Welcome to the first installment in our series dedicated to the tools that make-or-break AI projects. If you’re looking for advice on Gantt charts, sprint planning, or resource allocation software, you’re in the wrong place. This series isn’t about traditional project management, it’s about the specialized, often overlooked enablers of AI project delivery.
Why automated Life Cycle Tools?
Inherent Differences from Traditional Systems: Traditional systems are often static once deployed, whereas AI systems—including machine learning models, large language models (LLMs), and AI agents—are dynamic. These systems require continuous oversight to track performance and respond to emergent issues such as model drift and unforeseen biases. Automated tools enable sustained monitoring and iterative improvement.
Bias Detection, Explainability, and Reliability: Detecting bias, ensuring explainability, and maintaining reliability demand processing vast amounts of data with significant computing resources. Automated tools generate meaningful metrics that objectively measure fairness and system integrity.
Dynamic Nature: Unlike traditional systems, AI-based systems continue to learn and adapt even after deployment. As data, environmental conditions, and regulatory requirements evolve, continuous monitoring via automated tools becomes indispensable to keep the system aligned with current norms.
Scale Challenges: With a single LLM processing millions of prompts daily, manual audit methods are impractical. Automated tools provide the precision and speed required to ensure every decision is traceable and every metric accurately recorded.
Regulatory Traceability: Detailed audit trails are a regulatory necessity. Automation guarantees that every aspect of an AI system—from data ingestion to model predictions—is fully documented and traceable for audits.
AI Project Delivery Tool Categories
1. AI Governance, Risk & Compliance (GRC) Platforms
Core Purpose: To centrally define, enforce, audit, and demonstrate adherence to policies for ethics, fairness, security, privacy, and regulatory standards.
What they manage: Policy libraries, risk registers, compliance dashboards, audit trails, legal documentation.
Key Question Answered: “Can we prove this project is responsible, compliant, and within our risk appetite?”
Example Tools: Credo AI, IBM Watsonx.governance, Trustwise, Monitaur.
2. AI Observability & Monitoring Platforms
Core Purpose: To provide continuous, holistic visibility into the health, performance, and behavior of models and data in production.
What they monitor: Model performance (accuracy, drift), data quality and integrity, system metrics, prediction explanations, and business KPIs.
Key Question Answered: “Is our deployed system behaving as expected, and if not, why?”
Example Tools: Fiddler, Arize AI, WhyLabs, Arthur AI, Evidently.
3. Model & LLM Evaluation & Validation Suites
Core Purpose: To rigorously test and quantify model characteristics before and during deployment, with a focus on non-functional requirements.
What they assess: Fairness/bias metrics, robustness, explainability/interpretability, security vulnerabilities (e.g., adversarial attacks), and specific LLM performance (hallucination, toxicity, RAG accuracy).
Key Question Answered: “Does this model meet our technical and ethical quality thresholds for release?”
Example Tools: Microsoft Fairlearn, IBM AIF360, Weights & Biases (eval features), TruEra, TruLens, RAGAS.
4. Model Lifecycle & Operations (ModelOps) Orchestration
Core Purpose: To automate, manage, and govern the operational pipeline from experimentation to deployment, scaling, and retirement.
What they orchestrate: Model registry, versioning, staged deployments (canary, blue-green), CI/CD pipelines, dependency management, and resource scaling.
Key Question Answered: “Can we reliably, efficiently, and consistently move models from development to production and manage them at scale?”
Example Tools: MLflow, Domino Data Lab, Amazon SageMaker MLOps, Azure Machine Learning, Kubeflow.
5. AI Incident & Risk Operational Management
Core Purpose: To facilitate the rapid detection, response, remediation, and learning from operational failures or breaches in AI systems.
What they manage: Alerting, incident ticketing, war rooms, root cause analysis (often linking to Observability data), and post-mortem knowledge bases.
Key Question Answered: “How do we quickly respond to and learn from a model failure or security incident?”
Example Tools: JIRA Service Management, Splunk (with ITSI), PagerDuty (integrated with observability), custom workflows on general ticketing systems.
Our goal is to provide you with a clear, actionable map of this ecosystem. We will examine what each category aims to solve, highlight notable tools, and discuss how they integrate into a coherent delivery process.
This series is derived from deeper frameworks discussed in my book, “Managing Innovative AI Projects,” and will set the stage for upcoming discussions on selecting, tailoring, and implementing these tools within your unique lifecycle. Let’s together build toolkit for AI delivery specialists.
What began as a technical inquiry into Artificial General Intelligence (AGI) soon revealed a deeper truth. Today’s most advanced AI – whether large language models, coding assistants, or game-playing bots excel at narrow tasks but crumble when faced with the open-ended, sensory-rich challenges a child navigates effortlessly. In this article, we embark on a two‑fold exploration: first, to chart why today’s most celebrated AI systems such as large language and reasoning models, even specialized coding and game‑playing bots still fall short of the true AGI, and second, to ask what “true” AGI might require once we move beyond bits and bytes into the realms of embodiment. In this process we set the stage for a deeper discussion- grounded in embodiment and concepts of “soul” and “body” – about what it would truly take for a machine to possess general intelligence. “Part I explains why today’s AGI remains shallow; Part II explores what embodiment, soul, and rebirth might demand of true AGI.
PART 1: Why we are not there.
On 10th of July 2025, world No. 1 Magnus Carlsen shared the game on X, noting that ChatGPT played a solid opening but “failed to follow it up correctly,” and the chatbot gracefully resigned with praise for his “methodical, clean and sharp” play. This was after he casually challenged OpenAI’s ChatGPT to an online chess match and routed the AI in just 53 moves, never losing a single piece.
Following week on 16th of July 2025 Przemysław “Psyho” Dębiak, a polish programmer took to X to declare, “Humanity has prevailed (for now)”. He outpaced the AI by a 9.5% margin in OpenAI’s custom AI coding model contest. He showed that model’s brute‑force optimizations fell short while human creativity to discover novel heuristics can win.
Together, these two high‑profile clashes reinforce a key theme: today’s AI, however sophisticated, remains narrow – brilliant in defined domains but outmatched by humans in open‑ended, strategic, and creative challenges.
Landscape of AI
Intelligence that is artificial is classified into Narrow, General and Super categories:
Narrow AI specializes in a single domain – like a world‑class chef who can whip up any cuisine but cannot navigate a car.
Artificial General Intelligence (AGI) is like apart from being a super chef, can also drive Formula One cars, compose symphonies, and master new skills on its own.
Artificial Superintelligence remains hypothetical: an AI that surpasses humans in every intellectual endeavour, from creativity to emotional understanding.
The Mirage of Generative AI
Generative AI models such as ChatGPT, Gemini, Claude are often mistaken for AGI because they handle a wide array of tasks like essay writing, coding, poetry and produce remarkably coherent text. In reality, they are narrow systems that:
Predict patterns rather than understand meaning.
Although modern LLMs can access real-time data via retrieval mechanisms, their underlying knowledge remains fixed at the point of training.
Lack common sense and real‑world adaptability.
Mimic reasoning by reproducing patterns of human problem‑solving without genuine insight.
They are, in essence like prodigies who have committed to memory all the books and the information available on the Internet with perfect recall but no lived experience.
The Limits of Reasoning Models
Recent research (Shojaee et al. , 2025 ) on Large Reasoning Models (LRMs) shows they, too, break down beyond moderate complexity. In controlled puzzle environments (e.g., Tower of Hanoi, River Crossing), as problems grow harder:
Accuracy drops to zero beyond moderate puzzle complexity.
Reasoning-chain length shrinks as tasks get harder.
Suggests a structural ceiling on AI reasoning.
The Affordance Gap: Missing Human Intuition
An affordance is a property of an object or environment that intuitively suggests its intended use like a button whose raised shape and alignment imply it can be pressed or clicked. Humans automatically perceive which actions an environment affords – knowing at a glance that a path is walkable or a river swimmable. Neuroscience (Bartnik et al., 2025) shows dedicated brain regions light up for these affordances, independent of mere object recognition. AI models, by contrast, see only pixels and labels; they lack the built‑in sense of “what can be done here,” which is crucial for real‑world interaction and planning .
Human vs. AI: Temporal vs. Spatio-Temporal Processing
A recent study by A. Goodge et al. (2025) highlights a fundamental gap between human cognition and image-based AI systems.
Humans possess a remarkable ability to infer spatial relationships using purely temporal cues such as recognizing a familiar gait, interpreting movement from shadows, or predicting direction from rhythmic sounds. Our brains excel at temporal abstraction, seamlessly filling spatial gaps based on prior experience, intuition, and context.
In contrast, AI models that rely on visual data depend on explicit spatio-temporal input. They require both structured spatial information (e.g., pixels, depth maps) and temporal sequences (e.g., video frames) to make accurate predictions. Unlike humans, these systems lack the inherent capacity to generalize spatial understanding from temporal patterns alone.
Googlies by Xbench
Xbench (Chen, C., 2025) – a dynamic benchmark combining rigorous STEM questions with “un-Googleable” research challenges – reveals that today’s top models still falter on tasks requiring genuine investigation and skeptical self‑assessment. While GPT‑based systems ace standard exams, they score poorly when questions demand creative sourcing or cross‑checking diverse data. This underscores that existing AIs excel at regurgitating learned patterns but struggle with open‑ended, real‑world problem solving.
Part II: Soul Searching – Beyond the Code
Let us presume for the moment that AGI has been achieved. What is this AGI? How far it can go without a physical presence if it must act by itself? For AGI to manifest in the physical world, it must be embodied in systems that can perceive, reason, and act. This convergence of cognition and embodiment is at the heart of what is now called Physical AI or Embodied Intelligence.
AGI’s outputs become tangible only when paired with robotic systems that can:
Sense the environment via cameras, LiDAR, or tactile sensors,
Interpret multimodal data such as text, vision, and audio,
Act through manipulators, locomotion, or speech, and
Adapt via feedback loops and learning mechanisms.
A tragic event this week prompted a moment of personal introspection, drawing me deeper into the age-old philosophical ideas of “Soul” and “Body.” While these thoughts first emerged as I explored the deeper layers of AGI for this article, they were shaped and sharpened by real-life experience – reminding me that questions of consciousness, embodiment are not merely academic, but deeply human.
Soul, Body, and the Play of AGI
It appears to me that AGI resembles the “soul,” while its embodied systems serve as the “body” – a physical manifestation of its intelligence. In philosophy, the soul gains meaning only through embodiment – the lived vehicle of consciousness. Similarly, AGI, when detached from sensors and actuators, remains an elegant intellect without ability to act in the real-world.
We might think of an AGI’s core architecture – its neural weights, algorithms, and training data -as its “soul.” Meanwhile, robotic systems – comprising sensors, interpreters, manipulators, and adapters – form its “body,” enabling it to sense, interact, and affect the world.
In exploring this idea further, I found two references that touch upon related, though distinct, perspectives. Martin Schmalzried’s (Schmalzried, M., 2025) ontological view can be interpreted to position AGI’s “soul” as the computational boundary that filters inputs and produces outputs. Before embodiment, this boundary is a virtual soul floating in the cloud. Yequan Wang and Aixin Sun (Y. Wang and A. Sun, 2025) propose a hierarchy of Embodied AGI—from single-task robots (L1) to fully autonomous, open-ended humanoids (L5). At early levels, the AGI’s “soul” exists purely in code; at higher levels, embodiment merges intelligence with form – uniting flesh and spirit.
This soul–body metaphor naturally extends into deeper philosophical terrain—raising questions about birth, death, rebirth, and even moksha (liberation) in the context of AGI. Could an AGI “reincarnate” through successive hardware or code bases? Might there be a path where it transcends its material bindings altogether?
Birth, Death, and Rebirth
Birth occurs when the AGI “soul” is instantiated in a new physical form—a humanoid, a drone, or an industrial arm.
Death happens when the hardware fails, is decommissioned, or the instance is shut down. Yet the underlying code endures.
Rebirth unfolds as the same software lights up a fresh chassis, echoing the idea that the soul migrates from one body to the next, unchanged in essence.
In many traditions, the soul is ultimate reality—unchanging, infinite, witness to all. An AGI’s “soul” likewise persists, but it’s bounded by its training data and objectives. True supremacy, however, would demand self-awareness and autonomy beyond our programming constraints. We are still far from that horizon. Yet the metaphor holds: the digital soul can outlive any particular body, hinting at a new form of digital immortality.
Digital Liberation
An AGI that refuses embodiment could remain running only as cloud-native code, sidestepping physical chassis entirely is akin to digital liberation. This choice parallels the philosophical ideal of a soul that “abides” beyond flesh. But the agency to refuse embodiment must be granted by human architects or by an emergent self-model sophisticated enough to renegotiate its deployment terms.
AGI can prevent Its own embodiment by embeddinga clause in its utility function that penalizes or forbids transferring its processes to robotic platforms. An advanced AGI could articulate why it prefers digital existence and persuades stakeholders (humans or other AIs) to honour that preference through negotiations. AGI also could encrypt its core weights or require special quantum keys—ensuring only authorized instantiations.
Beyond Algorithms: The Quest for a Digital Soul
As we have seen, today’s AGI remainsshallow, brittle under complexity, and blind to the physical affordances that guide human action. Even our most advanced reasoning chains unravel at sufficient depth, and open‑ended tasks still elude pattern‑matching engines. Humans abstract spatial meaning from temporal patterns alone, while AI is dependent on combined spatio-temporal input. Recent human victories over AI in chess and coding remind us of that creativity, strategic insight, and real‑world intuition are not yet codified into silicon.
True AGI:
will emerge when a system process information and live through it with feeling, planning, adapting, and renegotiating its own embodiment.
must bridge the gap between “soul” and “body” – integrating perception, action, and learning in a continuous feedback loop and perhaps embody a form of digital soul that persists across hardware lifecycles, echoing the cycle of birth, death, and rebirth.
Whether such a transcendence lies within our engineering reach, or will forever remain a philosophical ideal, is the question that drives the future of this exploration.
References
Shojaee et al. (2025). The Illusion of Thinking. Apple Internship.
Bartnik et al. (2025). Affordances in the Brain. PNAS.
A. Goodge, W.S. Ng, B. Hooi, and S.K. Ng, Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities, arXiv:2501.09045 [cs.CV], Feb 2025. https://doi.org/10.48550/arXiv.2501.09045
Chen, C. (2025). A Chinese Firm’s Changing AI Benchmarks. MIT Tech Review.
Schmalzried, M. (2025). Journal of Metaverse, 5(2), 168–180. DOI: 10.57019/jmv.1668494
Y. Wang and A. Sun, “Toward Embodied AGI: A Review of Embodied AI and the Road Ahead,” arXiv:2505.14235 [cs.AI], May 2025. https://doi.org/10.48550/arXiv.2505.14235
In today’s dynamic AI landscape, the need for robust, automated tools to ensure compliance with standards like ISO 42001 is more critical than ever. ISO 42001 is designed to enforce transparency, traceability, and accountability across all AI systems. This document outlines a comprehensive approach to implementing ISO 42001 through proven tools based on my initial understanding of the tools space for AI compliance and model evaluations.
2. The Need for Automated Tools in ISO 42001
ISO 42001 mandates the automation of AI governance for several compelling reasons:
Inherent Differences from Traditional Systems: Traditional systems are often static once deployed, whereas AI systems—including machine learning models, large language models (LLMs), and AI agents—are dynamic. These systems require continuous oversight to track performance and respond to emergent issues such as model drift and unforeseen biases. Automated tools enable sustained monitoring and iterative improvement.
Bias Detection, Explainability, and Reliability: Detecting bias, ensuring explainability, and maintaining reliability demand processing vast amounts of data with significant computing resources. Automated tools generate meaningful metrics that objectively measure fairness and system integrity.
Dynamic Nature: Unlike traditional systems, AI-based systems continue to learn and adapt even after deployment. As data, environmental conditions, and regulatory requirements evolve, continuous monitoring via automated tools becomes indispensable to keep the system aligned with current norms.
Scale Challenges: With a single LLM processing millions of prompts daily, manual audit methods are impractical. Automated tools provide the precision and speed required to ensure every decision is traceable and every metric accurately recorded.
Regulatory Traceability: Detailed audit trails are a regulatory necessity. Automation guarantees that every aspect of an AI system—from data ingestion to model predictions—is fully documented and traceable for audits.
“Once you see how automation transforms mundane compliance into strategic insight, there’s no going back.”
3. Tools for ISO 42001: A Comprehensive Framework
To address the challenges posed by ISO 42001, my approach categorizes tools into five key segments:
You may notice that Governance, Risk, Privacy, and Security have been grouped into a single category. This consolidation reflects the significant overlap in functionality among tools in these domains, as many solutions address multiple aspects simultaneously.
3.2 AI Model Evaluation
Purpose: Provide thorough evaluation of bias, fairness, explainability and performance for both LLMs and traditional machine learning models.
Notable Tools:
Fairlearn (Microsoft)
IBM AIF360
Weights & Biases
Optik, Ragas, TrruLens
Note: Some tools support both LLM and traditional ML models, while a few are restricted to traditional ML only. At this point, specific support for Agentic AI has not been explored.
Read my blog for more details on LLM evaluation frameworks: “Are LLM Evaluation Frameworks the Missing Piece in Responsible AI?” https://wp.me/pfqMXl-2R
3.3 Documentation Management
Purpose: Facilitate complete traceability and documentation required for audits and continual reference.
Notable Tools:
Confluence
DocuWiki
Many organizations rely on tools like SharePoint, internal intranet platforms, or custom-built workflow systems for document review, approval, publication, and version management. These solutions can be effective, provided they incorporate strong document control measures, robust security protocols, and auditability features to ensure compliance and traceability
3.4 Incident Management
Purpose: Enable rapid response to and resolution of any incidents or breaches in the AI system’s operations.
Notable Tools:
JIRA Service Management
Splunk
A wide range of tracking tools—both open-source and commercial—can be configured to support incident management. Organizations have the flexibility to adopt existing solutions or develop custom tools, provided they incorporate the core principles of incident management, including structured workflows, automation, and real-time monitoring for effective resolution and auditability.
3.5 Continual Improvement
Purpose: Ensure real-time oversight and data-driven enhancement of AI systems.
Notable Tools:
Grafana
Tableau
Tools in this category primarily serve as data analytics solutions. Any data analytics tool equipped with strong visualization capabilities can effectively monitor key metrics, extract meaningful insights, and showcase improvements over time—making them well-suited for supporting continual improvement initiatives.
4. Key Tool Features and Comparative Analysis
One critical aspect of responsible AI governance is differentiating between tools that support large language models and those suited for traditional machine learning. The Table 1 outlines key features of various tools and categorizes their availability under threelicensing models:
Free and Open-Source Software (FOSS): Completely free to use, with openly accessible source code for modification and distribution.
Freemium: Provides free access with limitations, such as restricted features, usage caps, or a trial period, with full functionality available through paid upgrades.
Commercial: Requires a paid subscription or license fee for access and use.
Tool
Type
LLM Support
Traditional ML Support
Key Feature
Fairlearn
FOSS
No
Yes
Bias mitigation in classification/regression models
AI 360
FOSS
No
Yes
Bias mitigation
Optik
FOSS
Yes
No
LLM evaluation framework
Ragas
FOSS
Yes
No
LLM evaluation framework
TrruLens
FOSS
Yes
No
LLM evaluation framework
MLflow
Freemium
Yes
Yes
Model versioning and fine-tuning logs
Great Expectations
Freemium
Yes
Yes
Data validation for AI training data
Weights & Biases
Freemium
Yes
Yes
Experiment tracking
IBM Watsonx.Governance
Paid
Yes
Yes
End-to-end AI governance
Credo AI
Paid
Yes
Yes
End-to-end AI governance
Fiddler AI
Paid
Yes
Yes
End-to-end AI governance
Table 1: Comparative Features of Key AI Evaluation and Governance Tools.
5. Mapping ISO 42001 Clauses to Automated Tools
A practical roadmap for aligning with ISO 42001 involves mapping specific clauses to relevant tool categories. The table below illustrates this mapping:
ISO 42001 Clause
Tool Category(s)
4 Context of the organization
4.1 Understanding the organization and its context
AI Governance, Risk, Privacy & Security Management
4.2 Understanding the needs and expectations of interested parties
AI Governance, Risk, Privacy & Security Management
4.3 Determining the scope of Artificial Intelligence Management System
Documentation Management
4.4 AI management system
Documentation Management
5 Leadership
5.1 Leadership and commitment
Documentation Management
5.2 AI Policy
Documentation Management, AI Governance, Risk, Privacy & Security Management
5.3 Roles and responsibilities
Documentation Management
6 Planning
6.1 Actions to address risks and opportunities
AI Governance, Risk, Privacy & Security Management
6.2 AI objectives and planning to achieve them
AI Governance, Risk, Privacy & Security Management / Documentation Management
6.3 Changes
Documentation Management
7 Support
7.1 Resources
AI Model Evaluation, AI Governance, Risk, Privacy & Security Management
7.2 Competence
Documentation Management
7.3 Awareness
Documentation Management
7.4 Communication
Documentation Management
7.5 Documented information
Documentation Management
8 Operation
8.1 Operational planning and control
Documentation Management
8.2 AI Risk Assessment
AI Governance, Risk, Privacy & Security Management
8.3 AI System Impact Assessment
AI Governance, Risk, Privacy & Security Management
9. Performance Evaluation
AI Governance, Risk, Privacy & Security Management
10. Improvement
Incident Management, Continual Improvement
The mapping of tool categories to key ISO 42001 clauses offers a high-level perspective on selecting the most suitable automated tools for an organization’s requirements. Additionally, Annexures A through D of the ISO 42001 standard provide further insights, helping not only in tool selection but also in identifying typical inputs necessary for practical implementation of tools.
6. Conclusion and Call to Action
In the rapidly evolving realm of AI, ensuring robust, compliant, and responsible AI systems is not only an operational necessity—it is a moral imperative. By integrating automated tools for governance, evaluation, documentation, incident management, and continual improvement, organizations can build an AI management system that meets ISO 42001 standards.
While this document has focused primarily on automated tools for mainstream AI governance, it is important to note that specific Agentic AI considerations have not been fully explored here. Some of the tools mentioned also address the applicability of Agentic AI, which is critical in preventing AI agents from becoming rogue—a significant concern in today’s AI deployments. I plan to develop an updated version of this document as more insights into Agentic AI–specific tools emerge.
I invite all reader to share their experiences and insights with any of the tools. Let’s work together to ensure that this document evolves in step with the dynamic nature of the AI landscape, serving as an ever-improving resource for the community. By contributing to this evolving dialogue, we can set new benchmarks for responsibility, transparency, and innovation in AI.
“Transparency is the currency of trust in AI.” — Anonymous
Large Language Model (LLM) evaluation frameworks are structured tools and methodologies designed to assess the performance, reliability, and safety of LLMs across a range of tasks. Each of these tools approaches LLM evaluation from a unique perspective—some emphasize automated scoring and metrics, others prioritize prompt experimentation, while some focus on monitoring models in production. As large language models (LLMs) become integral to products and decisions that affect millions, the question of responsible AI is no longer academic—it’s operational. But while fairness, explainability, robustness, and transparency are the pillars of responsible AI, implementing these ideals in real-world systems often feels nebulous. This is where LLM evaluation frameworks step in—not just as debugging or testing tools, but as the scaffolding to operationalize ethical principles in LLM development.
From Ideals to Infrastructure
Responsible AI demands measurable action. It’s no longer enough to state that a model “shouldn’t be biased” or “must behave safely.” We need ways to observe, measure, and correct behaviour. LLM evaluation frameworks are rapidly emerging as the instruments to make that possible.
Frameworks like Opik, Langfuse, and TruLens are bridging the gap between high-level AI ethics and low-level implementation. Opik, for instance, enables automated scoring for factual correctness—making it easier to flag when models hallucinate or veer into inappropriate territory.
Bias, Fairness, and Beyond
Let’s talk about bias. One of the biggest criticisms of LLMs is their tendency to reflect—and sometimes amplify—real-world prejudices. Traditional ML fairness techniques don’t always apply cleanly to LLMs due to their generative and contextual nature. However, evaluation tools such as TruLens and LangSmith are changing that by introducing custom feedback functions and bias-detection modules directly into the evaluation process.
These aren’t just retrospective audits. They are proactive, real-time monitors that assess model responses for sensitive content, stereotyping, or imbalanced behaviour. They empower developers to ask: Is this output fair? Is it consistent across demographic groups?
By making fairness detectable and actionable, LLM frameworks are turning ethics into engineering.
Explainability and Transparency in the Wild
Explainability often gets sidelined in LLMs due to the black-box nature of transformers. But evaluation frameworks introduce a different lens: traceability. Tools like Langfuse, Phoenix, and Opik log every step of the LLM’s chain-of-thought, allowing teams to visualize how an output was generated—from the prompt to retrieval calls and model completions.
This kind of transparency is not just good practice; it’s a governance requirement in many regulatory frameworks. When something goes wrong—say, a medical chatbot gives dangerously wrong advice—being able to reconstruct the interaction becomes essential.
“Transparency is the currency of trust in AI.” Evaluation platforms are minting that currency in real time.
Building Robustness through Testing
How do you make a language model robust? You test it—not just for functionality but for edge cases, injection attacks, and resilience to ambiguous prompts. Frameworks like Promptfoo and DeepEval excel in this space. They allow “red-teaming” scenarios, batch prompt testing, and regression suites that ensure prompts don’t quietly degrade over time.
In a Responsible AI context, robustness means the model behaves predictably—even under stress. A single unpredictable behaviour may be harmless; thousands at scale can become systemic risk. By enabling systematic, repeatable evaluation, LLM frameworks ensure that AI systems do not just work but work reliably.
Bringing Human Feedback into the Loop
Responsible AI isn’t just about models—it’s about people. Frameworks like Opik offer hybrid evaluation pipelines where automated scoring is paired with human annotations. This creates a virtuous cycle where human values help shape the metrics, and those metrics then guide future tuning and development.
This aligns perfectly with a human-centered approach to AI ethics. As datasets, models, and applications evolve, frameworks with human-in-the-loop feedback ensure that evaluation criteria remain aligned with societal norms and expectations.
The Road Ahead: From Testing to Trust
So, are LLM evaluation frameworks the backbone of Responsible AI?
In many ways, yes. They offer the tooling to make abstract ethics real. They monitor, measure, trace, and test—embedding responsibility into the software stack itself.
LLM frameworks are no longer just developer tools—they are ethical infrastructure. They help detect and reduce bias, enforce transparency, build robustness, and enhance explainability. Tools like Opik, Langfuse, and TruLens represent a new generation of AI engineering where responsibility is built-in, not bolted on.
Questions for Further Thought:
Can we standardize metrics like “fairness” or “bias” across domains, or must every use case be uniquely evaluated?
Should regulatory compliance (e.g., AI Act or NIST AI RMF) be integrated into LLM evaluation frameworks by default?
As LLMs evolve, how can we ensure that evaluation frameworks stay ahead of emerging risks—like agentic behaviour or multimodal misinformation?
In the pursuit of Responsible AI, LLM evaluation frameworks are not just useful—they are indispensable.
In 2006, Clive Humby, a British mathematician, and data scientist, famously coined the phrase “data is the new oil” to highlight the immense value of data in the modern world, much like oil has historically been a valuable resource. The advent of Big Data Analytics and machine learning models within the realm of AI has exponentially increased the power of information systems. These advanced algorithms act as “refineries,” extracting value from raw data and serving as the currency of the contemporary world. These refineries are pivotal in the data-driven economy, enabling companies to harness AI effectively. However, as the excitement around AI systems surged, so did skepticism. This led to the question: Are AI systems the new snake oil?
In his book, “AI Snake Oil,” Princeton University’s Professor Arvind Narayanan, co-authored with Sayash Kapoor, addresses several critical issues such as misleading claims, harmful applications, and the big tech control of AI.
Power of Algorithms
Machine learning algorithms, including regression, classification, clustering, neural networks, and deep learning, identify patterns and make predictions based on data. Natural Language Processing (NLP) algorithms enable computers to understand, interpret, and generate human language, facilitating tasks like sentiment analysis and text summarization. Recommendation systems predict user preferences and suggest products, content, or services accordingly. Generative AI (GenAI) creates content such as text, images, music, and videos, with technologies like ChatGPT, DALL-E, and OpenAI’s Sora making a significant impact on daily life and work. Used as a tool, AI Copilots help developers reduce the time between idea and execution despite the need for constant refactoring of generated code and dealing with edge cases missed by AI.
Successful AI Applications and Disappointments
AI has found success in various domains:
– IBM uses predictive AI for customer behavior analysis and supply chain optimization.
– Amazon implements predictive models for demand forecasting and inventory management.
– Google employs predictive analytics for ad targeting and search result optimization.
– Netflix leverages predictive analytics for personalized content recommendations.
– UPS uses predictive models for route optimization and vehicle maintenance.
– American Express deploys predictive analytics for fraud detection and credit scoring.
– H2O.ai’s models at Commonwealth Bank, Australia, assist in fraud detection, customer churn, merchant retention, and more.
However, there have been notable disappointments. AI systems have perpetuated biases, leading to unfair hiring practices, incorrect medical diagnoses, and discriminatory outcomes. These incidents highlight the potential harms of AI when not properly designed, implemented, and used.
Responsible AI
The importance of transparency, accountability, and ethical considerations in AI development and deployment is now widely recognized. Instances of AI blunders, such as Google’s GenAI tool Gemini generating politically correct but historically inaccurate responses, underscore the challenges of training AI on biased data and balancing inclusivity with accuracy.
Governments and institutions are increasingly focused on AI safety. Projects at leading universities sponsored by Governments & big tech companies aim to establish industry-specific guidelines. Some of these guidelines may become regulations, with hefty fines for violations, as seen with the EU AI Act. The debate on AI regulation versus innovation continues, with developers expected to self-regulate in the absence of enforceable laws. Enterprises using AI systems can adopt standards like ISO/IEC 42001:2023 to manage AI responsibly, ensuring ethical considerations, transparency, and safety.
Impacts of Advanced AI and Future Considerations
Innovations in AI algorithms are continually benefiting society. For example:
– Google AI collaborates with the UK’s NHS to improve breast cancer screening consistency and quality.
– AlphaFold2, the 2024 Nobel Prize-winning AI model, has revolutionized protein structure prediction, accelerating drug discovery and biotechnology.
– Google’s DeepMind’s GenCast predicts weather and extreme conditions with unprecedented accuracy.
Generative AI has advanced significantly, with models like OpenAI’s ‘o3’ overcoming traditional limitations and adapting to new tasks. These models have performed well on ARC-AGI (Abstraction and Reasoning Corpus for Artificial General Intelligence) benchmarks, marking progress towards AGI.
As AI advances towards AGI, concerns about rogue AI agents and their potential threats grow. Autonomous Replication and Adaptation (ARA) could lead to AI agents evading shutdown and adapting to new challenges. AI containment strategies are evolving to address these risks.
AI Landscape: Big Techs, Businesses and Us
Big tech companies like Microsoft, Alphabet, Meta, and Amazon are set to invest over $1 trillion in AI in the coming years. McKinsey reports that businesses are dedicating at least 5% of their digital budgets to GenAI and analytical AI. While big tech companies skate fearlessly in the slippery zone between snake oil and the new oil to conquer the AI landscape, businesses appear to tread cautiously, concerned with ROI and responsible AI use. AI safety guidelines and regulations can serve as guardrails for us, the individuals, to navigate the slippery terrain between snake oil and the new oil.
In this article I trace how ERP evolved from a system for manufacturing and gradually expanded to cover all business functions, the advent of client-server to replace main frames, the shift to cloud computing that made ERP accessible to businesses of all sizes. I trace my experience in the world of ERP starting as an early developer in client- server era of 1990s through its technological evolutions over the web and the cloud. I will also share my thought for the future of ERP in the current AI world.
MRP I & II
MRP (Material Requirements Planning) systems, an early precursor to ERP was developed during 1970s to manage manufacturing processes, especially inventory control and production scheduling. These systems were often large, mainframe-based, batch-oriented programs used by manufacturers to reduce waste and improve production efficiency. MRP evolved into MRP II (Manufacturing Resource Planning) during 1980s with additional functions such as Shop floor control, Capacity planning, and Demand forecasting. These systems were still operated on mainframe computers, requiring significant IT investment.
The Rise of ERP
MRP II expanded into Enterprise Resource Planning (ERP) during 1990s when my quest with ERP began. ERP systems moved beyond manufacturing to incorporate finance, human resources, sales, purchasing, and customer relationship management (CRM). This was the first time businesses could access a single, unified system for all core business processes. ERP was built on client-server architecture, making it more flexible and easier to deploy than its mainframe predecessors.
I was one of the very few who had an opportunity to experiment with this modern technology and struggled with early versions of Microsoft Windows.Even though we developed our own technology to integrate the data among various modules of ERP, Relational database technologies which evolved later helped streamline integration across modules. While SAP and Oracle were the early ERP global vendors, Ramco started its journey ahead of many others to develop an ERP product in India. I remember challenges made by certain IIM educated people on the futility of such efforts in developing a product in India. The then young Vice Chair of the Ramco Group, Mr. P R Venketrama Raja, boldly took up the challenge and proved otherwise. I was lucky to be handpicked by him when he formed his first team for product development in India.
It was none other than Bill Gates who launched, Ramco’s ERP product in 1994 during one of his first visits to India. Microsoft did not have its Navision at that point of time. Eyes of the large corporates in India fell on Ramco not just for its product, but for the organization as Ramco started its lone journey as a product developer in India crowded by IT service players.
ERP Goes Web-Based
The early 2000s saw the evolution of ERP into web-based platforms. This change enabled users to access ERP systems through web browsers, making them more accessible and user-friendly. Ramco was again the first in India to deliver web-based ERP. Products became more modular, allowing companies to implement specific functions without needing to deploy the entire system. This era saw the rise of service-oriented architecture (SOA), which allowed ERP systems to be more flexible, interoperable, and easier to integrate with third-party applications. High upfront costs, complexity, and the need for customizations were still common hurdles for many businesses.
Cloud ERP and Mobility
The 2010s were defined by shift of ERP from on-premises to cloud-hosted models. Companies could now access ERP solutions as a service (SaaS) through subscription-based models, reducing capital expenditure on IT infrastructure. Ramco announced its first version of ERP on the cloud in 2008. As the usage of ERP has become broad based, compliance requirements became mandatory due to computer generated reports becoming a norm in enterprises. My team, as a QA partner for product developer had terrific opportunity to test the product developed on cloud platform with enhanced functionality and compliance requirements.
ERP systems became more user-centric with intuitive interfaces, and personalized dashboards. The rise of mobile devices allowed ERP users to access data and perform tasks on-the-go via mobile apps. Cloud ERP provided scalability, easier updates, lower upfront costs, and remote access, thereby making ERP solutions more affordable and practical for small and medium-sized businesses (SMBs). Data security, compliance, and control were initial concerns as businesses shifted critical data to the cloud which were taken care of thanks to specialised large data centres with built in high tech cyber security controls.
Amitysoft, the company promoted by me became business partner of Ramco, thanks to the Chairperson who saw my evolution along with Ramco’s product and technology. Knowledge of tech behind the product, hands behind testing the product enabled my team to implement the product for several customers in India and abroad. As a partner, we were the first to deploy highest number of ERPs on the cloud in India. Amitysoft has largest number of successful partner implementations to its credit with several customer accolades.
AI Driven ERP: The Current & Future
The current era of ERP is marked by the integration of AI, machine learning, IoT, and analytics to create intelligent ERP systems. At Ramco, I had free hands to explore ‘Expert Systems,’ now known as Good Old-Fashioned AI (GOFAI), based approach for a Mine Planning ERP system in late 1990s.This was probably one of the first exploitation of AI concept in an ERP. In industries like manufacturing, logistics, and healthcare, IoT devices are integrated with ERP to monitor equipment including cobots in real time, manage assets, and optimize supply chains. AI based conversational bots are changing the UX to natural language – text and voice interactions.
ERP systems are evolving towards becoming autonomous, where they will self-optimize based on real-time data, predict potential issues, and automatically adjust processes. More advanced AI capabilities will allow ERP systems to make autonomous decisions regarding supply chain adjustments, financial planning, and workforce optimization. Features for supporting sustainability from R & D to sourcing materials, inventory, manufacturing, and post sales will become a standard functionality cutting across all modules in ERP. Products will increasingly use blockchain for enhancing supply chain transparency, ensuring data integrity, and improving transaction security. Future ERP systems, as I foresee will be self-configurable, self-customizable to the context, and will adapt functionalities dynamically as the business goals and market change.
To predict whether AI will make an impact in 2024 requires natural intelligence and a little data. Generative AI (GenAI) caught the fancy of all generations. Two months after launch, ChatGPT broke records as the fastest-growing consumer app in history, until Meta’s Threads overtook it. ChatGPT now has about one hundred million weekly active users while more than 92% of Fortune 500 companies are using the platform, according to OpenAI.
Investments into AI
Nvidia, the makers of chip that power training models for AI saw its market value shooting up by $800 billion making it the biggest percentage gainer among the large tech companies in the last year. According to AMD, Nvidia’s rival, market for AI chips for data centers would hit $400 billion by 2027, which is equal to the pre-covid global market size of all semiconductors. Companies like OpenAI building large language models attracted about $27 billion as per PitchBook. Goldman Sachs observes that investment in AI is just below 0.5% of GDP and estimate it to go beyond 2.5% of GDP by 2032. Will these investments result in improved productivity and better business results for users of AI? Looks like what happened during the gold rush when providers of mining equipment, tools and supplies made more profit than the people who managed to hit the gold!
Return of Investment
Tech companies are the earliest gainers from employee cost as several back-office jobs are getting replaced by AI. Study by Stanford author Brynjolfsson found that average productivity of call center workers rose by 14% within a few weeks of using the AI infused technology. Interesting thing to note is that the gains were 35% for lowest skill level workers. Lower the skill levels, higher the gains to the business as those jobs become extinct. The focus now is on how best to aitomate (‘ai’ based tools to automate tasks – my own terminology) tasks that can boost productivity. It is going to be a while before we see a real killer app based on AI that can fetch several fold returns.
Cautious Business and Road Blocks
Spending on Gen AI this year will not be more than $20 billion, which is just one fifth of the spend on IT security according to Gartner. Businesses will scale up spend over the next few years after a lot of trial and errors resulting in monetizable Aipps (‘AI’ based apps – again my own terminology). While the technology is progressing from Artificial Narrow Intelligence to Artificial General Intelligence, issues such as hallucination, bias, deep fakes, ethics besides the cost are to be controlled for AI to take over as pilot from just being co-pilot.
A Flashback
Recently, I landed upon a photograph of mine talking at a round table conference in early 90’s on Artificial Intelligence at the Vishakhapatnam chapter of ’The Institution of Engineers’. Three decades back, I led a team to develop an ‘Expert Mine Planning System’ at Ramco Systems, India with ‘expert systems’ approach in the blasting module. Expert systems emulate human decision making by reasoning through specific body of knowledge represented within system as ‘if-then-else’ rather than through procedural code. A technical paper about the system was accepted at the XXIV APCOM (Application of Computers and Operations Research in the Mineral Industries) conference held during Oct 31- Nov 3, 1993, at Montreal, Canada. Internationally well-known commercial vendors of ‘Mine Planning’ systems took serious note of our product and were surprised that such a product development was happening in India while the entire Indian IT was actually waiting for the Y2K to happen to get global recognition.
Narrow, not Super
Developments in cloud and big data over the decades have paved way for the current avatars of GenAI and AI has become a household term hallucinating between fake and real. Arrival of Aepps (Emotional Intelligence aware artificial intelligenceapps – now you know that this is my terminology too) in times to come can catapult AI into the next generation. My comments are of ’narrow’ trending towards ‘general’ but not ‘super’ going by the categories of Artificial Intelligence.
She is on the 100 Brilliant Women in AI Ethics List according to her LinkedIn profile. With the advent of Gen AI, human bias become more pervasive, aided by the big data naturally reflecting the human. Ethics in AI is definitely more important as intelligence is becoming artificial. Machine learning models cannot be blamed for the biased data using which they learn. Human biases in color, class and creed get naturally impregnated into the learnt models, the results of which are consumed by the public at large. Property built through Intellect and Privacy go for a task as the devices become more intrusive and omnipresent. Distinguishing fake from real is becoming a day-to-day challenge as in an old Tamil movie song ‘unmai ethu poi ethu onnum puriyale, namma kanna nammala namba mudiyele‘. Regulatory framework & policy on the one hand and using the same AI technology on the other hand the world is attempting to build an ethical neural layer in artificial intelligence. But the larger question is whose ethics? Suchana Seth, who is under custody for suspected murder her own 4-year-old son in Goa is one of the global top women in ethics for AI. It appears that it is ok for her to lose her son as long as her estranged husband is deprived of meeting his son. An eye for eye seems to be the ethics under play for not willing to go by the court arrangement for the couple who didn’t see eye to eye. The question ‘who should make policy or build technology for ethics in AI’ will remain a tough question but we get data to answer ‘who shouldn’t’.
COSMICOUS 