{"id":9079,"date":"2025-12-01T08:00:17","date_gmt":"2025-12-01T07:00:17","guid":{"rendered":"https:\/\/holisticds.com\/articulo\/ai-implementation-5-steps\/"},"modified":"2025-11-26T16:07:17","modified_gmt":"2025-11-26T15:07:17","slug":"ai-implementation-5-steps","status":"publish","type":"articulo","link":"https:\/\/holisticds.com\/en\/articulo\/ai-implementation-5-steps\/","title":{"rendered":"How to start implementing AI in your company: 5 key steps"},"content":{"rendered":"

In 2025, talking about <\/span>\u201cimplementing AI\u201d<\/b> in a company sounds about as generic as \u201cinstalling electricity\u201d. Both can be used for a huge range of tasks, but every organisation uses them differently depending on their needs and processes.<\/span><\/p>\n

Artificial intelligence can be applied to tasks such as analysing data, serving customers, forecasting demand or automating processes, among many others. The mistake is to approach this technology as if it were a magical, one-size-fits-all solution that will \u201cfix\u201d every problem in the company. In practice,<\/span> the organisations that extract the most value from AI are those that integrate it strategically into their business processes.<\/b><\/p>\n

In this article, we set out what we see as the <\/span>5 key steps companies should take to successfully implement artificial intelligence.<\/b><\/p>\n

5 steps to get started with AI in your company<\/span><\/h2>\n

Implementing AI in a company is a transformation that involves people, processes and technology.<\/span><\/p>\n

These five steps provide a practical and strategic guide to getting started with AI in the organisation. From identifying the right profiles and selecting use cases with real impact, to launching pilots, building internal capabilities and, finally, scaling solutions that work.<\/span><\/p>\n

Step 1: Start with people<\/span><\/h3>\n

Contrary to what many might think, the first step is <\/span>not<\/b> choosing a model or a platform. It is <\/span>identifying the right people<\/b> inside the organisation to drive the change.<\/span><\/p>\n

You need a core group of sponsors who understand the business in depth (processes, bottlenecks, metrics, etc.) and who also have a <\/span>basic level of AI literacy.<\/b> They should know what AI can and cannot do, where its limits are and how its outputs should be validated. This combination is what allows you to connect real needs with technological capabilities and avoid AI ending up as another \u201cshowcase project\u201d with little real impact.<\/span><\/p>\n

In parallel, it is advisable to define clear and <\/span>responsible ground rules <\/b>from the outset, such as usage policies, human review, data privacy, security and risk management.<\/span><\/p>\n

This human dimension also <\/span>requires structured upskilling and reskilling<\/b> initiatives. According to the World Economic Forum (<\/span>link here<\/span><\/a>), the evolution of roles and skills over the coming years will increasingly depend on stronger digital competencies, data skills and collaboration with intelligent systems.<\/span><\/p>\n

Step 2: Identify use cases<\/span><\/h3>\n

Once the sponsoring team is in place, the next step is to<\/span> identify specific use cases<\/b> and prioritise them.<\/span><\/p>\n

Specialised AI partners can be very helpful at this stage by providing expert guidance<\/b>. You can also use structured decision-making methodologies, paying particular attention to variables such as:<\/span><\/p>\n