Don’t Count Your Agents: monday.com’s Chris Jordan on Measuring AI by Outcomes Across Southeast Asia and India

Key Highlights

“The number of agents someone uses is not itself a measure of success.”

Chris Jordan, Regional Vice President for Asia and Country Manager for Singapore at monday.com, says organizations adopting AI agents should judge them by the work they improve, not by how many are in use. In his view, the right measures are outcomes such as resolving customer tickets faster or managing projects with fewer mistakes. His advice is to start small: pick one workflow, define the outcome, decide where human approval is needed, give the process a named human owner, and expand an agent’s responsibilities as each addition proves its value.

In this conversation, Jordan explains how monday.com’s shift from managing work to executing it is changing customer conversations in Southeast Asia and India, and why the region’s differing levels of digital maturity mean a workflow that works in Singapore won’t transfer directly to Jakarta, Manila or Kuala Lumpur. He covers where he sees demand in retail, manufacturing and professional services, how to keep agents within existing permissions and governance, and what inclusive AI adoption requires. He also points to monday.com’s platform data, which he says shows employees in smaller companies using AI more heavily, as a sign that the biggest untapped opportunity lies with growing businesses.

You started in advertising before spending the last decade scaling SaaS businesses. Which lessons from those two very different worlds have proven most valuable in leading monday.com’s growth across Southeast Asia and India? 

Advertising teaches you to understand your audience before asking them to do something differently. That principle carries directly into SaaS. You need to understand the customer’s problem, explain the value clearly, and make it easy for people to take the first step. Across Southeast Asia and India, that means recognising that customers have different starting points. A smaller business introducing more structured processes needs a different conversation from an enterprise connecting work across departments. Our Singapore team brings together expertise across engineering, solution architecture, sales and product, helping us understand those requirements and support customers more closely. 

However, making something understandable is only the beginning because people must also be able to use it. That is particularly relevant to AI. For example, at monday.com, team members can configure and direct agents without a technical background. That connects the communication discipline of advertising with the adoption discipline of SaaS; explain how something helps, make it accessible and demonstrate its value through everyday work.

Southeast Asia is a diverse and fast-growing market. What are the biggest opportunities and challenges for monday.com in the region, and how do you balance a consistent regional strategy with local customer needs and buying behaviors? 

The opportunity spans across growing SMEs, family businesses and enterprises. Smaller businesses need accessible software that can develop with them, while enterprises want visibility and coordination across departments, with AI embedded into their workflows. There is substantial enterprise demand here - for example our largest global deal for monday Service in 2025 came from a customer in Southeast Asia. 

The challenge is that Southeast Asia compromises distinct markets, with different levels of digital maturity, regulatory environments and buying behaviours. A workflow that works in Singapore will not necessarily transfer directly to Jakarta, Manila or Kuala Lumpur. We need to understand how each customer operates and where they need support. That is why proximity matters. Our Singapore team’s capabilities cover engineering, solution architecture, sales and product. Having that breadth of expertise locally helps us respond to customer requirements. Our regional strategy centres on connecting work and making AI accessible, while implementation and support need to reflect each market.

Which sectors are driving the strongest growth for monday.com across Southeast Asia and India? What makes these industries particularly ready for work management and AI-powered solutions? 

Retail and commerce, manufacturing and professional services are key areas of opportunity for us across Asia. As supply chains shift towards the region, businesses in these sectors are modernising and moving away from rigid legacy tools. They need flexible platforms that can adapt as they scale. 

The demand we see across the region includes larger organisations looking for visibility, workflow coordination and practical AI adoption across teams. That is where our evolution from work management to work execution becomes relevant - connecting work across the business and making AI accessible to the people doing it. 

In retail, practical applications include sorting service tickets, drafting campaign content and updating supplier records. These give businesses specific operational tasks where they can introduce AI and assess its value.

monday.com has evolved beyond work management into an AI-powered work platform. How is this repositioning changing customer conversations, and how are businesses in the region benefitting from this broader approach? 

The shift is from a platform that manages work to one that helps execute it. That broadens the customer conversation to how people and AI agents can get work done together, with agents built directly into the platform. For businesses in the region, accessibility is central to this approach. Any team member can configure and direct agents without a technical background, giving growing and mid-sized companies access to AI capabilities without needing data scientists or heavy setup. This supports the way many APAC teams adopt software, starting within a team and expanding from there. 

Visibility and control are equally important. Boards provide a structured data layer, making agent activity queryable and reportable so teams can understand what agents are doing. Agents operate within the platform’s existing permissions, security and governance controls, with teams deciding what each agent can read, edit or create, and human oversight built in.

When you speak with customers today, what operational challenges come up most often? As AI agents become more common in the workplace, how should organisations rethink their workflows, team structures, and processes to get the most value out of them?

Fragmented systems, siloed departments and limited visibility are recurring challenges. Teams spend time finding information, updating records and chasing handovers, while leaders struggle to see where work is held up. To add AI to that environment, you need a clear view of the underlying process. 

I would start with one workflow. Define the outcome, identify the information an agent needs and decide where human approval is required. A service team might use an agent to classify incoming requests, while a person handles sensitive or unusual cases. Team structures need clear accountability as agents take on tasks. Each workflow should have a named human owner, and employees need to know how to review an agent’s work and escalate a problem. Managers should be able to see what both people and agents are doing, so they can adjust responsibilities as the process develops.

As AI becomes more deeply embedded in everyday work, concerns around data privacy, security and governance are growing. How can businesses across Southeast Asia and India adopt AI agents confidently while ensuring their data and workflows remain secure? 

One challenge is turning faster individual tasks into better results across the business. If AI helps one team complete work more quickly, a manual review or handover elsewhere can become the next bottleneck. Organisations need to identify where work is accelerating and adjust the work around it. 

I would start agents with a small, clearly defined responsibility and expand it through feedback. One example from our product team began with an agent listening to a team’s Slack channel and adding new tasks to a board. Its role then grew to creating project requirements from those discussions, before incorporating meeting notes to keep project information current. Each additional responsibility addressed a specific need. 

That also changes the human role. Employees spend more time reviewing outputs, providing context and intervening where needed. Access to the agent should reflect the task, so for example, they should not have editing permissions if they’ve only been asked to read something. For leaders, performance should remain tied to the employee’s goals and contribution. The number of agents someone uses is not itself a measure of success. What matters is whether they help deliver better outcomes, such as resolving customer tickets faster or managing projects with fewer mistakes.

APAC is one of the most demographically diverse regions, with varying levels of digital maturity and AI readiness. How can businesses drive inclusive AI adoption across different markets and workforces? Looking ahead, where do you see the biggest untapped opportunity for monday.com in the region? 

Inclusive AI adoption requires businesses to address differences in confidence and skills alongside access to the technology. Organisations must help people understand how to use AI and exercise judgment over its outputs. 

Making AI accessible within familiar workflows is another practical step. Natural-language interactions and ready-made capabilities for tasks like article summaries  give employees ways to begin using AI without specialist technical expertise.

Across APAC, that approach needs to reflect different levels of digital maturity. Our regional strategy combines accessible tools with support that responds to local customer requirements.

Looking ahead, I see a significant opportunity among growing businesses with limited technical resources. Our platform data shows that employees in smaller companies use AI more heavily, challenging the assumption that adoption is led by large enterprises. Alongside enterprise demand for connected workflows, that gives us an opportunity to support businesses at different stages of growth.