Digital transformation is an unavoidable challenge for the graphic communication sector, and the incorporation of AI in labeling is presented today as the fundamental lever to guarantee competitiveness. In a business environment dominated by small and medium-sized enterprises, process automation and intelligent data management are essential tools to leave administrative inefficiencies behind and improve profitability. This article analyzes in depth the state of technological adoption in the market, the daily problems it solves, and how a successful implementation based on market tools should be structured.
The starting point of the graphic sector reveals a clear digitalization at two speeds regarding the adoption of AI in labeling, as highlighted by the FESPA Print Census 2025, which shows that nearly 40% of printing and signage companies do not use artificial intelligence at all, and almost half of them have no automation tools implemented. This technological gap is confirmed in the Spanish context, where data from the INE for the first quarter of 2025 indicates that while 21.1% of companies with ten or more employees already use this technology, the percentage drops drastically to 13.4% in businesses with fewer than ten workers. Despite studies by the Cotec Foundation and ISEAK demonstrating that companies employing AI record average productivity levels 27% higher, there is a profound disconnect between the innovation developed by suppliers and its actual adoption by workshops. This situation is mainly due to 75% of these companies having fewer than 50 employees, directly limiting their investment capacity and generating a strong demand for accessible, modular solutions tailored to their size.
The need to integrate the AI in labeling It responds directly to the highly fragmented processes that characterize the day-to-day of a traditional workshop, where it is common to rely on manually prepared budgets on Excel sheets, informal communications via WhatsApp, and planning drawn on whiteboards or agendas. This lack of centralized tools causes an absolute lack of traceability between the different project phases, from sales and design to production, installation, and invoicing, which constantly leads to emergencies, last-minute changes, and costly rework. By working with a huge and heterogeneous catalog that includes illuminated signs, built-up letters, large-format digital printing, vinyl, or vehicle wraps, each order becomes a tailor-made project that greatly hinders standardization and precise cost measurement. Consequently, operations are carried out with blind profitability margins, mistakenly assuming that all products provide the same benefit simply by having liquidity at the end of the month, when the reality is that processes depend excessively on the undocumented knowledge of the owner or production manager.
To provide real value and not remain a mere technological promise, the AI in labeling must connect to concrete problems operating on three distinct levels: automation of repetitive tasks, intelligent assistance as a copilot, and decision-making based on data analysis. In practice, this translates into high-impact applications such as initial support via 24/7 operational chatbots that structure the briefing to avoid losing sales opportunities, assisted budgeting that calculates materials and labor based on company history, or the generation of photorealistic mockups and simulations that allow the client to visualize the sign on their facade before manufacturing begins. However, it is vitally important to understand that artificial intelligence acts as an assistant that suggests, so human supervision remains strictly mandatory. It is imperative that the human team approves final budgets, validates designs prior to production, and supervises any critical content that reaches the end customer, thus preventing the well-known "hallucinations" of generative AIs which, if not corrected, could result in a direct loss of commercial margin.
The correct path towards the integration of the AI in labeling It does not consist of acquiring software impulsively, but rather of following a progressive and structured phased implementation plan that guarantees technological assimilation by the entire company. This process must invariably begin with a short-term diagnostic phase to map existing processes and identify bottlenecks, followed by minimal digitization to centralize the catalog of materials, suppliers, and rates before attempting to automate anything. Once the data is organized, it is possible to move towards automating the conversion of quotes into work orders, optimizing margins, and finally, long-term scaling by integrating all business areas. To objectively measure the success of this transition, it is necessary to conduct a limited pilot of 60 to 90 days, establishing and evaluating key performance indicators (KPIs) before and after implementation, such as the average time to create quotes, the acceptance rate of those quotes, the actual gross margin obtained per project, and the reduction in administrative hours invested weekly.
The adoption of the AI in labeling entails a series of risks, operational barriers, and legal responsibilities that small and medium-sized enterprises cannot overlook if they wish to avoid sanctions and ensure a smooth transition. In Spain, approximately 45% of SMEs cite the lack of digital knowledge within their team and a scarcity of financial resources as the main obstacles to digitalization, in addition to a notable cultural resistance to change. From a regulatory compliance standpoint, the use of these technologies requires companies to strictly adhere to the GDPR in the processing of personal data, as well as the European AI Act, which imposes artificial intelligence literacy obligations effective from February 2025 and demands absolute transparency by informing the client when they interact with an automated system. To overcome these barriers, companies can leverage public support instruments such as subsidized training through FUNDAE, which allows for staff development while mitigating economic costs, or resort to aid from the Kit Digital program (and its regional variants) aimed at cybersecurity and the adoption of new technologies.
As the sector matures, the technology market has responded by developing a wide range of vertical MIS/ERP software solutions that facilitate the implementation of AI in labeling and allow the multiplicity of disconnected programs to be replaced by unified platforms. Today, there are consolidated alternatives both internationally and nationally, among which systems such as SignERP, Verial, MultiPress, Cyrious, or printIQ stand out, which assist in data centralization, integrated billing, and fluid connection between commercial and production departments. Programs like these significantly help the sector by providing precise analytics, automatic stock alerts, and profitability control. In this same line of innovation, the emergence of recent options in the market such as SignFloow are a perfect example of how these vertical platforms integrate layers of conversational intelligence to streamline budgeting and planning. All these tools, regardless of the chosen provider, pursue the same goal: to ensure that data enters the system only once and flows uninterruptedly to the final installation, eliminating double data entry and drastically optimizing workshop productivity.
The inclusion of advanced technologies in the graphic communication workflow is no longer optional. Addressing modernization requires a deep understanding of internal processes, data centralization, and the use of tools that connect from customer acquisition to the installation of the final product. By leveraging the potential of current software, respecting European regulatory frameworks, and adequately training staff, companies can transform their weaknesses into competitive advantages, leaving administrative chaos behind to operate with clear margins and much higher productivity.
