How a 10-Person Company Saves 15 Hours per Week with AI

Picture this: Monday, 8:15 a.m. The managing director of a 10-person engineering firm is sitting at his desk. On the screen: three proposal requests that need to go out by Wednesday. In the inbox: a research request from the project manager. On the desk: the monthly report that accounting has been waiting for since Friday. And the first client meeting is at 10.

In small teams, everyone knows this feeling. There is always more to do than hours in the day. Not because the team is poorly organized, but because ten people are doing the work that larger companies hire thirty for. Every hour spent on repetitive tasks is an hour stolen from the core business.

AI Automation Is No Longer a Corporate Privilege

Just two years ago, AI automation was something reserved for companies with their own IT departments and six-figure software budgets. That has fundamentally changed. The tools have become accessible, the costs manageable, and the barrier to entry lower than ever.

Many SMB owners either dismiss AI as distant future technology or overestimate it as a cure-all. The reality lies somewhere in between: AI does not replace a team. But it can potentially give a 10-person team 15 hours per week back. Not through one big breakthrough, but through many small optimizations in day-to-day work.

Time Savings as a Calculable ROI

Before we talk about specific workflows, let’s do some simple math. 15 hours per week at an average hourly rate of 60 euros: that’s 900 euros per week, roughly 3,600 euros per month, over 43,000 euros per year. For a 10-person company, that is not a rounding error. That is half a salary.

And the best part: this calculation can be tailored to any business individually. Which tasks consume the most time? Where is there repetition? Where is knowledge being gathered from scratch over and over again, even though it already exists somewhere?

In my experience, three workflows alone can already make a significant difference.

Workflow 1: Writing Proposals with an Internal Knowledge Base

Time saved: approx. 6 hours per week

Writing proposals is a surprisingly time-consuming process in many SMBs. Not because the proposal itself is so complex, but because you start from scratch every time. The sales lead hunts for an old proposal to use as a template, adjusts text blocks, looks up pricing, rewrites the scope of services. Two to three hours per proposal add up quickly.

With AI, the process looks different: the language model accesses an internal knowledge base containing past proposals, price lists, service descriptions, and client data. The employee sets the parameters: client, scope, budget. The AI generates a complete draft proposal, including matching text blocks, up-to-date pricing, and a personalized cover letter. The human reviews, adjusts, and approves.

Instead of two to three hours, the process takes 30 to 45 minutes. With three proposals per week, that quickly adds up to six hours saved.

A word on data privacy that I cannot skip at this point. Proposals contain sensitive business data: pricing structures, client information, cost calculations. If you feed this data into a cloud-based AI model, you need to know what happens with it. There are two clean approaches:

• Data Processing Agreement (DPA) with the cloud provider: The major AI providers offer contracts for business customers that guarantee data will not be used for training and that GDPR requirements are met. Before deploying, make sure to verify that such an agreement is in place.
• Locally hosted models: For particularly sensitive data, there are open-source models that run on your own infrastructure. The data never leaves your company. Models like Llama or Mistral are now capable enough for text generation and run on standard hardware.

In practice, a hybrid approach often works well: non-critical text via cloud APIs, sensitive proposal data through a local model. This requires a bit more setup, but protecting your business data is non-negotiable.

A more affordable alternative: replace personally identifiable data before sending it to the language model. Client names, addresses, and account numbers get swapped out for placeholders. The AI works with the anonymized data, and you insert the real values back into the result. With a bit of practice, this can be done manually surprisingly fast. And if you need the process regularly, you can largely automate it with a small script or custom software.

Workflow 2: Research and Information Processing

Time saved: approx. 5 hours per week

Research eats up more time in small businesses than most people realize. The project manager needs a market overview for a client meeting. The managing director wants to know which funding programs apply to a planned investment. A developer is looking for the right technology for a new project.

Without AI, that means: open the browser, open ten tabs, skim articles, compile the relevant information, and put it into a readable format. Per research task: one to two hours.

With AI, this can be shortened dramatically. A concrete example: the project manager needs an overview of current project management tools suitable for a team of eight. Instead of scouring comparison sites, they phrase the request to an AI tool with internet access. Within minutes, they receive a structured overview with price comparisons, pros and cons, and a recommendation based on the stated criteria.

The crucial point: the research is not done when the AI responds. The human still needs to validate the results, check sources, and cross-reference with their own experience. But the time investment drops from 90 minutes to 20 to 30 minutes. With five such research tasks per week, that adds up to roughly five hours saved.

Workflow 3: Reporting and Business Intelligence

Time saved: approx. 4 hours per week

Monthly reports, project status updates, client evaluations: in small companies, one person often creates these reports on the side, on top of their actual responsibilities. The result: reports get postponed, rushed under time pressure, or dropped entirely.

A typical scenario: the managing director exports figures from the CRM system and accounting at the end of the month. Then she builds charts in a spreadsheet, writes commentary, and formulates recommendations. Time required: three to four hours per month for the financial report alone, plus project reports for clients.

With AI, the process gets flipped: the raw data is fed directly to the language model. The AI produces a complete draft report with a summary, key metric comparisons to the previous month, trend analysis, and written recommendations. The managing director reviews the result, adds her personal assessment, and signs off on the report.

Instead of three to four hours, the process takes 45 minutes to an hour. And the report is often better structured, because the AI consistently follows a format that a human under time pressure tends to cut short. Spread across the month, including smaller status reports and ad-hoc analyses, this saves roughly four hours per week.

The Sum Makes the Difference

Taken individually, none of these workflows sounds revolutionary. Six hours here, five hours there, four hours on reporting. But combined, that is 15 hours per week given back to the team. That is nearly two full working days.

What companies do with that time varies. Some invest it in client acquisition. Others use it for projects that have been stuck for months. Others relieve team members who are working at their limit. The point is: it creates breathing room. And breathing room is exactly what small businesses lack the most.

The First Step: Your Own Analysis

Every business is different. The three workflows above are the most common, but by no means the only levers. In some companies, the biggest potential lies in customer service. In others, it is documentation. In others still, quality assurance.

This is exactly the analysis I do in my initial consultations: together, we look at where the most time-consuming routine tasks are in your business, which of those are suited for AI support, and what the concrete ROI looks like. No theory, just a calculation with your real numbers.

15 hours per week is not a promise that applies to everyone. But in almost every small business, similar potential is waiting to be unlocked. You just need to know where to look.