carry errors into professional decisions or customer-facing material.
- Concentrated resources: Large data and infrastructure requirements can make access and development dependent on a limited number of technology providers.
What should an organization check before deploying GenAI?
Assess a proposed use against the task, data, oversight and operational realities—not just the quality of a demonstration.
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- Define the task and sector fit. Identify the specific activity the system would support, who benefits, and what a useful result means. A task that involves drafting or classification may have different error consequences from one that informs a legal or financial decision.
- Review data sensitivity and privacy. Determine whether the workflow uses personal, confidential or regulated information, and establish what can be submitted to the system and under what protections.
- Set human review and accountability. Name who checks outputs, who can reject or correct them, and who remains responsible for decisions. Expert review matters where context and nuance affect the answer.
- Make use transparent and traceable. Decide when users or customers should be told that GenAI contributed, and how the organization will record or verify the references behind consequential outputs.
- Measure quality and productivity. Define a baseline and evaluate accuracy, consistency, time saved and error costs on the intended task. Do not assume a fluent output represents a real improvement.
- Check implementation and skills readiness. Confirm that staff have the training, authoritative reference material, security controls and escalation paths needed to operate the system responsibly.
A deployment should proceed only when the expected benefit is clear enough to compare with the risk of error, the data exposure involved and the cost of meaningful human oversight.
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