Autonomous ai integration and optimization system

Abstract

An automated method is provided to guide and update artificial intelligence integration, data value measurement, sustainability metrics, and user competency in an industrial environment. The method involves building a knowledge-based data structure combining data from multiple domains to assess AI performance, economic return, environmental impact, and user skills. A multi-agent processing engine is then used to analyze the data, identifying correlations among AI maturity, economic return, sustainability metrics, and competency requirements. Based on this analysis, recommended actions are generated to address various aspects of AI implementation, such as solution selection or deployment, data allocation, environmental adjustments, and training modules. The method also incorporates a self-learning feedback loop, where the knowledge-based data structure is updated with measured outcomes or new data to refine subsequent recommended actions over time.

Description

[0001] AUTONOMOUS Al INTEGRATION AND OPTIMIZATION SYSTEM

[0002] TECHNICAL FIELD

[0003] This disclosure relates to the field of data-driven artificial intelligence, and more specifically to integrated Al applications for industrial contexts.

[0004] BACKGROUND

[0005] The integration of artificial intelligence and data-driven technologies has become increasingly important in industrial settings as organizations seek to enhance productivity, innovation, and competitiveness. In recent years, diverse industries have recognized the potential of leveraging vast amounts of data to inform decision-making processes and automate complex operations. Despite this recognition, the adoption and implementation of Al technologies have followed varied trajectories across different sectors.

[0006] Industrial organizations have historically been product-centric and hardware-focused, with established methodologies for measuring and improving physical processes and outputs. As these organizations transition toward incorporating more data-driven approaches, they face a significant paradigm shift. This transition introduces several challenges that the field continues to grapple with.

[0007] One such challenge is the fragmented nature of Al and data initiatives within industrial organizations. Many Al projects remain isolated within specific departments or functions, operating as standalone proof-of-concept implementations rather than integrated components of broader business processes. This fragmentation limits the potential impact of these technologies across the value chain.

[0008] The measurement and evaluation of data assets and Al investments present another area of difficulty. Unlike physical assets, the value derived from data and Al implementations can be diffuse and challenging to quantify using traditional financial metrics. Organizations struggle to establish consistent frameworks for assessing the business impact and return on investment of their data and Al initiatives, making comparison across projects and prioritization decisions problematic.

[0009] Environmental considerations add another dimension of complexity. Al systems can require substantial computational resources, leading to significant energy consumption. As global awareness of climate change increases, questions arise regarding the energy efficiency of Al deployments and their overall environmental footprint. Industrial organizations seeking to address sustainability goals while pursuing Al adoption find themselves navigating potentially competing priorities.The workforce dimension presents additional obstacles. The rapid evolution of Al technologies has created a gap between available and required skillsets within many organizations. This gap spans technical proficiency, ethical considerations, and the ability to translate Al capabilities into business value. The distribution of Al knowledge is often uneven across different levels and functions within industrial organizations.

[0010] The industrial context itself introduces unique considerations for Al implementation. Manufacturing environments, supply chains, and product development cycles each present specific data challenges related to quality, accessibility, and integration. The intersection of operational technology (OT) and information technology (IT) systems further complicates data management and utilization.

[0011] Regulatory and governance frameworks for Al are still evolving, creating uncertainty around compliance requirements and best practices. For organizations operating across multiple jurisdictions, navigating varying regulations while maintaining consistent internal standards presents an ongoing difficulty.

[0012] Challenges remain in addressing these issues in ways that balance technical feasibility, economic viability, environmental responsibility, and workforce development. The field continues to seek approaches that can accommodate the multifaceted nature of Al implementation in industrial contexts without sacrificing one dimension for another.

[0013] It is therefore an objective of the present disclosure to enable comprehensive evaluation and optimization of data-driven technological assets across enterprise ecosystems, thereby overcoming the drawbacks of the prior art at least in part.

[0014] SUMMARY OF THE DISCLOSURE

[0015] The above and other objectives may be achieved by the subject-matter defined by the independent claims. Advantageous modifications of embodiments of the present disclosure are defined in the dependent claims as well as in the description and the drawings.

[0016] In a first aspect, the present disclosure provides a method for automatically guiding and / or updating artificial intelligence integration, data value measurement, sustainability metrics, and / or user competency, preferably for an industrial environment. The method may be computer-implemented.

[0017] The method may comprise building a knowledge-based data structure. The knowledgebased data structure may be configured to merge data from multiple domains. The knowledge-based data structure may include parameters indicative of artificial intelligence performance, economic return, environmental impact, and / or user skills. Accordingly, the incorporation of artificial intelligence (Al) within industrial environments is greatly enhanced by thoughtfully guiding and updating several advantageouscomponents, such as Al solutions, data valuation, sustainability, and user competencies. The method is capable of building a comprehensive knowledge-based data structure that combines data across the mentioned various domains. This amalgamation of crossdomain data supports a holistic view, enabling refined analysis and informed decisionmaking.

[0018] The method may comprise configuring a multi-agent processing engine. The multi-agent processing engine may be configured to iteratively analyze the merged data within the knowledge-based data structure to determine correlations among artificial intelligence maturity, economic return on investment, sustainability metrics, and / or competency requirements.

[0019] Accordingly, a sophisticated multi-agent processing engine is configured to iteratively analyze the merged data within the knowledge-based framework. The iterative analysis is able to uncover and comprehend correlations among Al maturity levels, economic returns on investment, sustainability metrics, and competency requirements. By evaluating these relationships, the system can provide insights that may guide strategic initiatives within the industrial setting.

[0020] The method may comprise generating, in response to the iterative analysis, recommended actions. The recommended actions may address at least one of selection or deployment of artificial intelligence solutions, allocation of data assets, adjustment of environmental or resource usage, and assignment of training modules or skillenhancement tasks.

[0021] Accordingly, the method is capable of developing a series of suggested actions tailored to optimize various aspects of the industrial operation. The recommendations might include the selection or deployment of Al solutions, with an aim to ensure that the chosen technologies are suitable for usage with organizational goals and operational needs. They might also involve strategic allocation of data assets to maximize utility and value, adjustments to environmental practices to better align with sustainability objectives, and the assignment of targeted training modules or skill-enhancement tasks intended to elevate user competency and meet evolving industrial demands.

[0022] The method may comprise updating the knowledge-based data structure with measured outcomes or newly received data to refine subsequent recommended actions. The updating may be carried out via a self-learning feedback loop.

[0023] Accordingly, a dynamic, self-learning feedback loop is provided that facilitates continuous improvement. After formulating recommended actions, the system is capable of updating the knowledge-based data structure with outcomes observed from implemented actions or newly gathered data. This recursive process allows the system to refine subsequent recommendations, adapting to changing conditions and enhancing performance for future iterations. By incorporating such a self-learning capability, the method contributes to the adaptability, well-informed integration of Al within industrialenvironments, and alignment with broader economic and environmental objectives. The interaction between the data-driven insights and proactive strategies fosters improvements in operational efficiency, sustainability, and workforce proficiency, promoting comprehensive progress and value creation across industrial sectors.

[0024] It may be provided that the knowledge-based data structure is implemented as a graph data model that stores nodes and edges. The nodes may represent entities selected from technology resources, business processes, consumption patterns, user competencies, and environmental parameters. The edges may represent relationships among said entities.

[0025] Accordingly, the method introduces an implementation detail where the knowledgebased data structure is suitable for usage as a graph data model. This graph model is crafted to store and reflect the intricate interdependencies within the diverse facets of the industrial environment efficiently. Within this graph structure, nodes can serve as representations for entities such as technology resources, business processes, consumption patterns, user competencies, and environmental parameters. These nodes are dynamic entities encompassing discrete elements related to artificial intelligence integration and operational workflows.

[0026] The graph model includes edges to characterize relationships among these entities, forming connective pathways that offer insight into how individual components interact. These edges can demonstrate the manner in which technology resources might influence business processes or how consumption patterns might affect environmental parameters. Furthermore, edges can indicate how user competencies relate to resource utilization or technology efficacy, offering a multi-dimensional view of the operational landscape.

[0027] By employing a graph data model, the method is conducive to exploring and managing complex networks of interrelated data, allowing iterative analysis through the multi-agent processing engine to be carryable with enhanced precision and depth. This representation benefits navigation and permits sophisticated query operations that reveal valuable correlations and patterns among the entities.

[0028] The utilization of a graph model is particularly advantageous in nurturing the method's self-learning feedback loop. As outcomes are measured or new data becomes available, the graph is amendable, allowing the representation to evolve and adapt. This adaptability helps ensure that each node and edge is relevant in depicting current conditions, thereby refining the recommended actions and bolstering the overall aim of continual improvement in artificial intelligence integration, operational efficiency, and sustainability within the industrial environment.

[0029] It may be provided that merging data from multiple domains includes collecting data from at least one of: enterprise resource planning systems storing financial and operational metrics, industrial Internet of Things platforms capturing machine sensor data, humanresources systems providing user skill profiles, and sustainability data repositories logging energy consumption and emission values.

[0030] Accordingly, such a process of merging data from diverse domains can enrich the knowledge-based data structure to foster comprehensive insights. The integration can be advantageous for meticulous data collection from various areas within the industrial ecosystem.

[0031] Firstly, enterprise resource planning (ERP) systems can serve as a valuable source, offering financial and operational metrics that reflect economic dimensions of enterprise performance. These systems typically capture transactional and aggregated data that may clarify aspects of business efficiency and financial returns, providing useful information for strategic planning and economic analysis within the method's framework. Secondly, industrial Internet of Things (loT) platforms can be beneficial in collecting machine sensor data, adding precision regarding the operational environment. loT platforms commonly record real-time data from equipment and machinery, such as usage patterns, operational states, and key performance indicators. This data can offer insights into the technical performance of technology resources, aiding in understanding conditions that potentially influence Al performance and resource allocation.

[0032] Thirdly, human resources (HR) systems can provide user skill profiles, aligning competency assessments with operational demands. These profiles offer detailed accounts of workforce skills, qualifications, and potential development needs. Integrating this data may facilitate matching user competencies with Al maturity and training requirements, promoting skill enhancement and personnel development.

[0033] Lastly, sustainability data repositories may connect to log energy consumption and emission values within the industrial setting. These repositories typically document environmental metrics, presenting data points that assess the ecological impact of operational processes. Such information can be useful for evaluating sustainability metrics and guiding decisions related to environmental adjustments and resource usage optimization.

[0034] By collating data from each of these domains, or subsets thereof, the method can build a robust, multidimensional dataset that captures the intricate dynamics of the industrial environment. This synthesis of data allows for sophisticated analyses capable of recommending actions that prudently consider economic returns, Al maturity, user competencies, and sustainability goals in a cohesive manner, thereby aligning the integration of Al with broader organizational and environmental objectives.

[0035] It may be provided that the parameters indicative of artificial intelligence performance include at least one of model accuracy, model inference latency, training iterations, or memory footprint.Accordingly, the method may incorporate parameters aimed at characterizing artificial intelligence performance in industrial environments. These parameters can serve as indicators, enabling nuanced assessments of Al solutions and their integration into operational workflows.

[0036] Model accuracy may represent the precision and reliability of Al systems in making predictions or generating insights. High model accuracy may indicate effective learning and consistent application of Al methodologies, making it a criterion for evaluating the viability and success of deployed solutions.

[0037] Model inference latency may illustrate the time Al models take to provide results in response to data input. Low latency is often preferable as it reflects swift performance, favorable for real-time applications and dynamic decision-making processes within industrial settings.

[0038] Training iterations may refer to the number of cycles conducted during the Al model training process, indicating the system's learning complexity and adaptability. The iterative nature influences the machine learning process, contributing to refining model accuracy and capabilities over time.

[0039] Memory footprint may indicate the computational resources an Al model uses, highlighting efficiency and resource consumption rates. A lower memory footprint may suggest optimized resource usage, aligning Al solutions with sustainability objectives by potentially reducing computational load and energy demands.

[0040] By integrating these, subsets thereof, and possibly other performance metrics into the knowledge-based data structure, the method can enhance its ability to assess and advise on high-performing Al strategies. Each parameter can offer unique insights, aiding in informed decisions related to Al solution selection, deployment, and ongoing evaluation, harmonizing with larger economic, environmental, and competency goals. It may be provided that the parameters indicative of economic return include at least one of capital expenditure, operating expenditure, revenue, or calculated return on investment over a defined time period.

[0041] Accordingly, the method may incorporate parameters that can signal economic return, capturing financial distinctions that are advantageous for evaluating operational effects and investment strategies within industrial environments. These parameters can provide an encompassing view of the financial impacts associated with integrating Al and decision-making agility for resource management.

[0042] Capital expenditure (CAPEX) may refer to the initial investments made to acquire, install, or develop Al systems, technology resources, and infrastructure. Understanding CAPEX can aid in predicting the long-term value and scaling capacity of operational enhancements and technological advancements.Operating expenditure (OPEX) may relate to ongoing costs incurred during the operation and maintenance of Al solutions and associated processes. Analyzing OPEX can help assess cost-efficiency and sustainability, contributing to achieving Al integration that addresses economic goals without surpassing operational budgets.

[0043] Revenue parameters may indicate income generated as a consequence of implementing Al solutions and optimizing industrial processes. Monitoring revenue variations can demonstrate the tangible benefit and economic impact brought by Al-enhanced strategies, uncovering potential profitability and growth trajectories.

[0044] Calculated return on investment (ROI) over a defined time period may represent the estimated financial outcomes relative to the investments made, offering a means to evaluate the value generated through Al integration. Assessing ROI can provide insights into the fiscal efficacy and strategic value yielded by the deployment of such systems, potentially encouraging future investment choices and refinements.

[0045] Integrating these parameters into the knowledge-based data structure can facilitates holistic financial assessments, supporting constructive recommendations to align Al strategies with organizational monetary objectives and optimizing resource allocation across the industrial environment. Considering CAPEX, OPEX, revenue, and ROI can enhance the method's ability to deliver insights that balance technological advances with fiscal considerations, aligning with enterprise goals.

[0046] It may be provided that the parameters indicative of environmental impact include at least one of total energy consumption, renewable energy usage, water usage, or CO2 emissions measured in accordance with a defined sustainability standard.

[0047] Accordingly, the method may consider parameters that influence environmental impact, incorporating sustainability metrics to assess and optimize the ecological effects of operations within industrial settings where artificial intelligence may be applied. These parameters can suggest insights into resource utilization and the environmental footprint associated with Al integration.

[0048] Total energy consumption may describe the amount of energy used over a specific period during operational processes, providing a basis for evaluating energy efficiency and identifying opportunities for conservation.

[0049] Renewable energy usage may reflect the proportion of energy sourced from renewable means, proposing sustainable practices within the environment. Favoring renewables can align operational strategies with environmental objectives, promoting the reduction of carbon footprint and resource sustainability.

[0050] Water usage may indicate the consumption of water within processes, highlighting conservation and management efficiencies. Effective evaluation of water usage can be valuable in optimizing resource use and addressing ecological responsibilities associated with sustainable industrial practices.CO2emissions measured according to a defined sustainability standard may offer an assessment of emissions produced from operations. By aligning emission assessments with established standards, organizations can benchmark their performance, ensuring compliance and seeking improvements in reducing greenhouse gas outputs.

[0051] Incorporating these environmental parameters into the knowledge-based data structure allows the method to emphasize sustainability, enabling evaluation and insights that support ecologically mindful decisions. By monitoring and optimizing elements such as energy consumption, renewable contribution, and emission outputs, the method supports efforts to reach sustainability benchmarks and balance environmental goals with technological advancements.

[0052] It may be provided that the parameters indicative of user skills include at least one of role-based responsibilities, completed training modules, defined proficiency levels, or qualification records tracked within the knowledge-based data structure.

[0053] Accordingly, the method may incorporate parameters related to user skills, focusing on capabilities and qualifications that may be advantageous for effective operation and interaction within industrial environments enhanced by artificial intelligence systems. These parameters can support aligning workforce competencies with technological needs and optimizing human resource utilization.

[0054] Role-based responsibilities may outline potential functions or duties anticipated from personnel within organizational structures. Identifying these roles can clarify the scope of responsibilities and assist in targeted workforce planning, enabling tasks to connect with broader operational and Al initiatives.

[0055] Completed training modules may reflect educational or skill-development programs users have engaged in, offering insight into their preparedness to work with Al systems. Tracking finished modules may aid in understanding training effectiveness, possibly encouraging ongoing learning and skill development in line with evolving technology. Defined proficiency levels may indicate the degree of expertise or competence achieved in specific areas, pointing out user strengths and potential areas for improvement.

[0056] Measuring proficiency levels can support matching skills with operational demands, bolstering human resources to meet Al integration challenges and goals.

[0057] Qualification records may include documented credentials, certifications, and educational histories that provide an overview of user qualifications. Compiling these records within the knowledge-based data structure can assist in monitoring skill sets, offering a factual basis for assigning roles or refining training needs.

[0058] By storing and analyzing these skill-related parameters, the method can improve its ability to assess workforce competency and propose strategies for skill enhancement and role optimization. A comprehensive approach to user skill evaluation can facilitate alignment with Al maturity, economic returns, and sustainability objectives, supporting acooperative interaction between human and technological resources as industrial environments progress towards enhanced efficiency and sustainability.

[0059] It may be provided that configuring the multi-agent processing engine comprises associating distinct software agents with respective data domains, such that each agent analyzes a subset of the merged data and / or communicates its inferences to other agents.

[0060] Accordingly, in configuring the multi-agent processing engine, distinct software agents can be associated with respective data domains, facilitating a specialized approach to data analysis and inference sharing. Each software agent may be responsible for examining a subset of the merged data, permitting tailored examination focused on specific dimensions of the knowledge-based data structure.

[0061] The configuration can allow each agent to engage in in-depth analysis on its assigned data domain. For example, an agent suitable for financial metrics might scrutinize parameters such as capital and operating expenditures, identifying economic relationships and potential optimization strategies. Meanwhile, an agent adapted to environmental parameters may assess resource usage, emission levels, and sustainability compliance, potentially revealing insights into ecological impacts.

[0062] Additionally, each agent can communicate its inferences to other agents within the processing engine. This collaborative exchange of insights can support a comprehensive analysis by integrating perspectives from diverse domains. Such inter-agent communication may help in identifying cross-domain correlations, enhancing the iterative analysis performable by the processing engine to produce well-rounded recommendations for artificial intelligence integration and industrial operation optimization.

[0063] By assigning tasks and promoting inter-agent communication, the multi-agent processing engine can enhance analytical efficiency and depth, supporting correlations among Al maturity, economic indicators, environmental metrics, and workforce skills. Consequently, this approach may bolster the method’s capability to generate strategic directions aligning diverse operational facets with organizational and sustainability goals, aiding adaptive progress and informed decision-making within industrial environments. It may be provided that each software agent is assigned a specialized task selected from: correlating artificial intelligence model performance with financial metrics, predicting sustainability outcomes based on usage patterns, recommending competency enhancements for users based on project requirements, and detecting opportunities to reuse existing data sets for multiple Al use cases.

[0064] Accordingly, each software agent within the multi-agent processing engine may undertake specialized tasks to enhance the precision and relevance of the data analysisprocess across various domains. These tasks can address focus areas that could align with organizational goals and / or technological integration objectives.

[0065] One optional task involves correlating artificial intelligence model performance with financial metrics. A software agent specializing in this function may examine links between Al system efficacy, such as model accuracy and latency, and economic factors like capital expenditure and revenue. Such correlations can provide insights into how Al may drive financial returns, supporting investment and resource allocation decisions grounded in evidence.

[0066] Additionally, predicting sustainability outcomes based on usage patterns represents another task. A software agent dedicated to this analysis may integrate environmental data, such as energy consumption and water usage, with operational practices to forecast sustainability impacts. Identifying patterns can enable organizations to optimize resource utilization and possibly better align with ecological standards, promoting responsible and sustainable industrial practices.

[0067] Furthermore, software agents might recommend competency enhancements for users based on project requirements. This may involve evaluating user skills in conjunction with Al maturity levels and project needs to offer advice on targeted training modules or skill development initiatives. Tailored recommendations could ensure that the workforce is prepared to meet the demands of Al integration and operational transformation.

[0068] Finally, detecting opportunities to reuse existing data sets for multiple Al use cases is another task an agent may manage. This may involve scanning the data landscape to identify possibilities for leveraging pre-existing information in supporting various Al applications. Recognizing these opportunities can enhance data economy, foster efficient resource use, and potentially minimize redundancy in data collection efforts. By assigning such specialized tasks to software agents, the multi-agent processing engine can process complex data more efficiently, facilitating actionable analysis and strategic insights that assist in optimizing Al integration across financial, environmental, competency, and data utilization dimensions. This focused approach encourages aligned actions and informed decision-making, potentially furthering the aims of enhancing industrial efficiency, sustainability, and progression through Al-driven transformations. It may be provided that determining correlations among artificial intelligence maturity, economic return on investment, sustainability metrics, and competency requirements includes applying at least one graph-based algorithm, machine-learning algorithm, and / or multimodal Al component.

[0069] Accordingly, to explore correlations among artificial intelligence maturity, economic return on investment, sustainability metrics, and competency requirements, the method may utilize advanced analytical techniques, which may include graph-based algorithms, machine learning algorithms, and / or multimodal Al components.Graph-based algorithms can offer a way to explore and map the complex relationships between nodes and edges within the graph data model that symbolizes the knowledgebased data structure. These algorithms can reveal patterns, structural dependencies, and connectivity insights helpful for understanding the interaction between various domains, such as the relationship between Al performance metrics and economic factors or sustainability parameters.

[0070] Machine-learning algorithms, including supervised, unsupervised, and / or reinforcement learning approaches, might be used to analyze data patterns and forecast outcomes across interconnected domains. These algorithms can develop predictive models drawing upon historical data and current conditions, providing informed projections about the evolution and integration of Al systems in the industrial environment. They can support automated decision-making processes, enabling dynamic adjustments based on iterative learning.

[0071] Multimodal Al components may contribute to a more expansive analysis by integrating diverse data types and signals, harmonizing different perspectives and insights across domains for comprehensive evaluation. These components can incorporate various Al modalities such as natural language processing, image recognition, or robotic process automation, each useful in understanding correlations and optimizing Al implementation. By using these sophisticated techniques, the method can process data with adaptability and precision, unraveling intricate relationships and generating actionable insights. This analytical approach might enhance the robustness of the recommended actions, promoting alignment within industrial endeavors to balance Al maturity, economic goals, environmental sustainability, and workforce skill development. Through integrating these advanced components, organizations are able to facilitate their adaptation to Al-driven changes, improve operational efficiency, and align with technological and ecological objectives.

[0072] It may be provided that generating recommended actions includes prioritizing possible actions according to a calculated impact factor that accounts for at least one of cost, estimated greenhouse gas reductions, workforce upskilling, and / or anticipated performance improvements.

[0073] Accordingly, in generating recommended actions, the method may include a prioritization strategy by calculating an impact factor that encompasses various dimensions of potential actions. This impact factor can serve as an indicator for assessing the relevance and potential effectiveness of possible actions, facilitating informed decision-making and strategic alignment.

[0074] Cost may form a component of the impact factor, providing insight into the potential financial implications of implementing recommended actions. By considering costs associated with each option, organizations can choose actions that fit within their budgetor offer favorable economic returns, supporting fiscal responsibility and strategic investment.

[0075] Estimates of potential greenhouse gas reductions may add an eco-centric aspect to the impact factor. Actions that may significantly reduce emissions can be emphasized for prioritization, aiding organizations in meeting sustainability objectives and regulatory guidelines, thus supporting environmental responsibility in operations.

[0076] Workforce upskilling may offer a perspective on the human resource dimension within the impact factor, reflecting on the educational and developmental outcomes of possible actions. Choosing actions that foster skill enhancement can support organizational adaptability, ensuring the workforce is prepared to navigate the evolving demands of Al integration.

[0077] Anticipated performance improvements may provide insights into the operational benefits associated with recommended actions. This aspect may evaluate potential efficiency gains and productivity enhancements, encouraging the prioritization of actions that bolster industrial performance and align with operational objectives.

[0078] Including an impact factor that encompasses these dimensions, or subsets thereof, can enhance the method's ability to propose recommendations that are informed and strategically aligned, potentially optimizing Al integration and industrial operations. This approach allows for targeted action, guiding organizations in balancing cost considerations, environmental commitments, human resource development, and performance advancements, contributing towards improvement in alignment with technological progress and sustainability objectives.

[0079] It may be provided that generating recommended actions further comprises providing a ranking of artificial intelligence models deemed to achieve a specified sustainability threshold measured by a predefined carbon intensity factor.

[0080] Accordingly, in the method's process of generating recommended actions, the patent application may incorporate a ranking system that facilitates the assessment of artificial intelligence models against a specified sustainability threshold. This ranking may consider the models based on a predefined carbon intensity factor, integrating ecological elements into Al model selection.

[0081] The predefined carbon intensity factor may operate as a metric expressing the environmental considerations, particularly the carbon footprint, associated with the operation of Al models. By calculating the carbon intensity, the method can identify models that effectively balance performance efficiency with sustainability aspects, aligning with ecological objectives.

[0082] The ranking system provides the option to compare various Al models, allowing organizations to assess which models align well with the criteria of the specified sustainability threshold. By focusing on Al models lower on the carbon intensity scale,organizations can explore solutions that potentially minimize environmental burdens, supporting efforts to reduce greenhouse gas emissions and pursue sustainability aims. Integrating this ranking as part of the recommended actions may encourage aligning Al initiatives with broader ecological goals, ensuring that technological advancements are consistent with environmental stewardship. This method can promote the consideration of Al technology that not only improves operational efficiency but also indicates the organization's commitment to responsible resource management and ecological impact awareness.

[0083] Additionally, this sustainability-focused ranking can support strategic decision-making, aiding organizations in deploying Al solutions that comply with industry environmental standards and advance a more sustainable approach to Al integration. Thus, the ranking system nurtures a commitment to progress within the industrial environment, helping organizations to manage technological growth alongside ecological responsibility effectively.

[0084] It may be provided that generating recommended actions further comprises automatically assigning targeted training curricula to specific user roles based on identified competency gaps for operating or managing selected artificial intelligence solutions.

[0085] Accordingly, the method may introduce automated assignment of training curricula tailored to user roles, based on identified competency gaps relevant to the operation or management of selected artificial intelligence solutions. This approach can support workforce skill enhancement, aiding personnel in acquiring the appropriate knowledge and abilities needed to interact with Al technologies and adapt to changing operational contexts.

[0086] By identifying competency gaps, the method can assess current user skills against those potentially needed for managing Al solutions, enabling an understanding of where additional knowledge or training could be advantageous. These assessments may utilize data from human resources systems, outlining role-based responsibilities, proficiency levels, completed training modules, and qualification records.

[0087] The training curricula may include modules designed to address the identified gaps, providing content and instructional activities that enhance relevant capabilities. Curricula might align with technological requirements, guiding employees to obtain practical knowledge that supports the integration of Al into their roles.

[0088] Automatically assigning curricula allows for efficient educational planning, making training responsive to Al integration and innovation. This automated process facilitates adaptation as Al solutions evolve and present challenges requiring distinct skills within industrial settings.By supporting this strategic skill enhancement, the method may encourage workforce capabilities to remain adaptable to new demands, potentially improving operational resilience and effectiveness. It emphasizes the fostering of human resources alongside technological advancements, embracing a holistic view that promotes growth through skilled expertise, addresses risks posed by competency gaps, and enhances synergy between personnel and Al systems. This approach advocates for industrial progress, supporting the alignment of human capital development with innovative technological strategies

[0089] It may be provided that updating the knowledge-based data structure includes storing performance outcomes over time, such that historical performance indicators, cost parameters, emission metrics, or skill progressions are compared to subsequent real-world measurements and used to refine future recommendations.

[0090] Accordingly, updating the knowledge-based data structure may include capturing performance outcomes over time, offering a basis for comparative analysis that can inform future adjustments to suggested actions. This systematic approach can allow the data structure to evolve dynamically, adapting to new insights and measurements to potentially enhance decision-making processes in Al integration within industrial environments.

[0091] The method may encompass various performance indicators, such as model accuracy or latency, alongside operational metrics like cost parameters and emission figures. By storing and organizing this information, the system can establish a historical record that serves as a reference for ongoing assessment. Additionally, skill progressions depicting user competency over time might be documented to track educational outcomes and skill development.

[0092] By comparing historical data with subsequent real-world measurements, organizations have the opportunity to assess the outcomes of previous suggested actions. This comparison can offer feedback that guides adjustments and aims to help keep recommendations aligned with current conditions, objectives, and evolving technology. The ability to refine future suggestions based on historical and current data creates a self-learning feedback mechanism. This mechanism strives to improve the accuracy and applicability of suggestions generated by the method, supporting potential enhancement across financial, technical, environmental, and human resource dimensions.

[0093] Through historical analysis and adaptive refinement, the method can support informed strategic planning, promoting alignment with organizational goals and sustainability standards. As a result, it may help foster efficient resource management, responsible environmental practices, and proactive skill development, facilitating continued growth and resilience in industrial settings as they navigate technological advancements and Al-driven transformations.It may be provided that the self-learning feedback loop is partially or fully automated, such that recommended actions are recalculated when newly received data exceeds a threshold difference in at least one parameter from the previously analyzed state.

[0094] Accordingly, the method may comprise a self-learning feedback loop that is operable with partial or full automation, designed to recalibrate recommended actions in response to observable changes in data parameters. This automation may support the system's adaptability and responsiveness, making it suitable for efficient integration of new information into the decision-making process within industrial settings enhanced by artificial intelligence technologies.

[0095] The mechanism may monitor parameter deviations, identifying when newly received data indicates a specified threshold difference compared to previously analyzed states. These thresholds can serve as triggers for recalculating recommendations, contributing to strategies that are relevant to the current context and technological landscape.

[0096] By automating aspects of this feedback loop, the method may facilitate timely adjustments and proactive response. It may reduce human intervention, allowing rapid processing and dynamic consideration of new insights, thereby decreasing the possibility of outdated or misaligned suggestions.

[0097] This automation is conducive to ongoing improvement and refinement, aligning operational tactics with evolving data, emerging trends, and technological advances. In doing so, it can enhance efficiency, fostering smoother integration of Al solutions, and nurturing responsiveness to ecological, financial, and workforce shifts.

[0098] The automated self-learning feedback loop may encourages a balance between stability and evolutionary adaptability, aiming to support an industrial environment that efficiently incorporates new information, maintains alignment with strategic objectives, and fosters innovation. Through adaptive recalibration driven by data thresholds, the method can highlight the value of real-time adjustments and resilient operational strategies, advancing organizational capabilities alongside rapid technological progress.

[0099] Another aspect of the present disclosure provides a data processing system. The data processing system may comprise means for carrying out any of the described methods. The data processing system may comprise a memory and one or more processors coupled to the memory, the one or more processors being configured to carry out any of the described methods.

[0100] Another aspect of the present disclosure relates to a computer program. The computer program may comprise instructions which, when the program is executed by a computer, such as the mentioned data processing system, cause the computer to carry out any of the described methods. A computer program may also be referred to as a program, software, a software application, an app, a module, a software module, a script, or code. A computer program may be written in a programming language, including compiled orinterpreted languages. A computer program may be deployed in any form, including as a stand-alone product or as a module, component, subroutine, or other unit suitable for use in a computing environment, such as on the mentioned data processing system. Another aspect of the present disclosure relates to a computer-readable medium having stored thereon a computer program as described above.

[0101] Another aspect of the present disclosure relates to a non-transitory computer-readable medium storing a set of instructions that, when executed by one or more processors of an apparatus, cause the apparatus to carry out any of the described methods. For example, a non-transitory computer-readable storage medium may be provided, storing instructions that, when executed by one or more processors, cause a device to perform a method for automatically guiding and updating artificial intelligence integration, data value measurement, sustainability metrics, and user competency for an industrial environment. The method may comprise building a knowledge-based data structure that merges data from multiple domains, the knowledge-based data structure including parameters indicative of artificial intelligence performance, economic return, environmental impact, and user skills. The method may comprise configuring a multiagent processing engine to iteratively analyze the merged data within the knowledgebased data structure to determine correlations among artificial intelligence maturity, economic return on investment, sustainability metrics, and competency requirements. The method may comprise generating, in response to the iterative analysis, recommended actions addressing at least one of: selection or deployment of artificial intelligence solutions, allocation of data assets, adjustment of environmental or resource usage, and assignment of training modules or skill-enhancement tasks. The method may comprise updating the knowledge-based data structure with measured outcomes or newly received data to refine subsequent recommended actions via a self-learning feedback loop.

[0102] Another aspect of the present disclosure relates to a system configured to automatically guide and update artificial intelligence integration, data value measurement, sustainability metrics, and user competency for an industrial environment. The system may comprise a data storage module storing a knowledge-based data structure that merges data from multiple domains, the knowledge-based data structure including parameters indicative of artificial intelligence performance, economic return, environmental impact, and user skills. The system may comprise a multi-agent processing engine connected to the data storage module and configured to iteratively analyze the merged data to determine correlations among artificial intelligence maturity, economic return on investment, sustainability metrics, and competency requirements. The system may comprise a recommendation engine generating, in response to the iterative analysis, recommended actions addressing at least one of: selection or deployment of artificial intelligence solutions, allocation of data assets, adjustment of environmental or resource usage, and assignment of training modules or skillenhancement tasks. The system may comprise a feedback mechanism updating theknowledge-based data structure with measured outcomes or newly received data to refine subsequent recommended actions.

[0103] It is an advantage of embodiments of the present disclosure that a single point of entry can be provided to accelerate data value creation in complex industrial environments. It is an advantage of embodiments of the present disclosure that Al technologies can be continuously integrated and scaled across the entire value chain by evaluating maturity, performance, cost, and sustainability metrics.

[0104] It is an advantage of embodiments of the present disclosure that data value can be measured in an evidence-based manner, including financial and additional businessrelevant parameters, to prioritize the reuse of data assets.

[0105] It is an advantage of embodiments of the present disclosure that resource consumption, such as energy usage and CO2 emissions, can be represented, evaluated, and steered to foster sustainable Al development.

[0106] It is an advantage of embodiments of the present disclosure that user competencies can be continuously enhanced through a data-driven qualification program, including mandatory ethical and regulatory training.

[0107] It is an advantage of embodiments of the present disclosure that a self-learning, multiagent framework can automate decision-making by correlating Al performance, economic return, environmental impact, and user skills.

[0108] It is an advantage of embodiments of the present disclosure that knowledge graph and machine-learning technologies can be used to master the complexity and interdependencies of data assets along the product life cycle.

[0109] It is an advantage of embodiments of the present disclosure that synergy effects and reusable building blocks can be recommended to optimize return on investment and accelerate Al adoption.

[0110] It is an advantage of embodiments of the present disclosure that dynamic insights and predictions can be generated to alert stakeholders about model performance drift or unmet sustainability targets.

[0111] It is an advantage of embodiments of the present disclosure that standardization and data model reuse can be leveraged to reduce time to market and align Al strategies with business objectives.

[0112] It is an advantage of embodiments of the present disclosure that resource needs, including GPU and CPU capacities, water, and energy, can be minimized through automated and intelligent optimization.It is an advantage of embodiments of the present disclosure that the complexity of hardware-driven industries can be addressed by integrating data-driven methods, thereby ensuring competitiveness and future viability.

[0113] It is an advantage of embodiments of the present disclosure that ESG measurement can be incorporated into Al decision-making processes, supporting responsible data usage and sustainable corporate strategies.

[0114] Particular and preferred aspects of the present disclosure are set out in the accompanying independent and dependent claims. Features from the dependent claims may be combined with features of the independent claims and with features of other dependent claims as appropriate and not merely as explicitly set out in the claims.

[0115] The above and other characteristics, features and advantages of the present disclosure will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, which illustrate, by way of example, the principles of the disclosure. This description is given for the sake of example only, without limiting the scope of the disclosure. The reference figures quoted below refer to the attached drawings.

[0116] The terms used herein should generally be construed as understood by the average person skilled in the art, unless explicitly indicated otherwise. The following explanations may guide the understanding:

[0117] As used herein, and unless otherwise specified, the term "knowledge-based data structure" refers to an organized framework, such as a database or other digital schema, that stores interconnected data and metadata in a manner enabling inference, analysis, and retrieval of information in support of decision-making processes. By way of example, this includes but is not limited to relational databases augmented with semantic layers, graph-based storage models housing various entity types and relationships, or ontology-driven repositories that codify domain knowledge.

[0118] As used herein, and unless otherwise specified, the term "multi-agent processing engine" refers to a coordinated set of software agents, each designed to operate on a distinct aspect or subset of the data, that collectively analyze, communicate, and negotiate insights or decisions in an automated or semi-automated manner. By way of example, one agent may manage economic metrics while another agent evaluates Al performance; together, they exchange findings to produce holistic recommendations. As used herein, and unless otherwise specified, the phrase "iteratively analyze the merged data" refers to repeatedly applying analytical or computational techniques to the integrated data, refining insights or results through multiple cycles as new information is incorporated or as intermediate results influence subsequent processing. By way of example, machine learning algorithms may be run multiple times, each iteration fine-tuning parameters or models based on updated feedback loops.As used herein, and unless otherwise specified, the term "artificial intelligence maturity" refers to a measure or assessment of the sophistication of Al solutions within an organization or system, including factors such as model complexity, level of automation, adaptability to changing data, and integration with operational workflows. By way of example, a low Al maturity level may be characterized by basic predictive models with limited automation, whereas a higher maturity level might include self-training neural networks integrated into real-time decision-making.

[0119] As used herein, and unless otherwise specified, the term "economic return on investment" refers to financial benefits, cost savings, or revenue enhancements realized in relation to the cost of deploying and operating Al solutions or associated initiatives over a particular time period. By way of example, it can include a percentage ROI figure comparing net benefits to total investment cost, or it may be represented through financial metrics such as net present value or payback period.

[0120] As used herein, and unless otherwise specified, the term "sustainability metrics" refers to quantifiable indicators that measure an organization’s or system’s environmental and resource usage impact, including energy consumption, carbon dioxide emissions, water usage, or other relevant ecological footprints. By way of example, sustainability metrics may follow international standards such as the GHG Protocol or ISO 14064 and may track both direct and indirect emission sources.

[0121] As used herein, and unless otherwise specified, the phrase "competency requirements" refers to defined knowledge, skills, or proficiencies necessary for personnel to effectively implement, operate, or manage Al solutions within an industrial environment. By way of example, a competency requirement may include advanced data analytics skills or safety certifications relevant to working with autonomous machinery.

[0122] As used herein, and unless otherwise specified, the phrase "recommended actions" refers to one or more proposed steps, strategies, or adjustments generated by the described system to advance or optimize Al integration, data usage, resource management, or workforce development. By way of example, such actions could suggest replacing a less efficient model with a more accurate one, reallocating budget toward data acquisition, or enrolling certain employees in specialized training.

[0123] As used herein, and unless otherwise specified, the phrase "self-learning feedback loop" refers to a process in which the system automatically refines its parameters, inference rules, or decision pathways based on newly received data or updated observed outcomes, thereby improving accuracy and effectiveness over time without manual intervention. By way of example, a self-learning feedback loop may repeatedly update recommended actions as sensor data from machinery indicate performance deviations under different operating conditions.

[0124] As used herein, and unless otherwise specified, the term "graph data model" refers to a data architecture employing nodes (entities) and edges (relationships) to represent andquery complex, interconnected information. By way of example, implementing a knowledge-based data structure as a graph data model may facilitate algorithms like shortest path, community detection, or centrality analysis to uncover correlations among Al, economics, sustainability, and skill data.

[0125] As used herein, and unless otherwise specified, the phrase "nodes representing entities selected from technology resources, business processes, consumption patterns, user competencies, and environmental parameters" refers to distinct elements or objects within a graph data model that encode real-world or conceptual items pertinent to Al integration and sustainability objectives. By way of example, a node may represent a production machine, a training course, a department’s water usage pattern, or an employee’s skill record.

[0126] As used herein, and unless otherwise specified, the phrase "edges representing relationships among said entities" refers to the linkage within the graph data model that captures the connections, dependencies, or associations between different nodes, providing contextual meaning and enabling relational queries. By way of example, an edge may indicate that a certain machine requires a specific Al model, or that an employee is certified to operate a particular piece of equipment.

[0127] As used herein, and unless otherwise specified, the phrase "enterprise resource planning (ERP) systems" refers to comprehensive software solutions that integrate and manage core organizational processes, including financials, supply chain, operations, and human resources. By way of example, ERP systems may store cost data for capital equipment purchases or consolidated financial statements indicating revenue generated by a particular product line.

[0128] As used herein, and unless otherwise specified, the phrase "industrial Internet of Things (I loT) platforms" refers to connected networks of sensors, devices, or embedded systems within industrial environments that collect machine- or process-related data, typically for monitoring, analytics, or automation. By way of example, HoT platforms could capture temperature readings from factory equipment or track vibration signals to preemptively detect mechanical failures.

[0129] As used herein, and unless otherwise specified, the phrase "human resources systems providing user skill profiles" refers to HR data repositories or platforms that store information on employee competencies, training history, certifications, or professional development activities. By way of example, such systems can track an operator’s completion of safety courses or an engineer’s advanced-level analytics certification. As used herein, and unless otherwise specified, the phrase "sustainability data repositories logging energy consumption and emission values" refers to databases or software environments that compile environmental metrics, such as kilowatt-hours consumed, CO2 emissions, or water usage, typically for the purpose of tracking and reporting sustainability performance. By way of example, these repositories could holdmonthly carbon footprint calculations for specific manufacturing sites or renewable energy usage details across company assets.

[0130] As used herein, and unless otherwise specified, the phrase "parameters indicative of artificial intelligence performance" refers to measurable data points or metrics that reflect how well Al models or algorithms function, including accuracy, inference speed, number of training epochs, or resource usage. By way of example, accuracy can be expressed as a percentage of correct predictions, while inference latency may be measured in milliseconds of processing time.

[0131] As used herein, and unless otherwise specified, the phrase "parameters indicative of economic return" refers to data points or indicators that quantify the financial performance or benefit of Al deployments, such as capital expenditure, operational expenditure, revenue impact, or computed ROI over a designated period. By way of example, a capital expenditure parameter might reflect hardware or software purchase costs, while operational expenditures could encompass maintenance or subscription fees.

[0132] As used herein, and unless otherwise specified, the phrase "parameters indicative of environmental impact" refers to metrics or measurements that gauge the ecological effects associated with Al or industrial processes, encompassing total or partial energy usage, renewable energy adoption, water consumption, or CO2 emissions. By way of example, these parameters may be reported in kilowatt-hours per month or metric tons of CO2 equivalent per production cycle.

[0133] As used herein, and unless otherwise specified, the phrase "parameters indicative of user skills" refers to data points that capture and represent individuals’ or teams’ competencies, training progress, professional certifications, or capacity to perform roles vital to Al implementation. By way of example, proficiency levels may be rated on a scale (e.g., beginner, intermediate, expert), while qualification logs could note whether a user has completed advanced machine learning courses.

[0134] As used herein, and unless otherwise specified, the phrase "distinct software agents with respective data domains" refers to separately operating modules or processes, each focusing on input or analysis pertinent to a particular type of data (e.g., finance, environment, Al performance, or workforce training), working collaboratively within the larger multi-agent processing engine. By way of example, a sustainability agent may prioritize emission data, while a finance agent focuses on cost-benefit analysis.

[0135] As used herein, and unless otherwise specified, the phrase "graph-based algorithm, machine learning algorithm, or multimodal Al component" refers to computational techniques or models used to detect patterns, derive insights, and produce forecasts or recommendations from integrated data. By way of example, a graph-based algorithm might be a pathfinding technique to discover relationships among nodes, whereas amultimodal Al component might incorporate text analytics alongside sensor data interpretation.

[0136] As used herein, and unless otherwise specified, the phrase "calculated impact factor" refers to a composite or weighted score that quantifies or ranks the prospective benefit, feasibility, or priority of a particular action by considering multiple metrics such as cost, productivity gains, emission reductions, or skill enhancement. By way of example, the impact factor may use a scoring rubric that awards points for lowering greenhouse gas emissions and for improving ROI, thereby generating a numeric score to prioritize recommended actions.

[0137] BRIEF DESCRIPTION OF THE DRAWINGS

[0138] The present disclosure may be better understood by reference to the following drawings: FIG. 1 illustrates a method in accordance with one embodiment.

[0139] FIG. 2 illustrates a schematic diagram of a system architecture in accordance with one embodiment.

[0140] FIG. 3 illustrates an exemplary graph data model in accordance with one embodiment. FIG. 4 illustrates a schematic block diagram of the configuration of a multi-agent processing engine in accordance with one embodiment.

[0141] FIG. 5 illustrates a correlation determination method in accordance with one embodiment.

[0142] FIG. 6 illustrates a recommendation generation method in accordance with one embodiment.

[0143] FIG. 7 illustrates a self-learning feedback loop in accordance with one embodiment. FIG. 8 illustrates the four key dimensions addressed by the DAI Matrix platform in accordance with one embodiment.

[0144] FIG. 9 illustrates a schematic block diagram of computer hardware usable for carrying out one or more aspects in accordance with one embodiment.

[0145] FIG. 10 illustrates an exemplary data model in accordance with one embodiment.

[0146] DETAILED DESCRIPTION

[0147] In the following, representative embodiments illustrated in the accompanying drawings will be explained. It should be understood that the illustrated embodiments and thefollowing descriptions refer to examples which are not intended to limit the embodiments to one preferred embodiment.

[0148] Certain embodiments provide a software-based platform that implements an underlying software platform methodology referred to as “DAI Matrix”. In certain embodiments, generative Al and multi-agent systems on graph databases are leveraged to automate the data value evaluation of Artificial Intelligence across the value chain and industry, with a strong focus on ESG premises.

[0149] In certain embodiments, the software-based platform enables an automated, multidimensional evaluation and steering of Al investments by integrating data value, sustainability (ESG), and competency expansion within a single platform. One goal is to automate the complex decision-making required to master the challenges of a hardware-driven industry in the age of data-driven technologies.

[0150] In certain embodiments, the software-based platform enforces a structured approach and guides the evaluation and management of Al based on a defined framework. In certain embodiments, features and characteristics include one or more of:

[0151] • Data Fusion: Integrates all relevant data, including technological, business, consumption, and customer data, into an Al system.

[0152] • Multi-Agent System: Utilizes agents that handle specific sub-tasks for decision preparation and recommendation. Examples include agents for insight generation, data submission, discovery, and everyday operational context.

[0153] • Dynamic Knowledge Representation & Processing: Aims to increase the efficiency of complex decisions and knowledge transfers.

[0154] • Data-Driven Recommendations: Recommends data-driven decisions based on current premises, considering connections between different areas of the business. Examples provided include matching Al models, infrastructure, and competencies to achieve specific goals like cost reduction or quality improvement.

[0155] • Holistic Data Representation: Provides a comprehensive view of the transformation through a self-learning platform, including history, current status, and future outlook.

[0156] • Multimodal Human-Machine Interaction: Supports interaction via text, graphics, speech, and acoustics.

[0157] FIG. 1 illustrates a method 100 for automatically guiding and updating artificial intelligence integration, data value measurement, sustainability metrics, and user competency for an industrial environment according to an embodiment. In building step 102, method 100 builds a knowledge-based data structure that merges data from multiple domains, the knowledge-based data structure including parameters indicative of artificial intelligence performance, economic return, environmental impact, and user skills. In configuration step 104, method 100 configures a multi-agent processing engine to iteratively analyze the merged data within the knowledge-based data structure to determine correlations among artificial intelligence maturity, economic return oninvestment, sustainability metrics, and competency requirements. In generation step 106, method 100 generates, in response to the iterative analysis, recommended actions addressing at least one of (i) selection or deployment of artificial intelligence solutions, (ii) allocation of data assets, (iii) adjustment of environmental or resource usage, and (iv) assignment of training modules or skill-enhancement tasks. In updating step 108, method 100 updates the knowledge-based data structure with measured outcomes or newly received data to refine subsequent recommended actions via a self-learning feedback loop.

[0158] FIG. 2 schematically illustrates the architecture of a system 202 for automatically guiding and updating artificial intelligence integration according to one embodiment. The system 202 may be configured to implement one or more of the characteristics of method 100. In the illustrated embodiment, the system 202 comprises a central knowledge-based data structure 204 implemented as a database system that stores merged data from multiple domains. Connected to the knowledge-based data structure is a multi-agent processing engine 206 that performs iterative analysis on the merged data. The system 202 further includes a data input interface 208 for receiving data from various source systems, including ERP systems 210 storing financial and operational metrics, industrial loT platforms 212 capturing machine sensor data, HR systems 214 providing user skill profiles, and sustainability repositories 216 logging energy consumption and emission values. A recommendation generation module 218 processes the outputs from the multiagent processing engine 206 to provide actionable insights through a user interface 220. The system 202 includes a feedback measurement module 222 that collects outcomes of implemented recommendations and feeds this information back to the knowledgebased data structure 204 via a self-learning feedback loop.

[0159] In embodiments, the knowledge-based data structure 204 may be implemented as a graph data model that stores nodes representing entities selected from technology resources, business processes, consumption patterns, user competencies, and environmental parameters, and edges representing relationships among said entities. This enables complex relationship modeling between various elements of the system, allowing for more sophisticated analysis and understanding of interconnections.

[0160] FIG. 3 illustrates an example of such a graph data model 300 as one implementation of the knowledge-based data structure 204. By way of example, the illustrated graph data model 300 comprises various nodes representing different entity types and edges representing relationships between these entities. Technology resource nodes 302 represent Al models, computational infrastructure, and data assets. Business process nodes 304 represent operational workflows and revenue-generating activities.

[0161] Consumption pattern nodes 306 represent usage metrics for various resources. User competency nodes 308 represent skill profiles and training requirements. Environmental parameter nodes 310 represent energy usage, emissions, and other sustainability metrics. The edges between nodes represent relationships such as “utilizes” between business processes and technology resources, “requires” between technology resourcesand user competencies, “generates” between business processes and consumption patterns, and “impacts” between consumption patterns and environmental parameters. The graph data model 300 enables complex queries and relationship traversals to identify correlations across different domains.

[0162] In embodiments, merging data from multiple domains may include collecting data from at least one of: a) enterprise resource planning systems storing financial and operational metrics, b) industrial Internet of Things platforms capturing machine sensor data, c) human resources systems providing user skill profiles, and d) sustainability data repositories logging energy consumption and emission values. This provides comprehensive data integration from diverse sources, enabling holistic analysis and more informed decision-making.

[0163] In embodiments, the parameters indicative of artificial intelligence performance may include at least one of model accuracy, model inference latency, training iterations, or memory footprint. This allows for detailed measurement and optimization of Al model performance across multiple technical dimensions.

[0164] In embodiments, the parameters indicative of economic return may include at least one of capital expenditure, operating expenditure, revenue, or calculated return on investment over a defined time period. This enables precise financial evaluation of Al initiatives, facilitating cost-benefit analysis and budget optimization.

[0165] In embodiments, the parameters indicative of environmental impact may include at least one of total energy consumption, renewable energy usage, water usage, or CO2 emissions measured in accordance with a defined sustainability standard. This enables accurate sustainability tracking and reporting, supporting environmental compliance and corporate responsibility goals.

[0166] In embodiments, the parameters indicative of user skills may include at least one of rolebased responsibilities, completed training modules, defined proficiency levels, or qualification records tracked within the knowledge-based data structure 204. This allows for precise tracking of workforce capabilities, enabling targeted training and optimal assignment of personnel to Al initiatives.

[0167] In embodiments, inclusion of privacy and security as key parameters in data evaluation may enhance the system 202’s ability to handle sensitive information. In a broad embodiment, a knowledge-based data structure 204 may incorporate privacy and security attributes as fundamental parameters when merging and correlating data from various enterprise systems. In an intermediate embodiment, a graph data model 300 may store privacy classifications and security levels for each data node, enabling evaluations that account for risk exposure, compliance constraints, and encryption requirements. In a specific embodiment, for each data asset, a designated privacy sensitivity rating and a minimum security protocol level may be assigned. A multi-agent processing engine 206 may then analyze these ratings alongside cost-benefit orsustainability metrics, generating Al usage recommendations that meet specified privacy thresholds and restricting access if requirements are not fulfilled.

[0168] In embodiments, configuring the multi-agent processing engine 206 may comprise associating distinct software agents with respective data domains, such that each agent analyzes a subset of the merged data and communicates its inferences to other agents. This architectural approach enables specialized analysis of complex data while maintaining coordination between different analytical components.

[0169] FIG. 4 illustrates a schematic block diagram of the configuration of the multi-agent processing engine 206 in one embodiment. The multi-agent processing engine 206 comprises multiple specialized software agents that operate on different aspects of the merged data in the knowledge-based data structure 204. The Al-financial correlation agent 402 analyzes relationships between Al model performance and business outcomes, processing parameters such as model effectiveness, implementation costs, and revenue impact. The sustainability prediction agent 404 forecasts environmental outcomes based on usage patterns, analyzing parameters such as energy efficiency, resource optimization, and emission projections. The competency recommendation agent identifies skill gaps and learning opportunities, processing parameters such as role requirements, current capabilities, and training pathways. The data reuse optimization agent 408 detects opportunities to leverage existing data assets across multiple use cases, analyzing parameters such as data compatibility, use case similarity, and integration feasibility. An agent coordination module 410 orchestrates communication and information sharing among the agents through standardized message protocols. The aggregated insights from all agents are compiled by an analysis integration module 412 before being passed to the recommendation generation module 218 (see FIG. 2).

[0170] In embodiments, synergy effects to reduce rework effort in full-time equivalent (FTE) hours may be quantified to optimize resource utilization. In a broad embodiment, a system may determine synergy effects among multiple Al initiatives by comparing projected rework or duplication efforts against incremental efficiency gains, thereby quantifying overall resource savings. In an intermediate embodiment, a synergy evaluation component may aggregate data from project histories, predicting potential reductions in rework by identifying overlapping Al model features, data sets, computational infrastructure, or skill requirements, and presenting net FTE-hour savings. In a specific embodiment, a data-aware synergy engine may monitor active Al initiatives and capture incremental man-hours allocated to rework tasks, recommending effort consolidation when overlapping data assets or technologies with sufficient similarity are found. The system may report numeric estimates of FTE hours saved, along with cost impact, and update the knowledge-based data structure 204 with real-time reductions. In embodiments, each software agent may be assigned a specialized task selected from: a) correlating artificial intelligence model performance with financial metrics, b) predicting sustainability outcomes based on usage patterns, c) recommendingcompetency enhancements for users based on project requirements, and d) detecting opportunities to reuse existing data sets for multiple Al use cases. This task specialization optimizes the system’s ability to deliver targeted insights in specific domains while maintaining an integrated approach.

[0171] In embodiments, determining correlations among artificial intelligence maturity, economic return on investment, sustainability metrics, and competency requirements may include applying at least one graph-based algorithm, machine learning algorithm, or multimodal Al component. This leverages advanced analytical techniques to discover non-obvious relationships and insights that might be missed by traditional analysis. In embodiments, the use of multimodal generative Al for interactive data representation and recommendations may further enhance user engagement and decision support. In a broad embodiment, a platform may employ generative artificial intelligence models capable of processing textual, visual, and auditory data to present insights, forecasts, and recommendations through an interactive interface. In an intermediate embodiment, an interactive recommendation system may process structured and unstructured data, producing explanatory text or graphics and allowing users to query the system via spoken words, typed queries, or image-based prompts. In a specific embodiment, a user interface 220 may integrate a generative language model with speech recognition and image analysis, so that users submitting voice commands or annotated images receive narratives or graphical diagrams illustrating possible outcomes, recommended actions, and explanations in natural language.

[0172] FIG. 5 illustrates a process flow of a correlation determination method 500 for correlation determination among artificial intelligence maturity, economic return on investment, sustainability metrics, and competency requirements according to one embodiment. The correlation determination method 500 begins with data extraction 502 from the knowledge-based data structure 204, pulling relevant parameters from each domain. Next, the data undergoes preprocessing 504 including normalization, outlier detection, and / or feature engineering. The correlation analysis 506 applies various analytical methods to the prepared data, including graph-based algorithms that traverse relationship patterns, machine-learning algorithms that identify non-linear dependencies, and / or multimodal Al components that process heterogeneous data types. The correlation results undergo validation 508 against historical patterns and domain-specific rules. A significance assessment 510 ranks the strength and reliability of detected correlations. Correlation determination method 500 concludes with dimensional integration 512 that synthesizes findings across all four key dimensions: Al technology integration, data value creation, sustainability impact, and competency expansion. The integrated correlation model serves as input for the recommendation generation module 218 (see FIG. 2).

[0173] In embodiments, generating recommended actions may include prioritizing possible actions according to a calculated impact factor that accounts for at least one of cost, estimated greenhouse gas reductions, workforce upskilling, or anticipated performanceimprovements. This enables balanced decision-making that considers multiple factors simultaneously, leading to more optimal outcomes across various dimensions.

[0174] FIG. 6 illustrates a process flow of a recommendation generation method 600 according to an embodiment. The recommendation generation method 600 may be carried out by the recommendation generation module 218. The process begins with goal analysis 602 that identifies objectives across the four dimensions based on user input and system state. The subsequent action identification 604 determines possible interventions to achieve the defined goals. These candidate actions undergo impact assessment 606 where each is evaluated according to multiple factors including cost, estimated greenhouse gas reductions, workforce upskilling requirements, and / or anticipated performance improvements. The recommendation generation module 218 then ranks the actions according to a calculated impact factor derived from the assessment metrics. For sustainability-focused objectives, an Al model ranking 608 evaluates and recommends artificial intelligence solutions that meet specified carbon intensity thresholds. For competency-related objectives, a training assignment 610 automatically generates personalized learning curricula based on identified skill gaps and role requirements. The process culminates in recommendation package generation 612 that include implementation guidelines, expected outcomes, and measurement criteria. These recommendations are delivered through the user interface 220 (see FIG. 2) with appropriate visualization and explanation components.

[0175] In embodiments, generating recommended actions may further comprise providing a ranking of artificial intelligence models deemed to achieve a specified sustainability threshold measured by a predefined carbon intensity factor. This facilitates the selection of environmentally responsible Al solutions that meet sustainability goals while maintaining performance requirements.

[0176] In embodiments, generating recommended actions may further comprise automatically assigning targeted training curricula to specific user roles based on identified competency gaps for operating or managing selected artificial intelligence solutions. This personalized approach to workforce development optimizes training resources and accelerates Al adoption by focusing on specific skill gaps.

[0177] In embodiments, mandatory ethics and regulatory compliance training may also serve as a prerequisite for advanced Al usage. In a broad embodiment, a system configuration may automatically withhold access to specified Al tools or datasets until designated users complete relevant ethics and regulatory compliance modules. In an intermediate embodiment, an automated training assignment module may verify each user’s completion of Al ethics and regulatory standards before conditionally unlocking advanced Al functionalities. In a specific embodiment, a system design may detect whether a user’s training status includes required modules on Al ethics, privacy regulations, and digital inclusion, blocking high-impact Al applications if not satisfied and providing a tailored training schedule until completion.In embodiments, updating the knowledge-based data structure 204 may include storing performance outcomes over time, such that historical performance indicators, cost parameters, emission metrics, and / or skill progressions are compared to subsequent real-world measurements and used to refine future recommendations. This temporal analysis enables trend identification and continuous improvement of recommendations based on actual outcomes rather than predictions alone.

[0178] FIG. 7 illustrates a self-learning feedback loop 700 for updating the knowledge-based data structure 204 according to one embodiment. The loop begins with the implementation of recommended actions 702 by system users. The outcomes of these implementations undergo tracking by measurement modules 704 that collect various performance indicators including Al effectiveness metrics, financial performance data, sustainability measurements, and / or competency advancement indicators. This measured data is compared against predicted outcomes 706 to calculate deviation metrics. The deviation analysis 708 identifies patterns in the differences between expected and actual results. These insights feed into a model refinement 710 process that updates the analytical models used by the multi-agent processing engine 206.

[0179] Parameters adjusted during refinement include correlation weights, prediction confidence intervals, and / or impact coefficients. The knowledge update 712 process then integrates the refined models and new measurement data into the knowledgebased data structure 204. An automated trigger 714 mechanism initiates recalculation of recommendations when newly received data exceeds defined threshold differences from the previously analyzed state. The continuous operation of this self-learning feedback loop 700 enables progressive improvement in recommendation accuracy over time as the system learns from real-world outcomes.

[0180] In embodiments, the self-learning feedback loop 700 may be partially or fully automated, such that recommended actions are recalculated when newly received data exceeds a threshold difference in at least one parameter from the previously analyzed state. This autonomous operation ensures recommendations remain current and relevant without requiring constant manual intervention, increasing efficiency and responsiveness.

[0181] FIG. 8 illustrates the four key dimensions addressed by the DAI Matrix platform according to one embodiment. At the center of the illustration is the unified data foundation represented by the knowledge-based data structure 204. Radiating from this central element are the four dimension axes. The Al technology integration 802 axis represents parameters including technology maturity levels, implementation status, and / or scaling potential. The data value creation 804 axis represents metrics such as financial return, operational efficiency gains, and / or innovation enablement. The sustainability impact 806 axis represents environmental measurements including energy consumption reduction, carbon footprint, and / or resource optimization. The competency expansion 808 axis represents workforce development metrics including skill coverage, learning effectiveness, and / or organizational readiness. The interconnecting dotted lines between the axes represent the interdependencies among dimensions, showing howprogress in one area affects capabilities in others. This integrated view enables decision-makers to understand the holistic impact of artificial intelligence initiatives across all relevant business dimensions.

[0182] FIG. 9 illustrates a schematic block diagram of computer hardware usable for carrying out one or more aspects disclosed herein. As can be seen, a data processing apparatus 902 comprises one or more processors, one of which is exemplarily shown as processor 904. The data processing apparatus 902 comprises a memory 906. The one or more processors 904 are communicatively coupled to the memory 906. The memory 906 comprises a computer program 908. The computer program 908 may implement some or all aspects of the disclosed methods and functionalities.

[0183] FIG. 10 illustrates an exemplary data model underlying the disclosed concepts in one embodiment.

[0184] In the following, exemplary applications of the concepts disclosed herein will be described in exemplary specific use cases:

[0185] Example 1: Implementation and Validation of the DAI Matrix Platform in an Automotive Manufacturing Environment

[0186] Embodiments of the DAI Matrix platform may be used for automating artificial intelligence integration, measuring data value creation, tracking sustainability metrics, and enhancing user competency in an automotive manufacturing facility. This is one example to demonstrate how a multidimensional approach could address the complex challenges faced by hardware-driven industries in their digital transformation journey. The system may be implemented using an enterprise-grade server cluster composed of nodes and storage capacity with redundant backup systems. Software components may include the DAI Matrix platform core, a knowledge graph database system, a multi-agent processing engine, machine learning frameworks (such as TensorFlow 2.8, PyTorch 1.12), a multimodal generative Al interface, and ESG measurement modules. Data sources integrated into the system may include the company's Enterprise Resource Planning (ERP) system, Manufacturing Execution System (MES), Quality Management System (QMS), Human Resources records, energy consumption monitoring systems, existing Al model performance metrics, and financial data.

[0187] Initially, the setup begins with the deployment of the DAI Matrix platform connected to all relevant data sources within the facility. A knowledge-based data structure is built according to a graph data model, establishing nodes representing technology resources, business processes, consumption patterns, user competencies, and environmental parameters, with edges representing the relationships among these entities. Data is collected and merged from multiple domains to establish baseline measurements across all four dimensions: Al technology integration, data value creation, sustainability impact, and competency expansion.The multi-agent processing engine is configured with distinct software agents associated with respective data domains, assigned to specialized tasks including correlating Al model performance with financial metrics, predicting sustainability outcomes, recommending competency enhancements, and detecting opportunities to reuse existing data sets. A recommendation system is implemented to generate and prioritize possible actions based on calculated impact factors that accounted for cost, greenhouse gas reductions, workforce upskilling, and anticipated performance improvements. A selflearning feedback loop is established to update the knowledge-based data structure with measured outcomes and newly received data.

[0188] The system can be evaluated in three distinct phases: initial deployment and baseline establishment (e.g., months 1-6), implementation of recommended actions and initial feedback (e.g., months 7-12), and advanced optimization and continuous refinement (e.g., months 13-18).

[0189] The implementation of the DAI Matrix platform can result in significant improvements across all four dimensions. Al model deployment time can decrease, while model accuracy can increase. Data processing efficiency can improve, and the return on Al investment can increase. Energy consumption per Al inference can decrease, and CO2 emissions can be reduced. Employee Al competency scores can increase, and the reuse rate of Al components can rise.

[0190] Example 2: Implementation of the DAI Matrix Platform in Healthcare Systems for Clinical Decision Support and Resource Optimization

[0191] The DAI Matrix platform may be deployed in a large healthcare network comprising hospitals, clinics, and / or a telemedicine center serving vast amounts of patients annually. The platform may connect to the healthcare network's electronic health record (EHR) system, clinical decision support systems, medical imaging databases, patient monitoring devices, pharmacy management systems, and administrative databases to create a comprehensive knowledge graph.

[0192] The knowledge-based data structure may be implemented as a graph data model with multiple nodes representing clinical pathways, medical devices, diagnostic tools, healthcare professionals, patient care patterns, medication protocols, and sustainability parameters. These nodes may be connected by edges representing clinical relationships, causal connections, treatment sequences, and staff-patient interactions. The graph database may employ secure partitioning to ensure HIPAA compliance while maintaining analytical capabilities across the network.

[0193] Data merging processes may collect and integrate information from clinical laboratory systems (providing diagnostic results), radiology information systems (storing imaging data and reports), pharmacy management systems (tracking medication inventory and usage), human resource systems (maintaining staff credentials and training records), and facility management systems (monitoring energy consumption, waste generation,and water usage). The integration layer may employ healthcare-specific data standards including HL7 FHIR, DICOM, and SNOMED CT to ensure semantic interoperability across all data sources.

[0194] The multi-agent processing engine may feature specialized agents for clinical pathway optimization, diagnostic accuracy enhancement, resource allocation, staff scheduling optimization, and sustainability monitoring. The clinical pathway optimization agent may analyze treatment patterns against outcomes to identify optimal intervention sequences. The diagnostic agent may correlate imaging analysis results with patient histories and laboratory findings to improve diagnostic accuracy. The resource allocation agent may predict patient admission patterns and recommends staffing adjustments. The sustainability agent may monitor medical waste, energy consumption, and pharmaceutical disposal to recommend environmentally responsible practices.

[0195] The system may employ a federated learning approach to analyze sensitive patient data without compromising privacy, using encrypted model updates rather than raw data transfers. Graph convolutional networks may analyze relationships between treatments and outcomes, while transformer-based natural language processing extracts insights from clinical notes and medical literature. Computer vision algorithms may process medical imagery to augment diagnostic capabilities.

[0196] Recommendation generation may prioritize actions based on calculated impact factors including patient outcome improvement potential, cost reduction, staff burden reduction, and environmental impact mitigation. The system may recommend specific Al diagnostic tools for particular patient profiles, identify opportunities to consolidate medical testing to reduce resource usage, suggest optimal scheduling patterns for specialized equipment, and / or recommend targeted training for clinicians based on their specific patient populations.

[0197] The platform's self-learning feedback loop may continuously compare predicted treatment outcomes against actual patient results, refining its clinical recommendations as new evidence emerges. When new medical research is published, the system may automatically evaluate its relevance to current clinical pathways and suggest evidencebased practice updates. Monthly sustainability metrics may track progress toward the network's environmental goals, with the system adapting its recommendations based on performance trends.

[0198] Example 3: Deployment of the DAI Matrix Platform in Electric Utility Grids for Smart Energy Distribution and Renewable Integration

[0199] An electric utility company may implement the DAI Matrix platform across its operations spanning power generation, transmission, distribution, and customer service to optimize Al deployment in smart grid management and renewable energy integration. The utility may serve millions of customers across a huge service area with a mix of conventional and renewable energy sources.The knowledge-based data structure may be built as a hierarchical graph database with nodes representing energy generation assets, distribution equipment, smart meters, customer profiles, weather patterns, and energy market factors. These nodes may be interconnected by edges representing energy flows, grid dependencies, maintenance relationships, consumption patterns, and predictive correlations. The graph may incorporate temporal attributes to capture time-series data essential for energy demand forecasting and renewable energy integration.

[0200] Data merging processes may integrate information from supervisory control and data acquisition (SCADA) systems monitoring generation and transmission, advanced metering infrastructure providing real-time consumption data, geographic information systems mapping physical grid assets, weather forecasting services providing meteorological predictions, energy trading platforms offering market pricing, human resource systems tracking workforce qualifications, and environmental monitoring systems measuring emissions and water usage. The integration layer may employ industry-specific protocols including IEC 61850, MultiSpeak, and CIM standards to ensure complete operational visibility.

[0201] The multi-agent processing engine may employ specialized agents for demand forecasting, renewable integration, predictive maintenance, outage management, and grid optimization. The demand forecasting agent may analyze consumption patterns, weather data, and economic indicators to predict load requirements. The renewable integration agent may optimize battery storage deployment and dispatch based on solar and wind generation forecasts. The predictive maintenance agent may identify potential equipment failures before they occur based on sensor data and historical maintenance records. The outage management agent may coordinate restoration efforts during service disruptions, prioritizing critical infrastructure.

[0202] Advanced algorithms may analyze grid operations, including recurrent neural networks for time-series forecasting of energy demand, reinforcement learning algorithms for optimizing energy dispatch decisions, computer vision for drone-based infrastructure inspection, and transformer models for analyzing customer communication patterns. These algorithms may operate within a federated architecture that preserves customer privacy while enabling system-wide optimization.

[0203] Recommendation generation may include Al model deployment suggestions that balance grid stability with renewable integration goals, data asset allocation recommendations for prioritizing sensor deployments in high-impact grid locations, sustainability adjustments to maximize renewable utilization during peak generation periods, and competency enhancement recommendations tailored to each operational team. The recommendations may be prioritized based on reliability impact, cost reduction potential, carbon emission avoidance, and regulatory compliance factors. The self-learning feedback loop may continuously refine grid operation models by comparing predicted versus actual load patterns, equipment performance, andrenewable generation. The system may automatically recalibrate when significant deviations are detected, such as following major weather events or when new distributed energy resources are connected to the grid. Performance metrics may be tracked and used to further optimize the platform's recommendations.

[0204] In yet another embodiment, a system is provided that transforms a given environment into a vibrant ecosystem where every piece of data is connected, understood, and instantly actionable. A multi-agent processing engine built on a knowledge-based data structure seamlessly unifies the data assets and use cases. In certain embodiments, this system may comprise one or more of the following agents:

[0205] • Insight Agent: Deciphers user questions and delivers concise, data-driven insights. It behaves like having a personal data scientist at the user's fingertips, transforming complex information into clear, actionable intelligence.

[0206] • Submission Agent: Engages in smooth, interactive conversations that automatically update the assets, making data maintenance not just seamless but enjoyable.

[0207] • Discovery Agent: Intelligently filters and curates information, ensuring that the user always finds exactly what thry are looking for, precisely when they need it. • Everyday Agent: Delivers real-world information to empower daily operations, making the system an indispensable part of the user's routine.

[0208] With such a system, every interaction is an invitation to explore new possibilities, streamline workflows, and unlock insights.

[0209] While various aspects and embodiments have been illustrated and described in detail in the drawings and foregoing description, such illustration and description is to be considered illustrative or exemplary and not restrictive. The invention is not limited to the disclosed embodiments. Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0210] In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measured cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.

[0211] Embodiments of the present disclosure may be implemented on a computer system. The computer system may be a local computer device (e.g., personal computer, laptop, tablet computer or mobile phone) with one or more Processors and one or more storage devices or may be a distributed computer system (e.g., a cloud computing system with one or more Processors and one or more storage devices distributed at various locations, for example, at a local client and / or one or more remote server farms and / or data centers). The computer system may comprise any circuit or combination of circuits.In one embodiment, the computer system may include one or more Processors which can be of any type. As used herein, Processor may mean any type of computational circuit, such as but not limited to a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a graphics Processor, a digital signal Processor (DSP), multiple core Processor, a field programmable gate array (FPGA), or any other type of Processor or processing circuit. Other types of circuits that may be included in the computer system may be a custom circuit, an application-specific integrated circuit (ASIC), or the like, such as, for example, one or more circuits (such as a communication circuit) for use in wireless devices like mobile telephones, tablet computers, laptop computers, two-way radios, and similar electronic systems. The computer system may include one or more storage devices, which may include one or more Memory elements suitable to the particular application, such as a main Memory in the form of random access Memory (RAM), one or more hard drives, and / or one or more drives that handle removable media such as compact disks (CD), flash Memory cards, digital video disk (DVD), and the like. The computer system may also include a display device, one or more speakers, and a keyboard and / or controller, which can include a mouse, trackball, touch screen, voice-recognition device, or any other device that permits a system user to input information into and receive information from the computer system.

[0212] Some or all of the method steps may be executed by (or using) a hardware apparatus, like for example, a Processor, a microprocessor, a programmable computer or an electronic circuit. In some embodiments, some one or more of the most important method steps may be executed by such an apparatus.

[0213] Depending on certain implementation requirements, embodiments of the present disclosure can be implemented in hardware or in software. The implementation can be performed using a non-transitory storage medium such as a digital storage medium, for example a floppy disc, a DVD, a Blu-Ray, a CD, a ROM, a PROM, and EPROM, an EEPROM or a FLASH Memory, having electronically readable control signals stored thereon, which cooperate (or are capable of cooperating) with a programmable computer system such that the respective method is performed. Therefore, the digital storage medium may be computer readable.

[0214] Some embodiments comprise a data carrier having electronically readable control signals, which are capable of cooperating with a programmable computer system, such that one of the methods described herein is performed.

[0215] Generally, embodiments of the present disclosure can be implemented as a computer program product with a program code, the program code being operative for performing one of the methods when the computer program product runs on a computer. The program code may, for example, be stored on a machine-readable carrier.Other embodiments comprise a computer program for performing one of the methods described herein, stored on a machine-readable carrier.

[0216] A further embodiment is a computer program having a program code for performing one of the methods described herein, when the computer program runs on a computer. A further embodiment is a storage medium (or a data carrier, or a computer-readable medium) comprising, stored thereon, the computer program for performing one of the methods described herein when it is performed by a Processor. The data carrier, the digital storage medium or the recorded medium are typically tangible and / or non-transitory.

[0217] A further embodiment is an apparatus as described herein comprising a Processor and the storage medium.

[0218] A further embodiment is a data stream or a sequence of signals representing the computer program for performing one of the methods described herein. The data stream or the sequence of signals may, for example, be configured to be transferred via a data communication connection, for example, via the internet.

[0219] A further embodiment is a processing means, for example, a computer or a programmable logic device, configured to, or adapted to, perform one of the methods described herein.

[0220] A further embodiment is a computer having installed thereon the computer program for performing one of the methods described herein.

[0221] A further embodiment is an apparatus or a system configured to transfer (e.g., electronically or optically) a computer program for performing one of the methods described herein to a receiver. The receiver may, for example, comprise a computer, a mobile device, a Memory device or the like. The apparatus or system may, for example, comprise a file server for transferring the computer program to the receiver.

[0222] In some embodiments, a programmable logic device (for example, a field programmable gate array) may be used to perform some or all of the functionalities of the methods described herein. In some embodiments, a field programmable gate array may cooperate with a microprocessor in order to perform one of the methods described herein. Generally, the methods are preferably performed by any hardware apparatus.

[0223] REFERENCE SIGNS

[0224] 100 method

[0225] 102 building step

[0226] 104 configuration step

[0227] 106 generation step

[0228] 108 updating step202 system

[0229] 204 knowledge-based data structure

[0230] 206 multi-agent processing engine

[0231] 208 data input interface

[0232] 210 ERP system

[0233] 212 loT platform

[0234] 214 HR system

[0235] 216 sustainability repository

[0236] 218 recommendation generation module 220 user interface

[0237] 222 feedback measurement module

[0238] 300 graph data model

[0239] 302 technology resource node

[0240] 304 business process node

[0241] 306 consumption pattern node

[0242] 308 user competency node

[0243] 310 environmental parameter node

[0244] 402 Al-financial correlation agent

[0245] 404 sustainability prediction agent

[0246] 406 competency recommendation agent 408 data reuse optimization agent

[0247] 410 agent coordination module

[0248] 412 analysis integration module

[0249] 500 correlation determination method

[0250] 502 data extraction

[0251] 504 preprocessing

[0252] 506 correlation analysis

[0253] 508 validation

[0254] 510 significance assessment

[0255] 512 dimensional integration

[0256] 600 recommendation generation method 602 goal analysis

[0257] 604 action identification

[0258] 606 impact assessment

[0259] 608 Al model ranking

[0260] 610 training assignment

[0261] 612 recommendation package generation 700 self-learning feedback loop

[0262] 702 implementation of recommended actions 704 tracking by measurement modules 706 compare against predicted outcomes 708 deviation analysis

[0263] 710 model refinement712 knowledge update

[0264] 714 automated trigger

[0265] 802 Al technology integration 804 data value creation

[0266] 806 sustainability impact 808 competency expansion 902 data processing apparatus 904 processor

[0267] 906 memory

[0268] 908 computer program

Claims

CLAIMS1. A computer-implemented method for automatically guiding and updating artificial intelligence integration, data value measurement, sustainability metrics, and user competency for an industrial environment, the method comprising:building a knowledge-based data structure that merges data from multiple domains, the knowledge-based data structure including parameters indicative of artificial intelligence performance, economic return, environmental impact, and user skills;configuring a multi-agent processing engine to iteratively analyze the merged data within the knowledge-based data structure to determine correlations among artificial intelligence maturity, economic return on investment, sustainability metrics, and competency requirements;generating, in response to the iterative analysis, recommended actions addressing at least one of:(a) selection or deployment of artificial intelligence solutions,(b) allocation of data assets,(c) adjustment of environmental or resource usage, and(d) assignment of training modules or skill-enhancement tasks; and updating the knowledge-based data structure with measured outcomes or newly received data to refine subsequent recommended actions via a self-learning feedback loop.

2. The method of claim 1, wherein the knowledge-based data structure is implemented as a graph data model that stores nodes representing entities selected from technology resources, business processes, consumption patterns, user competencies, and environmental parameters, and edges representing relationships among said entities.

3. The method of claim 1 or 2, wherein merging data from multiple domains includes collecting data from at least one of:(a) enterprise resource planning systems storing financial and operational metrics, (b) industrial Internet of Things platforms capturing machine sensor data,(c) human resources systems providing user skill profiles, and(d) sustainability data repositories logging energy consumption and emission values.

4. The method of any one of claims 1 to 3, wherein the parameters indicative of artificial intelligence performance include at least one of model accuracy, model inference latency, training iterations, or memory footprint.

5. The method of any one of claims 1 to 4, wherein the parameters indicative of economic return include at least one of capital expenditure, operating expenditure, revenue, or calculated return on investment over a defined time period.

6. The method of any one of claims 1 to 5, wherein the parameters indicative of environmental impact include at least one of total energy consumption, renewableenergy usage, water usage, or CO2 emissions measured in accordance with a defined sustainability standard.

7. The method of any one of claims 1 to 6, wherein the parameters indicative of user skills include at least one of role-based responsibilities, completed training modules, defined proficiency levels, or qualification records tracked within the knowledge-based data structure.

8. The method of any one of claims 1 to 7, wherein configuring the multi-agent processing engine comprises associating distinct software agents with respective data domains, such that each agent analyzes a subset of the merged data and communicates its inferences to other agents.

9. The method of claim 8, wherein each software agent is assigned a specialized task selected from:(a) correlating artificial intelligence model performance with financial metrics, (b) predicting sustainability outcomes based on usage patterns,(c) recommending competency enhancements for users based on project requirements,(d) detecting opportunities to reuse existing data sets for multiple Al use cases.

10. The method of any one of claims 1 to 9, wherein determining correlations among artificial intelligence maturity, economic return on investment, sustainability metrics, and competency requirements includes applying at least one graph-based algorithm, machine-learning algorithm, or multimodal Al component.

11. The method of any one of claims 1 to 10, wherein generating recommended actions includes prioritizing possible actions according to a calculated impact factor that accounts for at least one of cost, estimated greenhouse gas reductions, workforce upskilling, or anticipated performance improvements.

12. The method of any one of claims 1 to 11 , wherein generating recommended actions further comprises providing a ranking of artificial intelligence models deemed to achieve a specified sustainability threshold measured by a predefined carbon intensity factor.

13. The method of any one of claims 1 to 12, wherein generating recommended actions further comprises automatically assigning targeted training curricula to specific user roles based on identified competency gaps for operating or managing selected artificial intelligence solutions.

14. The method of any one of claims 1 to 13, wherein updating the knowledge-based data structure includes storing performance outcomes over time, such that historical performance indicators, cost parameters, emission metrics, or skill progressions are compared to subsequent real-world measurements and used to refine futurerecommendations.

15. The method of any one of claims 1 to 14, wherein the self-learning feedback loop is partially or fully automated, such that recommended actions are recalculated when newly received data exceeds a threshold difference in at least one parameter from the previously analyzed state.

16. A data processing system comprising means for carrying out the method of any one of claims 1 to 15.

17. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of any one of claims 1 to 15.

18. A system configured to automatically guide and update artificial intelligence integration, data value measurement, sustainability metrics, and user competency for an industrial environment, the system comprising:a data storage module storing a knowledge-based data structure that merges data from multiple domains, the knowledge-based data structure including parameters indicative of artificial intelligence performance, economic return, environmental impact, and user skills;a multi-agent processing engine connected to the data storage module and configured to iteratively analyze the merged data to determine correlations among artificial intelligence maturity, economic return on investment, sustainability metrics, and competency requirements;a recommendation engine generating, in response to the iterative analysis, recommended actions addressing at least one of:(a) selection or deployment of artificial intelligence solutions,(b) allocation of data assets,(c) adjustment of environmental or resource usage, and(d) assignment of training modules or skill-enhancement tasks; and a feedback mechanism updating the knowledge-based data structure with measured outcomes or newly received data to refine subsequent recommended actions.

19. A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause a device to perform a method for automatically guiding and updating artificial intelligence integration, data value measurement, sustainability metrics, and user competency for an industrial environment, the method comprising:building a knowledge-based data structure that merges data from multiple domains, the knowledge-based data structure including parameters indicative of artificial intelligence performance, economic return, environmental impact, and user skills;configuring a multi-agent processing engine to iteratively analyze the merged data within the knowledge-based data structure to determine correlations among artificial intelligence maturity, economic return on investment, sustainability metrics, and competency requirements;generating, in response to the iterative analysis, recommended actions addressing at least one of:(a) selection or deployment of artificial intelligence solutions, (b) allocation of data assets,(c) adjustment of environmental or resource usage, and(d) assignment of training modules or skill-enhancement tasks; and updating the knowledge-based data structure with measured outcomes or newly received data to refine subsequent recommended actions via a self-learning feedback loop.

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