Today, it is part —or should be part— of the strategic agenda of practically any company that wants to improve its efficiency, make better decisions, or differentiate itself in the market. According to the report The Enterprise in 2030 by the IBM Institute for Business Value, 79% of executives believe that AI will contribute significantly to their revenue before 2030, but only 24% have a clear vision of where that revenue will actually come from. That gap between expectation and real application is, precisely, one of today’s main challenges.
For an SME, this context carries a very clear interpretation: it is not about starting to use AI “because it is fashionable” or “out of inertia”, but about starting with clear criteria. The priority should not be to incorporate flashy tools, but to identify where they can add real value to the business.
Start with the problem, not the tool
One of the most frequent mistakes when approaching AI is doing so from a technology perspective instead of looking at the company’s needs. IBM states that many organizations continue to “add AI” to already existing processes to automate tasks or optimize flows, achieving incremental improvements but without truly transforming their way of working.
In an SME, the first step should be much simpler and more practical: detect which tasks consume the most time, which processes generate the most errors, where information gets lost, or at which points it is hardest to make decisions. From there, it does make sense to analyze whether AI can help.
For example, it can be useful to classify emails, summarize documents, assist in customer service, generate commercial drafts, forecast demand, detect repetitive incidents, or support data analysis. The important thing is not to deploy AI “in general”, but to solve a specific problem with a measurable goal.
AI needs data, context, and criteria
Another key message is that competitive advantage will not come so much from using generic algorithms as from applying AI connected to the actual logic of the business, its data, and its internal knowledge. The true return appears when the organization incorporates its own intellectual property and differential data into its products, services, and processes, instead of relying solely on standard solutions. For instance, an AI system trained on your own customers’ inquiry history will respond in a much more relevant way than a generic system.
Brought down to the realities of an SME, this means that AI works best when it is backed by well-organized information: customer data, sales histories, incidents, technical documentation, commercial criteria, or internal procedures. If that foundation does not exist, or is scattered, it is best to start by organizing it.
Therefore, implementing AI is not just about buying a license or testing an application. It also implies reviewing the quality of the information, defining usage rules, and deciding which tasks can be delegated and which must remain supervised by people.
From isolated testing to real integration
The future does not belong to companies that use AI occasionally, but to organizations capable of integrating it into their daily operations and effectively combining people and intelligent systems. The key will not be to indiscriminately replace human work, but to well design the collaboration between both.
For an SME, this idea is fundamental. Many initiatives fail because they remain isolated tests, without any connection to the company’s real processes. A tool is tested for a few weeks, creates some curiosity, but it does not integrate into the team’s routine or link to specific indicators.
Because of this, a good initial approach consists of selecting a small, useful, and manageable use case. It must have a clear owner, a defined process, and a simple way to measure results. For example: reducing commercial response time, automating repetitive administrative tasks, or improving quote preparation. When that first pilot works, the company learns, gains confidence, and can expand the scope with greater security.
Four practical steps for an SME
If an SME wants to start on the right foot, it can follow a basic roadmap:
- Identify a priority need: There is no need to transform the entire company at once. It is best to choose a specific problem that has an impact and is viable.
- Review available data and processes: Before implementing anything, you must check if there is enough information, if it is accessible, and if the process is minimally organized. A frequent error is wanting to implement AI without having the data minimally sorted out.
- Choose a proportionate solution: a complex implementation is not always necessary. In many cases, a simple tool, well-focused and well-utilized, brings more value than an ambitious, poorly integrated solution. An AI tool that helps your sales team draft quotes in half the time is a proportionate solution. A customized AI system that integrates all customer data and automates the entire sales chain is not a good way to start. In many cases, the simple tool well used adds more value than an ambitious, poorly integrated solution.
- Measure and adjust: AI must be evaluated like any other investment: time savings, error reduction, service improvement, or productivity increase.
AI can bring a lot of value, but only when it responds to a real need, is supported by useful data, and is incorporated in a coordinated manner into daily work. Many companies expect great results from AI, but they still do not have a clear idea of how to convert that expectation into revenue or into a tangible competitive advantage.
Having personalized support, such as that offered by the Economic Office of Galicia, can be key to a successful implementation. Request free specialized advice and take advantage of the available resources to boost your business.
