Artificial intelligence is being used more and more by companies in just about every industry. How do you make sure that implementation goes smoothly and what do you need to consider?
Just about every company is working on artificial intelligence (AI) these days, or at least looking at why it might be of interest to them. What can they do with it, how far are they willing to go into it and who gets to use it?
AI increases the productivity and work efficiency of your employees. Thanks to AI agents, certain tasks can even be completely automated without human intervention. “AI applications are evolving like the Internet in the 2000s, where companies wondered if it was worthwhile, until it later proved indispensable,” said Andreas Van Puyenbroeck, Category Manager and AI Ambassador for North West Europe at HP.
Technology is growing by leaps and bounds. It is not illogical that companies are not jumping on the bandwagon right away. Implementing AI in a company brings challenges. Each challenge is related to another; that whole forms an overall implementation. If one is not solved, your company will have to revise its strategy in no time.
1. Right infrastructure
A solid basic technological infrastructure is crucial to successfully implementing AI. That includes not only the hardware and software needed to run AI solutions, but also a solid network connection and sufficient physical or cloud storage for the data that AI will use. So companies urgently need to invest in cloud solutions or local servers, depending on their needs and preferences.
In addition, it is important to scrutinize current IT systems and ensure that they can be combined with new AI systems. If not, it is best to replace or upgrade them completely.
2. Privacy and cybersecurity
“If you use artificial intelligence, you also have to look into cybersecurity and data organization,” Van Puyenbroeck knows. Companies need to make sure they comply with privacy laws and regulations, such as the AI Act and NIS2 legislation in Europe. This not only ensures that they are complying with the law, but can also prevent huge fines.
Any cybersecurity strategy for AI must start from data privacy. This is necessary to prevent data theft, cyber attacks and other threats, especially since AI systems are often targets for attacks. It also immediately wins you the trust of your customers.
3. Sufficient management and incorporation of margin of error
AI systems can make mistakes, especially at this stage of technological development. “You’re really going to have to make sure that the code is written so that there are very clear boundaries for that software,” Van Puyenbroeck said. That’s why it’s important for companies to closely monitor their AI applications. They should constantly monitor AI performance, but also update and maintain the tools regularly.
A playbook for dealing with errors and analyzing them afterwards helps to respond quickly to problems and to minimize the impact of those errors. What risk do those mistakes pose to the company, stakeholders and customers? Again, privacy and cybersecurity are important pillars.
4. Staff training
An important but often overlooked issue, is staff training. Employees must have a good understanding of how AI systems work and how they can be used in helping or automating their daily tasks. This may involve workshops, courses or training to ensure employees are comfortable with using new technologies. After that, they will receive an AI license.
In addition, there must be a focus on the changing roles of employees in an AI-driven environment so that they can adapt to new responsibilities and tasks. “Of course, it all starts with the moment you start handing out the license, that as a company you explain why you are doing it. It makes no sense to introduce AI without any announcement or with one mail in the company. There has to be a story and reasoning behind it,” says Van Puyenbroeck.
5. Budgeting
We left perhaps the most important factor for last: allocating an AI budget. The financial aspects of AI implementation cannot be underestimated. Companies must consider the cost of developing or purchasing AI technology. Its implementation and ongoing maintenance costs can also add up.
In addition, there are costs associated with AI training for employees who start working with it. It is indispensable for a company to establish a realistic budget that not only covers the investment itself, but also takes into account future expansions, modifications and evolution of the technology. Of course, you never know 100 percent how to determine that budget, but a buffer is never a bad idea.