Organizations invest heavily in technology, skilled teams, and operational improvements, yet many continue to struggle with inefficiencies that new systems alone cannot resolve. For Hailin (Avery) Chi, an enterprise technology and business solutions professional with more than six years of experience, these challenges often originate in the processes, decision-making structures, and organizational relationships surrounding the technology.
As an Implementation Consultant and Solutions Architect at Riskonnect, Chi has worked with enterprise organizations across governance, risk management, operational resilience, and SaaS implementation. She is also developing Made4Vital, a business platform focused on helping organizations address complex operational challenges through a more systematic approach. In this interview with IntelligentHQ, Chi discusses how systems thinking influences her approach to problem-solving, why organizations frequently overlook the underlying causes of operational difficulties, and how AI and automation can expose weaknesses in existing business processes.
Your career has spanned enterprise technology, SaaS implementation, governance, risk, operational resilience, and incident management. How has working across these areas shaped the way you approach complex business problems today?
It taught me very early not to confuse the request with the problem.
In enterprise implementations, somebody might come in asking for a workflow, a report, a configuration change or a new capability. But once you start asking what they are actually trying to accomplish, who uses the information, what happens before and after that step, and where the decision goes next, the original request can look very different.
Working across business, technology, risk and operations reinforced that repeatedly. A technically correct solution can still fail operationally. A good process can fail because ownership is unclear. A system can work exactly as designed and still not produce the business outcome people expected.
So today I tend to move backward before moving forward: What outcome are we trying to create? What is actually happening now? Where is the gap? What is causing it?
Then we can talk about the solution.
In your experience, why do organizations so often focus on the most visible symptom of a problem rather than identifying the underlying constraint that is actually driving it?
Because the symptom is what hurts.
If reports take too long, buy better reporting technology. If the team is overloaded, hire more people. If sales are down, generate more leads. If work is slow, automate it.
Any of those could be right. They could also be very expensive ways to avoid the actual problem.
Organizations are remarkably good at adapting to dysfunction. Give people a broken process long enough and they’ll build three spreadsheets, two workarounds and a meeting around it. Eventually that becomes “how we do things here,” and nobody remembers why they started doing it in the first place.
That is why I spend so much time asking what is underneath the visible problem. More is sometimes exactly what a business needs. But without diagnosis, you don’t know whether you need more, or whether you’re adding resources to compensate for something that should have been fixed.
Many operational challenges cross traditional departmental boundaries. How do you approach problems that involve technology, people, processes, ownership, and business priorities at the same time?
I resist assigning the problem to a department too early.
The moment we call something an IT problem, HR problem, operations problem or risk problem, we have already put a boundary around how we look at it. Real business problems are rarely that considerate.
I map the outcome, the current process, the people involved, the systems and information moving through it, the decision points, ownership, dependencies and what happens when something goes wrong. Then I look for where those pieces stop connecting.
That translation layer has been a large part of my career. Executives, business users, risk teams, technical teams and developers can all be highly competent and still be describing different versions of the same reality.
Often the breakthrough isn’t another piece of expertise. It’s getting the expertise already in the room to connect.
Organizations can have strong teams, sophisticated technology, and significant resources and still struggle operationally. Why does that happen, and what does it reveal about the importance of organizational structure and process design?
Because having good components does not guarantee a good system.
You can have talented people compensating for a badly designed process. You can have expensive technology that employees use as an elaborate address book. You can have excellent data that reaches the wrong person too late. You can hire more people and increase headcount without increasing useful capacity.
In some ways, strong people can hide structural problems longer because they are good at working around them. Heroics keep the machine running.
The question I find more useful is: where is the organization using effort to compensate for bad structure, unclear ownership, a poor process, a missing capability or an unresolved constraint?
That is often where the hidden leverage is.
“Systems thinking” can sometimes sound abstract. What does systems thinking mean in practical business terms, and how can leaders use it to make better decisions about technology, operations, and organizational change?
To me, systems thinking is very practical: stop looking at the thing by itself.
If you change this process, what else changes? Who now has to make a decision? What information do they need? Which system supplies it? What happens upstream and downstream? What new dependency have you created? What behavior are you assuming from people? How will you know the change actually worked?
It is also the discipline of distinguishing local improvement from system improvement. Making one department faster does not necessarily make the business faster if the work simply piles up at the next handoff.
So systems thinking is not drawing an impressive diagram with fifty arrows. It is understanding enough of the relationships to avoid solving one problem by quietly creating three others.
Your work increasingly focuses on AI and automation as tools for creating enterprise leverage. How is AI making previously hidden weaknesses, such as fragmented processes, unclear ownership, or inconsistent decision-making, more visible inside organizations?
AI is an extraordinary leverage tool, and leverage works in both directions.
If the process is coherent, the information is usable, ownership is clear and the decision logic makes sense, AI can remove enormous amounts of repetitive, tedious and error-prone work. It can help people get to information faster and preserve more human capacity for judgment, creativity, relationships and higher-value decisions.
But if five people have five definitions of the process, AI needs an answer. If nobody owns an exception, AI exposes that. If important knowledge lives in somebody’s head, that becomes a constraint. If two departments make the same decision differently, automation forces the organization to confront which logic it actually wants to scale.
AI doesn’t create all of those weaknesses. In many cases, it makes it harder to keep pretending they aren’t there.
Before investing in a new technology or AI solution, what should leaders understand about the system surrounding the problem, and how can that understanding help them avoid automating or scaling a process that is fundamentally flawed?
Start with the business, not the tool.
What outcome matters? What happens today? Where does time, money, information or capacity get stuck? Who owns the process and the decisions inside it? Which exceptions matter? What should remain human? What would success actually look like?
Then ask whether technology is the right intervention.
I am very pro-technology and very pro-AI. I am also against making a bad process faster.
Automation gives an organization leverage. Before applying that leverage, leaders need to know what they are multiplying. Sometimes the answer is AI. Sometimes it is redesigning a workflow, clarifying ownership, eliminating a step, connecting information that already exists, or simply stopping something that no longer serves a purpose.
The goal isn’t to automate more. It is to create more value with the resources the organization already has, and use technology where it genuinely increases that value.
