The hidden treasure in your company

Knowledge management driven by artificial intelligence and the data economy are transforming the way in which organizations capture and take advantage of their knowledge, turning dispersed information into a strategic asset that drives productivity.

In every organization, regardless of its size or sector, there is hidden intellectual capital that represents years of accumulated experience.

This capital includes project reports that document lessons learned, market analysis with valuable insights, operating manuals that collect best practices developed over time, emails with clients that contain critical business information about preferences and needs, presentations that summarize successful strategies tested in the market.

For decades, companies have accumulated a veritable treasure trove of institutional knowledge that remains largely underused and underestimated.

This accumulation of knowledge represents one of the greatest paradoxes of modern business management: organizations have exactly the information they need to make better decisions, but they can't access it when it really matters.

This knowledge, scattered across file servers, shared cloud folders, individual email boxes, and fragmented document management systems, remains virtually inaccessible precisely when it's most needed to make informed decisions.

Generative artificial intelligence has opened up a genuinely transformative possibility that until very recently seemed impossible: Connecting all that dispersed knowledge and making it instantly accessible, contextualized and relevant to each specific query.

New knowledge management tools powered by artificial intelligence are radically redefining the operational efficiency of companies of all sizes, allowing even small and medium-sized companies to compete on more equitable terms with much larger organizations with greater resources, thanks to the intelligent and systematic use of their knowledge accumulated over the years.

As Nonaka and Takeuchi (1995) visionarily pointed out in their influential work on the knowledge-creating company, true competitive advantage in the modern economy does not lie so much in tangible assets as in the capacity of an organization to effectively create, capture and mobilize knowledge.

However, for decades this principle has remained more as a theoretical aspiration than as a practical reality, fundamentally because we lacked the necessary technological tools to make it a reality at an organizational scale.

The generative artificial intelligence revolution is definitely changing this equation, making available to any company, regardless of its size or technological budget, the ability to activate that latent intellectual capital that has been lying dormant in its digital files for so long.

Without an adequate system for capturing, organizing and distributing knowledge, much of the intellectual capital of any company is at risk of being irreversibly lost, either due to staff turnover, retirements, organizational changes or simply because of the passage of time that buries valuable documents under layers of the most recent information.

When an experienced employee leaves the organization, whether due to retirement, change of company or any other vital or professional circumstance, they take with them something much more valuable than their physical presence or their ability to work: they take with them their unique, deep and nuanced knowledge, accumulated over years of practical experience facing real situations, solving specific problems and developing expertise that is difficult to replicate.

An exhaustive study carried out by Panopto (2018) revealed a truly alarming fact that should make any manager reflect: 42% of an organization's institutional knowledge resides exclusively in the memory, experience and tacit skills of each individual employee, which means that when someone leaves the company, almost half of what that person knew how to do well, their professional tricks, their key contacts and their deep understanding of processes and contexts, is immediately out of reach of their colleagues and of those who will eventually occupy their position.

The direct and indirect economic costs of this systematic loss of knowledge go far beyond what is immediately apparent in terms of the costs of searching, selecting and training replacement personnel.

The Society for Human Resource Management (2024) calculated in its most recent analysis that replacing a qualified worker can cost the company between 50% and 60% of its annual salary only in direct hiring and basic training costs, but the hidden and long-term costs turn out to be substantially greater when the real impact on organizational productivity is carefully analyzed.

According to the same source, the decrease in productivity associated specifically with staff turnover costs companies globally the figure of 1.8 trillion dollars annually.

This astronomical figure is explained by the fact that when a senior expert leaves the organization, not only is their individual productivity lost, but dozens of junior and intermediate-level employees who were systematically dependent on their guidance, technical leadership, contextual knowledge and ability to solve complex problems experience a very significant and prolonged drop in their productivity until they manage, if at all, to compensate for that absence through other organizational mechanisms or the development of their own capacities.

Beyond the structural problem of staff turnover, there is another equally significant hidden cost that silently erodes the productivity of practically any knowledge organization: the time that knowledge workers spend daily searching for information that already exists documented somewhere in the organization but that is impossible or extremely difficult to locate when it is really needed.

An influential quantitative study conducted by McKinsey & Company (2012) on the social economy and collaborative technologies established that the average knowledge worker invests about 19% of their workweek — equivalent to approximately one full day out of every five working days — simply searching for and gathering information necessary to do their work effectively.

This time represents an enormous and systematic loss of productivity that most organizations do not even adequately account for in their operational efficiency analyses, mainly because institutional knowledge depends excessively on informal personal relationships, fallible individual memory and undocumented networks of contacts, instead of relying on structured, accessible and reliable organizational knowledge management systems.

As Davenport and Harris (2007) perceptively observed in their pioneering analysis of analytics-based competence, knowledge that cannot be found when needed is, for practical purposes, equivalent to knowledge that never existed, because it does not fulfill its fundamental function of informing organizational decisions and actions. This observation underlines the critical importance not only of capturing and storing knowledge, but of making it accessible, retrievable and usable in the right context and at the right time when it can generate value for the organization.

Artificial intelligence is rapidly emerging as the definitive and practical answer to activate that intellectual capital that remains dormant in organizations, trapped in dispersed documents and fragmented systems.

In particular, the technology known academically as RAG, an acronym in English for Retrieval-Augmented Generation, represents a fundamental evolution in the way in which machines process, understand and use business information to generate useful and contextualized answers.

Unlike traditional language models that can only respond based exclusively on the data they were originally trained with during their development phase, RAG agents incorporate a very important capability: they retrieve relevant and updated information from specific knowledge bases of the organization before generating each particular response, which means that they can consult and cite current and specific information about your company at the precise moment of responding to each query, instead of being limited to offer generic, outdated or based solely on patterns learned from public sources during their training (Chan et al., 2024).

Imagine having an artificial intelligence agent that can instantly access all your company's documents, emails, reports, presentations, databases and any other information repository.

Then imagine that this agent perfectly understands the semantic context of your question even when you formulate it in a colloquial or ambiguous way, and that it provides you with a timely, precise and directly actionable answer, meticulously citing the exact sources from which it obtains each piece of information that makes up its answer.

This is not a conventional search that simply returns you a list of potentially relevant files that you will then have to manually review one by one, spending hours finding the specific information you need; it is a genuinely intelligent agent that effectively reads complete documents, understands their content and context, identifies the semantic relationships between different pieces of information dispersed in multiple sources, and synthesizes all of this in a coherent and substantiated response that responds directly to your specific question. In daily practice, this technology radically and tangibly transforms the way in which you interact with your company's data and accumulated knowledge: instead of spending hours flipping through extensive reports, reviewing old presentations or carrying out dozens of unsuccessful queries on different disconnected systems, you get the precise answer you need directly in a matter of seconds, backed up by original documentation and with exact and verifiable references to primary sources that allow us to validate and deepen the information provided.

The data economy represents the integrated set of business initiatives, economic activities and organizational projects whose business model is essentially based on the systematic exploration and strategic exploitation of existing data structures, both internal and external, with the explicit objective of identifying new previously invisible business opportunities and of significantly and sustainably increasing the productivity of organizations, with special relevance for small and medium-sized companies that constitute the fundamental productive fabric of the European economy.

This concept, which has gained prominence in recent years within the economic policies of the European Union, considerably transcends the mere passive accumulation of information in databases to focus specifically on the organizational capacity to extract real, tangible and quantifiable economic value from data through its intelligent analysis, transforming them into actionable knowledge that effectively drives strategic decision-making, product and service innovation, the personalization of the customer experience and continuous optimization of operational processes (McAfee and Brynjolfsson, 2012).

For SMEs, which often operate with limited resources and face competition from both large national corporations and international companies with considerably larger budgets, the data economy represents a genuine historic opportunity to substantially level the competitive playing field.

A small company that manages to make intelligent use of its data — from detailed information on customer behavior and preferences to operational records that reveal inefficiencies and opportunities for improvement — can make strategic decisions with the same level of empirical foundation and analytical precision as its largest and most capitalized competitors, identify underserved or underserved market niches, customize commercial offers to specific segments of customers with particular needs, and optimize its value chain of in a continuous and iterative way (Brynjolfsson et al., 2011).

The European Commission, in its prospective analysis of the economic impact of digitalization, estimates that the data economy could contribute up to 829 billion euros per year to European gross domestic product by 2025, with small and medium-sized enterprises representing a very significant percentage of this added value if they manage to develop or access the technological tools and methodological knowledge necessary to effectively participate in this economic transformation.

The European Union has explicitly recognized the transformative potential of the data economy for European competitiveness in the global context and has developed an ambitious, comprehensive and unprecedented regulatory framework to enhance its accelerated development, while protecting the fundamental rights of citizens and companies in terms of privacy, security and control over their own data.

La European Data Strategy (European Data Strategy), formally presented by the European Commission in February 2020, establishes the strategic objective of creating a single European data space that allows the free, secure and regulated flow of information between economic sectors and member countries, while ensuring that Europe maintains and strengthens its global competitiveness in the emerging digital economy in the face of the dominant models of the United States and China. Within this general strategic framework, two specific regulations are particularly relevant and directly applicable to small and medium-sized enterprises that wish to participate actively in the data economy.

El Data Governance Regulations (Data Governance Act), which formally came into force in September 2023 after a complex European legislative process, establishes concrete and operational mechanisms to facilitate the secure and controlled exchange of data between companies, public administrations and citizens, creating the legal figure of trusted data intermediaries that specifically allow SMEs to access valuable and updated data sets that were previously completely out of reach for technical, economic or legal reasons.

This regulation is especially valuable for small companies because it dramatically reduces entry barriers to participating in sectoral data ecosystems, allowing a small textile company in Valencia, for example, to access aggregated data on consumer trends that were previously only available to large distribution chains capable of carrying out their own market studies.

For its part, the Data Regulation (Data Act), definitively approved by the European Parliament and the Council in 2023 and progressively applicable since September 2025, guarantees through binding legal provisions that companies have equitable and non-discriminatory access to data generated by connected products (Internet of Things) and related services they use, explicitly preventing large technological platforms from monopolizing access to valuable information generated by devices and sensors.

For an industrial SME that uses connected machinery, for example, this regulation guarantees their legal right to access the performance, use and maintenance data generated by their own equipment, data that the manufacturer could previously hold exclusively. This access to their own operational data allows SMEs to optimize their processes, predict maintenance needs and make informed decisions about investments in equipment (Gandomi and Haider, 2015).

The data economy and knowledge management driven by artificial intelligence are, in reality, two complementary sides of the same strategic currency for small and medium-sized companies that aspire to compete effectively in increasingly complex and digitized markets.

While knowledge management focuses primarily on capturing, systematically organizing and making accessible accumulated institutional knowledge of a qualitative nature (project experiences, lessons learned from successes and failures, operating procedures developed and refined over time, tacit expertise of experienced professionals), the data economy focuses specifically on extracting measurable economic value from quantifiable and structured information structures (transactional sales data, behaviors documented customer metrics, operational metrics for production or services, financial performance indicators).

When both disciplines are intelligently combined through integrative technologies such as RAG, companies can substantially enrich their decision-making processes with a truly complete and multidimensional vision that harmoniously integrates both the qualitative knowledge derived from the experience of their human experts and the solid quantitative evidence derived from the systematic and rigorous analysis of operational and market data (Davenport, 2018). For an SME operating in a competitive market, this synergistic integration is translates into specific and directly monetizable operational capabilities:

The data economy, when properly implemented, transforms operational data that would otherwise remain passive in databases into dynamic strategic assets that, intelligently combined with qualitative institutional knowledge, generate a sustainable competitive advantage that is genuinely difficult to replicate by competitors who have not developed these integrative capabilities (Barton and Court, 2012).

To fully exploit the transformative potential of artificial intelligence in organizational knowledge management, it is absolutely essential to have not only abundant data, but specifically with data of sufficient quality and with the organizational and technical capacity to extract from them information that is genuinely useful for the specific needs of the organization.

This is precisely where the discipline of data intelligence comes into play critically, which acts operatively as the high-octane fuel that exponentially enhances the performance and practical utility of artificial intelligence systems applied to knowledge management.

Knowledge management supported by AI technologies and data intelligence are two inherently complementary disciplines that, when properly and strategically integrated into organizational practice, create an extraordinarily powerful synergy for any organization, completely regardless of its absolute size, sector of economic activity or initial technological maturity (Chui et al., 2018).

Data intelligence is academically and professionally defined as the multidisciplinary discipline focused on systematically collecting, rigorously analyzing using statistical and machine learning techniques, interpreting correctly in the specific business context, and effectively presenting organizational data through understandable visualizations and narratives to extract genuinely valuable insights that concretely and measurably support business decision-making based on solid empirical evidence.

This discipline goes considerably beyond simply accumulating information in relational databases or data warehouses: it is fundamentally about systematically and methodically transforming raw data, often disordered and of varying quality, into actionable and directly applicable knowledge.

This is achieved through structured processes that necessarily include rigorous cleaning and standardization of data to ensure its quality. Also with systematic exploratory analysis to identify statistically significant patterns, the disciplined application of advanced statistical techniques and machine learning algorithms when appropriate, and effective visualization using dashboards and graphics that allow decision makers to quickly and intuitively understand the practical implications of the findings. (Provost and Fawcett, 2013).

In the specific context of organizational knowledge management, data intelligence operationally functions as the high-performance engine that exponentially enhances the practical effectiveness and real utility of artificial intelligence systems, allowing for a much deeper, more nuanced and contextualized understanding, as well as a more strategically sophisticated and accurate use of available business information.

IDC, a leading technology market analysis firm, projects in its prospective studies that by 2025 the global volume of digital data generated and stored will reach the truly astronomical figure of 175 zettabytes (a zettabyte is equivalent to a trillion gigabytes). In this way, it is inevitable that organizations develop the organizational and technical capacity to convert this potentially overwhelming avalanche of information into useful, relevant and differentiating knowledge, instead of simply being paralyzed and overwhelmed by the volume, speed and variety of available data (IDC, 2017).

Data intelligence can and should significantly and systematically increase the institutional knowledge repository of any organization through a continuous and iterative process of rigorous analysis and structured documentation of findings.

By examining large volumes of operational transactional data, detailed information on customer behavior and preferences, or relevant market and competitive data, data analysts can extract previously invisible insights that, once properly formalized and documented in accessible formats, become extremely valuable explicit knowledge for the entire company and for its decision-making processes.

For example, systematic and statistically rigorous analysis of historical sales data disaggregated by product, geography, channel and customer segment could reveal sophisticated customer behavior patterns or emerging market trends.

Previously, these patterns were not evident through casual observation or superficial analysis; these quantitative findings, once properly documented in analytical reports or integrated into knowledge systems, simply became a permanent part of the institutional knowledge repository. (Wedel and Kannan, 2016).

Rigorous empirical research in the field of information economy has consistently demonstrated that companies that manage to effectively and systematically integrate data analysis into their knowledge management achieve measurable and substantial improvements in their operational performance, with documented increases of 5% to 6% in overall productivity, thanks specifically to these new actionable perspectives derived from the systematic and disciplined analysis of their operational data (Brynjolfsson et al., 2011).

In addition to enriching the knowledge repository with new insights derived from data, data intelligence provides quantitative context that is highly relevant to the qualitative knowledge previously existing in the organization.

Enrich internal data with updated external context: Your company can automatically and intelligently combine its internal historical data accumulated over years of operation with relevant and up-to-date public information available on the external context, such as emerging market trends documented in sector studies, recent news from the specific industrial sector, macroeconomic data affecting demand, or political and regulatory events relevant to the business.

‍Connect knowledge dispersed between projects and departments: In knowledge-intensive professional services companies, where organizational expertise is inherently fragmented between multiple projects executed by different teams at different times, RAG technology allows us to quickly identify significant patterns between diverse past experiences practically instantaneously.

A consultant at a strategic consulting firm might ask: “What specific market entry strategies have we successfully recommended for food companies over the past five years, what were their concrete measurable results, and what critical success factors did we identify in each case?”

Without artificial intelligence and without a structured knowledge management system, meticulously collecting such dispersed information would require manually reviewing dozens of final client presentations, intermediate executive reports and internal documents of lessons learned stored in different network folders, project management systems and mailboxes of multiple consultants, easily consuming several days of highly qualified professional work.

With a RAG agent properly fed with historical project documentation, the system intelligently synthesizes information from multiple projects in a matter of seconds.

The RAG agent can identify significant strategic patterns (for example, that a certain distribution approach worked consistently better for family dairy businesses while another branding approach proved more effective for industrial meat companies), and provides a structured, comparative and referenced summary of the quantifiable results obtained in terms of sales growth, market penetration or profitability, allowing the organization to learn systematically from its collective experience rather than reinventing the rolls in each new project (Bughin et al., 2017).

‍Eliminate laborious and frustrating searches: Let's consider a typical everyday scenario in a medium-sized law firm where a lawyer urgently needs to verify if his firm has previously worked with a certain specific regional jurisdiction in complex intellectual property cases related to industrial patents.

Traditionally, this seemingly simple task would require laboriously diving between physical files in the firm's archive and digital files scattered over several hours without any guarantee of actually finding all the relevant existing information, with the constant risk of overlooking valuable precedents stored in unexpected places.

With a knowledge platform based on artificial intelligence and adequately nourished with the firm's historical documentary corpus, the lawyer simply asks the question in colloquial natural language without needing to know special search syntax and receives in a matter of seconds a precise and exhaustive answer with exact and clickable references to the specific 2019 file where they actually worked in that particular jurisdiction, including direct links to the relevant specific paragraphs of the legal conclusions, the successfully applied procedural strategies, and the case law precedents that were cited.

‍Discover hidden patterns between multiple and disparate sources: A medium-sized logistics distribution company that observes a worrying operational phenomenon of recurring order cancellations by certain segments of corporate customers can systematically investigate the root causes by asking the intelligent system directly: “Do you detect any statistically significant pattern or temporal correlation in the order cancellations registered during the last two years?”

The RAG agent, with its unique ability to cross multiple disparate sources of information both structured and unstructured, can identify causal connections that remained completely invisible.

A RAG agent, for example, can discover through temporal correlation analysis that cancellations systematically and significantly coincide with specific logistical delays on certain routes that were documented in internal emails between the operations department and external carriers, and that were also discussed in minutes of operational meetings archived years ago but never explicitly linked to transactional cancellation data.

‍Accelerate the training of new employees: The addition of new personnel to any organization traditionally consumes disproportionate amounts of time for veteran employees who must spend hours repetitively explaining operating procedures, internal policies and best practices that are theoretically already documented in manuals but are difficult to locate, understand or apply without expert guidance.

A knowledge agent based on artificial intelligence can dramatically accelerate the learning curve of these new employees by providing them not only with access to official manuals in static format, but also and fundamentally, concrete and contextualized examples of how these theoretical procedures were successfully applied in specific real situations.

A new employee who has just joined a digital marketing agency can ask the agent directly: “What is our complete and detailed process for high-end product launch campaigns?” , and the system will not only provide you with the official step-by-step manual with your flowcharts.

The knowledge agent can also cite and link concrete examples of previous successful campaigns executed by the agency, show relevant fragments of emails where specific segmentation and creativity strategies were discussed and debated, and even final presentations with quantifiable results.

In practice, the new member of the team not only reads the abstract theory of the procedure manual, but also simultaneously sees and learns from how it was actually applied in everyday operational practice, with real historical context and documented lessons learned, which allows its integration to be much faster, deeper and more productive than through traditional training methods based solely on reading manuals or unstructured “shadowing” of colleagues (Libai et al., 2010).

Institutional knowledge constitutes one of the most valuable and differentiating assets of any modern company that competes in information-based markets.

However, it has also been systematically the most underestimated, wasted and poorly managed asset due to the lack of adequate technological tools to effectively capture, organize and mobilize it.

For decades, much of that knowledge critical to competitiveness and innovation has remained inaccessible and trapped in files scattered across multiple disconnected systems.

What's worse, sometimes that knowledge has remained exclusively in the minds of individuals who eventually leave the organization taking with them that irreplaceable intellectual capital, without an effective, scalable and sustainable mechanism to mobilize it proactively when it is really needed to substantiate critical decisions.

Generative artificial intelligence, and in particular RAG technology, offers for the first time in modern business history the ability to release that hidden knowledge and make it available instantly, contextualized and relevant to the entire organization.

The intellectual capital of your organization exists and has always been there, latent and waiting patiently to be activated using the appropriate tools.

Email: contact@iautomator.net
‍Telephone: +34 689 395398

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