You don’t need “AI” in your product name. Soon it’ll be the default anyway. The real edge is integrating it across your business. But only where it has real value. Please don’t be driven by fear of missing out. We’re all still figuring things out.
AI’s impact on operations got real, fast. As tools matured, the world around us shifted, so we adapted. High-performing lean teams became cool again after a phase of seemingly endless venture capital, headcount inflation and talent wars.
Yes, there’s a lot of hype, and the full potential isn’t realised yet. I saw a similar cycle with data mining and especially blockchain, which I still practice and teach, focusing on cases where blockchain doesn’t make any sense.
That said, real benefits are already here (along with a lot of wasted money and computing power). I’m also very aware of the open questions: long-term maintainability, information security, IP (intellectual property) handling, risk tolerance for large enterprises vs startups and scale-ups, and ensuring that people don’t skip learning fundamental skills when using these tools.
One thing is clear: code is cheaper (at least when it comes to non-critical systems). The spotlight in software engineering moves to solution design, systems integration, security, quality assurance, and cost optimisation.
Humans focus on system design, architecture, security, QA (quality assurance), and cross-domain decisions. Our virtual colleagues handle drafts, cookiecutter templates (starter code), analysis, and routine checks.
Below are practical use cases for startups and scale-ups, from software engineering and product to beyond. I use 
for positive outcomes, 
for mixed results, and for negative outcomes.
Product
- Product specs (and tickets)
When your colleague drops a semi-structured idea in Slack, it’s now easy to turn it into a proper product specification with an LLM (large language model, an AI trained to understand and generate human-like text) and a few clarifying questions. Product owners no longer have to heroically translate someone’s shower thoughts into something tangible.
You can also request a business analysis, and your AI companion will outline tasks with rough estimates as a bonus. Some tools even auto-generate a basic demo. These days, it’s often faster to vibe-code (quickly hack something together) than to create clickable mocks.
- Rapid prototyping
Feature ideas, internal automations, presales demos, and so on. You can go from idea to demo in no time, regardless of the foundation model (base AI model, like OpenAI, Claude, Llama, Qwen, DeepSeek, Grok, Mistral, and others). As of now, that prototype is inherently insecure, but let’s save that chat for another time.
Also Read: The rise of agentic AI: What CFOs and Founders need to know
Software Engineering
- Coding co-pilots
Massive speed-up, if your team has strong fundamentals. As with any abstraction, juniors should still know where the efficiency comes from and what’s happening under the hood.
- Code reviews
Great as a complement, not a replacement for humans. We’re not yet at the AI maturity level where one AI can reliably review another.
AI excels at enforcing conventions, spotting duplicate logic, and automating routine validations within a single PR (pull request, a way to propose changes to shared code so others can review and approve them). Humans focus on system design, architectural trade-offs, legacy compatibility, and understanding components beyond a single code repository.
Any form of memory or reinforcement-learning-like feedback loop (training the system by trial and error) can boost review quality and filter noisy comments.
- Systems design
Systems design goes far beyond code. It’s about the entire architecture. No matter your context window (the amount of text an AI can “keep in mind” at once when responding), the number of integrations with external knowledge-base resources, or the model’s reasoning capabilities, humans still have to put the puzzle together and make the final call.
- Technical docs
LLMs can make technical documentation easier to understand and streamline third-party integrations by interpreting them in the context of your platform.
- Generating frontends
Vibe-coding internal frontend interfaces (like back-office admin tools) is a great use case for LLMs. Always keep an experienced human in the loop for scalability, solid API checks (making sure systems talk to each other correctly), and an information security review before anything goes live to customers.
Long-term maintainability is still TBD. We’ll learn as we go, and as our tools evolve.
- Scalability
As mentioned several times above, the long-term maintenance, stability, resilience, and security of solutions co-piloted by LLM-based agents remain open questions. We have anecdotal evidence suggesting that debugging and refactoring AI-generated code can be quite daunting, and that AI is not yet able to maintain the codebase itself with minimal human intervention. The seniority and domain expertise of the person guiding the AI matter greatly and are clearly reflected in code quality.
Will AI truly support reliable long-term maintenance and consistent quality, or will we eventually find ourselves discarding large portions once AI-generated code dominates? Time will tell.
Also Read: How can Malaysia leverage AI for growth and not see it as a threat?
Technology domains
- Ad-hoc data analysis (chat window)
Since multimodal (capable of handling both text and multimedia) chatbots can spin up, for example, a Python interpreter (Python is a widely used programming language) to perform simple calculations, you should definitely provide non-technical teammates with a self-service environment for getting data insights so that engineers can focus on more complex problems. This approach works especially well for spreadsheet-like data and simple data analysis or business intelligence tasks.
- AI + databases (APIs, middleware, and RAGs)
Given the costs of building and maintaining AI tools that connect to databases through APIs and middleware components (software that lets different systems talk to each other) within a Retrieval-Augmented Generation workflow (a method where AI first looks up facts in its knowledge sources before generating an answer), it’s still too early to know whether they will deliver a positive return on investment. But the vision is exciting. Non-technical users can get insights without having to ask the Business Intelligence, Data Analytics, and Data Engineering teams to tweak the pipelines or build custom dashboards.
Be mindful of hallucinations (confident but wrong answers). Use hardcoded SQL queries for the most common inquiries, post-query validation (double-checking results), well-tuned prompts, and safe fallbacks like “We don’t have that info in our database.”
- Data management
Give LLMs your schema (the structure of your database tables), API specs (a description of how your software component interacts with others), and historical requests and responses. You’ll save yourself a lot of time when enriching your data, generating mock data for quality assurance, turning unstructured data into structured data, and detecting and fixing data quality issues.
- QA (quality assurance) testing
AI is great for generating automated tests, including unit, regression, load, malformed-input, unhappy-path (failure-case), and basic security checks.
As of now, human QA testers do a better job at identifying edge cases, system integration issues (including end-to-end testing), UI/UX problems (such as compatibility and usability), and runtime errors (problems that occur while the program is running).
Looking ahead, a promising direction is to integrate LLMs into the CI/CD pipelines (automated build, test, and deployment processes) to catch runtime errors automatically.
- Internal technical support
Basic “Google it” or “Ask ChatGPT”-style guidance works even without perfect documentation. It’s a solid first line of support before a human steps in.
- External technical support
External support always requires a human in the loop and depends heavily on documentation quality (garbage in, garbage out). When reusing past answers, ensure any sensitive client-specific data is removed from training or tuning sets.
- Information security compliance
Auto-draft responses to vendor questionnaires based on your policies. Have humans review and tweak them instead of doing repetitive manual work. LLMs can also help consolidate and suggest improvements to your information security policies.
- Information security execution
LLMs and AI agents can digest logs and alerts, summarise insights, assist with incident reports, and translate natural language into configurations. However, don’t give them full access to live systems or allow autonomous actions in production. An internal LLM can easily become the weakest link in your stack, as it may be tricked into leaking data or performing unintended actions (current LLM safeguards are far behind those of any other components in your technical infrastructure).
Also Read: Is the future of AI decentralised? Cloud computing holds the key
Business domains
- Contract management
Map vendors, extract service-level agreements, detect risks, track renewals and deadlines, ensure compliance. As with any fact-checking, combine LLM outputs with classic entity recognition (identifying names, dates, and amounts, etc.) to cross-check factual correctness and maintain strong manual controls
- Educational content (incl. edutainment)
Use your virtual colleagues to create ELI5 (Explain Like I’m 5) explanations of technology for non-technical audiences or finance topics for non-business audiences. Create fun and insightful quizzes to make your employee training documents more digestible.
I’m sure you already have more ideas. Educational use cases are where LLMs shine.
- Daily productivity
This is a no-brainer. Note-taking, email catch-ups, integrations with your messaging tools, document processing, initial desk research, troubleshooting, and more. You’re probably already using LLMs this way in both your personal and professional life.
- Digital twin (AI avatar of you)
It’s tempting, setup costs are falling and latency is improving. But your virtual AI clone still typically struggles with complex conversations and tasks (especially if you avoid giving it sensitive data, which limits its adaptability). It may also make people uncomfortable, particularly when you lend your digital twin your voice and face, as is the case with some of the more advanced tools.
- Content creation (and marketing)
Polish and expand drafts, beat writer’s block, turn notes into case studies, blogs, videos, podcasts, for both tech and non-tech audience.
Just make sure to put plenty of yourself and your original thoughts into it so humanity isn’t endlessly rehashing public internet content. Most people can still tell when something is entirely AI-generated, although this might change in the near future.
- B2B lead generation (and B2B sales)
B2B (business-to-business) sales remains an art. If you’re B2C (business-to-consumer) or have shorter sales cycles, your mileage may vary. We’ve seen some cringe and spammy behaviours, more unsolicited emails and LinkedIn pings.
In an ideal world, customers talk more than salespeople (virtual or human). Maybe one day chatbots will handle both sides until a human needs to step in. We’re not there yet.
Also Read: The digital lag: How traditional consulting is failing to grasp the agentic AI revolution
Tooling
These tools cover ~80 per cent of our use cases, and they’ll work just fine for you if you’re not sure where to start.
- CustomGPTs and the OpenAI API for RAGs
- Gemini or Copilot (depending on whether you’re integrated with Google’s or Microsoft’s ecosystem)
- GitHub Copilot, Cursor, and Claude Code (AI companions for your software engineers)
- Hugging Face (great for experimenting with various models)
- Perplexity and ChatGPT apps (a daily productivity boost)
And here’s the extended list. Fingers crossed it doesn’t become outdated overnight.
Outro
Personally, I’m extremely excited about this whole domain. I believe not only startups and scale-ups can benefit big-time from integrating AI into software engineering, product, and beyond. This skill is also necessary to future-proof companies and individuals.
At the same time, I’d like to leave you with the following thoughts.
- Maintainability is unproven: We can’t review code faster than AI generates it, and AI still needs human reviewers. Information security is at stake here. Having one AI review another without oversight is risky.
- AI is great for building internal tools: I can strongly recommend AI for back-office apps, provided your APIs have robust validation. Customer-facing features are harder because making them secure and reliable is tricky. (Opinions will vary on this, and AI is improving. My view is that, as of now, LLM-based tools are inherently insecure.)
- Humans matter more, not less: As systems get smarter and more automated, people become more essential for troubleshooting. But staying sharp is harder when you’re less involved, less hands-on. (Someone called this the paradox of automation.)
- Go step by step with agentic AI: You’ve probably read about it and you’re already excited about having it, but it’s yet another layer of abstraction on top of potentially shaky layers in your specific environment. Meet prerequisites first. First and foremost, your database layer and documentation need to be ready for AI. And only then ask yourself whether AI makes sense for the use case at hand.
Also Read: From promise to payoff: AI’s test amid global trade tensions
Bottom line
As I’ve mentioned elsewhere, when building products, stay focused on customer and business value, with requirements driving technology rather than chasing tech for tech’s sake.
Use AI, LLMs, and multi-modal agents first for rapid prototyping, and then as one option (at times complementary) among many. Once you know what you want, your engineers will choose the right tool for the job, possibly an off-the-shelf non-AI solution or a piece of code. That helps you achieve your goals in a secure, scalable, and cost-effective way, rather than treating your ChatGPT chat window or OpenAI API key as a catch-all solution.
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Since the start of the year, only 83 reported cases have occurred, 2.2 times less than the total figure for 2024 and the lowest recorded figure since 2020. However, a single, successful attack dramatically boosted the total to US$2.34 billion.
