My view on business process improvement is simple: your systems should fit your business, not force your business to fit the software. But improving the fit can mean changing the process, configuring an existing tool, connecting systems, or building something new.
My background in supply chain software sales brought me into conversations about how businesses operate. Seeing companies work around software that did not fit their processes shaped the way I want to approach systems work at Fidelis.
The experience shaped how I think about business improvement. A software question is often also a question about responsibilities, information, and the way work moves between people.
Here are four principles I bring from that background into growth strategy and AI implementation.
1. Understand the handoff, not only the task
A task can work well on its own while the overall process remains frustrating.
An order can be entered correctly and still leave the next person without the information they need. An invoice can exist in one system while a customer-service question is being handled somewhere else.
When investigating a workflow, I want to understand what happens immediately before and after the visible task. Who supplies the input? Who uses the result? What happens when either side changes something?
For a hypothetical proposal-to-delivery process, faster proposal drafting may not be the most important improvement. A clearer handoff of the customer’s requirements could matter more.
That is why I do not want to recommend a system from a task name alone.
2. Repeated work is a clue, not a complete diagnosis
Copying information between tools is worth examining. So is rebuilding a report or answering the same internal question repeatedly.
But I would not assume that every repetition is waste.
Some checks exist for a reason. A second person may be confirming an amount, an approval, or a customer instruction. Removing the repetition without understanding its purpose can remove a useful control.
The question is which part of the effort should remain and which part a system could handle more reliably.
A useful improvement might prepare the information automatically while preserving the final check. That is different from eliminating human involvement simply because automation is possible.
3. Fit matters more than the number of features
A product can offer a long list of features and still leave an important part of the business unsupported.
The opposite can also happen: a capable tool is underused because it was never configured around the work or introduced clearly to the team.
I want to understand that difference before recommending a replacement.
Sometimes the answer is better use of what already exists. Sometimes it is an integration. Sometimes the process genuinely calls for a custom system.
The point is not to keep or replace software on principle. It is to decide which approach helps the business operate better without creating unnecessary complexity.
That mindset is also useful with AI. A new model or feature is relevant when it can improve a task the business needs done, not simply because it is available.
4. Growth depends on what happens after the sale
Sales experience keeps the commercial goal visible. The operational conversations remind me that winning work and delivering it are connected.
A business may need more opportunities. It may also need a better way to handle the opportunities it already has, communicate with customers, or maintain relationships after delivery.
Those are strategic questions before they become software requirements.
For example, consider a hypothetical referral process. A reminder can prompt a follow-up, but someone still needs to decide which relationships deserve attention, what useful reason there is to reconnect, and what happens if an introduction arrives.
Technology can support that approach. It should not substitute for having one.
How this shapes Fidelis
At Fidelis, I want the recommendation to follow the business need rather than the limits of a single product’s feature set. The combination I want to bring is commercial judgment, careful discovery, and the ability to implement an appropriate solution.
That does not mean knowing the answer before the first conversation. It means asking useful questions and being willing to change the recommendation as the business becomes clearer.
During discovery, I want to see real examples of the work and hear from the people involved. During design, I want to explain why an approach is worth pursuing. During implementation, I want to keep that business reason visible.
That is the thinking behind my approach to AI strategy and custom systems: understand how the business works, then decide what would make it work better.
You do not need to know the tools, or even where to start. Bring the business context. We can work through the next step together.