Implementation of Artificial Intelligence, Automation and Robotization in Financial Business Centers

The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

The relevance of the paper is due to the digitalization of the economy and the introduction of artificial intelligence in production processes. This paper attempts to assess the effectiveness of artificial intelligence for the automation of production. Thus, the purpose of the work is to evaluate the effect of the introduction of artificial intelligence into automated process control systems. For this, an algorithm for implementing artificial intelligence was developed, i.e., procedures and their sequence were identified when implementing artificial intelligence in automated process control systems. The following procedures were considered: selection of implemented artificial intelligence functions, selection of an artificial intelligence system, selection of hardware implementation and acquisition of artificial intelligence, formation of tests for artificial intelligence training, implementation of artificial intelligence, and evaluation of results of implementing artificial intelligence. When implementing artificial intelligence, one should choose artificial intelligence based on neural networks with deep learning. The ambiguity of the cost estimate existed when selecting hardware due to the lack of data from developed artificial intelligence versions. This complicates the definition of capital expenditures. A formula for calculating costs of implementing artificial intelligence costs in automated process control systems is proposed. The introduction of artificial intelligence into an automated process control system will not provide significant savings. Such conclusions are drawn on the basis of the calculation method.KeywordsArtificial intelligenceAutomated process control systemEconomic efficiency

Aim . The presented study aims to determine the impact of artificial intelligence as a modern breakthrough technology on productivity, to explore how the implementation of artificial intelligence technology will affect the preservation of jobs in different industries, what opportunities it will create for business in terms of increasing productivity along the entire value chain, and how this will affect GDP growth and key economic indicators in various countries. Tasks . The authors identify priority directions for the development and implementation of artificial intelligence in various economic sectors; analyze econometric results obtained during previous studies; substantiate the advantages and opportunities of artificial intelligence to facilitate its implementation in the business processes of organizations. Methods . This study uses the methods of analysis, systematization, and correlation analysis. Results . Various definitions of artificial intelligence, levels of its functionality, and fields of application are analyzed. The ways and prospects of using artificial intelligence in different countries are examined regressively by industry and geographical region, with an emphasis on the ways of using artificial intelligence systems (wired/special and adaptive) and automation technologies in the implementation of artificial intelligence. The potential effects of artificial intelligence at each stage of the company's value chain are described. Examples from different industrial sectors are provided. Based on the correlation analysis, the relationship between the implementation of artificial intelligence and productivity growth is presented. Conclusions . Implementation of artificial intelligence has a global economic impact on key economic indicators such as employment and GDP, which is especially important in the current crisis situation. The effect of artificial intelligence should be enough to maintain the rate of economic growth in the long term. The direct impact of artificial intelligence on GDP is due to increased income and employment in firms and industries engaged in the development or production of artificial intelligence technologies. Secondary (indirect) effects will come from other sectors that use certain artificial intelligence technologies to increase the efficiency of their processes and solutions and improve the accessibility of information. Regions implementing an artificial intelligence technology of higher quality can expect its impact on labor productivity to be even more significant.

BackgroundThe ability to perform complex tasks has seen artificial intelligence (AI) used to support radiology in clinical settings, including lung cancer detection and diagnosis. Evidence suggests that AI can contribute to accurate diagnosis, reduce errors, and improve efficiency. The National Health Service England (NHSE)–funded Artificial Intelligence Diagnostic Fund (AIDF) is currently supporting 12 National Health Service (NHS) networks to implement AI for chest diagnostic imaging. There is, however, limited evidence on real-world AI implementation and use, including staff, patient, and caregiver experience, and costs and cost-effectiveness. A National Institute for Health and Care Research Rapid Service Evaluation Team Phase 1 evaluation provided insights into the early implementation of these tools and developed a framework for monitoring and evaluation of AI tools for chest diagnostic imaging in practice.ObjectiveThis mixed methods evaluation of AI tools for chest diagnostic imaging aims to address previous research gaps by exploring the implementation of AI tools for chest diagnostic imaging, the impact and costs of implementing these service models, and the experiences of patients, caregivers, and staff.MethodsThis study will be a mixed method evaluation of implementation, experiences, impact, and costs of AI for chest diagnostic imaging in NHS services in England, with the evaluation informed by the Major System Change Framework. Trust-level case studies (3 in-depth and up to 9 light-touch) will be performed, including staff member, patient, and caregiver; NHSE AIDF team interviews; meeting observations; and analysis of key relevant documentation. Qualitative data will be analyzed using Rapid Assessment Procedures and inductive thematic analysis, supplemented by in-depth deductive thematic analysis. Data from case study sites and other relevant sources will be used to assess outcomes at the other sites and for comparators. A pragmatic economic model of the chest diagnostic imaging pathway will be developed to estimate key costs and resource use associated with AI tool deployment. Together with input from national stakeholders and staff workshops, the study findings will then be finalized for reporting.ResultsAs of September 2025, trust-level research and development approvals with participating sites are complete, and data collection has commenced. Results are expected to be reported by the end of February 2026.ConclusionsThe study will provide new insights into the facilitators and barriers to the adoption of AI technology in health care and the perceptions of both the general public and health care staff on its use. It will also inform best practices in approaches for service performance evaluation, for the implementation of AI into existing care pathways, and for the development of models to best support evidence-based decision-making. It will thus establish a framework upon which the greatest benefits of the use of AI in health care can be realized.International Registered Report Identifier (IRRID)DERR1-10.2196/81421

Purpose This service evaluation investigated frontline staff attitudes towards artificial intelligence (AI) implementation in NHS learning disabilities services to address critical knowledge gaps in workforce perspectives. Despite growing NHS AI adoption, systematic understanding of staff concerns remains limited, particularly regarding vulnerable populations who face heightened risks around consent capacity, communication barriers and potential exploitation. This study aims to capture staff perceptions of AI benefits, concerns and implementation needs to inform evidence-based, ethically-grounded Trust-level digital strategy that prioritises patient safety while supporting workforce readiness for technological change. Design/methodology/approach This mixed-methods service evaluation used an online questionnaire (n = 68) and semistructured focus group to explore staff attitudes in NHS specialist learning disabilities services. Participants included clinical professionals and nonclinical operational staff recruited through team meetings and electronic communications during July 2025–August 2025. The quantitative survey assessed AI familiarity using five-point scales, examining comfort levels, concerns regarding vulnerable patients, perceived benefits and training needs. A 30-minute focus group conducted via MS Teams explored clinical experiences, safeguarding concerns and implementation barriers. Descriptive statistics analysed quantitative responses while thematic analysis examined qualitative data. The study received Trust Practice Audit Implementation Group approval with voluntary participation and informed consent protocols. Findings Most staff (57%) demonstrated basic AI understanding, with 16% already using AI tools. Attitudes were predominantly cautious: 40% expressed neutrality and 35% voiced concerns about implementation with learning disabilities patients. Administrative efficiency emerged as the primary recognised benefit (62%), with limited support for clinical applications. Training priorities emphasised both AI fundamentals (47%) and ethical reassurance regarding bias and safety (47%). Qualitative analysis revealed four themes: heightened vulnerability concerns around patients’ capacity to distinguish AI from human interactions, significant safeguarding and exploitation risks, pragmatic engagement and training needs and governance. Originality/value This service evaluation addresses a critical gap by examining frontline workforce perspectives on AI implementation in an intellectual disabilities’ services, a population often marginalised in digital health transformation. It reveals unique vulnerabilities absent from general health-care AI literature, particularly around reality testing, consent capacity and exploitation risks through AI interactions. Unlike broader NHS AI surveys focusing on technical feasibility or public trust, this research captures specialist staff concerns about safeguarding implications and therapeutic relationship preservation. Findings provide evidence-based guidance for developing population-specific governance frameworks rather than applying standard protocols unsuitable for vulnerable groups. The equal emphasis on technical training and ethical reassurance offers practical insights for staged implementation strategies that balance innovation with patient safety.

Artificial Intelligence (AI) implementation incorporates challenges that are unique to the context of AI, such as dealing with probabilistic outputs. To address these challenges, recent research suggests that organizations should develop specific capabilities for AI implementation. Currently, we lack a thorough understanding of how certain capabilities facilitate AI implementation. It remains unclear how they help organizations to cope with AI’s unique characteristics. To address this research gap, we employ a qualitative research approach and conduct 25 explorative interviews with experts on AI implementation. We derive four organizational capabilities for AI implementation: AI Project Planning and Co-Development help to cope with the inscrutability in AI, which complicates the planning of AI projects and communication between different stakeholders. Data Management and AI Model Lifecycle Management help to cope with the data dependency in AI, which challenges organizations to provide the proper data foundation and continuously adjust AI systems as the data evolves. We contribute to our understanding of the sociotechnical implications of AI’s characteristics and further develop the concept of organizational capabilities as an important success factor for AI implementation. For practice, we provide actionable recommendations to develop organizational capabilities for AI implementation.

A key success factor of modern companies is the timely restructuring of business processes, taking into account the penetration of digital technologies in all spheres of society. Nowadays, the challenges have accelerated the implementation of modern business concepts. Continual improvements become the vital step in competitive market. Digitalization has become an unavoidable reality for companies. The article investigates the meaning of the terms "digitization", "digitalization" and "digital transformation". It is proved that digitalization provides companies with competitive advantages in all areas of activity, which is reflected in the optimization of production processes, costs minimization, decreasing errors, increasing speed of delivery, boosting the quality of finished products (services), improving the control of the company’s data and processes, increasing the effectiveness of communication. Examples of companies that use digital technologies and the results of their implementation in business processes are given. The results of survey confirm the positive effects of digitalization of business. Three main types of artificial intelligence are distinguished: artificial narrow intelligence, general artificial intelligence, artificial superintelligence and the differences of each are outlined. The benefits and the main threats of artificial intelligence are revealed. The global artificial intelligence industry is expected to grow from $59.7 billion in 2021 to $422.4 billion by 2028, according to Zion Market Research. The 2020 McKinsey Global Survey on Artificial Intelligence (AI) confirms that 50% of companies have reported using AI in at least one business function. The experience of the world’s largest companies in the implementation of various artificial intelligence tools in operational activities is presented. It is emphasized that artificial intelligence contributes to business development and global economic activity. The growth of key performance indicators after the implementation of artificial intelligence in the business processes of companies is presented.

The predominantly nonsignificant differences we found between head-worn (ARx) and handheld Seeing AI (artificial intelligence) implementations provide objective evidence to the ongoing debate about the relative advantages and disadvantages of form. People with vision loss must choose between these implementations based on functional needs and app accessibility, rather than form factor. To compare the functionality of AI implementations in head-worn devices versus handheld smartphones by objectively quantifying performance, usability, and acceptability when acquiring information from text and in daily activities. A cross-sectional, counterbalanced, crossover design was employed to assess performance using Seeing AI in two formats (ARx headset and on a smartphone) and to compare two head-worn formats that utilize different AI algorithms-Seeing AI and Meta AI. Completion and timing were quantified for items in two task categories: Text and Searching & Identifying. Usability was evaluated with the System Usability Scale. Data were compared with a baseline condition with no assistive technology, and performance among AI implementations was assessed. There was no significant difference in the number of participants who completed tasks and timing between head-worn ARx and smartphone-based Seeing AI implementations. A comparison of two AI algorithms (Seeing AI and Meta) in wearable implementations found equivalent gains in performance but significantly faster task completion times for the Meta glasses. The timing advantage of Meta derives from its ability to provide more information about most tasks more quickly, whereas Seeing AI often requires additional prompts to gather sufficient data to complete tasks. The reported acceptability and usability were statistically similar among the three AI implementations. We found no evidence to demonstrate an advantage in completing tasks using either head-worn (ARx and Meta glasses) or smartphone AI implementations.

BACKGROUND Artificial intelligence has been deemed revolutionary in medicine, but very few artificial intelligence solutions have been observed in Danish general practice. General practice in Denmark has an excellent system of digitization to develop and utilize artificial intelligence. However, a lack of involvement of general practitioners in the development of artificial intelligence exists. The perspectives of general practitioners as end users are essential to facilitate the development and implementation of artificial intelligence in general practice. OBJECTIVE This study aimed to characterize the prerequisites that must be met to enable the development and implementation of artificial intelligence in Danish general practice. METHODS This study applied semi-structured interviews and vignettes to gain perspectives on the potential for developing and implementing artificial intelligence among general practitioners. Twelve general practitioners were interviewed, resulting in an exhaustive dataset. The interviews were transcribed, and thematic analysis was conducted to identify the dominant themes throughout the data. RESULTS Four main themes were identified in the data analysis as prerequisites that general practitioners found important to consider when developing and implementing AI in general practice: ‘AI must begin with the low-hanging fruit’, ‘AI must be meaningful in the GP’s work’, ‘The GP-patient relationship must be maintained despite AI’, and ‘AI must be a free, active, and integrated option in the EHR’. CONCLUSIONS The four themes contributing to defining prerequisites for artificial intelligence can potentially lead the first steps of future development and implementation of artificial intelligence in Danish general practice. The participating general practitioners were positive towards developing and implementing artificial intelligence in their clinics, and it would be valuable to consider the defined prerequisites when considering new artificial intelligence tools for general practice.

Background: With growth in Artificial Intelligence (AI) adoption, challenges and hurdles are also becoming evident. Organizations implementing AI are challenged to find ways to leverage AI to produce optimum results and benefits for the organization. Understanding other organizations’ AI implementation journeys will help them start and implement AI. By understanding the different facets of AI implementation, they can strategize AI to gain business value. Though several studies have examined AI adoption, there are few studies on how firms implement it. We close this gap by studying AI adoption and implementations in various firms. Method: Using a qualitative approach of semi-structured interviews, we studied twenty global organizations of various sizes that have implemented AI. Results: The study categorizes the results into four major themes – facilitators, barriers, trends, and strategies for implementing AI. Our study reinforces the relevance of the TOE framework and Roger’s DOI theory in studying AI adoption. Organizational factors such as top management support, strategic roadmap, availability of skilled resources, and corporate culture influenced AI adoption. Their lack of data or poor data quality is a primary challenge. The privacy laws concerning data, as well as regulatory bottlenecks, further exacerbate this problem. We also identified and mapped the standard AI implementations to their AI technologies. We found that most of them exploit AI’s image and natural language processing capabilities to automate their processes. Regarding implementation, firms work with partners to obtain customer data and use federated learning. Conclusion: Understanding firms’ AI implementation journey will help us promote further adoption and experimentation. Organizations can identify areas where they can leverage AI to enhance value, prepare themselves for the future, start and proceed with AI implementation efforts and overcome barriers they might encounter.

Economic growth, employment creation and resilience of businesses and industry in the 4th Industrial Revolution which is intertwined with climate change realities depends much on the implementation of digital technologies and Artificial Intelligence (AI). The agricultural sector is no exception to these developmental realities. That is to say, the sector is equally compelled to implement AI in almost all the stages of agricultural production. From the cultivation of crops to transportation of the products to the target markets or the public. These will include having farmers and Extension Services implementing AI for crop yield detection, soil nutrients and moisture contents, climatic conditions predictions, milking and harvesting as well as weeds, pests and diseases identification and management. This paper explored the opportunities and challenges of AI in the implementation of Agricultural Advisory Services (AASs) for Sustainable Agriculture. These were achieved through extensive literature review which comprised of a conceptual framework for the implementation of AI in the AASs. The findings show the leveraging benefit of AI in the production costs among farmers, increase in farm productivity, and ease of access of AASs which was always almost a mission to achieve, especially, in the developing countries. Therefore, it is recommended that the relationship between youth participation in the agricultural sector and the implementation of AI and Digital Technology in the sector be explored, with the impact of the implementation of AI in the sector and the contribution of the sector towards developing countries’ Gross Domestic Products (GDPs). The ethical implications of AI in AASs and the Agricultural Sector as a whole must be explored to unveil issues that may hamper the future acceptance of these digital skills and innovations.

Purpose This paper aims to examine how sustainability-oriented companies manage the social disruption caused by artificial intelligence (AI) implementation while maintaining their environmental missions. Original insights are provided into the intersection of technological advancement, human factors and environmental management, focussing on organisational transformation challenges and strategies for balancing innovation with traditional environmental values. Design/methodology/approach A narrative literature review methodology was applied to synthesise and analyse the complex interactions between environmental management, AI implementation and organisational transformation. Multiple electronic databases were systematically searched using predetermined keywords related to green technologies, AI implementation and organisational change within sustainability-oriented organisations. The analytical process was structured to facilitate the integration of multiple theoretical perspectives and empirical findings. Findings Successful AI integration in sustainability-oriented organisations requires sophisticated approaches to knowledge management, leadership development and organisational design. Companies must design new models that combine technological capabilities with environmental expertise while addressing social impacts. Traditional environmental values can be preserved through thoughtful integration of AI systems. Research limitations/implications The study’s conceptual nature may limit immediate applicability across different organisational contexts and scales. The rapid pace of AI development means recent innovations may not be reflected in academic literature. The interdisciplinary nature required judgements about relevance that may have introduced selection bias. Empirical research is needed to validate proposed frameworks. Originality/value Novel insights are provided into specific challenges faced by sustainability-oriented organisations implementing AI, offering unique perspectives on managing the intersection of environmental values and technological innovation. Original frameworks are presented for understanding and addressing social dimensions of technological transformation in environmentally-focussed companies.

ObjectivesTo explore the views of intensive care professionals in high-income countries (HICs) and lower-to-middle-income countries (LMICs) regarding the use and implementation of artificial intelligence (AI) technologies in intensive care units...

\n The process of digital transformation will affect all organizations. In businesses that have already started this process, artificial intelligence (AI) solutions have begun to appear. What are the current or incoming challenges and business process changes for Brazilian companies on this journey? Projects on digital transformation pose new challenges and cause organizational changes in business, operational and administrative processes. AI initiatives will have a competitive impact on organizations leading digital transformation. Our objective was to identify changes, potential effects, and impacts of AI technologies on business processes, transformation dynamics, organizational structures, and management. This qualitative research examined the cases of five large companies: four multinational subsidiaries based in Brazil and one Brazilian company. Their industries were telecom & technology, professional services, logistics services, chemistry, and financial services. These companies have been engaged in a long-term digital transformation and AI implementation. Different companies have distinct organizational structures for portfolio management and project implementation. Their challenges and changes were identified through content analysis with a semi-structured interview protocol. Publicly available data and data provided by the companies were collected. The main reported challenges were prioritization and selection of AI projects, lack of people with the required abilities, change management issues, cultural resistance, and integration with existing processes and systems. The most affected business processes were assistance services: external (customers) and internal relationships (employees) and process of internal activities (document analysis, health, and fiscal and supplier registration). Improving professional and academic work in this field has great relevance at this moment, as professionals and scholars have begun to understand the transformative potential of AI technologies in our society.\n

Despite growing enthusiasm for artificial intelligence (AI) implementation in orthopaedic care, patient attitudes toward AI adoption remain unexplored. This study assesses patient perceptions of AI integration in diagnosis, treatment, and patient communication in orthopaedic surgery. A 28-question survey was administered to patients in hand surgery, orthopaedic oncology, and joint replacement surgery clinics. The survey queried patients' baseline comfort with AI and its perceived strengths, weaknesses, risks, and benefits. Among 300 patients, the median age was 59 years. Most (55.2%) were comfortable with AI-assisted radiologic diagnosis, 58.3% with robot-assisted surgery, and 34.7% with AI-driven communication tools. Higher education and income correlated with greater AI acceptance (P < 0.001). Patients with lower education levels perceived fewer benefits in daily AI use and a less positive impact of surgical outcomes (P = 0.03, P = 0.05). Common concerns included loss of patient-surgeon relationships (70.0%), surgeon overreliance on AI (56.9%), and lack of individualized care (51.5%). Men were more accepting of AI use in diagnosis and surgery (P < 0.03), whereas women were more concerned about AI perpetuating biases (P = 0.05). Older patients were less comfortable with using AI for diagnostics (P < 0.001). As AI implementation in orthopaedic care expands, women, older patients, and patients with lower education and household income levels may feel less comfortable with AI integration, threatening their quality of clinical care. Universal concerns about AI implementation include AI potentially weakening the patient-surgeon relationship. Targeted patient education efforts to address common concerns about AI adoption will enable orthopaedic surgeons to responsibly integrate these tools into practice.