How to succeed in the scaling of an AI project

HOW DO YOU HANDLE ARTIFICIAL INTELLIGENCE PROJECTS?

You can leverage artificial intelligence to make existing processes more efficient, save money, and invent new ones. However, the competition is fierce, and to quote one of our clients, "If we don't do this, someone else will." Leading the way is undoubtedly exciting, but artificial intelligence is a significant factor in competitiveness.

If you don't want to wait, here are instructions from our founder and CEO, Mika, on how to both lead and scale AI projects

FIND OUT THE STARTING LEVEL FOR YOUR COMPANY AND YOUR INDUSTRY

A company doesn't have to be big to test AI solutions. As a manager, you should initially acquire a basic knowledge of what is currently possible and what is not. Huge investments and groundbreaking product development are not always needed, but insights into the application possibilities of existing solutions.

For example, many customizable and scalable artificial intelligence solutions are used by industrials. That's why it's worth getting to know the starting level of the industry. To get your AI project off the ground, think on a practical level:

• who is responsible for the project
• who actually implements it
• what is the available budget
• what the strategy might look like.

Well-planned is therefore half-done in this case, too, when the implementation, on the other hand, takes little time. It is characteristic of us Finns to focus long-term on producing a perfect end result. Still, it is more fruitful in artificial intelligence projects to do short and efficient iteration rounds, from which we learn a lot quickly.

MEASURE AND LEAD THROUGH THE ORGANIZATION

A typical mistake in Finnish companies is the siloing of development projects: hard experts solve problems by themselves or in their own teams, and no one really has a grasp of the whole. Also, focusing too much on technology at the expense of the actual business problem is a sure way to destroy the project right from the start.

Instead, it's worth considering how the entire organization can function more rationally as an ecosystem and use artificial intelligence to make processes more efficient and improve products or services. To lead a transparent and functional artificial intelligence project, you should:

• define appropriate metrics that permeate the entire organization affected by the business problem or opportunity
• identify opportunities and potential problems
• focus on removing the most critical bottlenecks for the entire organization
• use a hybrid work model if your organization has a data science team.

MAKE INNOVATION A PART OF YOUR EVERYDAY LIFE

The best way to utilize artificial intelligence in business is when open ideation is a continuous process in everyday life. Finnish companies should adopt the ideation-innovation-product development circle, which rotates without stopping and always produces insights.

If innovation as a word annoys you and feels worn out, think about it this way: it's actually about systematic ideation and the fact that not sticking to formulas and an open attitude to the new are at the heart of the activity.

In practical work, it helps that time is used for innovation, it is encouraged, and it is made possible in everyday life. It is also worth setting clear problems and goals for the personnel. In this case, creating something new does not remain a buzzword in the strategy, but people commit to it and get excited about it.

START WITH QUICK WINS, BUT SCALE EVEN FASTER

Using artificial intelligence to develop a new, revolutionary service is not always necessary. It is already enormously beneficial when you can use it to save or free up resources.

However, the most important thing is to change the entire organization to support data. Instead of just looking for quick profits, you should aim for strategic, medium, and long-term profits and thus strive for scaling. So you can start with quick profits, but you should also start scaling quickly.

The project doesn't have to be big for it to be wise to use artificial intelligence. For example, the savings from a more intelligent process in a factory's production process or occupational safety can amount to millions of euros. Many times, the costs do not even correlate with the result, but high costs can only produce a marginally meaningful result. In contrast, reasonable costs and reasonable actions can achieve really impressive results.

If identifying quick profits or finding a genuinely scalable AI solution seems challenging, we can help.