The emergence of AI is revolutionising the ride-hailing industry through improved efficiency, personalisation, and safety.
inDrive, as one of the leading service providers in the industry, is effectively keying into it, deploying AI-powered tools that have led to a more convenient and efficient experience for both riders and drivers in 2025.
This has enhanced complex, time-consuming processes, route optimisation, predicted demand, and personalised user experiences.
The evolution of human-AI-partnership is what inDrive is exploring in its interactive characteristics, which include talking, adapting, forgetting, learning and making mistakes.
Adopting AI means building a more innovative company and not building better software because it has task-specific software, builds trust like a colleague, and encourages knowledge partnership.
With AI, inDrive has strengthened its customer service, delivering 85% faster resolution times by providing riders with quicker answers and support.
This includes 96% automatic verification of photos and documents without human intervention. It has helped prevent fraud through document verification, spotting fake IDS, and enabling more intelligent semantic searches within the company’s documentation. It has helped to understand the meaning behind questions, not just keywords.
It has also contributed to an increased personalised ride experience, customising interactions based on user preferences and history and enabling dynamic pricing recommendations that work with inDrive’s unique negotiation model between drivers and riders.
Leveraging AI Operating System Effectively in Ride-Hailing
To effectively leverage the AI operating system at inDrive, we transformed our company’s knowledge into something structured and accessible. Unlike most companies that run on undocumented tribal knowledge, which results in every process ending up as a tangled web of messages, spreadsheets, and overworked humans trying to keep it all together, we have used AI to help our business.
The AI edge is that new employees quickly get up to speed, documentation is clear, knowledge is centralised, and productivity is raised as people promptly access basic information.
It helps to erase the corporate amnesia of documents vanishing into the digital void, new hires spending weeks figuring out basic processes, support teams answering the same questions repeatedly, and the loss of knowledge when an experienced team member leaves.
The AI Implementation Paradox
At inDrive, we demystified the AI implementation paradox by restructuring our organisational knowledge into solution-focused formats that AI can work with. While many companies fail by continually pouring money into AI, we developed initiatives that will help our business by the way we want AI to behave like software — consistent, testable, and predictable.
But generative AI doesn’t work that way. It predicts likely outcomes based on patterns, not absolute truth.
Traditional software is deterministic: Input + Rules = Predictable Output (1 + 1 always equals 2). Generative AI is probabilistic: Input + Training Data = Likely Output (1 + 1 equals “probably 2, but it depends”).
Recompiling Your Organisation
The challenge isn’t adopting AI — it’s reformatting your organisation into something machines can understand and enhance. This transformation involves converting procedures into executable workflows with clear inputs and outputs, transforming business exceptions into logical decision trees, and turning institutional knowledge into structured guidance systems.
These changes replace outdated corporate tools such as static org charts that don’t reflect how work happens, playbooks that nobody reads or updates, annual OKRS disconnected from daily reality, and project plans with unrealistic timelines.
The new “AI Operating System” is a dynamic shift that builds on three foundations:
- Intent: Clear objectives driving action (“I need to reduce driver churn”)
- Context: Historical understanding to avoid repeated mistakes (“Here’s what we’ve tried”)
- Agents: AI executing tasks and learning from outcomes (“Here’s what I’m doing about it”)
inDrive’s Five-Layer AI Operating System Approach
At inDrive, we are implementing this through a structured methodology:
- AI Education: This involves teaching people how to use AI effectively, understanding its limitations while leveraging existing tools with AI capabilities, and shifting mindsets from seeing AI as software to understanding its probabilistic nature.
- Data Indexing: This is about creating comprehensive indexes of wikis, documents, procedures and communications, using the indexing process to finally organise information properly and building a foundation for everything that follows.
- Semantic Search: This involves deploying search capabilities that understand meaning, not just keywords, while ensuring teams can quickly find what they need, thus cutting hours wasted digging through document repositories.
- Assistant Layer: This layer focuses on building AI assistants that understand the company’s unique knowledge. It creates specialised assistants for specific departments and provides contextually relevant guidance when needed.
- Agent Layer: This enables AI to process work, not just provide information. It implements automation with appropriate human oversight while creating feedback loops to improve performance over time.
The Path Forward
The real impact of AI won’t come from chasing the latest technologies but from solving everyday problems that create massive organisational friction.
Companies that successfully structure their knowledge will gain tremendous advantages. Those that don’t will fall increasingly behind as the gap between AI-enabled and traditional businesses widens.
For executives, the message is clear: before investing in fancy AI models, first invest in transforming your organisation’s knowledge into something machines can work with.
By converting tribal knowledge into accessible, structured formats, you create a more resilient organisation where humans focus on creative work while AI handles the repeatable processes.
Culture eats models for breakfast. Organisational adoption matters more than technical sophistication.
