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Approach to how to adapt Artificial Intelligence in an organization.

The application of Artificial Intelligence (AI) in an organization is a challenge that must be addressed in a coordinated and strategic way. ILUNION Hotels has proposed the adoption and strategic planning of Artificial Intelligence through the Brill-IA-NT project, which is a strategic initiative that seeks to transform the company through the adoption of Artificial Intelligence (AI) and the creation of a data-based organizational culture. The project establishes a roadmap for the implementation of this technology in the organization. 

The project focuses on generating economic and social value for the identified stakeholders: solving problems and needs of the different business areas with information management and content creation, raising awareness and training among the organization’s employees and improving the experience of hotel customers.

Methodology used

The methodology of the Brill-IA-NT project is based on five key phases: competitive intelligence, data exploration, awareness, strategic planning and results analysis. Each of these phases has been designed to ensure the effective adoption of AI tools and the generation of positive impact on the company and its stakeholders.

Project Phases

1. Competitive Intelligence: Understanding the environment is the first step to innovate. ILUNION Hotels identifies key trends and solutions through its Competitive Intelligence system in order to manage them. In this phase, AI is identified as a current need, to which the company must adapt and solutions are sought. Additionally, in the identification of trends and solutions, artificial intelligence is used to analyze the competitive environment and identify opportunities for improvement. 

2. Data Exploration: Data exploration is crucial to becoming an AI-powered company. The quality of organizational data, analytical capacity, and the habit of data consumption at all levels of the organization have been key factors in this phase. ILUNION Hotels began to create a Data Driven culture in 2019, which has made the adoption of AI easier in recent years, as much of the data available is structured. The pillars have been: 

• The quality of organizational data

• The analytical capacity and the habit of data consumption at all levels of the organization

• Robust and prepared technological infrastructure

• Previously established culture of change in the organization

3. Awareness-raising, training and complementary actions: Awareness-raising and training workshops on the use of AI tools have been carried out. These workshops have promoted the adoption of departmental applications such as Copilot, Chat GPT, Perplexity, and Adobe Photoshop (with its AI module).

In addition, AI Champions (people with an interest and predisposition to lead the implementation of AI projects in each area) have been identified, and they have institutionalized the holding of AI committees with different areas to align expectations and AI projects.

Alliances have been created with relevant technology providers (Google, Microsoft, Telefónica, Salesforce) to study the possible impact of their AI solutions and carry out pilots that cover the identified needs.

The contact with allies in the hotel sector, with AI solutions already implemented, has facilitated the identification and assessment of solutions already tested by them.

4. Strategic Planning: In this phase, needs have been detected in all business areas and classified according to their impact, maturity of the technology. Planning and programming solutions based on these needs has been essential for the validation and prioritization of use cases.

Use cases were identified for the areas of Human Resources, Business, Marketing, Finance, Innovation, Sustainability and Accessibility, regrouping them into 19 needs and 7 different use cases: chatbot, predictive analytics, waste management, content creation, CV management, suppliers with specific solutions and technological solutions not available (in three cases).

5. Analysis of Results: Finally, indicators have been identified to be able to measure the impact of AI through the projects. In addition, a results analysis has been carried out to evaluate the impact of AI projects on ILUNION Hotels. This analysis has made it possible to identify the economic and operational benefits generated by the automation and optimization of processes.

Identified Use Cases

The Brill-IA-NT project has identified numerous use cases in various business areas, including people, business, marketing, legal-finance, innovation, sustainability, and accessibility. Some of the most relevant use cases are:

• People: Automatic evaluation of CVs, emotional and motivational support for employees, retention and attraction of talent.

• Business: Text generation, simultaneous interpretation system, chatbot, demand forecasting and dynamic pricing.

• Marketing: Automatic generation of descriptions, evaluation of customer reviews, creation of promotional texts.

• Legal-Finance: Analysis of accounting and financial reports, assistance in investment decisions.

• Innovation: User support, change management, project planning and monitoring.

• Sustainability: Sustainability reports, waste optimization.

• Accessibility: Visual impairment and spectrum of disabilities.

Emblematic Projects

Among the flagship projects implemented in the Brill-IA-NT project are:

• Internal Chatbot: A chatbot that transforms hotel operations and support provided from central services.

• Content Creation: Automatic generation of content for various platforms.

• Waste Management: Optimization of waste management in hotels.

• Digital Receptionist: Implementation of a digital receptionist to improve the customer experience.

• CV Management: Automatic evaluation of CVs for talent attraction and retention.

• Simultaneous Translation: Simultaneous interpretation system to improve communication in hotels.

• Analytic Predictions: Use of AI to make analytical predictions and improve decision-making.

Project Results

The Brill-IA-NT project has generated a significant economic impact on ILUNION Hotels, with an estimated increase in revenue and operating savings of €320,000 in 2024 and €1,540,000 expected in 2025. In addition, process automation and optimization has improved the customer and employee experience, and training and awareness actions have impacted 1,800 employees.

Main lessons learned from the implemented model and strategy

The main learnings from the Brill-IA-NT project are:

• The importance of an organizational culture based on data management to facilitate the adoption of AI projects.

• Identifying and strategically planning use cases across various business areas is critical to defining a needs-based roadmap.

• Creating an AI culture by promoting the adoption of AI tools through awareness and training workshops, and identifying AI Ambassadors as internal transformation advocates, is critical.

• The assessment of the economic and operational impact of AI projects should demonstrate the value of applying AI solutions.

In summary, ILUNION HOTELS’ Brill-IA-NT project has been a transformative initiative that is generating a positive impact on the company and its stakeholders, through the adoption of artificial intelligence, the creation of a data-based organizational culture and the achievement of results.

For years, innovation has been treated as a numbers game: take more swings, fail fast, and iterate quickly. This baseball-inspired mindset made sense—especially in environments where the cost of failure is low and feedback is immediate, like UX or consumer software. Try something small, learn, and adjust. Speed matters. 

This approach was reinforced by the conventional wisdom that “customers can’t tell us what they want.” And at first glance, that seemed true. Customers often struggle to articulate features or solutions—but that’s not their expertise or responsibility. 

“For suppliers to ask customers what features they want is like doctors asking patients what treatment they want. They don’t know.” 

While customers may not be able to tell us what features they want, they can tell us what they’re trying to accomplish, how they define success, and where they struggle given their current solutions. That’s a breakthrough. 

Why the Old Innovation Playbook Made Sense—Until It Didn’t 

Agile, Design Thinking, Stage Gate, and Lean Startup were all developed at a time when this distinction wasn’t fully appreciated. So, they focused on rapid prototyping and iterating to discover both the problem and the solution at once. That was the best thinking available—and at least it kept companies engaged with customers, even if the signals were hard to interpret. 

Today, we have a better understanding. With methods like Lean JTBD OS™, we can reveal unmet customer needs before designing or building solutions—by focusing on the job the customer is trying to get done, not the product they think they need. This doesn’t replace other innovation processes—it enhances them. It identifies and defines the target customers’ unmet needs so you can experiment only on the efficacy of your solution—not guessing about the customers’ problem as well. 

The Shift: From Guessing Twice to Aiming First 

Maybe innovation shouldn’t always be executed like baseball. Maybe—especially when the stakes are high—it should be executed like archery. 

In baseball, if you miss, you just swing again. That works when testing low-risk elements like button colors or pricing tweaks. But when launching industrial equipment, entering a new market, or defining a growth strategy, you can’t afford to miss. You don’t get unlimited swings. That’s when innovation must look more like archery—when the risk of missing the mark is too high to justify guessing. 

In archery, you aim first. You define the target clearly before you shoot. 

When you run experiments without first identifying what job the customer is trying to get done and where their unmet needs lie, you’re guessing twice—first about whether the need is real, and second about whether your solution addresses it. That’s avoidable risk, not agility. 

Why It Matters: Risk, Creativity, and Confidence 

By contrast, when you start with clarity about your customers’ important unsatisfied needs, your creativity improves. Not surprisingly, a well-defined customer need is one of the most powerful creativity triggers in innovation. Accurately defining the problem often sparks surprising, high-potential solution ideas. And your experiments become more efficient and effective because you’re testing how well your solution addresses a clearly defined need—not whether an unmet need even exists. 

What Edison and Jobs Got Right 

Thomas Edison and Steve Jobs understood this. Edison famously said he didn’t fail—he just found 10,000 things that didn’t work. But that’s misleading without context. What’s often left out is how he chose what problems to solve based on market demand. After a significant failure early in his career, he swore he’d never waste time on inventing without first confirming there was a real need. 

Additionally, Steve Jobs insisted on starting with the customer experience and working backward to the technology—not the other way around. Edison and Jobs sequenced their innovation process to first identify and validate the unmet needs in their target market and then generate ideas, prototype, iterate, and develop accordingly. 

A Simpler, More Precise Way Forward 

So, what’s changed? We now have frameworks—like Lean JTBD OS™—that let us: 

1. Understand what customers are trying to accomplish (the job)

2. Identify how they measure success

3. Discover where they still struggle

This allows us to uncover and prioritize unmet needs with greater clarity—even without large-scale surveys—before we ideate, prototype, or test. It helps reduce costly misfires, improve alignment, and gives teams a clearer path toward achieving product/market fit earlier in the process. Inspired by the foundational principles of Tony Ulwick’s Outcome-Driven Innovation®, Lean JTBD OS™ offers a simpler, more accessible way to reveal unmet customer needs before committing resources to ideas, prototypes, or product development. 

If your next move involves defining a new market, reimagining value creation, or making a major product or portfolio bet—you may not need more swings. You need better aim. When innovation stops being a numbers game and starts being about aiming first, your aim improves—and so does your impact. 

Want to go deeper? 

On July 2nd, I’ll be presenting Lean JTBD OS™ to the GIMI innovation community. This session will introduce a simpler, more accessible way to uncover and prioritize unmet customer needs—before development. Whether you’re leading innovation in a startup or scaling inside a large organization, you’ll walk away with practical tools and insights that reduce risk and increase your impact. 

Innovation is often portrayed as the elusive force that propels organizations ahead of the curve. Yet, despite the buzz, many leaders continue to fall prey to persistent myths that distort their innovation strategies—and limit their results. It’s time to challenge these assumptions and reframe the conversation.

1. Innovation Happens in a ‘Eureka!’ Moment

The myth of the lone genius having an instant epiphany—à la Archimedes’ “Eureka!” moment—is appealing but misleading. Innovation more commonly stems from iterative exploration, collaboration, and disciplined experimentation. As the MIT Sloan Management Review notes, breakthroughs often emerge from persistent effort and a culture that encourages exploration over time. Google’s design sprint methodology, for instance, exemplifies how structured iteration outpaces flashes of inspiration. Real innovation usually involves dozens of small ideas, tested and refined over time.

2. Only Big Tech Counts as Real Innovation

Too often, organizations equate innovation with radical technological breakthroughs, overlooking the rich spectrum of non‑tech advances. Service innovations—for example, Starbucks’ rapid‑service redesigns or IKEA’s flat‑pack retail model—redefine value propositions without new hardware. Process innovations—such as Toyota’s Kaizen continuous‑improvement system or Domino’s pizza‑tracking platform—drive quality and efficiency gains in everyday operations. Business‑model innovations—from Gillette’s razor‑and‑blade subscription to Netflix’s pivot from DVD rental to streaming—restructure revenue and delivery mechanisms, transforming industries without inventing new technologies. Organizational innovations, like Zappos’ adoption of holacracy, further demonstrate how changes in structure and culture can unlock creativity and customer focus. Savvy leaders build balanced portfolios that combine low‑risk, incremental improvements across these dimensions with occasional breakthrough bets—ensuring that innovation isn’t confined to “big tech” but pervades every corner of the business.

3. Innovation Can’t Be Taught

Many believe that innovation is an innate talent reserved for a few visionary individuals. However, research shows that creative thinking and problem-solving can be nurtured. Institutions like GIMI, Stanford, and MIT teach innovation as a discipline. Tools like design thinking, lean startup methodology, GIMI methodology and the 10 types of innovation framework prove that innovation capabilities can be systematically developed and applied across industries. The key is to create an environment where learning and experimentation are supported, and where failure is seen as a step toward improvement.

4. It’s the R&D Team’s Job

R&D and innovation labs play important roles, but isolating innovation in specific departments can be limiting. Innovation thrives when it’s embedded into the organizational culture. Cross-functional teams—blending marketing, operations, HR, and finance—bring diverse perspectives that often generate more holistic solutions. Procter & Gamble’s Connect + Develop model shows how involving the entire ecosystem, including external partners, enhances innovation impact. Organizations that democratize innovation and encourage contributions from all levels tend to achieve more sustainable results.

5. It Requires Huge Budgets and Risk

This myth can discourage experimentation. While some innovations do demand significant funding, many start as small, low-cost tests. Pilot programs, MVPs (minimum viable products), and sandboxing allow firms to learn fast and fail safely. 3M’s Post-it Notes, for instance, emerged from a failed adhesive experiment, not a high-stakes bet. Strategic risk-taking, when combined with disciplined portfolio management, mitigates downside while maximizing upside. It’s not about betting the farm—it’s about planting lots of small seeds and learning which ones grow.

6. Innovation Should Always Target Large Markets

The pursuit of large markets can blind companies to niche opportunities where growth is less contested and customer loyalty is stronger. Consider Warby Parker’s entry into affordable eyewear or Patagonia’s focus on environmentally conscious consumers. These companies gained traction by deeply understanding underserved markets, then scaling smartly. Innovation in niches often becomes the foundation for later mainstream expansion. It also allows for more personalized solutions and stronger customer relationships.

7. Constraints Kill Creativity

While it may seem intuitive that freedom fuels innovation, constraints often sharpen focus and spur resourcefulness. NASA’s space missions, conducted under extreme technical and financial constraints, are testaments to ingenuity under pressure. Constraints help teams prioritize, adapt, and reframe challenges in ways that encourage inventive thinking. The innovation paradox: limits can drive boundless creativity. Working within boundaries can inspire out-of-the-box solutions that would never have emerged in an unconstrained environment.

8. Startups Are the Only Innovators

Startups are nimble, but established companies have the scale and resources to drive impactful innovation—if structured correctly. GE’s FastWorks, Adobe’s Kickbox, and Google’s 20% time are all examples of intrapreneurship at scale. These programs empower employees to act like entrepreneurs within large systems, often leading to new products, services, or even spinoff ventures. Innovation is about mindset and systems—not size or age. Legacy organizations that embrace agility and experimentation can compete with the best startups.

9. Innovation Happens in Isolation

The lone inventor is a romanticized myth. Most successful innovations are collaborative endeavors, involving diverse stakeholders across functions and even organizations. Open innovation models—exemplified by platforms like Innocentive or LEGO Ideas—highlight the power of external inputs. Internally, cross-pollination of ideas between departments (e.g., marketing and R&D) is essential to move innovations from concept to execution. Collaborative innovation also builds buy-in and makes implementation smoother. For example, LEGO co-creates products with its community through its crowdsourcing platform, turning fans into innovation partners.

10. Innovation Is Just About Ideas

Ideas are essential, but execution is everything. The best ideas mean little if they don’t translate into tangible impact. Innovation involves selecting, testing, scaling, and integrating ideas into existing operations. Apple didn’t invent the smartphone, but its execution—with seamless design, intuitive UX, and ecosystem integration—set a new standard. As GIMI notes innovation is about creating but also capturing value.

The Takeaway

Innovation doesn’t require mythical genius, moonshot budgets, or luck. It demands intention, structure, and cultural alignment. By challenging these ten myths, leaders can unlock untapped potential within their organizations—and build innovation not as a moment, but as a muscle.