In this interview, e27 speaks with Bonnie Factor, Founder of Leading With Success PH and CuriosityGenAI LLC, about how organisations are moving from AI experimentation to real-world deployment. Through her work installing AI agents for SMEs and building AI labs for enterprises, Bonnie focuses on helping teams operationalise AI and integrate it into everyday workflows.
This conversation forms part of e27’s broader AI Pulse coverage, which examines how organisations across the region are building, deploying, and scaling AI in practical settings.
Organisation overview and role of AI
e27: Briefly describe what your organization does, and where AI plays a meaningful role in your work or offering.
Bonnie: We specialise in the installation of AI agents for SMEs and the development of AI labs for enterprises. AI plays a central role in enabling these organisations to automate workflows, experiment with AI-driven processes, and build internal capabilities for long-term adoption.
Concrete value creation with AI
e27: What is one concrete way AI is currently creating value within your organisation or for your users or customers?
Bonnie: OpenClaw, an open-source AI agent, can function as an AI Engineer, a Go-to-Market AI Engineer, and a Sales Support agent when equipped with trusted skills. Some users are even experimenting with giving it a budget to ideate and operate autonomously.
For our organisation, we are seeing strong value in its AI engineering capabilities. With little to no coding, it can perform advanced tasks such as detecting hallucinations, generating lead lists within minutes across different geographies and industries, and delivering outputs in structured formats like CSV files. It can also connect to social media platforms via API keys and manage content, effectively enabling one person to perform the role of a full Go-to-Market AI Engineer, which is currently one of the most expensive hires.
Key decisions and trade-offs
e27: What was a key decision or trade-off you had to make when adopting, building, or scaling AI?
Bonnie: A key trade-off was balancing time and cost. Time was needed to understand how to work with API keys, while costs came from token usage for LLMs and generative AI providers such as OpenAI Codex, Claude, Gemini, and models like Minimax and Kimi.
Also read: AI Pulse Exclusive: How Asia AI Association is advancing human-centred AI across the region
What worked and what was challenging
e27: Looking back, what has worked better than expected, and what proved more challenging than anticipated?
Bonnie: AI agents produced meaningful outputs faster than expected once deployed in real environments. With access to tools and workflows, they were able to generate lead lists, outreach drafts, and analysis even in early-stage setups.
What proved more challenging was not the technology itself, but integration and reliability. Ensuring consistent execution, handling edge cases, and connecting to real workflows required significant iteration. Attempting to replace existing processes too early also created resistance and slowed adoption.
This led to a key insight: instead of redesigning workflows upfront, it is more effective to deploy AI agents in parallel with existing processes. This allows teams to compare AI-native workflows with human workflows, observe performance, and gradually determine where automation is reliable and where human oversight is still needed.
Lessons leaders often underestimate
e27: What is one lesson about applying AI in real-world settings that leaders or founders often underestimate?
Bonnie: The most underestimated factor is not the technology, but change management. Leaders often assume AI adoption is a tooling problem, when in reality it is a people problem. Resistance emerges as soon as existing workflows are disrupted.
In practice, the fastest way to apply AI is not to replace current processes, but to run AI workflows in parallel. This reduces friction, allows teams to observe real outputs, and makes it clearer where automation works and where human judgment is still required.
Practical recommendations for organisations
e27: Based on your experience, what is one practical recommendation you would give to organisations that are just starting to explore or scale AI?
Bonnie: Start by deploying an AI agent or a small AI lab alongside your existing operations. Avoid redesigning or replacing workflows at the outset.
Allow the AI system to operate independently on a defined set of tasks and observe its outputs over time. This creates real evidence of what works, reduces resistance from teams, and makes it easier to identify where automation adds value and where human oversight remains necessary.
The next 12 months of AI
e27: Over the next 12 months, how do you expect your organisation’s use of AI, or the role of AI in your industry, to evolve?
Bonnie: Over the next 12 months, AI will shift from experimentation to operational deployment. Organisations will move from using AI as a tool to deploying autonomous agents that execute workflows end to end.
We expect the emergence of internal AI labs where agents run in parallel with existing systems, continuously generating outputs such as lead pipelines, analysis, and process automation. This allows companies to learn from real execution rather than theory.
As these systems stabilise, AI-native workflows will begin to integrate into core operations, with human roles shifting toward oversight, validation, and exception handling rather than manual execution.
Final thoughts
e27: Anything else you want to share with the audience?
Bonnie: AI adoption will not be limited by technology, but by how quickly organisations learn to work alongside it. Teams that move fastest will be those willing to experiment, observe real outputs, and adapt based on evidence rather than assumptions.
The opportunity lies not just in using AI tools, but in building internal capability to deploy and operate AI-driven workflows at scale.
Closing thoughts
As organisations continue to navigate the shift from experimentation to execution, Bonnie’s insights highlight a clear pattern: the real challenge is not the technology itself, but how teams adapt to it. From deploying AI agents in parallel with existing workflows to building internal AI labs, the focus is increasingly on creating systems that can be tested, observed, and refined in real conditions.
Ultimately, the organisations that will move fastest are those that prioritise learning by doing, reduce friction in adoption, and build the internal capability to work alongside AI.
For more interviews, analysis, and real-world perspectives on how organisations across the region are applying AI in practice, click here.
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Featured Image Credit: Bonnie Factor
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