Artificial intelligence as a cross-cutting and strategic lever

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La Artificial intelligence has infiltrated the heart of organizations at a speed that just a few years ago would have seemed like science fiction. It's no longer the exclusive domain of tech giants or R&D teams with a steady stream of PhDs: today it's in CRM, marketing, operations, analytics, software development, and even in how we measure a brand's reputation.

Looking ahead to 2026, AI is emerging as a cross-cutting, strategic, and radically transformative layer. for businesses and public administrations. We've moved from pilot tests and isolated projects to an industrialization phase: AI as a basic infrastructure, integrated into processes end-to-end, governed by quality and safety criteria, and aligned with very clear business objectives.

A cross-cutting artificial intelligence present in all sectors

In the last two years, the The development of artificial intelligence has broken down technical and cultural barriersWhat was once an almost experimental challenge, with much uncertainty and research components, are now solutions supported by mature platforms, pre-trained models, and accessible tools for less technical profiles.

According to multiple reports, near the 20% of Spanish companies already use AI systems in their daily operationsAnd that number keeps growing. This means that both technical teams and business professionals are working alongside... smart assistants, automations and data models that They optimize internal processes, personalize experiences, and enable new business models.

Professional profiles have also diversified: now key figures include the AI Engineer, data architects, and the AI software developerswho work in a coordinated manner with marketing, sales, finance, and human resources. The result is a much more cross-functional collaboration, with more agile and multidisciplinary development cycles.

All this translates into a mass and normalized adoption of AIIt is no longer perceived as something exotic, but as an everyday tool for making better-informed decisions, automating routine tasks, and supporting team creativity.

Towards technological maturity: AI is no longer experimental

The horizon of 2026 is shaping up to be a turning point towards the technological maturity of artificial intelligenceOrganizations are beginning to treat AI systems the same as humans. with critical: with engineering methodologies, rigorous testing and very clear quality standards.

Companies are prioritizing the development of robust, scalable, and reliable AI productsthat can evolve over time without breaking down at the first sign of trouble. We are entering the era of exhaustive testing, systematic validation, and advanced control mechanisms for to guarantee consistent, measurable and sustainable results in production.

This involves building frameworks of governance models, decision traceability, and human oversightEspecially when we talk about sensitive use cases such as financial risk, health, customer relations, or critical infrastructure management. AI is ceasing to be a laboratory “toy” and becoming essential infrastructure of business operations.

In parallel, a clearer strategic vision is taking hold: AI is integrated as a horizontal layer that connects data, processes, and decisions. in real time, instead of being a set of isolated solutions. From the first contact with the customer to logistics or the back office, AI is beginning to articulate the entire flow of information.

Trends for 2026: hyper-personalization, automation, and intelligent agents

In 2026 we will see how the Hyper-personalization and advanced automation are becoming central to the evolution of AIBroad segmentations or static rules are no longer enough: algorithms cross-reference historical behavior, real-time context, location, social media interactions, and transactional data to adapt to the user almost in real time.

This will make it possible dynamic digital experiences that change according to intention and context from the user's perspective. Marketing campaigns will be activated when there are high-probability conversion signals, recommendations will appear before the customer expresses their need, and journeys will be orchestrated flexibly thanks to advanced predictive models.

At the same time, the Business automation will extend to entire processesnot just to isolated tasks. Many companies will move from automating small, isolated activities to redesigning processes. end-to-end with AI: from data ingestion to the final decision, relying on multiple models and coordinated agents.

A key element will be the autonomous agents and multi-agent ecosystemsThese systems will be able to interpret data, execute complex tasks, and cooperate with each other within the organization: some will optimize sales, others will handle inquiries, others will analyze risks or generate content, exchanging context to maintain a seamless experience.

Thanks to this approach, Collaboration between humans and agents will be seamless and frictionless.We'll be able to start an interaction with a person, continue it with an agent, and return to a human without losing track of the conversation or the brand's tone. In CRM, for example, this will mean a huge improvement in response times, message consistency, and personalization.

Generative AI as a creative and productive engine

One of the most powerful trends is the Consolidation of generative AI as a creative lever in businessIt's not just about generate images, audio or videobut rather to devise products, services, value propositions and content tailored to the context of each company and each client.

Current generative models are capable of analyze large volumes of unstructured information (comments on social media, forums, reviews, call transcripts) and turn them into actionable ideas: from campaign concepts to messages tailored to very specific segments.

The real revolution lies in the fact that Creativity relies on massive amounts of data rather than solely on human intuitionDetecting patterns, anticipating consumption trends, and simulating response scenarios allows for the design of strategies that are much more closely aligned with the real market.

Furthermore, generative AI is beginning to significantly change the software development cycleSpecialized tools accelerate documentation, test design, security review, functional analysis, and code generation. In some cases, significant improvements are being achieved. reductions of up to 90% in the time spent on documentary tasks or report writingfreeing up teams to focus on architecture, product design, and quality decisions.

This combination of creative automation and strategic data vision It will make the difference between companies that only use AI as a complement and those that place it at the core of their product strategy, marketing, and technological development.

Advanced automation and intelligent assistants across the enterprise

In the early years of adoption, many companies limited themselves to testing AI in very limited pilot programsBy 2026, the picture is different: AI-based automation will become a cross-cutting reality, connected to large systems. core and aligned with business objectives.

Intelligent assistants have gone from answering simple questions to act as true digital collaboratorsThey manage schedules, prepare reports, identify business opportunities, and serve as the first point of contact with customers and suppliers, with accuracy rates far exceeding those of older rule-based chatbots.

In fields such as finance or logistics, AI is already analyzing millions of transactions and events to detect fraud in real time, optimize distribution routes, or anticipate incidents. In marketing, algorithms process opinions, reviews, and mentions on social media to extract signals that allow design hyper-personalized and more profitable campaigns.

One direct consequence will be the significant reduction in incident resolution times in critical systemsBy training models with historical service data, average resolution times are being reduced by around 30%, with a direct impact on system availability and the satisfaction of customers and internal users.

Furthermore, AI is becoming key in the modernization of legacy systemsAutomated analysis of massive codebases allows us to understand dependencies, real architecture, and critical points in a fraction of the time previously required, making modernization projects viable that until recently were considered unfeasible due to cost, risk, or duration.

Hyper-personalization in marketing and sales

Everything points to 2026 being remembered as the year in which Personalization in marketing and sales reaches an unprecedented levelWe will move from broad segmentations and simple recommendations to engines capable of anticipating what each person needs and when they are ready to receive a message or an offer.

The algorithms will analyze real-time consumption patterns and will correlate them with context (location, device, time of day), interaction history, and signals from social media or other channels. This will make it possible relevant communications at the exact moment in which the user shows a greater propensity to convert.

The impact will go beyond increasing sales: ability to build personalized and consistent relationships It will strengthen trust and loyalty, critical assets in an environment saturated with advertising. Customer loyalty will become a top-tier competitive advantage.

In parallel, sales teams will see their way of working transformed. They will no longer depend on outdated databases or generic reportsbut rather 360° views built from integrated structured and unstructured data. This will allow them make more informed decisionsto better prioritize opportunities and adapt the message in real time.

The most visible consequence will be a significant optimization of advertising investmentIt is estimated that advanced personalization can reduce spending on ineffective campaigns by around 40%, by focusing investment on genuinely interested audiences and highly targeted messages.

Convergence of AI, IoT, and edge computing

Another key vector of transformation is the integration between artificial intelligence, the Internet of Things (IoT), and edge computingUntil now, many implementations have progressed separately, but what's coming is a real convergence in industrial, energy, logistics, healthcare, and urban environments.

Connected devices already generate massive volumes of real-time dataAnd edge processing allows for on-site analysis, without always relying on the cloud. This reduces latency to milliseconds, which is critical for applications such as connected vehicles, smart grids, and industrial machinery.

In a production plant, for example, thousands of sensors can continuously monitor the status of the machinesBy analyzing data locally, AI can detect minimal deviations, anticipate failures, and activate automatic adjustments before the issue escalates, preventing costly downtime.

In healthcare, wearable devices and connected medical equipment can interpret biomedical signals in near real time, offering early warnings without the need for a permanent connection or constant sending of data to a central server.

Smart cities will also benefit: transportation systems, lighting, and waste management will make local decisions based on AI algorithms. reducing energy costs and improving the quality of life for citizensThe challenge, however, will be to strengthen cybersecurity, since more distributed processing implies more potential points of attack.

Digital Spain 2026 and the public strategy in AI

At the institutional level, the The Spain Digital 2026 agenda has been consolidated as the country's digital transformation roadmapIt is an update of the strategy launched in 2020 that incorporates priorities for the coming years and adds two cross-cutting axes: the PERTE (Strategic Projects for Economic Recovery and Transformation) and the RETECH initiative, focused on high-impact digital projects proposed by the autonomous communities.

During the last few years, there has been a strong push to Investment in connectivity, R&D, digitalization of public administration and support for SMEssupported by European recovery funds. Part of these resources has been allocated to strengthening citizens' digital skills and modernizing public sector technological infrastructure.

Digital Spain 2026 acts on three major dimensions: infrastructure and technology, economy and peopleIt maintains ten strategic axes (connectivity, 5G, cybersecurity, data economy and AI, digital public sector, companies, driving sectors, audiovisual hub, digital skills and digital rights) and adds two cross-cutting axes focused on large projects and territorial networks of technological specialization.

Among the most relevant objectives, goals such as the following stand out: guarantee high-speed broadband coverage for virtually the entire population, to lead the 5G rollout in Europe, strengthen the cybersecurity ecosystem and ensure that at least 25% of Spanish companies use artificial intelligence and big data within a period of five years.

The strategy is complemented by specific plans such as the National Digital Skills Plan, National Cybersecurity Plan, Public Administration Digitalization Plan or the programs to promote the digitization of SMEs, all of them with a significant role for AI as a lever for change.

Industrialization of AI: governance, security, and new roles

As organizations deploy AI at scale, it becomes essential moving from uncontrolled experimentation to an industrialized modelwith clear frameworks for governance, security, and accountability.

The transition to an “AI-Centric” approach involves integrate AI into every relevant process, into the systems core and in decision modelsensuring that all of this meets audit, explainability, and control requirements. Companies that achieve this will be able to accurately measure the impact of AI and expand its use with less internal resistance.

In this context, Autonomous agents represent the next evolutionary leapWe are no longer just talking about models that make recommendations, but about systems capable of executing concrete actions within well-defined limits, such as reallocating budgets, prioritizing incidents, or performing simple financial operations.

This forces us to design very strong governance frameworksIt is necessary to define what each agent can do, under what rules, with what human supervision, and with what traceability mechanisms. Initiatives such as internal "agent marketplaces" are emerging, which allow for their deployment under centralized control and aligned with responsible AI principles.

All of this has a direct impact on the labor market: roles are reconfigured and new ones emerge new profiles specializing in the design, deployment, and monitoring of AI systemsFar from eliminating the human dimension, AI shifts people towards higher-value tasks: strategy, customer relations, creativity, risk management, and complex decision-making.

In this scenario, the Technological and organizational maturity will be the decisive factor.Organizations that integrate AI across the board, with a clear purpose and skilled talent, will be the ones that lead in competitiveness, productivity and responsiveness to an increasingly changing environment.

Everything points to artificial intelligence becoming established as the axis that articulates data, processes and decisions in companies and administrationsIts value is already tangible: it improves timelines, reduces costs, opens up new business models, and allows for much more precise measurement of intangibles such as reputation and trust. In the coming years, the difference between falling behind and taking the lead will lie in daring to deploy it across the board, strategically, and with good governance, moving from isolated trials to responsible, industrial-scale adoption.

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