Gen AI in Spain: Innovating despite limited investment

While businesses in Spain acknowledge the critical role of generative AI in their future success, our recent research highlights a cautious approach to investment and a conservative outlook on strategy success compared with global trends. This year, projected spending averages $23.5 million per company, significantly lower than the global benchmark of $47 million.

However, there’s a palpable sense of urgency among Spanish businesses, with a striking 73% expressing a strong desire to accelerate their generative AI initiatives. Spanish companies seem to be at a crossroads, recognizing the necessity of embracing this technology to gain a competitive edge while carefully weighing the unique challenges and opportunities within their market.

The fact is, regional variances—such as regulatory compliance, infrastructure and available expertise—as well as internal factors like a business’s own technology foundation, will influence the success of implementing generative AI strategies. Consequently, the pace of generative AI adoption and its application will vary unevenly across the globe.

To gain a comprehensive understanding of global generative AI adoption, we conducted a study involving 2,200 business leaders across 23 countries and 15 industries, including 100 participants from Spain. The study evaluated various generative AI adoption trends, such as investment levels, use cases, the importance of generative AI strategies for business success and organizational readiness to embrace the technology.

We also analyzed 18 regional and internal business factors that will either inhibit or accelerate business adoption of gen AI (see the end of the report for the full list of factors). Respondents evaluated each factor’s potential impact on their generative AI strategy, rating it as either positive or negative on a scale of high to low impact.

From the results, we calculated a “momentum score” for each country or region. The momentum score represents the level of confidence business leaders have about their ability to roll out their generative AI strategy based on internal business factors and the prevailing local conditions of their country or region.

For all the regions covered, inhibitors to adoption outranked accelerators, meaning that all momentum scores skewed negative. In effect, businesses globally feel constrained by their operating environment.

But to understand how different regions varied relative to each other, we averaged the ratings to establish a baseline global momentum score. This approach enabled us to identify regions that are more optimistic about their ability to adopt the technology compared with a global average.

Spain’s momentum score is 22% below the global average, primarily driven by concerns over the scarcity and high cost of skilled AI professionals, public perception of the technology and the perceived lack of mature AI products in the market. Additional hurdles include a pessimistic view of existing technological infrastructures and data privacy issues.

Despite these challenges, Spanish businesses exhibit a positive outlook in certain crucial areas. They are notably confident about the market demand for AI solutions, their data preparedness, adaptability of existing operating models and access to sufficient computing power. Addressing the identified challenges while capitalizing on these strengths will be pivotal to accelerating Spain's generative AI adoption journey.

Spain’s gen AI scorecard

Base: 100 senior business leaders in Spain
Source: Cognizant and Oxford Economics
Figure 1

As for where businesses’ generative AI investments will be aimed in the near term, we looked at two distinct uses of the technology: productivity, such as helping people work more quickly and get more done, and innovation, which involves bigger changes to business and operating models.

In contrast to the global trend, where productivity enhancement is the primary focus for generative AI, Spain presents a more balanced perspective. Expectations are evenly distributed across productivity gains (35%), business innovation (34%) and redesigned operating models (35%) (see Figure 2). This suggests that generative AI will be a significant driver of change in Spanish businesses.

Our study also highlights a shift in the concept of productivity when driven by generative AI. Unlike previous automation efforts focused on efficiency and cost-cutting, the new dynamic emphasizes innovative thinking around business use cases for generative AI. We will explore these fresh perspectives in detail later in this report.

Greater focus on productivity than innovation

Q: Which of the following best describes the role generative AI will play in your organization's business strategy in the next two years? (Percent of respondents naming each as a top-three choice)

Base: 100 senior business leaders in Spain
Source: Cognizant and Oxford Economics
Figure 2

This report identifies the regional and business factors that could either inhibit or accelerate generative AI momentum in Spain. It also provides an industry-specific look at how generative AI will be used, a regional focus on business readiness and strategies for Spanish businesses to successfully implement generative AI.

Inhibitors and accelerators: The forces driving AI momentum

To dig deeper into these mechanics, rather than comparing to a global average, we’ll now examine how business leaders rate inhibitors and accelerators within their region. By doing so, our study provides a detailed temperature check on what respondents view as the main inhibitors and accelerators to generative AI in their region.

With this assessment, leaders can take advantage of what’s working well in their local environment, while strategizing on overcoming challenges.

A look at gen AI accelerators in Spain

Respondents were asked which factors inhibit or accelerate their organization's adoption of generative AI. Score represents a percentage point difference to the country's momentum score compared to the global baseline.

Base: 100 senior business leaders in Spain
Source: Cognizant and Oxford Economics
Figure 3

Despite Spain’s lower investment and more guarded outlook on generative AI, several factors are contributing to the acceleration of its adoption:

Market demand is a top driver of generative AI momentum in Spain. According to a 2024 report by the European Commission, AI adoption among businesses in Spain is higher than the EU average, as is the average annual growth in the use of AI. Further evidence of gen AI market demand is the fact that 73% of Spanish businesses in our study are at least piloting generative AI systems that engage directly with consumers, demonstrating their awareness of the general public’s interest in interacting with this technology.

The Spanish government has played a pivotal role in fostering an AI-friendly environment. Its National AI Strategy includes a commitment to invest €1.5 billion to improve supercomputing resources needed for AI, ensure a sustainable approach to data center expansion and develop a Spanish-language LLM. These initiatives demonstrate the government's dedication to building a robust AI ecosystem, further stimulating market demand and business adoption.

Data readiness is another top accelerator. Spain's progressive stance on open data is noteworthy, especially with its 2009 launch of the Aporta initiative, in which government, business and civil representatives work together to promote the use of open data for economic growth and social innovation. Spain's Open Data Portal provides a wealth of publicly available information to fuel the development and refinement of generative AI models. This treasure trove of data, spanning diverse sectors and domains, empowers researchers and businesses to create more accurate, contextually relevant and innovative AI applications.

Spain’s accomplishments on open data are well recognized; the country ranks seventh among all EU countries in an open data maturity report by the European Commission.

The availability of compute power has also emerged as a significant gen AI catalyst in Spain. The nation is home to one of the world’s most powerful supercomputers, the MareNostrum 5. This supercomputer, with a peak performance of 314 petaflops, is accessible to a wide range of European scientific and industry users, playing a critical role in supporting AI research and development. Its presence not only enhances the computational capabilities available within Spain but also solidifies the country's position as a hub for AI innovation within Europe.

Understanding Spanish gen AI inhibitors

Respondents were asked which factors inhibit or accelerate their organization's adoption of generative AI. Score represents a percentage point difference to the country's momentum score compared to the global baseline.

Base: 100 senior business leaders in Spain
Source: Cognizant and Oxford Economics
Figure 4

The cost and availability of AI talent stands out as a major inhibitor. The global scarcity of AI expertise makes it difficult to attract and retain top talent, particularly for high-demand roles such as data scientists and machine-learning engineers.

According to a recent report by IndesIA—an association promoting the use of AI in the industrial sector of Spain—24% of the country’s 120,000 technology job offers made in 2023 required data-focused skills, and that is expected to rise to 30% this year. Meanwhile, 30% of vacancies in AI and machine learning could not be filled. If the talent gap goes unaddressed, the organization warns, the demand-supply gap could quadruple by 2025.

This scarcity is likely amplified by financial constraints, especially for smaller enterprises, which further hinders the acquisition of necessary talent. The brain drain phenomenon is particularly prevalent in Spain, as skilled AI professionals may seek opportunities abroad in countries that offer software developers significantly higher salaries.

Consumer perceptions present another hurdle. While generative AI holds immense potential to enhance customer experiences, concerns about the accuracy and ethical implications of AI-generated content persist. The need to build trust and transparency with the public is crucial, especially as, according to a report from the European Consumer Organization, consumers in Spain are more likely than those in other EU countries to believe AI will result in abuse of their personal data, manipulation of their decisions by businesses and unfair discrimination. Spanish consumers are also more likely to think it's unclear who would be accountable for AI-driven damage or harm.

The maturity of generative AI-related products and services also contributes to the slower momentum. Despite significant advancements, businesses are still grappling with the evolving capabilities, potential applications and ethical implications of these technologies. This ongoing learning curve necessitates caution and careful consideration. Spanish companies may be hesitant to fully embrace generative AI until its potential benefits and risks are more clearly defined and understood.

Data privacy and security concerns further complicate matters. While Spain's progressive stance on open data is commendable, ensuring the responsible and secure handling of sensitive information remains paramount. The need for investment in robust data protection measures and clear regulatory frameworks is evident, particularly in light of the EU's General Data Protection Regulation (GDPR) and its strict requirements for data handling and privacy.

Spain is taking steps to address these concerns by establishing a new regulatory agency, the Spanish Agency for the Supervision of Artificial Intelligence (AESIA), which is part of its National AI Strategy, to oversee the development and use of AI in the country. This agency aims to ensure that AI is used responsibly and ethically, and that the privacy and security of citizens are protected.

Sector spotlight: Stark differences in industries’ gen AI adoption priorities

Of course, there are many use cases and strategies for using generative AI. As we’ve said, Spanish businesses are almost equally focused on realizing productivity gains with generative AI as they are with business innovation and redesigning operating models, at least in the next two years. However, a look at what’s driving their business cases sheds a new light on productivity from how it’s been seen historically.

Traditionally, businesses have equated automation productivity gains with cost-cutting: driving down the cost of output by reducing the number of people needed to get work done.

While generative AI-driven automation will likely lower headcount to some degree, cost savings is no longer the only end goal. Instead, as seen through the metrics respondents will use to drive business cases, we see as much if not more interest in using generative AI to increase revenues or develop new products and services as reducing costs.

The metric Spanish businesses say will be most important for justifying generative AI expenditures is increasing revenues, named by 60% of respondents, followed by improving product and service quality (52%) and creating new products and services (47%), all of which outranked cost savings (46%) (see Figure 5). In other words, the concept of productivity no longer stops at cost-cutting—businesses appear to be redirecting productivity gains into initiatives aimed at growth.

Revenue is a top metric for justifying gen AI use cases

Q: Which of the following metrics are most important in terms of justifying your organization’s generative AI business cases? (Percent of respondents naming each as a top-three choice)

Base: 100 senior business leaders in Spain
Source: Cognizant and Oxford Economics
Figure 5

By adopting this more granular view of productivity goals and business drivers, we analyzed the differences in how various industries intend to utilize the technology.

Rather than focusing on the distinction between productivity vs. innovation, we grouped the metrics into two high-level categories of business use cases:

• Enhancing current business performance (revenue, cost savings, time-to-market, productivity)
• Building something new (new revenue sources, new or improved products, innovation)

We then assigned each of the metrics a score to see the relative gap between a number-one-ranking metric and a number-three-ranking metric. By calculating the average score across industries, we could clearly see how each industry’s responses deviated from the baseline.

Our analysis reveals stark differences among Spanish industries in terms of the business use cases they’ll likely prioritize (see Figure 6).

Industries diverge on business cases

Note: This figure depicts each industry’s relative deviation from a baseline of “zero,” using a ranked scoring of the top-three metrics respondents cite as important for justifying their generative AI use cases. It reveals a weighted view of each industry’s overall priorities for gen AI deployment.

Base: 100 senior business leaders in Spain
Source: Cognizant and Oxford Economics
Figure 6

• The life sciences sector has its sights set on enhancing current business performance with generative AI. Almirall, a Spanish pharmaceutical company focused on medical dermatology, is collaborating with Microsoft to harness the power of generative AI in drug discovery. This strategic partnership aims to leverage AI to rapidly analyze extensive datasets, enabling the identification of new therapeutic targets and the discovery of synthesizable molecules.
  
  By accelerating the identification of promising drug candidates, Almirall aims to streamline its research and development processes and ultimately deliver innovative treatments more quickly and efficiently.
  
  
• The insurance sector is similarly focused on streamlining operations. MAPFRE, a leading insurance company in Spain, is exploring the potential of generative AI to automate various processes such as claims processing, policy renewals and customer service inquiries, allowing human agents to dedicate their time to more complex and value-added tasks. This approach promises to not only improve efficiency but also elevate customer satisfaction through faster response times and streamlined interactions.
  
  
• The communications, media and technology sector, meanwhile, is intent on exploring innovative applications of generative AI. For instance, Telefónica has integrated generative AI deep into its core systems to allow for full enterprise adoption and scaling. This also ensures privacy and security for its customers while enabling employees across the organization to innovate with the technology. The company hopes to use gen AI to provide higher value products and offers to its customers, using a digital customer assistant and a recommendation engine for its marketing teams.
  
  
• The energy and utilities sector is similarly focused on creating new ways of doing business with generative AI. Iberdrola, a leading Spanish energy company, is leveraging the power of generative AI to drive innovation and sustainability in the energy sector. Through a collaboration with Amazon Web Services, Iberdrola is establishing a generative AI center of excellence to develop and deploy AI applications across its operations. This initiative aims to optimize energy production, enhance customer service and accelerate the transition to clean energy.

Business constraints: Talent and tech infrastructure

A remaining question is whether businesses are ready to drive real value from these use cases.

The answer, according to our research, is mixed. To better understand how prepared executives believe their business is to adopt generative AI, we asked respondents to rank their organization’s maturity on a scale of 1 to 4 by selecting a statement that best described their organization in the following five areas, from low maturity to high:

• Organizational agility
• Leadership commitment
• Skills and talent
• Strategy and approach
• Technology and infrastructure

The output highlights areas where business leaders believe they have already achieved maturity, as well as those where they recognize a significant need to evolve capabilities to ensure the success of generative AI investments.

Leadership support is sound, but fundamentals are lacking

Respondents were asked to rate the maturity of their organization's operations in relation to generative AI. (Percent of respondents rating each as a 3 or 4, with 4 representing the highest level of maturity.)

Base: 100 senior business leaders in Spain
Source: Cognizant and Oxford Economics
Figure 7

Spanish respondents demonstrate a clear commitment to generative AI, with leadership commitment and strategy receiving high maturity scores by 69% and 65% of respondents, respectively. This indicates a strong desire from the top to embrace this transformative technology and develop clear plans for its integration.

However, a significant challenge remains in the area of skills and talent, with only 37% of respondents feeling adequately prepared. While it is encouraging that 54% of businesses are investing in training programs for specific roles, the relatively low percentage (27%) implementing enterprise-wide training highlights the need for a more comprehensive approach to upskilling the entire workforce.

Another area of concern is technology and infrastructure, which was rated favorably by just 38% of respondents. This suggests potential challenges in data management, processing capabilities and the overall technological readiness of organizations to support the demands of generative AI.

The situation is further compounded by apprehensions about data security and privacy. A concerning 49% of respondents believe their data security practices need improvement, and a striking 53% feel unprepared to comply with customer privacy and contract requirements in the context of generative AI. These concerns highlight the critical need for Spanish businesses to prioritize investment in robust data protection measures and ensure compliance with stringent regulations.

Path to success: Strategic recommendations for Spanish AI evolution

The challenge ahead is to fully leverage the factors that can drive generative AI strategy success while overcoming the inhibitors.

To navigate these challenges, executives should prioritize the following actions:

• Develop pragmatic partnerships to shore up skills: Spanish businesses need to create an ecosystem of partnerships that foster talent development and innovation. Strategic partnerships with educational institutions can provide tailored training programs, equipping the workforce with essential AI skills.
  
  Collaborations with startups can bring innovative solutions and rapid prototyping capabilities, while partnerships with government bodies can offer funding, regulatory support and infrastructure development. Indeed, 52% of respondents express a desire for direct government funding to support retraining and reskilling efforts.
  
  Recognizing the need for pragmatic support from experts, 42% of respondents are looking to partner with AI consultants. The goal is to find a partner that can help overcome challenges, assume risks and transform the business, preparing it for the future.
  
  
• Invest in AI literacy and training: Comprehensive training programs can bridge the skills gap and encourage adoption, facilitating the integration of AI into existing operations and the development of new business models. Businesses need to prioritize AI literacy to drive growth and competitiveness.
  
  On a broader scale, countrywide learning initiatives that introduce the technology at the early education levels can help build awareness of AI’s capabilities and spark national interest in its development. Businesses could look to Spain’s National AI Strategy, which intends to strengthen STEM, informatics and specifically AI learning at the primary and secondary education stage, expand post-grad studies in AI, and establish lifelong learning to keep competencies in line with labor market demands.
  
  
• Begin setting compliance guidelines now: Respondents from Spain believe their businesses will experience the full impact of generative AI in just under five years. This timeframe provides business leaders with a crucial window to establish clear guidelines for data handling and ethical use. Creating transparent documentation of AI processes and decision-making will enhance accountability. Additionally, training employees on regulatory requirements can ensure compliance is ingrained throughout the organization from the ground up.
  
  
• Focus on increasing data security measures: Implementing robust security measures such as encryption protocols and access controls ensures that sensitive data used by generative AI systems remains secure. Additionally, conducting regular security audits and vulnerability assessments can help companies stay on the offense when it comes to data breaches. Fostering a culture of transparency and collaboration can safeguard the country’s generative AI systems and build trust in its deployment and use.

*The full list of regional factors we evaluated includes: the flexibility of the existing operating model, market demand for gen AI-enabled products and services, data readiness, quality of output from gen AI, availability of compute power, cost/availability of gen AI-related technologies, shareholder/investor sentiment, regulatory environment, sustainability, national infrastructure, cost/availability of capital, data privacy and security, existing technology infrastructure, current and prospective employee perceptions, flexibility of the existing business model, maturity of gen AI-related technologies, consumer perceptions and cost/availability of talent.

Learn about the impact of generative AI on jobs and the economy in our report New work, new world.