One Startup Studio, Multiple Startups: How to Manage Different Automations for Different Products

Automating well means understanding how a company works, which steps generate value, and which ones, conversely, create friction.

In a single startup, this work is already complex. In a Startup Studio, it is even more so, because there isn't just one product to grow, but multiple initiatives evolving in parallel. Each startup has its own market, its own team, its own business model, and a different operational maturity.

That is why, when talking about automation in a Startup Studio, the question cannot be: “which process can we replicate from one startup to another?”. The correct question is: “which logic can we reuse, adapting it to the specific context of this product?”.

Common methodology, not identical automations

The risk, when working on multiple projects simultaneously, is falling into one of two extremes.

On one hand, you can try to standardize everything. You take a workflow that worked in one context and apply it to others, with few changes. It is a choice that is seemingly efficient, but often not very effective. A process designed for an already structured company can be too rigid for a project still in validation. Similarly, a logic suited for a product with complex sales may be of little use for a simpler, more direct model.

On the other hand, you can make the opposite mistake: building everything from scratch every time. Each startup chooses its own tools, defines its own flows, and creates automations without a shared logic. In the short term, this gives autonomy to the teams, but in the long term, it makes it harder to maintain, compare, and improve processes.

The solution lies in the middle: having a common, but not uniform, operating system. Common means there is a shared method for reading processes, designing automations, and updating them over time. Not uniform means each solution must remain closely aligned with the product, the growth stage, and the way the team works.

First the process, then the automation

Good automation is born from an understanding of the process.

We also discussed this in the article dedicated to moving from chaos to system, delving deeper into why mapping processes before automating them is the first step to building useful and sustainable workflows.

Before automating, it is necessary to understand what information enters the system, who uses it, which decisions it must support, and where bottlenecks are created. Often, the problem becomes a lack of clarity.

A manual step may be slow, but still make sense. Another may be repetitive, conditional, and therefore suitable for automation. The difference is not always obvious until the process is observed as a whole.

For this reason, in the Startup Studio, the work consists first of all in mapping how a startup works, how it transfers information between people and tools, which steps slow down the team, and which activities can be made smoother.

Only after that does it become possible to decide what to automate, what to simplify, and what to deliberately keep manual.

Reusable modules, adaptable logics

Working on multiple products allows us to recognize repeating dynamics. As every company grows, it reaches a point where the flow of information must become more orderly and coordination between people more stable. Operational activities increase, tools to update multiply, and the risk is that the team wastes time on repetitive steps instead of focusing on the decisions that matter.

It is from these recurring needs that reusable modules are born: solutions developed to solve similar problems in different contexts.

The same logic can take different forms depending on the product. In one startup, it may serve to make the handoff between marketing and sales smoother. In another, it may help the product team better read user signals. In yet another, it can support coordination between operations and customer management.

The principle remains the same, but the context, maturity level, and type of action to trigger change. This is where a Startup Studio can create efficiency without losing precision: by reusing what it has learned, but without forcing different products into the same mold.

The same applies to AI. It can accelerate certain activities, help read information better, and support the team in the most repetitive steps. But to be useful, it must be embedded within clear processes. Otherwise, it risks becoming an extra layer of complexity instead of a tool to reduce it.

The right level at the right time

Not all startups need the same level of automation at the exact same moment.

When a project is still in its early stages, automation must remain light. Its main task is to bring order, not to build complex infrastructures. It serves to avoid losing information, to keep track of activities, and to make learning easier.

At this stage, automating too much could become counterproductive. Poorly structured processes will inevitably transform over time, and the team must be capable of quickly changing how they work to face that change.

When the product grows, however, the need changes as well. Activities often become more recurring and complex, to the point of requiring more people involved. This increases the risk of important information getting lost. At that point, automation serves not only to save time but to make the work more reliable.

For this reason, the correct level of automation is not absolute. It depends on the maturity of the product, the stability of the process, and the complexity that the company has to manage.

From custom method to Delivery Service

This approach does not apply only to startups born within the Studio. It is also one of the reasons why we developed the Startup Bakery Delivery Service: a service created to make our operational, technological, and process know-how available to external companies, supporting them in managing complexity and scaling their business.

By working on different products and companies, we have built a wealth of expertise that can generate value not only for early-stage startups but also for established realities, growing companies, and scaleups undergoing structural reorganization.

Many companies reach a point where the problem is not just validating the product or finding the first customers, but sustaining that growth. In these cases, simply adding a new tool is rarely enough. The central point becomes understanding where the organization loses efficiency, control, or visibility.

With the Delivery Service, we bring our way of working outwards: we observe actual processes, identifying critical steps. Finally, we provide a vision of what is useful to maintain and we introduce automation and new processes only where they can generate a concrete impact. It may be a matter of making commercial management more solid, improving onboarding, or facilitating internal coordination. The starting point, however, always remains the same: understanding how the company works before intervening on the tools.

Technology, on its own, only makes a company more scalable when it strengthens a way of working that is already understood, simplified, and made measurable.

This is how a Startup Studio can work on multiple startups in parallel without losing consistency. And this is how the same approach can be brought to external companies, helping them make their growth more orderly, sustainable, and scalable.

Automating well does not mean making all companies identical. It means giving each one the right tools to grow with more control, more clarity, and less unnecessary complexity.