Setting up an automated flight-risk early warning system using low code automation

Why not build an early-warning nudge system that helps you retain critical employees before they disengage? High-growth companies frequently face retention challenges. Top innovation companies want to hold on to their top talent, reduce regrettable attrition, and keep high performers engaged. Yet most retention conversations still start too late. By the time someone is exploring opportunities, the intervention window has often closed.

In Customer Success and Revenue Operations functions within the most successful innovation companies, leaders rarely wait for problems to surface. They monitor customer churn signals long before a customer walks away, using automated systems, sentiment patterns, and instant alerts to spot issues early, yet companies rarely ever get to a similar level of orchestration and data analysis when it comes to flagging early warning signals for their employees.

Taking a similar approach to employee listening can have a huge impact on the success of the company in the long-term.  This is achieved not from creating an environment of surveillance, but recognising structural patterns that push top talent toward the exit months before they actually leave. Prominent workflow automation platforms like Zapier, Make, and n8n, offer a practical, scalable, and secure infrastructure to build and scale a prototype within your organisation.  

This article explores how an automated flight-risk early warning system works, which signals matter most, how leaders can intervene, and how such low-code platforms make it feasible without huge manual overhead.

Why automated retention matters now

Retention is harder for several reasons:
• Tight compensation competition
• Lower employee loyalty to specific brands
• Faster external hiring markets
• Internal mobility is often a second thought
• Workloads increase faster than recognition

Most companies are still reacting. They receive resignations and try to counter. By that point, the employee has already emotionally disconnected from the role, and the real opportunity to intervene has passed.

An automated flight-risk system moves your people strategy from reactive to proactive. An early warning system allows interventions weeks or months earlier, when high performers are still engaged and open to constructive conversation, without requiring constant manual monitoring.

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What an automated system is and isn't

This is about analysing data we as People teams already own and have access to. This is not about employee surveillance e.g. scraping personal messages or tracking private conversations. It is not about scanning Slack DMs or reading emails. Employees in high-growth companies are data-driven and understand the benefits of an employee listening programme which uses the same data-driven approach as the business. They also value transparency so this is always an important consideration for change management.

Platforms like Zapier, Make, and n8n can orchestrate smart workflows that surface insights from people data sets such as:
• Engagement survey
• Performance review cycles • 360 Feedback
• Mobility patterns based on Job Families
• Compensation progression
• Exit interview patterns

These data sources already exist. The difference is that People Ops rarely analyse data to produce predictive analytics or nudges. With early detection, we can convert this into a preventive retention strategy instead of late damage control.

Signals that the Automated PeopleOps Workflow could monitor

Patterns that often precede voluntary exits include:

Declining engagement: Engagement scores dropping over multiple cycles often indicate stress, unclear growth, or recognition gaps. Zapier can automatically track trends across survey cycles and flag sustained drops.

High performance with low mobility: Top performers are more likely to leave when progression stalls. n8n can automatically identify high performers with stagnant career progression by pulling HRIS data on performance ratings and promotion history.

Repeated concerns in feedback: Recurring themes such as "limited growth visibility" are meaningful early signals. Make can use NLP modules or structured notes to detect these patterns automatically.

Exit interview trends: Historical exit interviews often reveal systemic issues. AI integrations in Make can extract common themes and assign risk scores to departments or teams.

Workload and burnout markers: Frequent overtime, repeated workload spikes, or sudden performance dips are common early indicators. These can be tracked automatically via HRIS or business systems like Jira.

Automation transforms these insights from one-off reports into continuous monitoring. Alerts become timely, consistent, and actionable.

Case studies highlight the potential

Enterprise examples show the power of predictive retention:

HP: Analysed two years of employee data to create “Flight Risk” scores for over 300,000 workers. Managers were trained to interpret scores and intervene with promotions or pay adjustments, saving an estimated $300 million in recruitment and productivity costs.

Nielsen: Identified 120 at-risk high performers using predictive models. Lateral moves for 40% of them reduced attrition to zero for six months, while broader initiatives cut global turnover by 2 points, yielding $10 million in savings.

These examples demonstrate that early intervention is measurable, actionable, and cost-saving.

How an automated PeopleOps nudge system actually works

Imagine if when specific, predefined thresholds are crossed, Zapier, Make, or n8n automatically posts a simple, human-readable alert to employee’s Manager and People Partner:

Potential flight-risk indicator for [employee name]. Declining engagement over two survey cycles combined with recent performance notes suggest a proactive check-in.

Behind this alert is a lightweight scoring model built on structured People Ops data. Engagement trends, performance cycles, promotion history, and feedback themes are continuously evaluated in the background.

The system does three things well:

• Detects patterns early rather than reacting to single data points
• Surfaces only meaningful signals, reducing noise for People Teams
• Creates timely nudges, not dashboards that require manual review

Instead of analysing reports once a quarter, People Ops receives real-time prompts that guide action. A manager conversation, a skip-level check-in, or a development discussion can happen while the employee is still engaged and open to dialogue.

This approach makes it possible to monitor hundreds or thousands of employees consistently, without manual effort, while keeping the focus on human intervention rather than automated decision-making.

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Practical automated workflows

The real power of an automated flight-risk system shows up in the workflows themselves. These are not abstract models. They are simple, repeatable automations that turn existing People Ops data into timely nudges for action.

Each workflow follows the same basic pattern:

Data source → signal detection → nudge → human intervention

Scenario 1 – Engagement survey monitoring

Engagement survey data from platforms like CultureAmp or Qualtrics flows automatically into Zapier. Simple scoring rules monitor changes over time, not one-off results.
When engagement drops across two consecutive survey cycles, the system flags this as a potential early signal rather than a crisis.

An alert is posted to Slack with:

• The employee name
• Their team
• The engagement trend over time

People Professionals are prompted to schedule a proactive check-in. The goal is not performance correction, but understanding context while the employee is still engaged and open to conversation.

Scenario 2 – Promotion and performance monitoring

n8n pulls structured HRIS data covering performance ratings, tenure, and promotion history.

Rules identify employees who:

• Consistently perform at a high level
• Have not experienced role progression or promotion over a defined period

This combination is one of the strongest predictors of voluntary attrition.

When triggered, the workflow sends an automated alert to People Ops, prompting a development or career visibility conversation. In some cases, it may also notify the manager with a lightweight career planning template.

Scenario 3 – Exit interview pattern integration

Exit interviews are not used to recover individual employees. Instead, they provide critical system-level insight.
Make processes historical exit interview notes using NLP modules. Recurring themes such as limited growth, workload pressure, or manager effectiveness are automatically detected and grouped.
Patterns are scored and mapped to teams, departments, or roles. When thresholds are crossed, alerts trigger systemic interventions rather than one-off actions.

This shifts exit interviews from passive documentation into active organisational learning.

Scenario 4 – Quarterly dashboards/Trends Visibility

All alerts, signals, and interventions feed into a central dashboard in Power BI, Tableau, or Google Data Studio and human interventions are logged with any outcomes.

People Teams can visualise:

• Top drivers of flight risk
• Recurring issues by team or manager
• Which interventions correlate with improved engagement or retention

This creates a feedback loop where People Ops continuously learns which actions actually work.

These workflows transform retention from periodic analysis into continuous listening. They are scalable, repeatable, and low-friction.

People Ops spends less time pulling reports and more time doing what matters most: meaningful human interventions at the right moment.

From detection to intervention

Detection on its own delivers no value. Flagging risk without action simply creates another dashboard that People Ops is expected to monitor. The real impact comes when signals automatically trigger thoughtful, human-centred responses.

The most effective systems built in Zapier, Make, or n8n extend beyond alerts into response orchestration. When a risk threshold is crossed, the workflow does not stop at notification. It actively supports the next best action.

Each alert is paired with a recommended intervention based on the underlying signal:

• A sustained engagement drop triggers a skip-level one-to-one, automatically proposing time slots and providing a short conversation guide focused on context, workload, and growth.
• Promotion stagnation generates a structured nudge to the manager, including a lightweight career development template and prompts for discussing progression, scope expansion, or lateral opportunities.
• Workload or burnout indicators auto-generate rebalancing conversation guides, helping managers assess priorities, redistribute work, or reset expectations before exhaustion sets in.
• Repeated exit interview themes automatically create projects in task management systems, ensuring systemic issues are addressed at the organisational level rather than revisited repeatedly in future exits.

Follow-up tracking runs in the background. If an intervention is not completed within a defined window, reminders trigger automatically. Outcomes are logged over time. Did engagement scores recover? Did performance stabilise? Did the employee remain with the company?

This creates a closed feedback loop where People Ops can see which interventions actually reduce attrition and which ones fail to move the needle. Over time, patterns emerge that inform better retention strategies, manager enablement, and workforce design.

By automating the mechanics and preserving human judgement, these systems ensure that early detection consistently leads to timely, meaningful intervention, not reactive firefighting after a resignation is already in motion.

Ethical framing and employee trust

Transparency is essential. Communicate which data feeds into Zapier, Make, or n8n workflows and which do not and what data sets are “confidential” versus “anonymous”. Emphasis that personal communications are never monitored. No covert surveillance occurs.

The system protects talent, prevents burnout, and enhances growth opportunities. Done correctly, it is people-centric, not intrusive.

Strategic impact

Automated early warning systems allow People Ops to act before resignations occur. Schedule skip-level conversations. Redesign roles strategically. Recognise employees at key moments. Target development and internal mobility interventions.

Turnover affects culture, delivery, and innovation. With leaner teams expected to achieve more, retention becomes one of the most critical levers PeopleOps can control.

Zapier, Make, and n8n make these systems practical without engineering resources.

Future potential

Flight-risk systems can evolve further with AI and predictive analytics integrated into these platforms:

• Manager effectiveness scoring
• Burnout probability estimation
• Compensation competitiveness alerts
• Internal mobility likelihood scoring
• AI-generated retention playbooks
• Personalised career growth recommendations

OpenAI API integrations in Make and n8n already enable advanced predictive modelling. These workflow platforms transform People Ops from an administrative function into a strategic advisory team.

Why build this yourself with Make, Zapier, or n8n

People leaders already understand why early warning systems matter. The real question is how to build one without buying another expensive analytics platform or depending on engineering teams for months.

Workflow automation platforms like Make, Zapier, and n8n make this practical because they:

• Cost significantly less than specialised talent analytics or retention SaaS tools
• Scale gradually, allowing teams to start small and expand as signals and confidence improve
• Require no dedicated engineering resources, enabling People Ops to own and evolve the system directly
• Connect fragmented data sources that rarely live in a single data lake, including HRIS, survey tools, feedback systems, and spreadsheets. Where a data lake does exist, integrations are straightforward and these platforms operate as good middleware

Instead of outsourcing retention intelligence to black-box software, People Ops can assemble a transparent, flexible system tailored to their organisation.

These platforms allow teams to begin with a single workflow, such as engagement trend monitoring or promotion stagnation alerts, and layer in additional signals over time. There is no large upfront implementation, no rigid data model, and no long-term lock-in.

When implemented ethically and thoughtfully, this approach delivers the same early-warning capability as enterprise-grade tools, while remaining affordable, adaptable, and People Ops–led.

The result is a system that is:

• Easy to start
• Low cost to start, predictable pricing models
• Simple to evolve
• Focused on human action rather than dashboards

Make, Zapier, and n8n turn predictive retention from a vendor-led promise into a capability People Ops teams can build, own, and improve themselves.

Choosing the right approach

There is no single “correct” architecture for building an early warning system. The right approach depends on data maturity, existing tooling, and how much flexibility People Ops needs.

Workflow automation platforms

For many organisations, platforms like Zapier, Make, and n8n are the fastest way to get started.

• Zapier is well suited to simple, event-based workflows where speed and ease of setup matter most. It works well for teams that want to pilot a small number of signals, such as engagement survey drops or promotion delays, with minimal configuration.
• Make offers more flexibility for handling complex logic, branching workflows, and light data transformation. It is a strong option when teams want to incorporate AI-based text analysis, such as exit interview themes, without building custom pipelines.
• n8n provides the most control and extensibility. It works well for organisations that want to manage more advanced logic, self-host workflows, or integrate closely with internal systems, while still avoiding a full engineering build.

Across all three platforms, the key advantages are low cost, rapid iteration, no dependency on engineering teams, and the ability to pull data from multiple tools that do not naturally live in ngineering capacity, and a need for fast, human-centred action rather than delayed insight.

Final thoughts

An automated flight-risk early warning system applies proven predictive principles from RevOps to People Ops. By using structured HR data and workflow automation platforms, organisations can detect attrition risk before it becomes a crisis.

Implemented ethically and thoughtfully with Zapier, Make, or n8n, it:

• Protects top performers
• Reduces regretted turnover
• Turns retention from reactive firefighting into proactive engagement
• Delivers measurable strategic value at scale

These platforms make the system scalable, consistent, and actionable, allowing People Ops to focus on what matters most: meaningful human interventions that retain and grow talent.

If you want help building these systems

We help People Ops and HR teams from High-Growth Global Companies to accelerate their adoption of AI-native technology. If you need help to bring your ideas to reality, let us know!

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