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How are companies in the Valencian Community adopting
artificial intelligence
?
What are Valencian companies doing with AI today, what impact are they generating, and what challenges are they facing?
Study conducted by Pleyad in collaboration with the Fundació Parc Científic Universitat de València
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Opportunity
and Urgency
In just a few years, artificial intelligence has gone from being an experimental field to becoming a technology that shapes competitiveness and
redefines how companies operate
.
Investment, regulatory pressure, and market expectations are accelerating, but actual adoption in companies is progressing at different speeds: while the use of AI tools has spread rapidly,
the major step now is integrating the technology
into companies' processes, organizational models, and strategies.
EmpresIA was created to understand this moment of transition from within the Valencian business community: to understand how they are incorporating AI, what value they are generating, and what conditions are shaping their ability to move forward
A Brief History
1942
1942
1942 Joseph Schumpeter publishes his theory of creative destruction; disruptive innovation has the capacity to transform entire sectors.

The idea
that technology advances in “waves”
is established.
1956
1956
The field of
Artificial Intelligence
is founded at the Dartmouth meeting.

First promises of automating reasoning.
1980s–1990s
1980s–1990s
First practical applications in industry, logistics, and diagnostics.

High expectations, but technical limitations
.
2000s
2000s
Digital transformation as the fifth wave. Companies digitize their management, migrate to the cloud, and
generate massive volumes of data
.

The infrastructure that will make today's AI possible is created.
2017
2017
The Transformers: Google presents
“Attention is all you need”
, an article that introduces the architecture driving modern generative AI.
2022
2022
Publication of ChatGPT: the explosion of generative AI.

AI leaves the laboratory and enters mass use.
Experimentation is becoming democratized
.
2025
2025
Dawn of the sixth wave.

AI begins to be
integrated into products, processes, and decisions
.
2030
2030
A window of opportunity.

International projections estimate that AI could add at least 3–4% to global GDP.

The opportunity is concentrated in the decade 2025–2035
.
AI is already transforming how companies compete.

The impact of the future is being decided today: in how we organize ourselves, how we use data, and how we adopt technology.
The Analysis:
The Six Dimensions of the Model
The study is based on an
analytical model
articulated in six dimensions to understand how companies integrate artificial intelligence into their operations. These dimensions analyze
AI adoption as a progressive process
, conditioned by strategic, organizational, and cultural factors.

The approach does not evaluate specific technologies, but rather examines how decisions are made, solutions are implemented, value is generated, obstacles are overcome, and lessons are learned, offering a
realistic and useful perspective
for understanding AI adoption in organizations.
Hover over the diagram for more information
Dimension 1
Strategic Vision
From what ambition, purpose, and competitive position does each company approach AI, and how does this technology integrate into its overall strategy.
Dimension 2
Motivations and expectations
What challenges, needs, or opportunities drive the first AI projects, and what does the organization expect to gain in the short and medium term.
Dimension 3
Implementation process
How does one move from an idea to a production solution: phases, stakeholders, and decision-making dynamics
Dimension 4
Impact and results
What is truly changing with AI, combining quantitative indicators and qualitative effects.
Dimension 5
Barriers and success factors
What hinders projects, and what critical conditions allow them to move forward: challenges and factors that make the difference between scaling and remaining in testing.
Dimension 6
Lessons learned and future
What have companies learned after their initial adoption cycles, and where are they now directing their initiatives, capabilities, use cases, and collaborations.
Dimension 1: Visión
How AI is integrated into business strategy
Integrating artificial intelligence into business strategy doesn't follow a single model; rather, it
reinforces how each company already competes
. AI acts as an accelerator of existing priorities and highlights internal tensions.

The difference lies not in the use of technology, but in
how the role that AI should play
in the competitive positioning of each organization is defined.
Competitive positioning patterns
Hover over the diagram for more information
Operational efficiency and stability
Operational efficiency and stability

Markets with tight margins and intensive processes.

With AI:

  • Automation
  • Process control
  • Data quality
Differentiation and perceived value
Differentiation and perceived value

Competition through experience, personalization, and expertise.

With AI:

  • Better experience
  • Less friction
  • Recommendations
Social impact and legitimacy
Social impact and legitimacy

Environmental or social purpose at its core.

With AI:

  • Traceability
  • Accessibility
  • Resolution of systemic inefficiencies
Strategic Tensions
Companies operate amidst tensions that affect almost the entire business landscape; these are symptoms of trying to adapt to a context that evolves faster than their internal capabilities.
Strategic ambition
vs
Actual execution capacity
Speed of innovation
vs
Operational stability
Personalization
vs
Scalability
Purpose and impact
vs
Short-term results
What this means in practice
1.
AI functions as an amplifier of the existing strategy, not as a shortcut to replace a lack of direction
2.
The strongest companies manage tensions by priority, not by trying to resolve them all at once
3.
The strategic question is no longer "whether or not to use AI," but rather what role AI should play in each company's specific competitive strategy
Dimension 2: Motivaciones
Motivations Why Companies Decide to Incorporate AI
Companies navigate between
operational urgency and strategic ambition
. AI allows them to both alleviate specific frictions and explore capabilities that were previously unavailable.

The most successful projects are those that connect both dimensions: they start with real business problems but use AI to unlock new possibilities.
Triggers for adoption
Hover over the diagram for more information
Decisions that are too slow
Decisions that are too slow
  • Too much data
  • Limited analytical capacity
Processes that don't scale
Processes that don't scale
  • Repetitive tasks
  • Backoffice
  • Operational load
Customer experience and response times
Customer experience and response times
  • Waiting times
  • Friction
  • Response delays
Reliance on expert knowledge
Reliance on expert knowledge
  • Knowledge concentrated in a few people
Competitive regulatory pressure
Competitive regulatory pressure
  • Competition
  • Pricing
  • Regulation
Structural data disorder
Structural data disorder
  • Silos
  • Duplication
  • Technological debt
Need Pull vs Technology Push
AI projects are driven by
two distinct impulses
: the need to solve specific business problems (“need pull”) or the opportunity to explore what technology allows (“technology push”).

This difference marks the
starting point of the projects
and conditions both their approach and their potential to generate value.
We can't afford to grow our staff at the rate that demand grows
There's a lot of information, but it's not easy to digest; we reacted late and did post-mortem analyses
The workload was unsustainable; we dedicated our free time to finishing tasks
We have projects that were unimaginable a year and a half ago
Now, after a week, I can already show the user what I want to do; that way, I know if it makes sense or not
The core of our company is directly AI. It's not a strategy; we were born from it, with that distinctive vision
What this means in practice
1.
The best projects don't arise solely from technological trends, but from very specific operational frictions in the business that AI can alleviate
2.
When AI is driven solely by urgency, there is a risk of patching things up; when it is driven solely by opportunity, it moves away from the real problem
3.
The most successful projects define what "success" means from the outset and design the proof of concept to generate evidence as soon as possible.
Dimension 3: Implementation
How AI projects are implemented
Artificial intelligence projects do not advance linearly, but
rather go through successive experimental stages.


The difference between isolated pilots and scalable solutions lies in having
coherent governance and development models
that connect experimentation with daily operation.
AI project implementation phases
From proof of concept to scaling, AI projects tend to adopt an
agile and experimental approach
, as a response to uncertainty
Hover over the diagram for more information
1. Proof of Concept
1. Proof of Concept

Does it work on a small scale

  • Quick and inexpensive prototypes
  • Validates hypotheses with data
  • Uncovers limitations
2. Pilot
2. Pilot

Does it generate impact

  • Real-world environment, real users
  • MVP with KPIs
  • Reveals dependencies and limitations
  • Leads to redesigns
3. Deployment
3. Deployment

Is it stable?

  • Integration with corporate systems
  • Adjustment of processes and roles
4. Scaling
4. Scaling

How is it expanded and maintained?

  • Adding new use cases
  • Extending to more teams and processes
  • Maintaining and updating models
1. Proof of Concept
1. Proof of Concept

Does it work on a small scale

  • Quick and inexpensive prototypes
  • Validates hypotheses with data
  • Uncovers limitations
2. Pilot
2. Pilot

Does it generate impact

  • Real-world environment, real users
  • MVP with KPIs
  • Reveals dependencies and limitations
  • Leads to redesigns
3. Deployment
3. Deployment

Is it stable?

  • Integration with corporate systems
  • Adjustment of processes and roles
4. Scaling
4. Scaling

How is it expanded and maintained?

  • Adding new use cases
  • Extending to more teams and processes
  • Maintaining and updating models
Governance and development
There is no single model for AI governance and development:
each company configures its own balance
between technical control, alignment with the business, and speed of execution
Who governs AI projects
1/3
IT / Digital Transformation
Option 1
IT / Digital Transformation
Technical control
Architectural coherence
Security
Bottlenecks
Reduced scope for experimentation
2/3
Innovation / Business
Option 2
Innovation / Business
Strategic alignment of use cases
Proximity to the real problem
Dispersion
Isolated or non-integrated solutions
3/3
Hybrid models
Option 3
Hybrid models
Shared decisions
Common criteria
Cross-functional coordination
Greater complexity
Slower pace
Blocking of experimentation
1/3
IT / Digital Transformation
Option 1
IT / Digital Transformation
Technical control
Architectural coherence
Security
Bottlenecks
Reduced scope for experimentation
2/3
Innovation / Business
Option 2
Innovation / Business
Strategic alignment of use cases
Proximity to the real problem
Dispersion
Isolated or non-integrated solutions
3/3
Hybrid models
Option 3
Hybrid models
Shared decisions
Common criteria
Cross-functional coordination
Greater complexity
Slower pace
Blocking of experimentation
Who develops the solutions
1/3
In-house development
Option 1
In-house development
Greater control over data
Greater alignment with the business
Requires strong technical skills
2/3
Outsourced development
Option 2
Outsourced development
Faster access to advanced capabilities
Accelerated time to market
Lack of business knowledge
Vendor dependency
3/3
Hybrid models
Option 3
Hybrid models
Business and data control
Faster speed in specialized components
Risk of multiple dependencies
More complex project governance
1/3
In-house development
Option 1
In-house development
Greater control over data
Greater alignment with the business
Requires strong technical skills
2/3
Outsourced development
Option 2
Outsourced development
Faster access to advanced capabilities
Accelerated time to market
Lack of business knowledge
Vendor dependency
3/3
Hybrid models
Option 3
Hybrid models
Business and data control
Faster speed in specialized components
Risk of multiple dependencies
More complex project governance
What this means in practice
1.
The most successful projects define a proof of concept to reduce uncertainty, not just to test the technology
2.
Companies that manage to scale integrate AI into real processes and systems from early stages
3.
Implementation flows much more smoothly when there is clear minimum governance
4.
Data quality and availability become the main bottleneck; companies that prepare for this before starting avoid subsequent roadblocks
Dimension 4: Results
Impact and Results
The initial impact of AI on businesses is predominantly manifested in
improvements in efficiency and internal organization
, rather than in immediate increases in revenue.

This lays the necessary organizational and data foundations so that, later on
economic results
can materialize.
Impact and Results
Hover over the diagram for more information
Strategic Impact
Strategic Impact
  • New value propositions
  • Differentiation from the competition
Organizational Impact
Organizational Impact
  • Redesign of flows and roles
  • Improvement in data quality
Commercial Impact
Commercial Impact
  • More homogeneous service
  • Better response time
  • Peak demand buffering
Operational Impact
Operational Impact
  • Higher volume
  • Reduction of time and repetitive tasks
  • More reliable processes
Tangible versus intangible: beyond metrics
Quantitative indicators
Quantitative indicators
  • Hours saved or tasks automated
  • Increased workload managed by the same team
  • Reduced response times in key processes
What data doesn't capture
What data doesn't capture
  • More informed and faster decisions
  • Less internal friction in accessing information
  • Greater capacity for experimentation
  • Improved data quality and traceability
Soft ROI
In company narratives, the idea of a "soft ROI" appears repeatedly, associated with those improvements that do not yet impact financial results, but that create the necessary conditions to generate economic value in the medium term
What this means in practice
1.
Companies that are best leveraging AI understand that operational and organizational impacts come before financial results
2.
The most successful projects define a few simple indicators from the outset and connect them to qualitative improvements in data, decision-making, and customer or user experience
3.
Organizations that haven't defined how to measure these effects find it more difficult to justify new investments and scale projects beyond the pilot phase
Dimension 5: Barriers
Barriers and Critical Factors
The obstacles to AI projects are not primarily technological, but rather
organizational and economic.


These difficulties are concentrated in internal culture, data quality and governance, and realistic estimation of implementation and maintenance costs.
Organizational and Cultural Barriers
Organizational and Cultural Barriers
1/4
Silos and Organizational Immaturity
Silos and Organizational Immaturity
Teams work separately, and projects become disconnected from the business or the organization
2/4
Resistance to New Ways of Working
Resistance to New Ways of Working
Part of the organization defends legacy processes and methods, slowing down adoption
3/4
Inflated Expectations and Incorrect Delegation
Inflated Expectations and Incorrect Delegation
AI is expected to solve everything “magically”
4/4
Fear of Job Displacement
Fear of Job Displacement
AI is perceived as a risk, and people react with caution and distrust
Technical Barriers
Technical Barriers
1/4
Data Quality and Access
Data Quality and Access
AI projects become data cleanup and governance projects
2/4
Integration with Legacy Systems
Integration with Legacy Systems
Solutions work in pilot programs, but get bogged down when deployed to production and integrated with existing systems
3/4
Pace of Technological Change
Pace of Technological Change
Technologies, such as models and APIs, are updated too rapidly
4/4
Security and Compliance
Security and Compliance
Managing sensitive data and regulatory requirements add complexity
Economic Barriers
Economic Barriers
1/4
True Implementation Costs
True Implementation Costs
The effort lies in custom development and integration, not in licenses
2/4
Cloud and Computing Costs
Cloud and Computing Costs
Consumption can skyrocket if not anticipated or optimized from the outset
3/4
Maintenance and Evolution
Maintenance and Evolution
Recurring costs arise, for example, associated with monitoring, retraining, and upgrades
4/4
Vendor Dependence
Vendor Dependence
Non-modular architectures can hinder switching vendors or components
What this means in practice
1.
The most successful companies align their ambitions with their actual organizational capacity, rather than forcing roadmaps that are difficult to meet
2.
Preparing and governing data in advance prevents delays and technical bottlenecks during project execution
3.
Adoption improves when cultural change is explicitly addressed (communication, training, participation), and not solely delegated to IT
4.
Decisions are more robust when the total cost of ownership, including maintenance, integration, and consumption, is calculated from the outset
Dimension 6: Learnings
Where are we going and how
Following the initial adoption cycles, companies are projecting a second,
more selective and strategic phase of AI.


The focus shifts
from exploration towards consolidating
cases that have already proven their value and strengthening internal capabilities, before opening new fronts or multiplying initiatives.
Lessons learned
Adjustments to the initial approach
  • Narrow the use case
  • Integrate UX from the beginning
  • Prior preparation
Avoid broad approaches without a clear framework
Initial approach
Methodological adjustments
  • Short and agile cycles
  • Rapid prototyping
  • Iterative MVPs
Validation and the ability to pivot before exhaustive planning
Methodological adjustments
Technological adjustments
  • Scalable architecture
  • Generative AI-First
  • Modernization of the core stack
Early decisions impact evolution
Technological adjustments
Operational and organizational adjustments
  • Focus on narrowed domains
  • Close support
  • Reinforce the role of PM/Analyst
Alignment, support, and focus are crucial
Operational and organizational adjustments
Conditions
for success
Data quality
Clear return on investment
Integration capabilities
Compliance and security
Total cost of ownership (TCO) estimate
From experiment to scaling
Enhanced
Enhanced
  • Back-office Automation
  • Predictive Analytics
  • Integration with Enterprise Tools
New Capabilities
New Capabilities
  • Internal Copilots
  • Autonomous Agents
  • Multimodality (Voice/Chat)
Horizontal Extension
Horizontal Extension
  • Same processes, other units
  • New geographies/locations
  • Areas with high repetition
End-to-End Integration
End-to-End Integration
  • Connecting complete workflows
  • Orchestrating automations
  • Real-time monitoring
Companies' roadmap is not just about multiplying AI use cases, but about scaling those that have proven to generate value.

The focus shifts from isolated projects to strategic decisions, where AI ceases to be a technological experiment and becomes a core business asset.
What this means in practice
1.
The next step is not to multiply use cases, but to scale selectively where clear evidence of value already exists
2.
Investment decisions are increasingly driven by criteria such as time-to-value, data availability and quality, and internal capacity to sustain the solution
3.
Talent and continuous learning become fundamental factors: without teams capable of absorbing change, projects stall
4.
The relationship with the innovation ecosystem becomes more pragmatic, focused on complementing internal capabilities with specialized expertise
Conclusion
Closing the Circle: From Findings to Action Key Learnings
The study's findings paint a clear picture of
how companies are incorporating AI
and what conditions explain their progress or stagnation.

Beyond specific cases, the results allow us to extract
cross-cutting lessons and practical criteria
to guide future decisions.
Key Learnings
Key Learnings
  • AI works best when it's supported by well-structured processes, not when it tries to replace them
  • The initial impacts are operational and organizational; financial impacts come later and depend on consolidating the former
  • Maturity isn't measured by the number of pilot projects, but by how many reach production and are sustained over time
Conditions for Moving Forward
Conditions for Moving Forward
  • Start with a use case that connects to a specific business problem
  • Have a minimum level of technical preparation: accessible and high-quality data, a flexible architecture, and a realistic cost model
  • Align internal expectations: what can AI contribute today and what cannot yet
EmpresIA is a starting point: a common foundation of language, experiences, and evidence so that each company can build its own AI roadmap, with less noise and more sound judgment.

The value of the study lies not only in its conclusions, but also in the questions it raises for each company: what is the purpose of AI in our context, where does it truly add value, and what are we not yet prepared to address.