Automation promises speed, reliability, and scale. Yet, if you walk through enterprise programs today, you’ll often see a familiar story: a few quick wins, then a plateau. The scripts multiply, the tools expand, but adoption stalls.

What causes that stall isn’t a lack of technology. It’s a lack of participation.

The organizations that scale automation effectively do so not because they chose a single “right tool,” but because they created the conditions for teams to contribute and collaborate. Automation is a team sport, and the sooner leaders embrace that, the sooner programs move from siloed experiments to enterprise impact.

It’s tempting to believe success hinges on choosing the best tool. Standardize on one language, one framework, one single source of truth, one vendor and expect the rest to fall into place.

The reality is different. Every enterprise has a mix of teams, each with unique skills and responsibilities:

• A team that manages infrastructure provisioning might be fluent in Terraform or OpenTofu.
• A team focused on network configuration may rely on Ansible.
• Developers writing custom integrations will naturally reach for Python.

Forcing every team into a single tool may look tidy on a chart, but in practice, it creates friction, resistance, and workarounds. Automation assets multiply outside the official platform, and the program stalls.

Success doesn’t come from enforcing uniformity. It comes from accepting diversity and orchestrating contributions into a cohesive whole.

If there’s no single best tool, then the differentiator is collaboration. Effective programs operate more like sports teams than tool factories. Everyone has a role, and the rules of the game ensure those roles add up to more than individual effort.

Here’s what that looks like in practice:

• Clear Contribution Paths: Teams know how to submit automation into a shared system, and what standards it must meet to move forward.
• Guardrails, Not Gates: Governance sets boundaries – security checks, documentation requirements, naming conventions – but doesn’t block progress with unnecessary hurdles.
• Automation on the Automation: Governance itself should evolve. Apply automation to reviews, documentation, and testing to modernize release management.
• Shared Visibility: Dashboards, repositories, and orchestration platforms make contributions visible across silos, so work isn’t duplicated.
• Recognition for All Contributions: Writing a module is valuable. So is documenting it or integrating it with ticketing and monitoring systems. Every contribution counts when it improves adoption.

When automation becomes a team sport, contributions multiply, and scale follows.

Too often, organizations assume “contribution” means writing automation scripts. That mindset excludes a huge portion of potential value.

True contribution is broader:

• Reusable Assets: Templates, playbooks, and pre-built integrations.
• Documentation: Instructions that make automation accessible beyond the original author.
• Best-Practice Examples: Patterns that reduce errors and guide new contributors.
• Process Integrations: Updates to tickets, change records, or sources of truth that keep automation connected to business workflows.

The lesson: contribution isn’t about who codes the most. It’s about building a framework where every team can add value.

Say the word “governance,” and you can watch the energy drain from a room. Too many leaders picture static policies, outdated checklists, and bottlenecks.

But in high-performing automation programs, governance looks different. It evolves with technology. It creates guardrails that make participation safe and repeatable, while keeping the program moving.

Think of it as the guardrails on a racetrack: they don’t slow you down, they give you confidence to move faster. Effective governance provides:

• Reusable templates and patterns aligned to security and compliance.
• Contribution guidelines that clarify expectations without requiring constant hand-holding.
• Automated checks for consistency, rather than manual approvals that delay progress.
  

When governance is treated as an enabler, adoption accelerates.

Another trap organizations fall into: measuring automation success by the number of scripts created or servers configured. Those are outputs, not outcomes.

Leaders who scale automation look at different metrics:

• Participation Breadth: How many teams contribute, not just consume.
• Consistency: How many contributions meet governance standards without rework.
• Reusability: How often assets are adopted beyond their original team.
• Velocity: How quickly contributions move from creation to production.

Measuring success this way shifts focus from individual wins to organizational health. It also signals to teams that collaboration is the metric that matters.

Scaling automation isn’t about picking a winner in the “best tool” debate. It’s about building a program where:

• Every team sees a path to contribute.
• Governance guides without constraining.
• Contributions – code or not – are visible, reusable, and repeatable.
• Success is measured by collaboration, not just execution.

That’s what separates stalled automation programs from scalable ones.

At Itential, we see this every day. Our orchestration platform is designed to accept contributions from any tool, enforce governance automatically, and create shared visibility across silos. It’s how enterprises move from fragmented automation to enterprise-wide impact.

Because automation isn’t a solo act. It’s a team sport.

🔎 Want to go deeper? Watch my recent webinar replay with William Collins below or check out the full series here.
💬 Interested in continuing the dialogue? Reach out to me on LinkedIn.