At Enjins, we’ve been doing tech DDs with a focus on data & AI since 2018. But with AI going mainstream and LLMs/Agentic AI making it harder to tell what’s valuable tech and what’s just a wrapper on ChatGPT, guess what? Demand for AI-focused DD exploded.
Personally, I loved diving into these AI kitchens. We saw it all: from Git repos that were nothing more than a single call to an LLM, to sophisticated combinations of hardware, software, and end-to-end AI systems that built a serious moat.
So, reflecting on this summer, here’s my top 5 ingredients of a real AI moat. Disclaimer; These are the factors that stood out to me as most differentiating. Some ventures nailed them, others could have gone deeper. Curious to hear your thoughts too—here we go.
1. Data Asset
- LLMs are available to everyone. Proprietary data isn’t. Companies that build unique datasets and actually leverage them for customer value are hard to copy.
- Bonus points if getting that data is tough: e.g. only through your own hardware (sensors), or via corporate partnerships that take months of negotiations to lock in.
2. Model Differentiation through Domain Knowledge
- Fine-tuning with domain expertise is where real value shows. If a company can prove humans-in-the-loop actually exist in practice and that they have direct access to experts, they can beat generic alternatives.
- Bonus points if this has been in place long enough to show multiple iterations and improvements across the full ML/GenAI pipeline.
3. Strong MLOps / LLMOps Capabilities
- One prototype is fine when you’re small, but in scale-up mode, models multiply fast. A mature MLOps/LLMOps setup shows a team can move from prototype to production quickly, shortening cycles and boosting impact.
- Bonus points in this category go to AI and tech teams with strong awareness of the available tools for the AI lifecycle. Fully self-build MLOps and LLMOps deployment streets are no longer needed, but strong opinions on why tools are picked and implemented can demonstrate thorough understanding of the rapidly changing AI field.
4. Business Fit & Workflow Integration
- AI only matters if it creates real business value. Not accuracy, but measurable impact in the product or process. Teams that can demonstrate how the AI is integrated end-to-end and what upside it provides for customers score points.
- Bonus points if AI is aligned with the growth plan. Planning to scale abroad? Then the data & AI setup should already support it, without huge re-engineering or skyrocketing costs. So has the algorithmic design been done in such a way that there is a clear thought on how to add new countries, products or customers?
5. Trust, Transparency & Compliance
- In sensitive or regulated industries, AI systems must have built-in transparency, explainability, and governance that meet regulatory requirements to win customer trust. Without proper governance, AI opens doors to regulatory exposure and biased decisions. Something I’ve witnessed firsthand across financial services clients from our product venture Deeploy, who demand both innovation and accountability.
- Bonus points in this category go to AI and tech teams that take a proactive stance on regulations like the GDPR and EU AI Act. Rather than simply ‘we have it on the roadmap’ for next year quotes, demonstrate that you view AI through the lens of responsibility. Emded these considerations into your design and development process, mitigating risks early on and build genuinely responsible tech that scales.
All five components reinforce each other. The more boxes a company ticks, the deeper and broader the AI moat, and the harder it is for competitors to cross.
