As artificial intelligence tools proliferate across industries, a counterintuitive trend is emerging. Far from delivering the promised era of streamlined efficiency and reduced workloads, AI implementation is creating new layers of complexity, supervision, and management tasks. According to Carmen Torrijos, AI Lead at the digital consultancy Prodigioso Volc\u00e1n, this intensification is a direct side effect of the technology’s accessibility, leading professionals to venture beyond their core competencies without adequate systemic support.<\/p>\n
A recent study from Harvard Business School suggested that AI, instead of saving labor, is “intensifying” work by generating additional supervisory and management duties. Torrijos, a computational linguist with over a decade of experience in natural language processing and applied AI, confirms this observation from her frontline work in corporate digital transformation. “It’s a side effect derived from the fact that we can now do things that are outside our usual work area or what we know how to do,” she explains.<\/p>\n
Torrijos illustrates the point with a common scenario: a communications professional using AI-powered “vibe coding” to prototype a digital product. This creates intensification on two fronts. First, the individual tackles an unfamiliar task enabled almost “by magic” by AI, experiencing both freedom and potential frustration. Second, the work is displaced. When the prototype moves to the product team, engineers must now reform or dismantle this preliminary work, dedicating extra time to reasoning with the communications team or fixing errors.<\/p>\n
“This is very positive in the sense that it breaks down silos and generates new conversations,” Torrijos acknowledges. “But it’s also true that it generates noise and rework.” She identifies a cultural culprit: an overemphasis on individual initiative. “In recent years we have highly rewarded individual initiative. To avoid multiplying work, we must foster more collective productivity with AI and generate systems of common, agreed-upon use that everyone can leverage.”<\/p>\n
The solution, therefore, is not to retreat from AI adoption but to pivot towards collaborative frameworks. The goal is to move beyond the fragmented, individual experimentation that currently dominates many organizations and build shared, standardized systems that prevent redundant effort and align AI use with collective goals.<\/p>\n
Torrijos’s team at Prodigioso Volc\u00e1n has developed a tangible example of this collective approach with an innovative project called “La Escalera” (The Staircase). This initiative acts as a generational listening neighborhood-laboratory powered by AI, designed to help marketing and communication professionals deeply understand their audiences.<\/p>\n
The concept is rooted in a specific need: to condense the worldview of a demographic, like Generation Z, into a language model. By fine-tuning the model with instructions and feeding it generational studies, the team creates a simulacrum of a person from that generation. “Not only have we created and defined it, but we can converse with it,” Torrijos says. Professionals can propose business ideas, slogans, campaigns, or product concepts and receive feedback on how that generation might receive them.<\/p>\n
The project is wrapped in a compelling narrative and visual layer: a four-story building. Each floor houses a different generation\u2014Zoomers, Millennials, Generation X, and Boomers\u2014with both a female and male avatar on each level. This allows an idea to “travel the entire staircase,” garnering rapid feedback from all generations coexisting within companies and target audiences.<\/p>\n
“What this project has contributed to brands,” Torrijos reflects, “is the understanding that AI models are a very agile and human way of interpreting complex data about people. With the narrative of La Escalera, the utility is understood very quickly.” The project demonstrates how AI can be deployed not as a solitary tool for individual productivity, but as a shared platform for collective insight, aligning teams around a common, data-informed understanding of their market.<\/p>\n
The collective responsibility for AI extends into the ethical realm. As AI assistants become brand assets and primary channels for customer interaction, the biases embedded in their training data\u2014drawn from the vast, imperfect corpus of the internet\u2014pose a significant threat. “We have to ensure that what they convey always rows in favor of social progress,” Torrijos warns. “If they reinforce stereotypes or obsolete worldviews, even subtly, they can be a significant force against it.”<\/p>\n
She outlines a three-tiered approach to tackling bias. The first level concerns the foundational data and configuration of the large language models, largely trained outside European regulatory frameworks and thus often outside direct control. The second involves the proprietary data and instructions used to fine-tune these models for specific applications. The third, and most immediately actionable, is individual user awareness. “When you are well-versed in the issue of bias, you are more alert to what the AI returns and have a more critical attitude,” she notes.<\/p>\n
The ethical use of AI is currently a point of intense global tension. Torrijos highlights the divergence between the European perspective on technology’s social impact and that of other regions. Concepts like privacy, intellectual property, and the right to non-discrimination are not universally understood or protected. “We battle every day with ethics, but it is fundamental to maintain the objective and effort; a great many things that matter to us are at stake,” she asserts.<\/p>\n
A promising development, she points out, is the explicit obligation for AI literacy introduced in Article 4 of the EU’s AI Act. It mandates that organizations developing or using AI systems must ensure people understand AI within their professional profile. “Fostering AI education from an industrial regulation requirement is a pioneering and new step, and without a doubt indicates that we are moving forward.”<\/p>\n
The massive energy consumption of AI models presents another systemic challenge. The current paradigm of mass free access, beneficial for model training, faces economic unsustainability. “The subscription business model is not viable,” Torrijos states bluntly. The costs are astronomical, and not enough users are willing to pay the required price, pushing companies toward alternative revenue streams like commercial alliances, government deals, and advertising.<\/p>\n
This leads to a critical crossroads for user experience. While companies like Anthropic and Perplexity reject integrated advertising to preserve conversational intimacy and trust, others like Google and OpenAI are advancing firmly toward ad-supported models. Torrijos believes this could trigger user backlash. “I’m not sure if we will accept advertising in AI chats with the same naturalness as sponsored search results, and I think it could really bother us and generate a rejection effect.”<\/p>\n
For brands considering advertising within AI platforms, Torrijos advises caution. “From a reputational and brand image standpoint, I think at this moment you have to be very careful making an advertising investment in a platform that could be heavily criticized for this option.” She advocates for a more organic, long-term strategy: modulating global digital content so that AI systems naturally find and convey key brand messages\u2014a process akin to AI Search Engine Optimization (AISO). “It takes more time but is more authentic, works in favor of your brand, and can give you greater long-term performance.”<\/p>\n
The ongoing construction of the AI ecosystem presents a unique opportunity to address gender disparity in technology. Torrijos observes that while the sector inherits a heavily male-dominated base, the influx of AI into diverse fields is creating new pathways for women. “We are very much in time to integrate more women who arrive via different paths: from students choosing STEM careers to professional women reinventing themselves or becoming hybrid profiles applying AI from health, linguistics, education, research, social sciences, or art.”<\/p>\n
Qualitatively, she sees progress with more women in public discourse on AI, creating vital referents. However, quantitative data remains stark. In European tech employment, the percentage of women has dropped to 19%, with Spain at 23%. Executive roles are even scarcer. “It is not enough with more women studying STEM; that cannot be a beacon metric,” she argues. The expansion of strategic capabilities brought by AI can, however, design new, non-linear career paths more open to connection with other sectors, potentially welcoming many more female profiles into decision-making spaces.<\/p>\n
Looking ahead, Torrijos predicts a significant shift. “Throughout the next year, many people will move from sporadic use of AI to constant use in work activity; that leap is still to be made.” Subsequently, focus will shift from individual usage\u2014which will become assumed\u2014to the construction of collective solutions that genuinely transform processes.<\/p>\n
This will not eliminate the need for skilled professionals in software development, design, UX, writing, or translation. Instead, collaborators will reach them at a more advanced conceptual stage. “They will teach us things. This will bring new frictions, and we will have to learn to overcome them.” A long-term challenge will be finding equilibrium between autonomous AI agents and the imperative risks to privacy, security, and data control.<\/p>\n
For any business or individual beginning the AI integration journey, Torrijos’s advice returns to the core theme of collective, informed action. Building a solid foundation of education is paramount, understanding both technical concepts and the evolving regulatory landscape. Extensive individual experimentation is necessary before integrating AI into real processes. “The greatest danger,” she cautions, “is thinking that we can already use AI at a productive level without technical profiles, and delegating entire projects or critical processes to it. It is a mistake. To work with AI, the most important thing is the team.” The future of effective, ethical, and equitable AI, therefore, hinges not on the solitary prompt engineer, but on the strength and diversity of the human systems we build around the technology.<\/p>\n","protected":false},"excerpt":{"rendered":"
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