Alessio Vinassa on How to Reduce Automation Risks at Scale

My view as Alessio Vinassa on how to reduce automation risks is straightforward: assign accountable owners, test failure scenarios and prove that people can intervene before scaling. Digital investment strengthens resilience only when the business can detect errors, contain their impact and maintain essential operations when automated systems fail.

Why did pandemic-era technology adoption strengthen resilience?

Digital tools strengthened resilience by giving businesses alternative ways to serve customers, coordinate teams and change operations when physical channels became unavailable.

During the pandemic, remote collaboration, digital customer access and cloud infrastructure became operational necessities. Businesses could redirect work and maintain relationships without relying entirely on offices, shops or in-person processes.

In my perspective on leading through change, I referenced McKinsey’s finding that the pandemic compressed several years of technology adoption into months. That acceleration demonstrated the value of digital capabilities, but it did not prove that every implementation was resilient.

The distinction matters. A system that helps a company move quickly under one set of conditions can become a dependency that restricts its response under another.

I see digital capabilities as the nervous system of a resilient business. But a nervous system must detect abnormal signals and support an appropriate response, not simply transmit instructions faster.

How to reduce automation risks before scaling

Reduce automation risks by defining what a workflow may do, who owns its consequences and what happens when its assumptions stop holding.

Automation becomes a liability when execution continues after the underlying logic is no longer valid. A workflow can operate exactly as configured while producing the wrong business outcome.

Consider a hypothetical automated purchasing process. If its supplier feed contains outdated stock information, it may keep placing orders that cannot be fulfilled; successful execution logs would not reveal the customer impact.

Before expanding that workflow, I would require answers to five questions:

1. Purpose: Which business outcome is the automation meant to improve?
2. Boundaries: Which actions, values or exceptions require human approval?
3. Ownership: Who is accountable for the outcome, not just the software?
4. Dependencies: Which data feeds, permissions and external services must remain reliable?
5. Intervention: Who can pause execution, and what process replaces it?

My operating rule is structure before intensity. Applied here, it means defining controls before increasing transaction volume or connecting more systems.

This is also the difference between adopting technology and understanding how digital transformation creates value. Faster execution is useful only when the outcome remains acceptable and the exposure remains controlled.

What should an automation stress test include?

An automation stress test should expose the workflow to bad inputs, unavailable dependencies and operational pressure, then verify detection, intervention and recovery.

A demonstration proves that a process can work under selected conditions. A stress test examines what happens when those conditions deteriorate.

I would start with four scenarios in a controlled environment, using synthetic data or isolated systems where necessary. The objective is to reveal weaknesses without creating a live customer incident.

Scenario	What to test	Evidence to capture
Unreliable data	Introduce missing, outdated or contradictory inputs.	Whether the workflow rejects, flags or blindly processes them.
Dependency failure	Make a critical external service unavailable.	Whether work stops safely, queues correctly or creates duplicate actions.
Demand surge	Increase workload beyond the expected operating range.	Queue growth, error rates and the capacity of the fallback process.
Human intervention	Ask the designated operator to pause and recover the workflow.	Time to intervene, access problems and unresolved transactions.

Set acceptance criteria before running the exercise. Depending on the process, these might include maximum interruption time, permitted financial exposure or the number of transactions requiring reconciliation.

There is no universal safe threshold. Payroll, marketing distribution and customer refunds have different consequences, so controls should reflect the risk rather than follow a convenient template.

Record what actually happened, assign remediation owners and retest. A fallback plan that nobody has exercised is an assumption, not evidence of readiness.

Who should own automation governance?

The business process owner should be accountable for automated outcomes, with technical, security and compliance responsibilities explicitly assigned.

Governance fails when everyone assumes someone else owns the exception. Engineering may own system reliability while operations owns customer delivery, but someone must have clear authority to stop a workflow when those objectives conflict.

My resilience approach has three priorities: visibility, optionality and responsiveness. Each translates into a practical governance requirement.

• Visibility: Monitor business outcomes as well as technical availability, including exceptions, complaints and accumulating backlogs.
• Optionality: Maintain a viable alternative for essential work, with the access, people and capacity needed to use it.
• Responsiveness: Give named operators decision rights, escalation routes and rehearsed procedures.

A manual fallback is not credible if only one unavailable employee understands it. Nor is an alternative platform independent if it relies on the same failed service.

This is where business continuity planning becomes an operational discipline rather than a document. Teams need to practise switching paths and reconciling work afterward.

Culture matters too. People must be able to report that automation is producing questionable results without being treated as obstacles to efficiency.

How should leaders evaluate digital resilience investments?

Evaluate digital investments against both their efficiency gains and the business’s ability to contain and recover from failure.

My experience includes a business reaching seven-figure monthly revenue before an external shock reversed much of its progress and halted operations for several weeks. That was not an automation case study, but it reinforced a lesson relevant here: revenue does not equal resilience.

A digital investment proposal should therefore include more than implementation cost and projected hours saved. I want to see critical dependencies, failure exposure, control ownership, testing costs and the resources needed for recovery.

Release funding and expand permissions in stages where practical. A limited rollout provides an opportunity to observe exceptions before the same weakness reaches every customer or transaction.

Retest after material changes to integrations, business rules, permissions or transaction volumes. Passing yesterday’s test does not establish that a changed system remains safe.

In my advisory work with founders and leaders, the aim is better judgment, not dependence on someone else to solve every problem. Automation deserves the same discipline: use it to extend capability without surrendering accountability.

Key takeaways

Digital resilience depends on governed execution, not automation alone.

• Pandemic-era pivots demonstrated the value of digital options, not the safety of every automated workflow.
• Assign outcome ownership, operating boundaries and stop authority before scaling.
• Test bad data, unavailable services, demand surges and human intervention.
• Fund monitoring, fallback capacity and recovery alongside efficiency improvements.

FAQ

When does automation become a business liability?

Automation becomes a liability when it can repeat or amplify harmful actions without timely detection and intervention. The risk increases when unreliable inputs, concentrated dependencies or unclear ownership go unchallenged.

How often should automated processes be stress-tested?

Test before significant deployment and after material changes to rules, integrations, permissions or operating conditions. Set additional recurring tests according to the consequences of failure and the speed at which the process changes.

Does automation governance slow innovation?

Poorly designed governance can create unnecessary delays, but proportionate controls clarify where teams can move independently. Define approval boundaries and escalation triggers so routine work proceeds quickly while consequential exceptions receive scrutiny.