AI implementation of the week – All hours marked & billed, thanks to Harvest helper

At Wunder, we use AI in dozens of ways across client projects and our own operations to improve productivity, clarity, and efficiency. In this article, we want to share a concrete solution we have designed and actively use. Harvest helper has solved a bottleneck and pain point: getting everyone to mark all their hours—and to mark them accurately enough to support billing, resourcing, and decision-making. Based on our experience, we believe many organizations struggle with the same challenge. So, let us introduce: Harvest helper by Wunder.  And does this solution scale to other time tracking tools as well? The answer is yes it does!  With other urgent questions, please visit the FAQ in the end of this article. 

Two wins, one solution: less manual work, more friendly reminders

In expert organizations, a few missing or unclear entries per person per week can quietly add up to hundreds or even thousands of euros that never make it to invoicing. Over time, these gaps distort profitability, resource planning, and leadership decisions, often without anyone noticing until it is too late.

Traditionally, preventing this has relied on a familiar but inefficient workaround: someone, in person, reminding everyone else. A project manager, team lead, talent expert or finance colleague takes on the role of chasing timesheets, correcting entries, and explaining—again—why accurate time tracking matters. It is time-consuming, hard to scale, and is rarely anyone’s dream role.

Harvest helper delivers two wins at once. It reduces the need for manual follow-ups while ensuring employees receive timely, constructive reminders about the importance of marking their hours accurately and on time. The result is better data, smoother billing, and fewer administrative distractions—without pressure, surveillance, or added management workload.

Why this AI implementation exists

In professional services and expert organizations, timesheet data sits at the core of business health. It directly affects revenue, forecasting accuracy, invoicing, and compliance. Despite its importance, feedback on timesheets is often delayed, inconsistent, or handled manually by managers who already carry significant responsibility.

When guidance arrives weeks later, employees have little opportunity to correct their practices. Managers, meanwhile, are pulled into administrative quality control instead of focusing on leadership and value creation. Over time, small issues accumulate into operational friction that quietly undermines performance.

Harvest helper was designed to address this challenge at its root by providing earlier, clearer, and more consistent feedback—without adding bureaucracy or cognitive load.

What Harvest helper does

Harvest helper is an AI-powered solution that analyzes weekly timesheet data and delivers private, constructive feedback directly to each employee via Slack. Each week, employees receive a short, easy-to-understand summary of their previous entries, including utilization, billability, and the overall quality of their time tracking.

Rather than simply reporting numbers, the AI highlights patterns, explains expectations in plain language, and offers practical suggestions for improvement. Because the feedback arrives quickly and in context, employees can adjust their practices while the information is still fresh.

The entire process runs automatically in the background. There are no dashboards to monitor, no manual follow-ups to coordinate, and no additional work required from managers.

Designed to build trust, not surveillance

A defining principle of Harvest helper is trust. The system is explicitly not intended for performance evaluation, disciplinary action, or managerial monitoring. We know that the routine of marking the hours is harder for some than for others, so a little friendly support and reminder does not hurt, but helps.

Feedback is delivered only to the employee who owns the data. Supervisors do not automatically receive the messages, and the tone is deliberately supportive rather than corrective. The goal is to create a safe environment for self-improvement, where employees can reflect on their own data without fear of judgment.

This approach significantly improves adoption and aligns with modern leadership practices that emphasize autonomy and transparency. Most of us (especially the very ambitious and creative ones) can de facto get carried away when the task or project we work on is inspiring - and it’s only a “mechanical favor” that someone/something reminds us to also mark the hours, not only work intensively for hours! 

Privacy-first and EU AI Act–ready by design

Privacy and compliance are built into the technical architecture of Harvest helper, not added as an afterthought. Before any data is analyzed by the AI model, employee names are removed, and only strictly necessary timesheet information is processed. Sensitive personal details are explicitly excluded.

Once the AI has generated its analysis, the feedback is securely re-associated with the correct employee within the workflow. This separation of identity and analysis significantly reduces data protection risks and limits third-party exposure.

Because the solution operates in an employment context, it has been designed to meet even conservative interpretations of the EU AI Act, including requirements related to transparency, traceability, and human oversight. The result is an AI implementation that is not only effective but also regulation-ready.

Role-aware intelligence that reflects real work

Automated systems often fail when they ignore context. Harvest helper avoids this by taking job roles into account when generating feedback.

Different roles naturally have different utilization and billability patterns and goals. The AI adjusts its expectations accordingly, ensuring that support-heavy or non-billable roles are not unfairly flagged. At the same time, strong performance is recognized and reinforced, helping employees understand what good looks like in practice.

This role-aware logic ensures that feedback feels fair, relevant, and grounded in the realities of everyday work.

Business impact without added complexity

When employees receive timely, understandable feedback, timesheet quality improves. Cleaner data leads to fewer invoicing disputes, faster billing cycles, and more reliable forecasting.

Because the system automates basic quality checks, managers regain time that would otherwise be spent on manual reviews. Employees gain clarity without pressure. All of this happens without adding meetings, dashboards, or layers of process.

Small weekly improvements, applied consistently, create meaningful operational gains.

Technology foundation

Harvest helper is built on a modern, transparent technology stack. It uses Google Gemini Flash for AI analysis, open-source and self-hosted N8N for orchestration, and a secure cloud environment on Google Cloud Platform. The solution integrates seamlessly with existing tools such as Harvest for time tracking and Slack for communication.

Clear documentation, defined prompts, logging for traceability, and explicit user disclaimers ensure that the AI remains understandable and controllable. Human judgment stays central, with AI acting as an assistant rather than an authority.

FAQ on Harvest helper

Is Harvest helper used for performance evaluation?
No. The solution is designed solely to support employee self-awareness and improvement. Feedback is delivered only to the employee and is not automatically used for performance reviews, promotions, or disciplinary actions.

Who can see the AI-generated feedback?
Only the employee receiving the message. Supervisors do not automatically receive the output, which supports trust, adoption, and responsible use of AI.

How does the solution support EU AI Act compliance?
The system applies privacy-by-design principles, minimizes personal data usage, provides transparency to users, maintains logs for traceability, and supports human oversight. It is designed to meet high-risk AI system requirements where applicable.

Does the AI understand different job roles?
Yes. The AI adjusts expectations based on role-specific utilization and billability patterns, ensuring that feedback is context-aware and fair.

What business outcomes does this AI implementation support?
Organizations typically see improved data quality, fewer invoicing issues, reduced management overhead, and increased employee awareness around utilization and billing practices.

A practical example of responsible AI in action

Harvest helper demonstrates how AI can be embedded into everyday operations in a way that is ethical, compliant, human-centered, and commercially effective. It shows that AI does not need to be large or complex to be impactful—only well designed.