Why Omnisend rewards employees for meaningful AI adoption

Artificial intelligence has already changed how many teams plan, write, analyze, code, test, and make decisions. For tech companies, the question has moved beyond whether people use AI at work. The more useful question is how much value the user creates.

At Omnisend, we’ve started answering that question in a very practical way: by including meaningful AI adoption in employee performance and compensation conversations.

Omnisend has introduced cash bonuses for employees who use AI effectively in their work. The decision has been described as an unprecedented step in Lithuania. For us, it reflects a simple belief: when people find smarter ways to work, that effort should be recognized.

Why AI became part of the conversation

AI is already part of how many tech teams work. It helps with research, planning, analysis, testing, and other tasks that can slow people down when done manually.

But simply opening an AI tool is easy. Creating something useful with it takes curiosity, ownership, and a clear understanding of the problem being solved.

That distinction matters.

At Omnisend, the goal is to recognize employees who use AI to improve their work and help the business move forward. This can mean saving time, reducing manual effort, improving a process, helping a team make better decisions, or creating a solution that others can adopt too.

That evaluation is grounded in outcomes. The focus is on practical value, not surface-level activity.

What meaningful AI use looks like

To clarify the process, Omnisend focuses on three main categories: efficiency, impact, and implementation.

Efficiency asks a practical question: Does AI help save time or costs in daily work? This could be anything from reducing repetitive manual tasks to speeding up research, analysis, documentation, QA, ideation, or internal workflows.

Impact looks at the bigger picture. Does AI-assisted work create a positive effect on business goals? A useful prompt, workflow, or automation has more value when it improves how a team works or contributes to measurable progress.

Implementation focuses on adoption. If someone creates a useful AI-based process, do other people use it? Can it become part of how a team works? Can it help more than one person?

These categories help keep the conversation grounded. They also make AI adoption feel less abstract. Employees are encouraged to think about real problems, useful solutions, and lasting habits.

An incentive built around curiosity

One important point is that this policy is designed as encouragement.

Good AI habits need time to develop. People need space to test different approaches, learn from each other, and understand where AI genuinely makes their work better.

That can look different from team to team. One person might use AI to speed up research or documentation. Another might use it for code reviews, testing, internal tools, customer insights, or process improvements. The most useful ideas usually come from employees who know their work well enough to spot the slow, repetitive, or unclear parts.

Making AI a natural work habit

Bernard has also shared that the idea was inspired in part by practices at Meta, where teams are encouraged to use AI through structured goals, dashboards, and internal initiatives.

For Omnisend, the larger goal is to make AI adoption a consistent habit across the company. That includes everyone from office managers to software engineers.

In the long run, we expect AI to become as natural in daily work as tools like Slack, Google Docs, or project management platforms. The difference is that AI needs a more thoughtful learning curve. It asks people to rethink how they approach tasks, evaluate output, and share what works with others.

That is where culture becomes important.

A company can give people access to tools. It can offer guidelines, examples, and training. But the strongest results happen when employees feel trusted to experiment and supported when they share what they learn.

A culture of practical experimentation

Omnisend tends to value ideas that work in practice. Clearer processes, better workflows, fewer repetitive tasks, stronger outcomes — that’s where AI can be genuinely useful.

This initiative is one more way to show that experimentation has value.

When someone finds a better way to work, we want that knowledge to spread. When a team improves a process, we want to understand how it happened. When AI helps someone spend less time on repetitive tasks, that is worth recognizing.

The point is not to turn every task into an AI task. The point is to help people build judgment around where AI helps, where it does not, and how to use it responsibly.