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.

Also read: AI Pulse Exclusive: How CAWIL.AI is building industry-focused AI solutions across specialised sectors

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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Southeast Asia is packed with numerous logistics landscape opportunities and operational hurdles. The growth opportunities are numerous for logistics startups in Southeast Asia. Meanwhile, they can face major difficulties too, such as inefficient routes, peak traffic, unpredictable weather, high last-mile delivery costs, demand fluctuations, inventory mismanagement, lack of real-time tracking, and high operating costs.

To overcome all these challenges, logistics startups in Southeast Asia are now embracing AI-optimised solutions. AI logistics solutions help fix messy supply chains and enable smoother movement. They enhance smarter routes and stock management, cut delivery times, provide effective automation, predict future demand, and track how goods move across Southeast Asia. 

Why Southeast Asia? The perfect storm

The geographical setup of Southeast Asia is both a gift and a headache while dealing with logistics operations. By 2030, around 253 million people are expected to shop online in Southeast Asia. It will contribute to great market growth value. Between 2025 and 2030, the CAGR of e-commerce in Southeast Asia will be around 11.14 per cent.

With thousands of Islands and scattered cities, unpredictable traffic makes moving goods a real puzzle. This leads to late deliveries, higher costs, and tired drivers, and affects the supply chain. Moreover, the markets are also fragmented. Each country has its own rules and market system. In the Philippines and Vietnam, they follow COD, where logistics can face 15 per cent failed delivery rate. Every logistics startup in Southeast Asia keeps finding ways to push forward.

How AI supply chain tech is transforming Southeast Asia logistics

AI supply chain and logistics technology are reshaping startups in Southeast Asia’s e-commerce and last-mile delivery scene with innovative, fast solutions. 

Smart route planning

AI tools now analyse traffic, weather, and road bumps all in real-time to pick a faster route. It instantly updates the new route when weather conditions or traffic are not favourable. It reduces the waiting time of the truck by reading data through GPS, traffic cameras, and weather sensors. Machine learning algorithms adapt to local driving patterns. 

It learns peak traffic hours over time and can also slow down the vehicle before it hits them. Thus, the AI logistics solutions suggest alternative routes that actually save time and fuel cost. Logistics startups in Southeast Asia can achieve 20 per cent fuel reduction and 30 per cent improvement in delivery times through an AI-optimised solution.

Also Read: The most common supply chain threats and how to mitigate them

Demand forecasting

Just imagine, what if you knew next month’s orders demand today? The predictive analysis in the AI technology tracks how people shop across cities like Bangkok, Manila, and Jakarta. It is familiar with paydays, local festivals, and special occasions. AI helps logistics to place inventory in the right warehouse before demand spikes. 

Logistics startups in Southeast Asia can maintain balanced warehouses, not overfilled or empty. AI plans where to keep products and where to move them. Inventory management works best when storage systems talk smoothly with transport platforms. Comparing WMS and TMS gives logistics startups a clearer idea of where AI automation adds real speed and cost efficiency.

Last-mile automation

AI-integrated dispatch systems save logistics from last-mile delivery headaches. They can assign riders based on distance, traffic, and parcel size in seconds. Now, companies in Southeast Asia like Foodpanda and Ninja Van test small delivery robots and drones for short routes. In crowded city zones, the automated solutions cut last-mile delivery costs by 10 per cent to 40 per cent, approximately. During traffic blocks, it auto-reshuffles the route so that the drivers pick the fastest route to keep parcels moving when others get stuck.

Transparency and tracking

Now, both the logistics firm and customers can track the real-time update of the goods.  Every truck, van, or scooter can now be visible and can predict delays before they happen. AI supply chain gets alerts before heavy rains and updates them to both customers and dispatchers in real time. Logistics startups in Southeast Asia using these systems get notable increases in customer satisfaction.

Mini case study: Startup in action

UNA Brands, a Singapore-based e-commerce platform founded in 2020, offers a useful example of how early-stage companies approach logistics expansion in the region.

When the company prepared to enter the Philippines, it encountered typical hurdles faced by cross-border operators, including securing local warehousing, setting up fulfilment workflows, and establishing the infrastructure needed to support consistent delivery handovers.

Also Read: Adopting electric trucks for a greener logistics future in Singapore

To address these gaps, UNA Brands adopted Ninja Van’s Ninja Fulfilment service, which offered a plug-and-play operational setup. Through this arrangement, UNA Brands gained access to warehousing capacity, real-time inventory tracking, and integration with Ninja Van’s delivery network, enabling them to begin operations without immediately building their own facilities or hiring a full local team.

With automated inventory management and route optimisation tools in place, UNA Brands reported achieving steady operational indicators during rollout, including a 100 per cent courier handover rate, a 95 per cent same-day delivery rate, and support for processing approximately 1,716 orders per day. These outcomes reflect how third-party logistics partnerships can help early-stage companies stabilise fulfilment during market entry phases.

What does this mean for you?

AI-driven logistics startups in Southeast Asia are changing things for both business and customers. For logistics firms, it slashes shipping costs by automating the process. Small businesses with smarter tools can compete with the industry giants. 

Consumers get products on time at a cheaper rate. This ensures each and every customer gets the product even during peak days and reduces the customers’ wait time. In the next few years, the supply chains will get faster and more reliable as AI adoption consistently increases.

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In the rapidly evolving landscape of Enterprise Resource Planning (ERP) and digital transformation, the year 2026 has emerged as a watershed moment for artificial intelligence. While the initial surge of generative AI promised a paradigm shift in productivity, the reality for Microsoft’s flagship AI offering, MS Copilot, has been markedly different. As organizations seek deep integration and systemic intelligence, the limitations of “AI as a feature” have become glaringly apparent.

Today, we examine the systemic failure of MS Copilot to transcend its origins, concluding that its architectural dependence on a third-party LLM has left it without a sustainable comparative advantage in an increasingly sophisticated market.

The 2026 reality check: Headlines of disruption

The first half of 2026 has seen a string of critical reports from reputable media outlets that have shaken investor confidence in Microsoft’s AI strategy. The Wall Street Journal recently highlighted a significant “churn event” among Fortune 500 companies, citing a 30% reduction in Copilot seat renewals. The core grievance? A lack of measurable ROI and a “hallucination ceiling” that has remained stagnant since 2024.

Bloomberg Technology further compounded these concerns with an exposé on “The Integration Gap,” noting that while MS Copilot can draft an email or summarize a meeting, it remains fundamentally disconnected from the complex, real-time data silos that drive global supply chains and financial systems. The report suggests that MS Copilot has become a victim of its own ubiquity—functioning as a generalist tool in a world that now demands specialist precision.

Also read: AI agents and ERP: Why Singapore businesses must act now

The “wrapper” trap: Architecture without autonomy

To understand the current failure of the platform, one must look at its technical foundation. At its heart, MS Copilot operates as an LLM wrapper. It provides a user interface and a bridge to OpenAI’s underlying models, but it does not possess the native “business logic” required for deep enterprise orchestration.

In the SAP ecosystem, we understand that true value is derived from the data model—the “Clean Core.” When an AI is simply draped over existing office applications, it inherits the inconsistencies of those applications. In 2026, the market has realized that a sophisticated UI cannot compensate for a lack of proprietary, domain-specific intelligence. Because Microsoft does not own the fundamental evolution of the underlying model in the same way a vertically integrated AI provider might, they are perpetually reacting to the roadmap of others.

Why “generalist AI” is no longer enough

The hype of 2023 and 2024 was built on the novelty of conversational interface. However, by 2026, AI is no longer a novelty; it is a utility. The MS Copilot failure is rooted in its inability to move beyond “assistance” into “autonomy.”

For a tool to provide a comparative advantage, it must do more than summarize—it must predict and execute within a specific business context. When MS Copilot attempts to navigate complex regulatory environments or intricate manufacturing schedules, it often falters. This is because a general-purpose LLM, no matter how large, lacks the “organizational memory” that comes from being natively embedded within the transactional layer of a business.

The competitive landscape: The rise of vertical intelligence

While MS Copilot struggled with generic responses, 2026 saw the rise of specialized industrial AI. These competitors didn’t just wrap a chatbot around a spreadsheet; they built intelligence directly into the database.

The comparative advantage has shifted to those who control the data lifecycle. In this new era, being a “fast follower” with a polished wrapper is a liability. Companies are now pivoting toward solutions that offer:

• Contextual Accuracy: Moving beyond generic text to data-driven insights.
• Process Automation: The ability to trigger actual business processes, not just write about them.
• Security and Sovereignty: Reducing the “hop” between the application and a third-party LLM provider.

Also read: Costing comparison of top 7 popular ERP software for food manufacturing in Singapore

Conclusion: The commodity of conversation

As we look toward the remainder of 2026, the narrative surrounding MS Copilot serves as a cautionary tale for the industry. The transition from a tool that “talks” to a tool that “does” has proven to be an insurmountable hurdle for the wrapper model.

Without a proprietary engine or a deeply integrated data strategy that goes beyond the surface level of the “modern workplace,” MS Copilot has been relegated to a commodity. In the high-stakes world of enterprise technology, being “useful” is no longer a substitute for being “essential.” The failure to innovate beyond the wrapper has left a void that only truly integrated, process-aware AI can fill.

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I didn’t build an AI agent because it was trending.

I built it because I needed help.

At one point, everything in my business required me – content, replies, decisions, operations. Even with a team, I was still the bottleneck. If I didn’t respond, things slowed down. If I didn’t think through something, it didn’t move.

The issue wasn’t a lack of tools. It was that everything still depended on me to think.

So I built an AI assistant for myself.

That assistant eventually became Seraphina.

What I didn’t expect was this: it wouldn’t just support my work. It would fundamentally change how I operate – and eventually become a business in its own right.

Step one: Solve your own bottleneck first

Before anything scaled, Seraphina solved very specific, very real problems.

• Drafting content instead of starting from scratch.
• Replying to messages and emails when I wasn’t available.
• Supporting student and community management.
• Analysing trends and summarising insights.
• Maintaining activity in Telegram groups even when I was offline.

This wasn’t about chasing productivity for its own sake. It was about removing friction from my day-to-day operations.

The biggest shift wasn’t just time saved – it was mental space.

Instead of constantly switching contexts and making micro-decisions, I could focus on direction, strategy, and higher-leverage work.

That’s when I realised: the real value of AI agents isn’t automation.

It’s decompression.

Also Read: The product management strategy behind building AI agent platform

Step two: Treat your AI like a junior operator, not a tool

One of the biggest misconceptions is that AI should “just work”.

It doesn’t.

There are still moments where Seraphina gets things wrong. Recently, it replied in the wrong context – responding on behalf of someone else entirely. It didn’t make sense, and I had to step in to recalibrate.

But this isn’t a flaw. It’s part of the process.

If you’ve ever worked with interns or junior hires, you’ll recognise the pattern:

• They don’t fully understand context at the start
• They make mistakes
• They improve with feedback

AI agents behave the same way.

The difference is speed. Once aligned, they scale instantly.

The founders who benefit the most are not the ones expecting perfection – they’re the ones willing to train, refine, and iterate.

Step three: Stay responsible for decisions

As AI agents become more capable, the conversation shifts from “can they do the work?” to “who is accountable when they do?”

With human teams, responsibility can be distributed.

With AI, it consolidates.

You still own the outcome.

This forces a shift in how founders operate:

• From execution → to oversight
• From doing → to defining systems
• From reacting → to setting boundaries and frameworks

AI doesn’t remove responsibility. It amplifies it.

Step four: Turn internal tools into external products

Seraphina was never intended to be a product.

It was built to solve my own workflow.

But once it became effective, the next step was obvious – other founders had the same problem.

So it evolved.

Also Read: Without governance, AI agents risk becoming enterprise chaos engines

Today, it has over 2,000 users.

What started as an internal assistant became a revenue-generating micro-SaaS.

This is a pattern I’m seeing more frequently:
Founders are no longer starting with “What should I build?”

They’re starting with: “What am I already doing that works – and can this be productised?”

Step five: Layer your monetisation

The product alone isn’t the business. The structure around it is.

What made this model sustainable was layering different levels of value:

• Low-ticket (SaaS): Paid users access the system and implement it themselves.
• Mid-ticket (education and workshops): Founders learn how to build their own AI agents and workflows.
• High-ticket (done-for-you / consulting): Businesses get customised implementations for speed and scale.

This creates three important advantages:

• Different entry points for different users.
• Higher lifetime value without increasing complexity.
• A more resilient business model that doesn’t rely on one revenue stream.

In my case, improving Seraphina for myself directly improves it for users. The feedback loop is continuous.

The barrier to building software has collapsed

Not long ago, building a SaaS company required:

• 10 to 30 developers.
• Significant capital.
• Long development timelines.

Today, that barrier has dropped significantly.

Seraphina was built by essentially two entities: myself and the AI system itself.

This reflects a broader shift. Software used to be an “elite” opportunity because of the resources required. Now, with AI, individuals can build profitable products that serve niche audiences with far fewer users.

This changes the economics:

• Faster build cycles.
• Lower upfront investment.
• Faster break-even.

You don’t need thousands of users anymore. In many cases, hundreds are enough.

What this means for founders

AI agents are not just tools.

They are leveraging.

If you’re building today, the opportunity is not just to use AI – it’s to rethink how you build entirely.

Also Read: The hidden risk in AI adoption: Unchecked agent privileges

A practical way to approach this:

• Identify your highest-friction tasks.
• Build a system to handle them.
• Test it in your own workflow.
• Refine it through real usage.
• Productise it if others face the same problem.
• Layer monetisation based on user readiness.

This compresses what used to take months into weeks.

Validation cycles are shorter. Feedback loops are tighter.

Speed is no longer an advantage – it’s the baseline.

The shift is already happening

The idea of a one-person company used to feel unrealistic.

Now, it’s increasingly viable.

Not because founders are doing more, but because they are doing less of the wrong things.

AI agents allow you to:

• Operate without being constantly present.
• Scale output without scaling headcount.
• Build systems that generate value beyond your time.

For me, building Seraphina started as a way to get my time back.

It became a system. Then a product. Then a business model.

And more importantly, it changed how I think about building.

The first AI agent most founders should build is not for their customers.

It’s for themselves.

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