An Extensive Review of Organizational AI Adoption Challenges and Consequent Integrated AI Appliance Proposal for Adoption Facilitation and Impact Studies

An Extensive Review of Organizational AI Adoption Challenges and Consequent Integrated AI Appliance Proposal for Adoption Facilitation and Impact Studies Pius Onobhayedo * Peter Cardon  Paul Osemudiame Oamen  December 1, 2025 Abstract Although AI is widely believed to have transformative potential in organizations, recent reports reveal that many organizations are grappling with value derivation therefrom and the ability to take ownership of due ethical and regulatory demands, among other responsible uses of technology. Our goal is to examine these challenges with a view to proposing an approach to effective AI adoption by organizations and pave the way for further impact studies. As a first step, we reviewed and clarified these challenges, categorizing them into Weak or Non-Existent Strategy, Poor Data Readiness and Privacy Concerns, Inadequate Integration with Existing Technology Stack, Inadequate Human Knowledge Skills and Attitudes/Abilities, Scalable and Secure Infrastructure Challenges, Ethical Governance Concerns, Regulatory Framework Lag, Responsibility and Accountability Concerns as well as Reliability Concerns. Next, we carried out a thematic review of constituent AI technology innovation concepts and tools that have adoption potential in organizations vis-`a-vis Enterprise Resource Planning (ERP). In the light of these reviews, we used inductive reasoning to propose an approach to AI adoption and create a tool (OAAD) that exemplifies our recommendations, and which could facilitate well-informed adoption and real-life impact research. To set a compass for our effective adoption approach proposal, we expanded on Yang et al. (2024) and defined organizational AI readiness as the organization’s capacity and disposition to * University of Southern California. Email: pius.onobhay[email protected]  University of Southern California. Email: [email protected]  University of Aberdeen. Email: [email protected] 1 deploy and use AI technology tools in ethical, responsible and accountable ways that add value to the organization. Finally, we make some recommendations for progressive impact studies in line with our proposed adoption experimentation. Keywords: artificial intelligence; adoption; organization; adoption experimentation; organizational AI appliance 1 Introduction Artificial Intelligence (AI) is widely believed to have a potential transformative effect on organizations. As far back as about ten years ago, Gartner’s top 10 technology trends prediction for 2017 gave a prominent place to AI among other emerging transformative technologies like blockchain, augmented and virtual reality (Panetta, 2016). The release of ChatGPT in November 2022, founded on OpenAI’s Large Language Model (LLM) and subsequent advancements in reasoning algorithms, further accentuate the attention to AI and its transformative potential (Marr, 2023). Ideas on how this can be actualized have also been evolving. Gartner had proposed a Mesh app and service architecture (MASA) that requires significant changes to development tooling and best practices, to meet the challenges of digital business (Panetta, 2016). The debut of ChatGPT shifted attention to Generative AI which may be defined as “computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data.” (Feuerriegel et al., 2024, p. 111). More recently, Agentic AI which we parsimoniously refer to as goal-oriented, autonomous and adaptive AI task execution systems, seems to now be the strongly touted, as a path to realizing this transformative aspiration (Acharya et al., 2025; Huang, 2025; Reddy, 2025). Perceived enthusiasm about AI potential notwithstanding, many organizations seem to be grappling with how to strategically or profitably incorporate AI into their digital solutions framework (Challapally et al., 2025; Hradecky et al., 2022; Khandabattu, 2025; McKinsey, 2025; Neumann et al., 2024; Potluri and Serikbay, 2025; Selten and Klievink, 2024; Shah et al., 2024; Sharma et al., 2022; Vial et al., 2023; World Economic Forum, 2024; Yang et al., 2024). For example, in their study involving a systematic review of over 300 publicly disclosed AI initiatives, structured interviews with representatives from 52 organizations, and survey responses from 153 senior leaders collected across four major industry conferences, Challapally et al. (2025) of MIT’s Project NANDA (Network Agent AI in a Decentralized Architecture) reported a significant disconnect between investment in AI and positive impact on profit and loss, a disconnect which they referred to as the AI Divide. In their findings, only about 5% of organizations are extracting value from AI investment while the rest are 2 getting zero return. Similarly, McKinsey (2025) reported from their global survey, a climb in AI usage in organizations, yet more than 80 percent say that they have not seen tangible impact on enterprise-level EBIT (Earnings before interest and taxes) from their use of Generative AI. In their 2025 hype cycle artificial intelligence report, Gartner asserted that Gen AI has entered the trough of disillusionment with low-maturity organizations finding it difficult to identify suitable use cases and mature organizations battling with literacy (Khandabattu, 2025). Even where there are experiences of success, challenges have also been reported. For example, Dinculeana (2024), in a study of AI adoption in some European banks, concluded that the role of AI in banking customer service is multifaceted and laden with both promises and challenges like regulatory compliance, ethical considerations, data management, technological integration, and organizational change which are all critical factors influencing the successful deployment of AI technologies. Echoing some industry players, Hornyak (2025, p. 1) similarly asserts that agentic AI still faces major challenges. For example, he quotes the Databricks CEO Ali Ghosdsi as having cautioned that “AI agents will compound errors as task complexity grows, making human supervision vital, at least in the near term”. The background we present here suggests the need for more clarity about path to effective adoption by organizations, especially where strategic alignment is crucial. Although no technology (including AI) provided as means for people to carry out their organization’s activities may be considered inherently strategic, we hold the assertion of Mendes et al. (2024) that any asset or component that may jeopardize a critical mission may be considered strategic. This level of risk in our opinion requires a more proactive approach to research engagement with organizations, in a way that simultaneously provides expertise to the participating organizations in their adoption process. 1.1 Defining Objectives Our overarching goal in this work is to inductively propose an approach to effective AI adoption by organizations and pave the way for further impact studies. To achieve this goal, our specific objectives include to  examine and categorize known barriers to effective adoption,  carry out thematic review of emerging AI innovation systems with potential for enterprise adoption vis-`a-vis alignment, enhancement and/or strategic replacement of features of Enterprise Resource Planning (ERP), a mainstream organizational technology integration pattern,  propose an approach to effective adoption 3  create a tool that exemplifies our adoption proposal which could facilitate well-informed adoption decisions and further impact research. The relationship with ERP expressed in the second specific objective above is our attempt to facilitate clarity about how emerging AI innovations could fit into existing organizations’ technology platforms. Besides applying lessons from reported attempts to adopt AI, we see the need to additionally draw lessons from one or more widely adopted technology system and explore effective AI adoption in the light of alignment, enhancement and/or strategic replacement of the status quo; AI adoption should not destroy past gains. Our choice of ERP is informed by the fact that it is a well-established and largely standardized technology type that is typically implemented in any given organization type, for integrated use by a wide range of organizational stakeholders, across value chains (supply chain, demand chain, back office, etc.). The term ERP was coined by T. Lee WyLie of Gartner Group as criteria for evaluating the extent to which the software actually integrated both across and within the various functional silos (Nazemi et al., 2012; Robert Jacobs and Ted Weston, 2007). From the 1990s ERP system practically became the standard for replacing legacy systems (Nazemi et al., 2012) and it is still considered essential for today’s business (Reuters, Thomson, 2025). A detailed literature review of ERP is beyond the scope of this article. However, for clarity, we itemize here the core constituent ERP features to be 1) Rule-based business logic for organizational functional units e.g., Customer Relationship Management (CRM), Asset and Financial Management, Supply Relationship Management, Inventory Management, Sales, Facilities Management, Warehouse Management, Project Management, Human Resource Management (McGaughey and Gunasekaran, 2007); 2) Database Management System (DBMS) for integrated data availability, integrity, security and independence (Sumathi, 2007) across the functional units; and 3) Authentication and Authorization for access control. Features closely associated with ERP include 1) Management Information System (MIS)/Business Intelligence (BI) which traditionally serves as Decision Support System (DSS) involving descriptive analytics, that provide information for management to take decisions (Demigha, 2021); 2) Workflow Engines for automating tasks execution steps in a given business process; 3) Document Management System/Enterprise Content Management Systems for managing all types of documents and digital content across the organization; 4) Knowledge Base/Knowledge Management System which serves the purpose of user self-service for knowledge contribution and retrieval (i.e., knowledge sharing); 5) Identity Management Systems/Single Sign-On (SSO) across functional units; and 6) Enterprise Integration Patterns (EIP) for standardized integration with disparate in-house systems or even third-party solutions, as proposed by Hohpe (2003). 4 1.2 Methodology In the first place, in line with the definitions of Grant and Booth (2009) we carried out a stateof-the-art literature review of recent academic and industry publications that speak to the challenges of AI adoption by organizations, to identify and categorize the published potential barriers to AI adoption. We primarily focused on publications after the release of ChatGPT in line with our goal of proposing an adoption approach that addresses contemporary barriers. We only accommodated publication earlier than the year 2022 where it gives perspective to the state-of-the-art. We then carried out a thematic review and selected for critical analysis, AI technology innovations which by their inherent features could be considered milestones in the advancement of AI technology applicability to organizations vis-`a-vis ERP, as explained in our objectives above. Finally, using inductive reasoning, we synthesized an approach to AI adoption in organizations that address the observed barriers and implemented a tool that exemplifies our proposal. 2 Observed Barriers to Effective Adoption Yang et al. (2024) defined AI readiness as a firm’s capacity to deploy and use AI in ways that add value to the firm. This definition to a large extent captures what we refer to in this work as effective adoption, which is inclusive of both successful adoption mechanism and value derivation therefrom. In this section, we present our findings in literature on observed obstacles to effective adoption which often speaks to a firm’s AI readiness. For clarity of identification, we have used subheadings in this section to categorize the barriers we encountered in our review. 2.1 Weak or Non-Existent Strategy Prominent among the observed barriers is an adoption-strategy gap which in our opinion merits research attention as it could significantly affect organizations bottom-line. AI needs to be adopted for the right reasons say Neumann et al. (2024), to solve previously unsolved problems, and not for solving problems you first must create. Adoption without strategy alignment is evident in the findings by Shah et al. (2024) in a study across Europe, AsiaPacific and the US, tagged Arm AI Readiness Index. While over 80% of organizational global leaders believe that it is urgent for their organizations to embrace AI and a significant number aligning their budget to match their belief, less than 40% have measurable key performance indicators (KPIs), underscoring the need for strategic roadmaps. Prior to the ChatGPT milestone, Hradecky et al. (2022) already observed among Western European organizations 5 in the exhibition industry, that despite their belief that AI will increase efficiency, reduce costs and enhance customer experience, most organizations do not have a future strategy to implement AI. They encountered in their study a lack of vision and progressivity from CEOs who are primarily responsible for the strategic direction of the organization, as a key obstacle to AI adoption. Without an adequately clear sense of direction, AI in public organizations also remains limited both in terms of the number of applications used and in terms of the depth of their integration (Selten and Klievink, 2024). 2.2 Poor Data Readiness and Privacy Concerns Data readiness has been a common source of concern. Shah et al. (2024) observed that 60% of organizations they studied face challenges with data readiness. Reported concerns include data accuracy, privacy and trust (Gurjar et al., 2024; Madanchian and Taherdoost, 2025; Praveen et al., 2025; Romeo and Lacko, 2025; Williams, 2024); robustness of the firm’s data infrastructure for quality data availability, which may not be unconnected with available financial resources and management vision, especially among small firms (Yang et al., 2024); as well as data in silos (Shah et al., 2024) as opposed to data integration for adequate data pipelining. Weak integration of AI technology stack itself constitutes another form of barrier reported (Challapally et al., 2025; Madanchian and Taherdoost, 2025; Williams, 2024). Challapally et al. (2025) asserted that complexity of AI solutions integration and lack of fit with existing workflows lead to stalling of organizational adoption of custom AI solutions. Madanchian and Taherdoost (2025) identified expenses associated with such integration as a deterrent. Khanna and Bhusri (2025) similarly observed what they considered a key gap, that companies only use AI application for a very specific business function and do not adopt crossdisciplinary integration with the remaining operational functions, which results in inefficient AI implementation. In their study, up to 50% of respondents cited integration with existing IT systems as the most significant challenge. 2.3 Inadequate Human Knowledge Skills and Attitudes/Abilities (KSA) Even though AI advancement has been touted to possibly result in significant employee disengagement, experience suggests that adequate employee training on the flip side, is required for AI to be adopted effectively (Leoni et al., 2024; Shah et al., 2024; Williams, 2024; Yang et al., 2024). For example, Yang et al. (2024) observed that employees’ digital skills and management’s AI literacy impacts the AI readiness of the professional firms they 6 studied. Likewise, Leoni et al. (2024) identified the presence of unqualified personnel to be one of the most significant barriers to implementing the appropriate AI tools. Esmaeilzadeh (2024) similarly emphasized the importance of human capital, continuous learning and a supportive environment for AI integration to thrive. For Shah et al. (2024), the People pillar faces a critical skills gap, with only 30% of the organizations they studied, offering comprehensive AI training. Gartner similarly asserted in their 2025 hype cycle report that mature organizations are battling to find skilled professionals (Khandabattu, 2025). 2.4 Scalable and Secure Infrastructure Challenges Security and scalability concerns occupy a place in AI implementation challenges (Khanna and Bhusri, 2025; Williams, 2024). This challenge may be even more pronounced as organizations see the need for AI within their network boundaries as an edge deployment, for security and privacy reasons, rather than send private data to cloud LLMs. Inadequate scalable and secure infrastructure at the edge thus exacerbates barrier to AI adoption (Shah et al., 2024). S´anchez et al. (2025) indicated that small and medium enterprises are particularly vulnerable to challenges in this respect, due to technological complexities in AI adoption, among others. 2.5 Ethical Governance Concerns Even prior to the release of ChatGPT, Taeihagh (2021) drew attention to the importance of AI governance stating that understanding and managing risks posed by AI is crucial to realize the benefits of the technology. Shah et al. (2024) observed that ethical governance is underdeveloped, with only 35% of organizations studied addressing ethical concerns. Marocco et al. (2024) pointed out three kinds of ethical concerns - the implications of AI making lifeor-death decisions, the risk of discrimination due to biased data or programming, and the ethical challenges of replacing humans with AI. They also asserted that managers with ethical concerns, become less curious about AI systems and consequently reduce their willingness to adopt. Rushing out AI is likely to backfire says Davies (2025) drawing on a survey among 900 professionals responsible for implementing AI across the UK, US and Canada in which a significant number of professionals were skeptical, expressing fear, pressure and potential risk to both employees and customers. For Dinculeana (2024), one of the most pressing ethical challenges is the potential for algorithmic bias as AI models trained on historical data can inadvertently reinforce existing biases in the data. 7 2.6 Regulatory Framework Lag The rapid advancement of AI technologies often outpaces the development of regulatory frameworks (Dinculeana, 2024). Praveen et al. (2025) identified regulatory complexities as a key barrier in AI adoption. Marocco et al. (2024) on a positive note observed that regulatory guidance helps reduce uncertainty, boosting managers’ confidence in AI technologies. On the contrary, regulatory uncertainty hinders confidence in AI adoption (Poon et al., 2025). In this regard, Yang et al. (2024) distinguished between large and small firms. In a study of professional firms, they observed that large firms are more hindered by gaps in regulatory frameworks compared to other AI readiness factors like infrastructure. 2.7 Responsibility and Accountability Concerns The term responsible AI emerged even prior to the first ChatGPT release, to refer to demand for a set of principles that ensure ethical, transparent, and accountable use of AI technologies consistent with user expectations, organizational values, and societal laws and norms (Mikalef et al., 2022). Along this line, the black-box nature of AI models is a source of discomfort, especially among managers who feel responsible for their assigned role in the organization. Such managers may be apprehensive about delegating authority to AI which led Marocco et al. (2024) to infer from their study, the importance of designing AI-based systems that enable managers interact with, modify and oversee AI-generated recommendations. To build trust in automated decision-making, managers may demand more explainability and interpretability in AI models (Procter et al., 2023; Romeo and Lacko, 2025). AI should be made accountable, not only to the individual, but also to the organization says Procter et al. (2023). Unfortunately, when AI agents fail, responsibility is unclear (Kundaliya, 2025). 2.8 Reliability Concerns Reliability of AI-based systems has sometimes been called into question especially when it comes to taking decisions in organizations. While not overlooking the fact that AI systems are normally designed to improve, as learning systems, it is important to rise beyond hype and understand what the current level of AI is when planning adoption. Succinctly put by Stief (2025, p. 1), “we are currently between basic and strong AI”. The often-assertive nature of output from Generative AI (even when it is hallucinating) seems to sometimes result in a situation where AI becomes crutch instead of tool for collaboration, a phenomenon which BetterUp Labs (2025) in collaboration with the Stanford social media lab refers to as workslop. They defined workslop as AI-generated content that appears good but lacks 8 substance. Based on insights from their online survey of 1150 US desk workers in September 2025, they concluded that workslop makes individuals feel frustrated, confused and disengaged; teams waste cycles, duplicate efforts, and lose trust; and organizations lose time, are misled by false productivity and experience stalled AI adoption. They recommended that organizational leaders set guardrails to curb it, modelling thoughtful use of AI, and fostering the use of AI collaboratively rather than seeking to use it to avoid work. Similarly, in a study primarily among British workers, employees raised alarm over the rise of AI agents in the workplace, warning that they are unreliable, unresponsive to feedback, and in some cases creating more work instead of reducing it (Kundaliya, 2025). In the same vein, Anderson et al. (2025, para. 8), while recognizing the benefits of thoughtful use of Generative AI in coding, drew attention to the hidden costs. Developers they interviewed indicated a number of problems that come with coding with AI – code duplications, integration problems, dependency conflicts, a lack of context awareness, etc. which can lead to the compounding of technical debt. Alluding to the seriousness of the situation, they further cautioned organizations to treat AI tool’s tendency to increase technical debt as a strategic risk, not just an operational nuisance. 3 Milestone AI Innovations with Organizational Adoption Potential vis-`a-vis ERP Integration Here, we present our thematic review of AI innovations that we consider to be milestones based on our perception of their relevance to organizational usefulness and the clarity of problems that they address. The AI innovations we present in no particular order are not necessarily product-specific but are more of conceptual foundations, approaches or algorithms and in some cases associated tools, that have increasingly become more prominent since the advent of LLMs like ChatGPT and that we consider to have the potential to functionally and technically align, enhance or replace aspects of classical ERP, our chosen mature technology reference point. 3.1 Retrieval-Augmented Generation (RAG) Before the first release of ChatGPT, Lewis et al. (2021) used the term Retrieval-Augmented Generation (RAG) to refer to the phenomenon in which they enhanced factual accuracy of large pre-trained language models in knowledge-intensive tasks, by combining pre-trained parametric memory (weights intrinsic to LLMs’ neural network) with non-parametric memory (numerical representation of domain-specific data stored as indexed vector embeddings), 9 for inference speed. 3.5 Document Understanding Documents are substantive part of any organization where a lot of information pertaining to an organization’s knowledge base resides. AI has the potential to move document information extraction beyond basic OCR to semantic knowledge discovery, applying machine learning at different points in the workflow including effective computer vision, even for partially legible physical documents. Many documents exist in multimedia forms (e.g. combined text and images, audiovisual electronic documents, etc.). Knowledge extraction from such documents logically stands to benefit from AI model algorithms that are multimodal. Among the transformer-based algorithms we have discussed so far, only CLIP supports more than one content mode – text and images. The rest are unimodal. Multimodal language models can be used for unified document tokenization – printed or handwritten text, image, layouts, etc. An example is Qwen3-VL with distilled variants like Qwen3-VL-2B-Instruct, Qwen3-VL-2B-Thinking which can recognize broad range of entities in documents, carry out OCR with multiple language recognition, etc. They are available for free download from Hugging Face. 3.6 Agentic AI The buzz around Generative AI paved the way for yet another buzz – Agentic AI – in the quest for AI based autonomous task execution. AI wave has evolved from basic predictive AI wave to Generative AI wave and then to Agentic AI wave (Falconer, 2025b). The term Agentic AI seems to have been coined by Andrew Ng and used in a Deeplearning.ai online magazine, The Batch. In the episode dated April 10, 2024, he used the title “agentic AI design pattern” to label a link to a previous article in which he started to describe agentic design patterns (Ng, 2024a,b). The term Agentic AI has since become conventional among industry players and academics. Acharya et al. (2025) refer to it as qualitative leap in the development of Artificial Intelligence, with capability to set complex intermediate goals, autonomously adapting to a changing and uncontrolled situation and autonomously managing their resources as the agent steers itself towards the goal set for it. Core to the technical capability of AI agents are reinforcement learning (RL), goal-oriented architecture and adaptive control mechanisms (Acharya et al., 2025). The key components in writing an agent include defining the environment for agent interaction (e.g. events), implementing reasoning logic (e.g. as in a model), enable actions and incorporating adaptation. We advocate organizational capacity building or expert partnerships for agile creation of 16 agents in line with business demand dynamics, in order to facilitate due control. Many frameworks have emerged to assist in the development of single or multi-agents that can collaborate. Examples include PydanticAI (https://ai.pydantic.dev/), Haystack (https://haystack.deepset.ai/), AutoGen (https://microsoft.github.io/autogen/stable/index.html) and Strands Agents (https://strandsagents.com), LangChain (https://github.com/langchain-ai/langchain) and CrewAI (https://github.com/crewAIInc/crewAI), all of which are opensource projects. Even though a careful choice of any of these frameworks could go a long way to facilitating Agent development, we believe it is important to be clear about the essence of any Agent, and be as flexible as possible in the development, as the need arises. Agents should fit into the organization and not the other way round. Besides frameworks, some new protocols have emerged for standardization of Agent operations. Model Context Protocol (MCP) was created by Anthropic for the standardization of access to tools, data, contexts, outside the model itself (Anthropic, 2024; Hou et al., 2025). A2A Communication for agent-to-agent communication was created by Google and launched by The Linux Foundation (2025) The Linux Foundation (2025), to pave the way for interoperability across differing technology platforms. There seems to be growing advocacy for event-driven AI agents for better scalability, avoiding potential bottlenecks associated with point-to-point, albeit decentralized architectures, as may be experienced in microservices architecture. Manditereza (2025) for example sees such a scalability challenge with A2A protocol which relies on HTTP and gRPC’s direct point-to-point architectures and advocates an event-driven architecture for agent-to-agent communication. Similarly, the future of Agentic AI is event-driven where EDA (event-driven architecture) acts as “central nervous system” for dynamic information flow, says Falconer (2025a,b) of Confluent. He posits this architecture as a natural fit for MCP. Along this line, Song (2025) of Alibaba categorized Agentic AI applications into two types based on their triggering mechanisms namely, user-triggered agents and system-triggered agents. He foresees a future where majority of agents operate in industrial setting as system-triggered agents which must run continuously, act autonomously and recover from failure without manual intervention. In this light, he proposes leverage on Apache Flink’s capabilities in real-time distributed event processing, state management and exact-once consistent fault tolerance as framework for building such system-triggered event-driven AI agents. A project named Apache Flink Agents (https://github.com/apache/flink-agents) has been initiated in the Apache community for this purpose but still at an early stage. Agents may also be broadly classified into two types – ReAct agent and Workflow agent – depending on the level of autonomy of the inherent workflow decisions. ReAct (short for Reasoning and Action) was first introduced by Yao et al. (2022) to describe the use of LLMs to generate in an interleaved manner, both reasoning traces and task-specific actions, the 17 latter including access to external resources and tools as needed. In workflow terms, a ReAct type agent does not require a predetermined workflow path but autonomously figures out through a reasoning process the next steps (which may include iteration) based on previous step’s outcome in relation to user-defined goals. The primary understanding of what an autonomous agent is aligns with this ReAct concept. However, from an organization’s perspective, it is relatively easy to identify where discomfort could come from for managers who are particular about being in control of decisions as core to their responsibilities. Adopting ReAct thus calls for some basis for trustworthiness. Workflow agents on the other hand may be more connatural with the way organizations classically predetermine their workflow steps in handling data-driven tasks but with the added leverage on agentic autonomy in some predefined task execution steps. Apache Flink Agent framework supports both ReAct and Workflow agent types. The official documentation explicitly states that the Workflow agent paradigm is inspired by the need to orchestrate complex, multi-stage tasks in a transparent, extensible, and data-centric way, leveraging Apache Flink’s streaming architecture. 3.7 Authentication, Authorization and Guardrails We group these three concepts together because they all have in common the need for the organization to exercise some form of control on the use and operations of AI technology. On the one hand, authentication (the process of establishing that users are who they claim to be) and authorization (the process of giving users their due access rights only) are well established concepts in technology use in organizations. On the other hand, the use of guardrails is more recently being explored as a way to exercise control over the operations of autonomous agents. Although authentication and authorization approaches are well-established, attempts to standardize them for AI agents are still at an early stage. Efforts have been made to extend existing authentication and authorization systems but “OAuth, mTLS (mutual transport layer security), RBAC/ABAC (role-based access control/attribute-based access control), and cloud-native IAM (identity and access management) solutions merely provide an entry point to securing AI agents as they are incapable of coping with the changing agent behaviors, ongoing agent execution, and delegation complexities” (Chinni, 2025, p. 4). AI agents come with a new set of security risks like prompt-based manipulation or prompt injection (exploiting user input for unintended behavior), model pollution (targeting the corruption of model foundational knowledge e.g., with bias), institutional identity confusion and privacy leaks (Chinni, 2025; He et al., 2024). Model pollution and privacy leaks, for example, could occur when models are fine-tuned with user data if care is not taken. (Chinni, 2025, p. 6) further 18 asserted that such problems “require the next generation of guardrails: contextual filtering on prompts, dynamic privileges, agent-specific IAM, and effective human-in-the-loop controls”. He et al. (2024, para. 7) proposed an authenticated delegation approach which keeps humans in the loop. They described their approach as a “process of instructing an AI system to perform a task that requires access to tools, the web, or computer environments in such a way that third parties can verify that (a) the interacting entity is an AI agent, (b) that the AI agent is acting on behalf of a specific human user, and (c) that the AI agent has been granted the necessary permissions to perform specific actions”. As specific solutions emerge for applying classical authentication and authorization to Agents, it is clear that there needs to be a way that is dynamic enough to meet the dynamic nature of security threats associated with Agentic AI. Any successful initial authentication and authorization notwithstanding, the guardrail concept seems to provide a way for mitigating risks in a dynamic way. In practical terms, guardrails are AI operations technology stack that help to “ensure that organization’s AI tools, and their application in the business, reflect the organization’s standards, policies, and values” (McKinsey, 2024, para. 1). McKinsey further classified guardrails into five types – appropriateness, hallucination, regulatory-compliance, alignment and validation. Use of guardrails is thus a way for organizations to take responsibility for the governance of their AI operations, setting and implementing safe boundaries, when faced with risks associated with the black-box nature typical of language models. Wellimplemented guardrails by definition should go a long way to boosting confidence in AI system, especially with respect to privacy, security and trustworthiness concerns. With particular focus on security, Dev et al. (2025) propose a threat modelling approach to building guardrails, to mitigate risks like confidentiality attacks to ML training data, sensitive data leakage from output, AI auto-hack/reward hack, model stealing/proxy ML models and reverse engineering, among others, as these threats are discovered. Different innovators have created tools that could lead to the standardization of guardrail implementations. Notable among these is Guardrails-ai with an opensource codebase at https://github.com/guardrails-ai/guardrails. It is a Python framework for building applications made up of input/output guards that detect, quantify and mitigate the presence of specified risks. 3.8 AI Operations Transparency: Observability, Explainability and Evaluability With the clamor for responsible AI, the discomfort associated with the black-box nature of models along with the autonomy of agents, calls for a design-for-transparency framework 19 which embraces observability and explainability. Closely related are reliability concerns that stretch transparency demand to the ability to actually evaluate the operations of AI. When the stakes are high, organizations may wish for visibility (observability) to enable it to steer the language models (LM) towards reliability (Carter, 2023) and also appreciate the rationale for a given outcome (explainability) which could facilitate trustworthiness (Kastner et al., 2021). Explainable AI (XAI as it is commonly called) is about having explanation for why the model predicted something and why it is trustworthy (Mishra, 2021). In the following subsections, we throw more light on each of these three aspects of AI operations transparency. 3.8.1 Observability As black boxes that produce nondeterministic outputs based on natural language inputs, the vast range of possible outputs makes it impossible to exhaustively test all inputs and scenarios for quality assurance, as may be done with rule-based client applications (Carter, 2023). The only way to monitor quality is to embed telemetry into the interaction environment and observe how users interact with LM at production time. The foundation for observability is thus data from usage footprints (Agrawal, 2023). Traditionally, software, network and systems engineering rely on observability using a set of telemetry data types that has become conventionally referred to as MELT, an acronym for metrics (numeric measurements like CPU, GPU usage over time), events (discrete actions performed that cause state changes like user login), logs (detailed records of discrete events) and traces (request journeys including the successes, failures and responses) data types. Analogically, this can be applied to AI observability (Carter, 2023; Koc et al., 2025). An IBM staff writer Badman suggests additional metrics which could be used to monitor AI language model’s specific interaction quality, namely, token usage (operational expense watching), model drift (observe accuracy deterioration), response quality (tracking hallucination, factual accuracy, consistency of output for similar input, relevance of response to user input, latency) and responsible AI monitoring (monitor bias occurrence, Personally Identifiable Information – PII – in generated content, compliance with ethical guidelines, content appropriateness). Telemetry may be implemented in proprietary ways. To prevent vendor locking, an opensource framework named OpenTelemetry was created with vendor-agnostic APIs, as an observability framework that brings all the data types together in one platform (Young and Parker, 2024). Carter (2023) asserts that by using traces with OpenTelemetry, you can track information about user inputs, AI model outputs, and the result of any operations you perform on AI model outputs before showing a result to an end user. Specific telemetry design can vary from simple LM call with static prompt to more complex processes like dynamic prompt building, RAGs, agents chaining, etc. Operational workflow has evolved 20 over time. For example, OpenTelemetry SDK compliant metrics can be exported for scraping by Prometheus from the exposed endpoint (typically /metrics in the URL) and visualized using tools like Grafana which can use Prometheus data source. In place of Prometheus, Jaeger, another open-source platform, can be used for traces. 3.8.2 Explainability Unlike observability that is external to the model, explainability can be intrinsic to the machine learning algorithm itself though not exclusively (Mishra, 2021). Depending on the algorithm, models may be inherently explainable or may require post-hoc explanatory methods. A model that is based on a simple linear regression between the input and output can be inherently interpreted as such. In more complex black-box situations, external, post-hoc methods are used to analyze the input and output, for explanations. Different methods with their respective python code libraries have emerged for this purpose e.g., SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-Agnostic Explanations) which appear to be the two most popular (Salih et al., 2024). LIME based explanations are limited to a particular input instance that gives rise to a single prediction. SHAP covers a wider scope; besides specific instance explanation (i.e., local), it can be used to explain how the model behaves across all data (i.e., global). Both shap and lime libraries are available for use from the python package repository pypi but shap is actively maintained while lime appears not to be maintained anymore. 3.8.3 Evaluating AI Operations As AI gains widespread adoption attention, an urgent question is how we evaluate the systems for effectiveness, safety, ethical compliance, etc. (Duranton, 2024; Japkowicz and Boukouvalas, 2024). The complexities involved in AI make human evaluation tedious and quest for efficiency has paved the way for the use of AI itself as AI evaluation judges (Yu, 2025; Zhuge et al., 2024) among other ways. Beyond human-as-a-judge, the AI based evaluation paradigm has evolved from traditional metrics (like BLEU, ROUGE for NLP evaluation) to single LLM-as-a-judge and then to multi-agent-judges (e.g., debate and committee-based approaches) and finally to agent-as-a-judge (process-based evaluation approach) (Yu, 2025). While LLM-as-a-judge only evaluates the final output, agent-as-a-judge evaluates intermediate steps as well as the final, thus enhancing informativeness (Yu, 2025; Zhuge et al., 2024). Chen et al. (2025) also describes multi-agent-as-judge which involves using multiple LLMs for multi-dimensional evaluation, in a way analogous to multi-dimensional human evaluation, which they found to outperform single LLM-as-a-judge and better aligned with human 21 expert’s rating. Innovators have created tools for evaluations which organizations can leverage. Notable examples include DeepEval (https://github.com/confident-ai/deepeval), an open-source tool, which can be used for more than 40 types of evaluations, along with trained judges (i.e., LLMs and also SLMs fine-tuned for evaluation purposes e.g., Selene-1-Mini-Llama-3.1-8B available on Hugging Face). Selene-1-Mini-Llama-3.1-8B is a highly performant multi-purpose evaluator which can be further fine-tuned for more specific domains (Alexandru et al., 2025). 3.8.4 Combined Tooling Some innovations include all three (observability, explainability and evaluability) in the same tool, to various degrees. Examples include LangFuse (https://github.com/langfuse) and Phoenix (https://github.com/Arize-ai/phoenix/) both of which are open source and actively developed. In a complementary way, they can also integrate with the more specialized tools like shap libraries for explainability as well as DeepEval and models like Selene-1-Mini-Llama3.1-8B, for more rigorous evaluation. 4 Synthesis of an Adoption Approach In the foregoing section, we have highlighted AI innovations that could find their way into organizations, with potential to complement, enhance or perhaps replace different aspects of ERP. In this section we apply inductive reasoning to propose an adoption approach in the light of our enumerated adoption challenges and the state of AI innovations. As a first step and take-off point in our inductive reasoning process, we expand on Yang et al. (2024) and define organizational AI readiness as the organization’s capacity and disposition to deploy and use AI technology tools in ethical, responsible and accountable ways that add value to the organization. We believe that this definition sets an adequate compass for our goal of proposing effective AI adoption approach. It not only addresses value creation, but it also addresses behavioral contexts for achieving the target values. Our definition takes a human-centric behavioral approach to organizational AI in as much as organizations are human purpose-driven institutions in line with Chester Barnard’s formal organization definition as “a system of consciously coordinated activities or forces of two or more persons” Barnard (1968, p. 81). So, the ethical, responsible and accountable ways embodied in our working definition, pertain to judgement about the human actors who may use AI tools as instruments to achieve their purposes. Our goal here is not to argue about whether machines can or cannot be ethical, responsible, or accountable. However, for the avoidance of doubt, it suffices to state that we take for granted that machines 22 have no free will but are conditioned by human design and have no ontological possibility of deliberately going against such designs. Next, as shown in Table 1, we map the keywords in our organizational AI readiness definition to our identified adoption challenges. In line with our objective to propose and exemplify an approach to effective AI adoption, we leverage on the same structure to suggest implementation approaches that could mitigate the challenges, in the light of the strengths and limitations of AI innovations that we have identified (see Column 3 of Table 1). Finally, we describe OAAD, which we designed and created to exemplify our suggestions, as well as to proactively facilitate both AI adoption readiness and further impact studies, an approach we have termed adoption experimentation. Table 1: AI Readiness Keywords Mapping to Adoption Challenges and Operational Ideas Keywords Challenges Recommendations Capacity to Deploy (Ability to implement a functional system) Poor data readiness 1. Carry out data readiness assessment on data generated from operations a. Completeness: identify data repositories associated with all relevant operations (ERP, etc.). b. Governance: identify regulatory compliant access control policies and mechanisms associated with organizational data from operations. 2. Fill data capture gaps across operations using open standards for flexibility. 23 Keywords Challenges Recommendations Inadequate integration across organization 1. Carry out an operational data integration assessment a. Cross-query accessibility across data from all relevant organizational operations using relevant business cases. 2. Fill integration gaps across operations using relevant Enterprise Integration Patterns (EIPs) with open standards, for flexibility. 3. Implement a system for harmonizing authentication and authorization. 4. Implement systems that can simultaneously be used to solve current problems whether AI-enabled or not and also be used for further AI empowerment, for better guarantee of continuous integration. 5. Create and deploy event-driven agents triggered by output events from integrated systems Inadequate infrastructure scalability 1. Carry out infrastructure scalability assessment 2. Every choice of technology stack should have clear horizontal scalability architecture 24 Keywords Challenges Recommendations Disposition to Deploy (Willingness to deploy a functional system) Privacy concerns 1. Carry out data privacy audit for proposed technology. 2. Adopt alternatives that better guarantee due privacy e.g. local models in place of models external to the organization as data boundaries demand. 3. Establish guardrails for due privacy protection. Security concerns 1. Carry out threat modelling for proposed technology using emergent frameworks for AI, e.g., MITRE ATLAS (Adversarial Threat Landscape for ArtificialIntelligence Systems) for adversarial threats, NIST AI RMF for governanceoriented risks and OWASP Top 10 for LLMs for practical security vulnerabilities like prompt injection. 2. Establish guardrails for mitigating identified potential threats Ethical concerns [Same recommendations as in Ethical Ways keyword below] Regulatory gap concerns 1. Review regulatory policies released preAI, but that could apply to AI e.g., GDPR and establish how they could apply to your organization. 2. Review AI-specific regulatory policies 3. Suggest regulatory policies to relevant authorities for gaps identified through available channels 25 Functional Role Subsystem Justification 1. Stateful computations over data streams 2. Machine learning tasks in stateful computations 3. Emerging framework for Agentic AI Apache Flink (https://flink.apache.org/) 1. In-built scale-out architecture with multiple task managers and job multiple partitioning support 2. High-availability clustering 3. Dynamic extensibility with runtime deployment of new tasks including those based on suitable agentic AI frameworks 4. Mainstream adoption 5. Backed by Apache Foundation 6. Active maintenance development 1. Service Orchestration and Workflow 2. Emerging framework for Agentic AI Apache Airflow (https://airflow.apache.org/) 1. scale-out architecture with multiple executor clusters like Celery, Kubernetes 2. High-availability clustering with multiple Airflow workers and Redis broker. 3. Plugin architecture for implementing new process automations including those based on suitable agentic AI frameworks 4. Support for Human-in-the-Loop service orchestration workflows 5. Mainstream adoption 6. Backed by Apache Foundation 32 Functional Role Subsystem Justification Object store for 1. metadata 2. local AI models 3. state data 4. files in general Minio (https://www.min.io/) 1. In-built robust scale-out architecture 2. In-built high-availability clustering 3. Mainstream adoption 4. Backed by MinIO, Inc. Multiple storage types on same platform 1. Structured data store 2. Vector embeddings store 3. Graph store PostgreSQL (https://www.postgresql. org/) with PG vector scale extension (https://github.com/ timescale/ pgvectorscale) and with Apache Age extension (https://github.com/ apache/age) 1. Scale-out architecture e.g., with Citus shards 2. High-availability clustering e.g., with Patroni and Etcd 3. Mainstream adoption 4. PostgreSQL is backed by a strong community development (more than 20 years) 5. PG Vector scale backed by Tiger Data 6. Apache Age backed by Apache Foundation 1. Scalable AI compute engine for delegated ML Workloads (optional) Python Ray (https://www.ray.io/) 1. In-built scale out architecture with head node and multiple worker nodes 2. In-built high-availability cluster of head node and multiple worker nodes 3. Backed by Anyscale Inc. 33 Functional Role Subsystem Justification 1. Traces, evaluations, prompt management and metrics to debug and improve Language Model application LangFuse (https://langfuse.com/) 1. Combined Observability, Explainability and Evaluation 2. Based on OpenTelemetry and can consume OpenTelemetry traces from other providers like DeepEval 3. Supports use of Clickhouse for more performance in handling of traces 4. Backed by Y Combinator 1. Systems monitoring and alerting Prometheus (https://prometheus.io/) 1. Flexible monitor for applications, systems, and services 2. Backed by Linux Foundation 1. Data visualization for system monitoring Grafana OSS (https://grafana.com/oss/ grafana/?plcmt=oss-nav) 1. Ease of integration with Prometheus data sources 2. Backed by Grafana Labs 1. Reverse Proxies, Load balancing Nginx (https://nginx.org/) and HAProxy (https://www.haproxy.org/) 1. High performance load balancing and reverse proxy 2. In active maintenance 3. Nginx is backed by F5, Inc. and a strong and active open-source development community 4. HAProxy is backed by HAProxy Technologies and a strong and active open-source development community 4.2 Choosing Foundational Models By foundational models we refer to those SLMs that are downloaded and kept in the local object store for various functional purposes on OAA, as shown in Table 3. They can be used 34 Figure 1: Schematic Diagram of Subsystems Interactions as is or after further purposive fine-tuning. They can also be replaced with more suitable models as more effective and efficient innovations emerge. Table 3: Constituent Subsystems of OAAD Functional Role SLM Justification Vector embeddings generation Embedding Gemma (https://huggingface.co/google/ embeddinggemma-300m) 1. Highly performant and efficient. 2. Suitable for low resource environment 3. Suited for fine-tuning on specialized domains 35 Functional Role SLM Justification QA Retrieval and Graph Extraction Granite-4 (https://huggingface.co/ ibm-granite/ granite-4.0-1b) (Smaller quantized versions are available at https://huggingface.co/models? other=base model: quantized:ibm-granite/ granite-4.0-1b) or NVIDIA-Nemotron-Nano-9B-v2 (https://huggingface.co/nvidia/ NVIDIA-Nemotron-Nano-9B-v2) (Smaller quantized versions are available at https://huggingface.co/models? other=base model: quantized:nvidia/ NVIDIA-Nemotron-Nano-9B-v2) 1. Hybrid Mamba-2 and Self-attention for efficiency 2. Suitable for low resource environments 3. Suited for fine-tuning on specialized domains OCR and knowledge extraction from documents Qwen3-VL-2B-Instruct (https://huggingface.co/Qwen/ Qwen3-VL-2B-Instruct) [Smaller quantized versions are available at https://huggingface.co/models? other=base model:quantized: Qwen/Qwen3-VL-2B-Instruct] 1. Optimized for document understanding 36 Functional Role SLM Justification Evaluation: Small language model-as-ajudge (SLMJ) Selene-1-Mini-Llama-3.1-8B (https://huggingface.co/AtlaAI/ Selene-1-Mini-Llama-3.1-8B) [Smaller quantized versions are available at https://huggingface.co/models? other=base model:quantized: AtlaAI/Selene-1-Mini-Llama-3.18B] 1. Highly performant as can be seen on https://huggingface.co/ spaces/allenai/reward-bench 4.3 Choosing Key Python Libraries By key libraries, we refer here to those python packages that we have chosen to be core to the operations of OAA. OAAD declares them as requirements, out of the box. They can be replaced with newer versions for upgrade. Table 4 shows our choices for various functional requirements. Table 4: Key Python Libraries of OAAD Functional Role Library Justification Evaluation of model interactions DeepEval (https://pypi.org/project/ deepeval/) 1. Comprehensive evaluation 2. Actively maintained Explainability of agent operations Shap (https://pypi.org/project/shap/) 1. Covers a wide scope; besides specific instance explanation (i.e. local), it can be used to explain how the model behaves across all data (i.e. global) Actively maintained 37 Functional Role Library Justification Guardrails creation Guardrails-ai (https://pypi.org/project/ guardrails-ai/) 1. Hardly any significant open competitor 2. Actively maintained Model fine-tuning PEFT (https://pypi.org/project/peft/) and Other packages (https://pypi.org/project/ transformers/) (https://pypi.org/project/ accelerate/) (https://pypi.org/project/ bitsandbytes/) (https://pypi.org/project/ datasets/) 1. For parameter efficient model fine-tuning 2. Library contains target AdaLoRA or QLoRA (if fine-tuning quantized model) 3. Actively maintained 38 Functional Role Library Justification GraphRAG 1. Embeddings generation 2. Graph generation LangChain Ecosystem (https://pypi.org/project/ langchain/) (https://pypi.org/project/ langchain-huggingface/) (https://pypi.org/project/ sentence-transformers/) (https://pypi.org/project/ langchain-postgres/) (https://pypi.org/project/ langchain-community/) and Other packages (https://pypi.org/project/ transformers/) (https://pypi.org/project/ accelerate/) (https://pypi.org/project/ bitsandbytes/) (https://pypi.org/project/ psycopg/) 1. LangChain ecosystem aligns with our choice of LangChain and 2. LangGraph for agentic AI framework. Agentic AI framework [GraphRAG libraries above] + LangGraph (https://pypi.org/project/ langgraph/) (https://pypi.org/project/ torch/) 1. Primary justification is as shown in the subsection on Choosing Agentic AI Framework 2. Actively maintained Agentic AI framework (experimental alternative on maturity watchlist) Flink Agent (https://github.com/apache/flinkagents) 1. Uses LangChain4J under the hood, which follows the philosophy of LangChain but for Java. 2. Best aligned with our recommended Kafka/Flink for data pipelining 39 4.4 Choosing Agentic AI Framework In choosing agentic AI framework for out-of-the-box illustrations, we deferred to innovations that we consider most compatible with our proposed subsystems and that could also readily facilitate agents’ development in line with our recommended no-black-box policy, to boost adoption confidence. To be closer to an objective comparison, we asked Grok4 to rank, with justification, a number of known agentic frameworks in terms of suitability for the following goals:  Use of local SLMs (external LLMs as supplementary).  Locally run LangFuse for observability, eval, metrics, etc., as well as use of python libraries like shap and judge models like https://huggingface.co/AtlaAI/Selene-1-MiniLlama-3.1-8B  Kafka and Flink-based data pipelines for real-time data  Agents as airflow plugins or agents as flink UDF as may be necessary for no real-time or real-time data pipelining  GPU usage support  Guardrails using https://github.com/guardrails-ai/guardrails  RAG/GraphRAG using pgvectorscale for vector embeddings and Apache age for graph databases and integrated tools for knowledge graph creation  Model fine-tuning - batch and real-time - with AdaLoRA or QLoRA  Locally run Google’s embedding gemma from hugging face for embeddings generation  Locally run Granite-4 (or Nvidia-nemotron-v2) for Retrieval QA  Human-in-the-loop workflow support  Enterprise SSO support  “No black-box” policy  Openness  Battle-tested 40 LangChain and LangGraph combination had the highest average score of 8.8/10 compared to Haystack (8.0/10), Airflow AI SDK (8.2/10), Flink Agents (7.2/10), Pydantic AI (7.8/10), Crew AI (6.9/10), AutoGen (6.6/10), Strands Agents (6.6/10). We therefore adopted the duo as our agentic AI framework for our OAA. In addition to this, we adopted Apache Flink Agents framework (as an experimental alternative on maturity watchlist), because of its 100 5 Conclusion and Recommendations In this work, we set out to inductively propose an approach to effective AI adoption by organizations in a way that simultaneously facilitates longitudinal impact studies, an approach we refer to as adoption experimentation. To achieve this overarching goal, we began with a state-of-the-art review of known challenges with AI adoption by organizations. We categorized these challenges into Weak or Non-Existent Strategy, Poor Data Readiness and Privacy Concerns, Inadequate Integration with Existing Technology Stack, Inadequate Human Knowledge Skills and Attitudes/Abilities, Scalable and Secure Infrastructure Challenges, Ethical Governance Concerns, Regulatory Framework Lag, Responsibility and Accountability Concerns as well as Reliability Concerns. As a first step in inductive reasoning, we drew on our state-of-the-art review and defined organizational AI readiness as the organization’s capacity and disposition to deploy and use AI technology tools in ethical, responsible and accountable ways that add value to the organization. We also traced the evolution of AI technologies that could apply to organizations vis-`a-vis ERP. We inductively mapped the adoption challenges we identified to the keywords in our organizational AI readiness definition and to our recommended adoption approaches to mitigate each challenge identified (see Table1). We further exemplified our recommendations by creating OAAD as a tool for generating integrated AI appliances in organizations. Our core recommendations may be itemized as follows: 1 Take an integrated platform approach to AI technology adoption akin to experience with ERP. The integrated system should include clearly defined points of data integration with existing technology solutions as exemplified by OAAD, especially with ERP that typically has wide stakeholder reach in organizations. 2 Establish an approach to authentication and authorization that harmonizes with existing infrastructure. 3 Have preference for integrated platform that seamlessly enables you to solve dynamic business problems, whether your digital solutions require AI or not. 41 J. Lang, Z. Guo, and S. Huang. A comprehensive study on quantization techniques for large language models. Technical report, November 2024. URL https://arxiv.org/ pdf/2411.02530v1. Preprint. James Larson and Sarah Truitt. GraphRAG: Unlocking LLM discovery on narrative private data. Microsoft Research Blog, February 13 2024. URL https://www.microsoft.com/en-us/research/blog/ graphrag-unlocking-llm-discovery-on-narrative-private-data/. L. Leoni, G. Gueli, M. Ardolino, M. Panizzon, and S. Gupta. AI-empowered KM processes for decision-making: empirical evidence from worldwide organisations. Journal of Knowledge Management, 28(11):320–347, 2024. doi: 10.1108/JKM-03-2024-0262. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K¨uttler, Mike Lewis, Wen-tau Yih, Tim Rockt¨aschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive NLP tasks. In ArXiv.org, 2021. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov, and Peter G. Allen. RoBERTa: A robustly optimized BERT pretraining approach. Technical report, 2019. Mitra Madanchian and Hamed Taherdoost. Barriers and enablers of AI adoption in human resource management: A critical analysis of organizational and technological factors. Information (Basel), 16(1):51, 2025. doi: 10.3390/info16010051. K. Manditereza. A2A for enterprise-scale AI agent communication: Architectural needs and limitations. HiveMQ Blog, August 25 2025. URL https://www.hivemq.com/blog/ a2a-enterprise-scale-agentic-ai-collaboration-part-1/. S. Marocco, B. Barbieri, and A. Talamo. Exploring facilitators and barriers to managers’ adoption of AI-based systems in decision making: A systematic review. AI, 5(4):2538– 2567, 2024. doi: 10.3390/ai5040123. Bernard Marr. A short history of ChatGPT: How we got to where we are today. Forbes, May 19 2023. URL https://www.forbes.com/sites/bernardmarr/2023/05/ 19/a-short-history-of-chatgpt-how-we-got-to-where-we-are-today/. Ronald E. McGaughey and Angappa Gunasekaran. Enterprise resource planning (ERP): Past, present and future. International Journal of Enterprise Information Systems, 3(3): 23–35, 2007. doi: 10.4018/jeis.2007070102. 48 McKinsey. What are AI guardrails?, November 14 2024. URL https://www.mckinsey. com/featured-insights/mckinsey-explainers/what-are-ai-guardrails. McKinsey. The state of AI: Global survey, March 12 2025. URL https://www.mckinsey. com/capabilities/quantumblack/our-insights/the-state-of-ai. J. P. Mendes, M. Marques, and C. Guedes Soares. Risk avoidance in strategic technology adoption. Journal of Modelling in Management, 19(5):1485–1509, 2024. doi: 10.1108/ JM2-10-2023-0221. Patrick Mikalef, Kieran Conboy, Jon Erik Lundstr¨om, and Aleˇs Popoviˇc. Thinking responsibly about responsible AI and “the dark side” of AI. European Journal of Information Systems, 31(3):257–268, 2022. doi: 10.1080/0960085X.2022.2026621. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. In 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, 2013. Prateek Mishra. Practical explainable AI using Python: artificial intelligence model explanations using Python-based libraries, extensions, and frameworks. Apress, 2021. doi: 10.1007/978-1-4842-7158-2. E. Nazemi, M. J. Tarokh, and G. R. Djavanshir. ERP: a literature survey. International Journal of Advanced Manufacturing Technology, 61(9–12):999–1018, 2012. doi: 10.1007/ s00170-011-3756-x. Oliver Neumann, Katharina Guirguis, and Reto Steiner. Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1):114–141, 2024. doi: 10.1080/14719037.2022.2048685. Andrew Ng. Autonomous coding agents, instability at stability AI, Mamba mania, and more. The Batch (DeepLearning.AI), April 10 2024a. URL https://www.deeplearning. ai/the-batch/issue-244/. Andrew Ng. Four AI agent strategies that improve GPT-4 and GPT-3.5 performance. The Batch (DeepLearning.AI), March 20 2024b. URL https://www.deeplearning.ai/ the-batch/how-agents-can-improve-llm-performance. Kasey Panetta. Gartner’s top 10 technology trends 2017. Gartner, October 18 2016. URL https://www.gartner.com/smarterwithgartner/ gartners-top-10-technology-trends-2017. 49 Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Hao Cao, Xin Cheng, Michael Chung, Xingjian Du, Matteo Grella, G. V. Kranthi Kiran, Xuzheng He, Haowen Hou, Jiaxi Lin, Przemys law Kazienko, Jan Kocon, Jiaming Kong, Bart lomiej Koptyra, et al. RWKV: Reinventing RNNs for the transformer era. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14048–14077, 2023. doi: 10.18653/v1/2023.findings-emnlp.936. Eric G. Poon, Christy Harris Lemak, Javier C. Rojas, John Guptill, and David Classen. Adoption of artificial intelligence in healthcare: Survey of health system priorities, successes, and challenges. Journal of the American Medical Informatics Association : JAMIA, 32(7):1093–1100, 2025. doi: 10.1093/jamia/ocaf065. R. M. Potluri and D. Serikbay. Artificial intelligence (AI) adoption in HR management: Analyzing challenges in kazakhstan corporate projects. International Journal of Asian Business and Information Management, 16(1):1–18, 2025. doi: 10.4018/IJABIM.376012. R. Praveen, A. Shrivastava, G. Sharma, A. M. Shakir, M. Gupta, and S. S. S. R. G. Peri. Overcoming adoption barriers strategies for scalable AI transformation in enterprises. In 2025 International Conference on Engineering, Technology & Management (ICETM), pages 1–6, 2025. doi: 10.1109/ICETM63734.2025.11051446. Rob Procter, Peter Tolmie, and Mark Rouncefield. Holding AI to account: Challenges for the delivery of trustworthy AI in healthcare. ACM Transactions on Computer-Human Interaction, 30(2):1–34, 2023. doi: 10.1145/3577009. X. Qian and D. Klabjan. A probabilistic approach to neural network pruning. Technical report, 2021. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In Proceedings of Machine Learning Research, volume 139, pages 8748–8763, 2021. Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. Robust speech recognition via large-scale weak supervision. In Proceedings of Machine Learning Research, volume 202, pages 28492–28518, 2022. C. K. Kumar Reddy. The Power of Agentic AI: In Industry 6.0. Springer, 1 edition, 2025. Nils Reimers and Iryna Gurevych. Sentence-BERT: Sentence embeddings using siamese BERT-networks. In EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in 50 Natural Language Processing and 9th International Joint Conference on Natural Language Processing, pages 3982–3992, 2019. doi: 10.18653/v1/d19-1410. Reuters, Thomson. What an ERP system is and how it impacts your business, April 15 2025. URL https://tax.thomsonreuters.com/blog/ what-is-an-erp-system-and-why-does-it-matter-for-your-business/. F. Robert Jacobs and F. C. Ted Weston. Enterprise resource planning (ERP)—a brief history. Journal of Operations Management, 25(2):357–363, 2007. doi: 10.1016/j.jom.2006.11.005. E. Romeo and J. Lacko. Adoption and integration of AI in organizations: a systematic review of challenges and drivers towards future directions of research. Kybernetes, 2025. doi: 10.1108/K-07-2024-2002. Azad M. Salih, Zahra Raisi-Estabragh, Ilaria B. Galazzo, Petia Radeva, Steffen E. Petersen, Karim Lekadir, and Gloria Menegaz. A perspective on explainable artificial intelligence methods: SHAP and LIME. 2024. doi: 10.1002/aisy.202400304. Friederike Selten and Bram Klievink. Organizing public sector AI adoption: Navigating between separation and integration. Government Information Quarterly, 41(1):101885, 2024. doi: 10.1016/j.giq.2023.101885. D. Shah, J. Soldatos, M. Hinkle, N. Hoher, N. von I. Seip, V. Just, K. Kechichian, K. Benkrid, M. McDonagh, V. Jesaitis, and W. Abbey. Arm AI readiness index, 2024. Mahak Sharma, Sunil Luthra, Sudhanshu Joshi, and Anil Kumar. Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy. Government Information Quarterly, 39(4):101624, 2022. doi: 10.1016/j.giq.2021.101624. X. Song. Flink agents: The agentic AI framework based on Apache Flink. Community Over Code Asia 2025, July 25 2025. URL https://asia.communityovercode.org/sessions/ streaming-913378.html. K. Soule and D. Bergmann. IBM Granite 4.0: Hyper-efficient, high performance hybrid models for enterprise. IBM Announcements, October 2 2025. URL https://www.ibm.com/new/announcements/ ibm-granite-4-0-hyper-efficient-high-performance-hybrid-models. K. M. Stief. Artificial? yes. intelligent? not really, October 1 2025. URL https://www. computing.co.uk/analysis/2025/artificial-yes-intelligent-not-really-copy. 51 Suhas Jayaram Subramanya, Rohan Devvrit, Kadekodi, Ravishankar Krishnaswamy, and Harsha Vardhan Simhadri. DiskANN: Fast accurate billion-point nearest neighbor search on a single node. In NeurIPS 2019, 2019. URL https://www.microsoft.com/en-us/research/publication/ diskann-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node/. S. Sumathi. Fundamentals of relational database management systems. Springer, 1 edition, 2007. doi: 10.1007/978-3-540-48399-1. E. S´anchez, R. Calder´on, and F. Herrera. Artificial intelligence adoption in SMEs: Survey based on TOE–DOI framework, primary methodology and challenges. Applied Sciences, 15(12):6465, 2025. doi: 10.3390/app15126465. Araz Taeihagh. Governance of artificial intelligence. Policy and Society, 40(2):137–157, 2021. doi: 10.1080/14494035.2021.1928377. The Linux Foundation. Linux foundation launches the Agent2Agent protocol project to enable secure, intelligent communication between AI agents, June 23 2025. URL https://www.linuxfoundation.org/press/ linux-foundation-launches-the-agent2agent-protocol-project-to-enable-secure-intelligent-communication-between-ai-agents. Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. VideoMAE: Masked autoencoders are data-efficient learners for self-supervised video pre-training. In Advances in Neural Information Processing Systems 35, 2022. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. 2023. Gregory Vial, Ann-Frances Cameron, Theodore Giannelia, and Jing Jiang. Managing artificial intelligence projects: Key insights from an AI consulting firm. Information Systems Journal, 33(3):669–691, 2023. doi: 10.1111/isj.12420. L. Wang, S. Chen, L. Jiang, S. Pan, R. Cai, S. Yang, and F. Yang. Parameter-efficient fine-tuning in large language models: a survey of methodologies. Artificial Intelligence Review, 58(8):1–64, 2025. doi: 10.1007/S10462-025-11236-4. H. Wei, Y. Sun, and Y. Li. DeepSeek-OCR: Contexts optical compression. Technical report, October 2025. URL https://arxiv.org/pdf/2510.18234. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems 35, 2022. 52 S. Williams. Surge in AI adoption amongst sales challenges in Singapore. CFOTech Asia, August 5 2024. URL https://cfotech.asia/story/ surge-in-ai-adoption-amongst-sales-challenges-in-singapore. World Economic Forum. AI governance trends: How regulation, collaboration, and skills demand are shaping the industry, 2024. URL https://www.weforum.org/stories/2024/ 09/ai-governance-trends-to-watch/. Jie Yang, Yvette Blount, and Alireza Amrollahi. Artificial intelligence adoption in a professional service industry: A multiple case study. Technological Forecasting and Social Change, 201:123251, 2024. doi: 10.1016/J.TECHFORE.2024.123251. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. ReAct: Synergizing reasoning and acting in language models. In 11th International Conference on Learning Representations, ICLR 2023, 2022. YCombinator Hacker News. Is there a half-life for the success rates of AI agents?, June 2025. URL https://news.ycombinator.com/item?id=44308711. Ted Young and Austin Parker. Learning OpenTelemetry. O’Reilly Media, Incorporated, 1 edition, 2024. F. Yu. When AIs judge AIs: The rise of agent-as-a-judge evaluation for LLMs. Technical report, August 2025. URL https://arxiv.org/pdf/2508.02994. Alex L. Zhang. Recursive language models, October 15 2025. URL https://alexzhang13. github.io/blog/2025/rlm/. Q. Zhang, Z. Liu, S. Pan, and C. Wang. The rise of small language models. IEEE Intelligent Systems, 40(1):30–37, 2025. doi: 10.1109/MIS.2024.3517792. Yutao Zhu, Hao Yuan, Shujin Wang, Jinyi Liu, Wenjie Liu, Chao Deng, Haonan Chen, Zhicheng Liu, Zhiyuan Dou, and Ji-Rong Wen. Large language models for information retrieval: A survey. 2025. doi: 10.1145/3748304. M. Zhuge, C. Zhao, D. R. Ashley, W. Wang, D. Khizbullin, Y. Xiong, Z. Liu, E. Chang, R. Krishnamoorthi, Y. Tian, Y. Shi, V. Chandra, and J¨urgen Schmidhuber. Agent-asa-judge: Evaluate agents with agents. Technical report, October 2024. URL https: //arxiv.org/pdf/2410.10934. 53