How Make is building the orchestration layer for AI-native companies

Make has been building workflow automation tools for more than 10 years. The company initially became known for its highly visual and intuitive approach to automation, helping users connect applications, automate workflows, and orchestrate operations without needing advanced technical skills.

Over time, Make expanded from traditional no-code automation into AI-native orchestration. Today, the platform supports more than 3,000 integrations, serves over 150,000 customers worldwide, and works with companies ranging from individual creators to large enterprises. The business also experienced rapid growth over recent years, multiplying revenue by 15x while scaling the team from fewer than 100 employees to more than 300.

Fabian Q Veit joined the story after previously serving as COO at Celonis, where he helped scale one of Europe’s largest enterprise software companies. He later led the acquisition of Make in 2020 and now serves as CEO.

I sat down with Fabian Q Veit, CEO of Make, to discuss where companies struggle with AI transformation, how Make uses its own platform internally, and why orchestration and observability are becoming critical in AI-native organizations.

Disclaimer: The organizational choices and technical solutions shared in this newsletter aren’t meant to be copied and pasted as-is. Always keep your company’s context in mind before adopting something that works elsewhere! 😊

Build internal AI champions before scaling adoption

Many companies approach AI transformation with overly broad plans from day one. Fabian explained that AI adoption works better when organizations start with concrete use cases and motivated employees already curious about the technology.

At Make, the idea is to create momentum progressively across the company:

• identify enthusiastic employees
• give them tools quickly
• encourage experimentation
• share internal wins
• scale successful workflows

Leadership still plays an important role. Leaders need to create enough energy internally so teams understand that AI matters strategically and operationally.

Make also believes AI adoption should not stay limited to technical teams. Business users closest to workflows often understand operational pain points better than anyone:

• customer operations
• sales execution
• reporting
• marketing workflows
• internal coordination

“The people closest to the business processes know the problems they want to overcome” - Fabian Q Veit

This is why accessibility and visual workflows became central to Make’s product strategy.

Structure AI transformation around skills, will, and environment

Fabian shared a framework Make uses internally and with customers to evaluate AI readiness. It is built around three dimensions:

• skills
• will
• environment

The first layer is skills. Companies need to understand how comfortable employees are with AI workflows and where training is required.

The second layer is will. Some employees naturally embrace experimentation faster than others. These people often become internal multipliers for adoption.

The third layer is environment. Leaders need to create conditions that support experimentation:

• access to tools
• hackathons
• workflow redesign
• operational autonomy
• room for testing

According to Fabian, these three dimensions need to evolve together.

“If you get those three right, the skill, the will, and the environment, that’s what you need” '- Fabian Q Veit

This framework helps companies treat AI transformation as an operational shift across teams, not only a technical initiative.

Create AI leaders inside every department

Make operationalized AI adoption internally through a program called “Make Runs Make.”

The initiative originally focused on ensuring employees actively used Make internally. Over time, the company expanded it into a broader AI transformation program.

Make created a group of internal experts called the “Samurai.” Each department has at least one person focused on AI automation, agents, and workflow orchestration.

Their role is to:

• identify use cases
• prioritize automation opportunities
• estimate ROI potential
• share learnings
• accelerate adoption

The company also introduced minimum AI automation expectations across teams. Employees are encouraged to build workflows that improve their own day-to-day work.

Fabian shared an example from the product marketing team. One employee built “product launch agents” capable of generating:

• launch announcements
• short-form copy
• long-form copy
• multi-platform content
• reusable workflows

The human still reviews and improves outputs, but the operational workload changes significantly.

“Without any engineering background, you’re really building agents today that help unlock significant business value” - Fabian Q Veit

Use AI automation to scale revenue faster than headcount

Fabian shared one particularly interesting business outcome.

According to him, Make grew roughly 50% in revenue and customer scale over the last year while remaining relatively flat in organizational size.

This operational leverage came from automating repetitive workflows across teams:

• reporting
• launch preparation
• customer operations
• data processing
• workflow execution

Fabian also shared a use case from Celonis around travel expense reporting.

Previously, several employees manually reviewed expense submissions against company policy. Make automated large parts of the process:

• reading submitted documents
• checking policy compliance
• flagging exceptions
• routing edge cases to humans

As a result, around 80% of requests could flow automatically through the system.

This type of automation becomes especially valuable for fast-growing companies trying to scale without creating excessive operational overhead.

“If you need to do something three times, you should think about how to automate or agentify it” - Fabian Q Veit

Build observability before AI operations become too complex

Fabian explained that organizations are already running hundreds or thousands of workflows and agents internally.

As AI operations scale, companies need visibility into:

• what is running
• how systems connect
• where data flows
• where human review happens
• how workflows behave over time

This is why Make increasingly positions itself as a visual orchestration layer for AI-native operations.

The company focuses heavily on:

• monitoring
• governance
• logging
• visual mapping
• orchestration visibility

Fabian described this as moving from a “black box” toward a “glass box,” where companies can inspect and understand automated operations more easily.

“We want to be this visual landscape where you can look inside and understand what’s going on” - Fabian Q Veit

This also changes the role humans play inside organizations. Teams spend less time executing repetitive workflows and more time supervising systems, reviewing outputs, and orchestrating exceptions.

Expand from automation toward agentic orchestration

Make originally became known for visual no-code automation. Today, the company is evolving toward what Fabian calls the “agentic automation spectrum.”

Some workflows remain highly deterministic:

• rule-based automations
• predefined execution paths
• stable operational systems

Others become more autonomous:

• AI agents
• reasoning systems
• adaptive workflows
• goal-driven execution

Most companies will operate somewhere between these two approaches.

Fabian explained that different business processes require different levels of flexibility and control. Sensitive workflows often still require strong human oversight and deterministic execution.

At the same time, AI dramatically changes how workflows are built.

Earlier no-code workflows required users to map every step manually. Many actions can now start directly from natural language prompts.

This reduces setup complexity while making automation accessible to a broader range of teams.

Fabian summarized this evolution very simply:

• language becomes the fastest way to build
• visual systems become the fastest way to understand

Adapting Make as AI reshaped user expectations

One of the biggest challenges for Make was adapting its product while the AI ecosystem evolved extremely quickly.

The company initially grew around no-code automation and visual workflows. Modern AI models expanded what users expected from automation tools.

Some workflows that previously required drag-and-drop setup could suddenly be generated directly from prompts.

This changed:

• onboarding expectations
• workflow creation
• interaction models
• customer behavior
• competitive positioning

Fabian explained that users increasingly expected workflows to feel almost instantaneous.

At the same time, AI expanded the number of use cases customers could automate.

Make therefore had to evolve:

• its product
• positioning
• technology stack
• operational model
• workflow experience

while continuing to support a rapidly growing customer base.

• AI transformation works better when companies combine leadership conviction with strong bottom-up experimentation inside teams.
• Operational AI adoption accelerates when business users closest to workflows can directly build and test automation use cases.
• Skills, motivation, and organizational environment all need to evolve together for AI transformation to scale successfully.
• Internal AI champions can become highly effective multipliers when they are embedded directly inside operational teams.
• AI automation creates operational leverage by helping companies grow revenue and customer scale without proportional headcount growth.
• Observability and governance become increasingly important as organizations deploy hundreds of automated workflows and AI agents.
• The future of workflow tools will likely combine natural language creation with highly visual operational supervision.
• Companies need different levels of automation depending on workflow sensitivity, reliability requirements, and operational risk.
• AI-native orchestration is gradually transforming humans from task executors into workflow supervisors and system orchestrators.
• The companies creating the most value with AI are continuously evolving both their products and their organizational workflows simultaneously

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