How Program Management Accelerates the National AI Revolution

The integration of agentic Artificial Intelligence (AI) into enterprise Software-as-a-Service (SaaS) platforms such as Salesforce is transforming organizational governance and risk management practices. This study examines how trustworthy agentic AI, Salesforce SaaS capabilities, and product governance collectively influence risk control effectiveness. Employing a mixed-methods design, data were collected from 212 survey respondents and 15 expert interviews across multiple industries. Reliability and validity analyses confirmed the robustness of the measurement constructs, while regression and structural equation modeling revealed that governance maturity is the strongest predictor of effective risk controls. Trustworthy agentic AI significantly contributes to governance quality through features such as transparency, fairness, and robustness, while Salesforce SaaS functions as a strategic enabler by providing secure and scalable infrastructures. Mediation and moderation analyses further highlighted governance as a mediator and compliance adherence as a moderator in enhancing AI-driven risk controls. The findings emphasize that governance should be treated as a strategic enabler rather than a compliance obligation, ensuring ethical, resilient, and sustainable AI adoption in SaaS environments.

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

This study employs a qualitative design grounded in documentary analysis and a systematic review of the literature to examine how data quality, institutional capacities, and governance arrangements condition the adoption of artificial intelligence (AI) in Bahia’s public administration. The investigation evaluates how governmental information infrastructures, digital maturity levels, and organizational coordination shape the state’s ability to transition from data availability to data intelligence. The analysis identifies a recurring pattern across Brazilian states in which technical, institutional, cultural, and legal limitations accumulate at multiple territorial scales and restrict the effective use of AI systems. These constraints manifest in data quality dimensions such as completeness, timeliness, structure, interoperability, and licensing; in institutional capacities, including agency participation, workforce skills, and coordination mechanisms; and in governance maturity, reflected in regulatory consistency, ethical oversight, and accountability structures. Together, these factors determine the feasibility of deploying AI tools that support transparency, operational efficiency, and evidence-based decision-making. The study contributes a replicable methodology for diagnosing readiness conditions, assessing bottlenecks, and prioritizing policy interventions in contexts characterized by heterogeneous institutional capacity and limited statistical visibility. By integrating empirical evidence from data inventories, digital ecosystem assessments, and state program documentation, the analysis outlines a strategic pathway to strengthen foundational infrastructure and enable the responsible use of AI in the public sector.

For modern enterprises to guarantee the quality and accuracy of their fundamental business data, master data management (MDM) is essential. However, a lack of connectivity between data governance and data quality throughout deployment is the reason why many MDM programs fail. This paper introduces a six-phase MDM framework that specifically integrates stakeholder-driven governance and data quality dimensions into each phase of the MDM lifecycle. Based on the PRAXEME methodology and designed using Business Process Model and Notation (BPMN), the framework emphasizes real-time monitoring and facilitates departmental collaboration. A healthcare case study illustrates its efficacy in enhancing data comprehensiveness, minimizing redundancy, and boosting operational productivity. The proposed framework offers a comprehensive approach that aligns business and IT objectives and reinforces process transparency.

As enterprises undergo rapid digital transformation, the volume, velocity, and variety of data have grown exponentially, leading to unprecedented challenges in data management, security, and compliance. Traditional, manual data governance frameworks are no longer sufficient to handle the scale and complexity of modern data ecosystems, often resulting in data silos, poor data quality, and regulatory non-compliance. This paper proposes a comprehensive AI-Driven Unified Data Governance Framework designed for modern enterprise platforms. By integrating artificial intelligence (AI) and machine learning (ML) at the core of the data governance lifecycle, the proposed framework automates critical functions such as data discovery, classification, quality assurance, lineage tracking, and policy enforcement. We present a layered architectural model that seamlessly operates across hybrid and multi-cloud environments, supporting paradigms like data mesh and data fabric. Through four detailed enterprise case studies spanning financial services, healthcare, global retail, and smart manufacturing, we demonstrate the empirical benefits of AI-driven governance, including up to 72% reduction in compliance reporting time and significant improvements in data quality and operational efficiency. Furthermore, we provide a comparative analysis of leading enterprise governance platforms and introduce a six-level AI Governance Maturity Model. Finally, the paper explores future trends, including the integration of Large Language Models (LLMs), autonomous self-healing data pipelines, and federated learning, providing a strategic technology roadmap for the next decade of enterprise data governance.

Facts, figures, symbols, items, and occurrences are all examples of data. The information is helpful for making decisions. As organizations rely on data to inform their decisions, it is gathered regularly and from various sources. Because of their variety and ease of use, Google forms are often used to create surveys for event planning, interview staff or consumers, and collect a wide range of data. Users can ask questions in many formats with this technology. As electronic data proliferates, ensuring high data quality in both government and business applications grows. Data quality is a key aspect of decision-making and inter-organizational interaction. In this paper, we review the established rules by Google Forms to ensure the quality of the information gathered from them.

The adoption of Business Intelligence (BI), Big Data, and Advanced Data Analysis techniques (such as Artificial Intelligence) by organizations for decision support is seen as incontrovertible and imperative to be competitive in the global market. If first companies are concerned with the implementation on data platforms for Data Analysis/data science, as per example, Self-Service Business Intelligence, Cloud Data Lakes, Machine Learning frameworks, Mobile Intelligence, regulations (e.g., General Data Protection Regulation – GDPR) and information security (infosec) force organizations to put in place data governance programs and data maturity assessment. The foundations for business results on using data platforms are trusted data, improved risk and analytic decisions, cost reduction and operational efficiency, and regulatory compliance. Being the deployment of BI platforms necessary to store and extract knowledge from data, the data governance is mandatory to assure the central data management, data quality, regulation, security, and privacy. Another important topic, to establish the data roadmap and assess the maturity within an organization, is the adoption of mechanisms to measure the return of investments in BI and Big Data platforms. If currently organizations tend to be data centric, a transversal approach from tools to data governance and maturity control is required.

Long-Term Care Reforms Live On With Court Ruling

Integrating Land Use and Water Quality for Environmental based Land Use Planning for U-tapao River Basin, Thailand

Management Insights

Biological health assessment (BHA) has developed as an imperative gauge in efficient management of freshwater resources and fish assemblages. The principal objective of this research is directed at the development and application of a new index under the umbrella of the famous index of biological integrity (IBI). Further, we intended to carry out comparative assessments of the new index with two existing indices and testified to their rational in Geum River watershed, which is the third largest river water basin in South Korea. We analyzed the biotic integrity of 149 different study sites in the streams and rivers of Geum River watershed, perusing fish assemblages and water quality data. The results revealed the newer index, i.e., multi-metric (mm) IBI 06 (mmIBI 06 metrics), as comparatively more efficient than previously used indices, i.e., mmIBI 08 and mmIBI 11. Furthermore, the linear regression and correlational analyses indicated harmonic relation of mmIBI 06 with mmIBI 08 (R2 = 0.85) and mmIBI 11 (R2 = 0.87). Nonetheless, linear regression modeling discovered a very strong positive relation between mmIBI 08 and mmIBI 11 (R2 = 0.91), thereby implying previously used indices demonstrate better approximation. In significant contrast to both old indices, the newer index exhibited improved facility to better classify the study sites between the assortments of ‘excellent to very poor’ compared to old indices elucidated one-step lower, i.e., from ‘excellent to poor’. For instance, the newer index categorized 60 sites as ‘very poor’, requiring immediate attention owing to biological degradation. Additionally, the newer index endures grander ability to indicate sites requiring immediate management or restoration attention with a plausible site classification, especially in relation to the invasive alien species (IAS). The water chemistry was mainly influenced by rampant anthropogenic actions compounded by intensive monsoon precipitation that may relinquish highly suitable grounds for invasive alien fish species. This may eventually lead to severe biological degradation and successive deterioration of habitat by the IAS. In conclusion, the newer index endures ample capacity to indicate the fish community disturbances in rivers and streams. Further, correlation, linear regression, as well as principal component analysis (PCA) analyses on biotic indices and water chemistry showed higher approximations. Therefore, our newer index would be a valuable BHA tool to classify and elucidate the streams and rivers by indicating sites necessitating preferred attention and restoration measures.

PurposeStandards are written by practitioners for practitioners. It is therefore logical that project managers should comply with project management standards. Benefits management is a domain within programme management. The focus of benefits management is to deliver benefits of initiatives beyond the closure of a normal programme or project. This is not the case with projects within the information systems (IS) discipline, implying that IS programme and project managers are not adhering to standards. The purpose of this paper is to determine whether the best practices associated with benefits management are applied to IS initiatives in order to maximise the benefits of these initiatives.Design/methodology/approachSenior and middle managers in South African organisations were interviewed to determine how benefits are managed within their various projects. The purpose of the interviews was to determine adherence to standards and especially benefits management and, second, to determine whether these organisations are achieving any benefits and ultimately value.FindingsThere is an overwhelming non-adherence to benefits management best practices within the IS discipline, and IS programme and project managers do not have the slightest idea how to perform benefits management. Irrespective of this, organisations do believe that they are receiving benefits and value from these IS projects.Research limitations/implicationsThe research was only done in South Africa with the specific focus of IS. The results are thus very specific and opens the door for more comprehensive research that focusses on various industries, countries and standards.Practical implicationsThe results have several implications ranging from how standards are written to the professionalism of IS programmes and project managers. Organisations are not achieving the optimal benefits from investments. The fact that organisations do realise benefits from a broken process, implies that more benefits can be realised when the entire benefits realisation process is followed. Governance controls should also be put in place to ensure that programme and project managers are adhering to standards.Originality/valueStandards are dominating the project management discipline and there is a general assumption that programme and project managers are adhering these standards. This research queries the value of standards as the results indicate that there is limited adherence to standards and best practices.

The probabilistic and possibilistic methods of reliability analysis of structures have been developed independently. However, the designers will prefer the flexibility of modeling the structural parameter uncertainty either probabilistically or possibilistically depending on the nature and quality of the available data. Thereby, the complex structural model will involve both types of uncertain parameters and can be referred as hybrid uncertain system. The present paper deals with the safety evaluation of such hybrid uncertain system in possibilistic format. The limit state functions involving both the random and fuzzy variables need to express in terms of the fuzzy variables. This necessitates transformation of random variables to equivalent fuzzy variables satisfying the consistency condition. In present study, the entropy based transformation theory of expressing the uncertainty information applied in various fields of engineering, is used. The possibilistic reliability index and the possibilistic design point are obtained by numerical optimization technique to evaluate the possibility of failure. A numerical example illustrates the capability of the transformation approach to evaluate the safety of the structure under hybrid uncertainties.

BackgroundThe UK Multiple Sclerosis Register (UKMSR) was established in 2011 to capture ‘real world’ data from people with MS (pwMS) and the NHS. Capturing clinical data from the NHS for research is time consuming for clinical staff, can involve complicated governance, security and data quality issues. UKMSR has implemented quality controls, validation rules, new capture systems and site reporting to improve this.MethodCompare the Minimum Data Set (MDS) consisting of 7 items including demographics and EDSS for quality and quantity in the first 3 years (pilot phase) to the most recent 3 years of data captureResultsCompletion of MDS (100% valid and completed values) (2011–2014) 4 sites, n=884. MDS received= 159 (17.9%), Time to add site ~3 Months (2017–2020) 48 Sites, n= 8836. MDS received = 5791 (65.5%), Time to add site ~6 WeeksConclusionThe UKMSR has improved the clinical capture of MDS from a vastly increased number of patients and clinical sites. Improved validation standards and systems have increased data quality. Sites are added more quickly and rapidly notified of data quality issues for correction, making the data more useful to the site and ultimately of more utility to researchers.richard.nicholas@btinternet.com

Data Governance, Data Integrity, and Data Quality