Corporate AI Strategy
Moving beyond the hype, hyperbole, and frenetic, unchecked adoption... Our AIGP-certified professionals offer strategic advisory services tailored to your organization's needs and the practical employment of AI as a business accelerator. From developing AI strategies aligned with business goals to assessing AI readiness and maturity through the lens of ISO/IEC 42001 and the proposed ISO/IEC 27090, we provide actionable insights to inform, protect, and accelerate AI adoption.
AI Governance Frameworks
Phenomenati vCAIOs assist in designing and implementing robust AI governance frameworks that encompass regulatory compliance, ethical guidelines, and risk management practices. Our recommended governance structure:
- begins with the creation of a cross-functional AI Governance Committee,
- proceeds with the establishment of a corporate AI Strategy (above) informed by the 5 OECD AI Principles... as well as the UNESCO Principles, the Asilomar AI Principles, the CNIL AI Action Plan, and the IEEE Initiative on Ethics of Autonomous and Intelligent Systems,
- continues with the deliberate Design of AI into Business Systems and Processes (below),
- and ultimately leverages industry standards and frameworks such as ISO/IEC 42001, the NIST AI RMF, and the European Council's proposed HUDERIA to establish an AI Management System (AIMS) that ensures transparency, accountability, and fairness in AI deployment.
The most basic of first steps here is to establish an enterprise-wide Inventory of where AI is currently in use across the company. From there, use Phenomenati's concept of a "DAIR" matrix to assess the potential Risks to the business from each documented use.
Designing AI Into Business Systems and Processes
AI systems and the business processes they enable must be developed with a clear understanding of the specific business problems they are intended to solve. Whether you are exploring the use of:
- language models (e.g., natural language processing [NLP] for transcription, sentiment analysis, etc.),
- transformer models (e.g., generative pre-trained transformers like ChatGPT, machine translation, decoders, etc. for content generation),
- analytical models (e.g., linear/statistical approaches such as linear regression, logistic regression, support vector regression [SVR], support vector machines [SVMs], naive Bayes, K Nearest Neighbor [KNN], etc. for spam filtration, intrusion detection, or sentiment analysis),
- decision trees (e.g., ID3, CART, random forest, etc. for credit scoring, fraud analytics),
- neural networks (e.g., feedforward, modular, GANs, perceptron, etc. for applications like facial recognition or computer vision),
- expert systems (e.g., rule-based, frame-based, fuzzy, neural, and neuro-fuzzy inferencing systems, supporting applications like CaDet used in cancer detection),
- multi-modal models (e.g., fusion for machine perception, gesture or emotion recognition, video summarization, automated exam proctoring, medical diagnoses, etc.),
- EdgeAI (e.g., for smart Industrial Control Systems [ICS]),
- etc.
... Thorough system analysis and design ensures that AI architectures, technologies, implementations, and integrations are robust, reliable, and secure. This includes addressing potential biases in AI models, ensuring data privacy and security in deployments, integrating seamlessly into existing system and network environments, and establishing clear accountability for AI-driven decisions. By embedding AI thoughtfully and systematically, Phenomenati helps our clients to mitigate risks, foster trust among stakeholders, and create sustainable value.
Leveraging Open Source AI Models
There are a few fundamental topics which should concern the CAIO when the organization is considering deploying open source AI Models as part of any internal business system:
First, the CAIO should ensure that the company only employs open source models managed and distributed through reputable repositories such as GitHub, TensorFlow Hub, or Hugging Face, which each host a variety of AI models.
Next, the company must evaluate models based on well documented Criteria such as direct relevance/applicability to the business need(s), maturity, transparency, sustainability, performance metrics, community reviews, and documentation quality. Of course, the business should prioritize models that are well-maintained, regularly updated, and have a strong user community for support and collaboration.
Next, it's imperative that the CAIO emphasize the critical need to scan open source AI models for malicious content. Open source AI models provide valuable resources for innovation and development across industries, but they also pose potential risks if not thoroughly vetted. Scanning these models helps mitigate the risk of inadvertent inclusion of malicious code or vulnerabilities that could compromise data security, privacy, or operational integrity.
And finally, the company should follow best practices (discussed below) for integrating, testing, and customizing these models to fit the specific business needs; while adhering to intellectual property rights, licensing, and ethical guidelines.
Developing and Deploying AI Systems and Processes
To provide oversight of the development and deployment of new AI systems, your Phenomenati vCAIO will apply deep knowledge of the technology stack that underpins contemporary artificial intelligence systems and is fundamental to successful implementation. At its core, AI systems rely on several key components:
- Data Collection and Storage – AI systems depend on vast amounts of data, collected from various sources such as sensors, databases, and APIs. Data collection pipelines and data storage solutions like data lakes or cloud databases are essential for storing and managing this data effectively and securely. Database technologies such as NebulaGraph, HyperGraphDB, Neo4j, and TetraScience are relevant here.
- Data Preprocessing and Cleaning – Before AI algorithms can effectively analyze data, it must be preprocessed and cleaned to remove noise, handle missing values, and standardize formats. Tools like Apache Spark or TensorFlow Data Validation are used for these tasks.
- Machine Learning Algorithms – These algorithms (see below) are the heart of AI systems, comprising supervised (e.g., regression, classification), unsupervised (e.g., clustering, anomaly detection), and reinforcement learning algorithms. Popular libraries include TensorFlow, PyTorch, and scikit-learn.
- Natural Language Processing (NLP) and Computer Vision (CV) – For tasks involving text or image data, NLP libraries like NLTK or spaCy and CV frameworks such as OpenCV or TensorFlow Object Detection API are essential.
- Model Training and Optimization – Training AI models (see below) involves iterating through data to adjust model parameters for better accuracy. Techniques such as gradient descent and backpropagation are commonly used, with optimizations handled by tools like TensorFlow Extended (TFX) or Apache Mahout.
- Model Deployment and Management – Once trained, models are deployed into production environments. Technologies like Kubernetes or Docker facilitate scalable deployment, while model management platforms such as MLflow or Kubeflow help monitor performance and manage versions.
- Hardware Acceleration – AI computations often require high-performance hardware like GPUs or TPUs. Cloud providers offer GPU instances (e.g., AWS EC2, Google Cloud AI Platform) optimized for AI workloads.
- Edge Computing – For AI applications requiring real-time responses or operating in resource-constrained environments, edge computing platforms (e.g., TensorFlow Lite, AWS IoT Greengrass) enable running models on edge devices, such as smart ICS controllers and smart phones.
- AI Ethics and Governance (see below) – Ensuring ethical use of AI involves frameworks for fairness, transparency, and accountability. Tools for model explainability (e.g., LIME, SHAP) and governance platforms (e.g., IBM AI OpenScale) help manage these aspects.
- Continuous Learning and Adaptation – AI systems evolve with new data and feedback. Technologies like online learning algorithms and reinforcement learning frameworks (e.g., DeepMind's AlphaGo) enable continuous improvement.
Understanding and integrating these components within a cohesive technology stack is essential for developing, deploying, and managing effective AI systems that meet business objectives while adhering to ethical standards and regulatory requirements.
AI Project Oversight
"What does success look like?" It's imperative to emphasize the critical need for informed project oversight when leveraging AI technology in innovative ways. Expectations of AI investments are often inflated, and ignore the enduring commitments required to extract sustained value. AI projects often involve complex models (algorithms and large datasets) that require careful and continuous management to ensure accuracy, reliability, and ethical use.
Effective oversight of the full AI Model Lifecycle involves not only technical expertise but also a deep understanding of regulatory requirements and ethical considerations. By establishing robust governance frameworks and engaging stakeholders throughout the project lifecycle, your Phenomenati vCAIO will help manage expectations, mitigate risks, ensure compliance with data privacy laws, and uphold ethical standards.
Investing in Continuous Machine Learning to Train AI
It is essential to recognize the critical need for ongoing investment in the training of AI systems across the enterprise through relevant Machine Learning (ML) techniques and technologies. Continuous training ensures that your AI systems remain effective, adaptive, and aligned with evolving business needs and technological advancements.
Selecting and investing in relevant, specific machine learning (ML) approaches to continuously train your models:
- supervised learning (e.g., Support Vector Machine, Support Vector Regression, etc. requiring well labeled data),
- unsupervised learning (e.g., requiring huge volumes of unlabeled data)
- semi-supervised learning (e.g., using a small amount of labeled data, with a very large amount of unlabeled data)
- reinforcement learning (e.g., marketing and advertising, image processing, gaming, automated robots in warehouses or assembly lines),
- deep-learning (e.g., Recurrent or Convolutional neural networks for applications such as recommendation, virtual assistants, fraud detection, autonomous vehicles),
- federated learning (e.g., training that uses multiple, separated local datasets without exchanging data across those),
- TinyML within an ICS architecture based on the Purdue Framework (e.g., Industrial Control Systems)
- etc....
... Not only enhances operational efficiency and decision-making but also reinforces your commitment to maintaining leadership in leveraging AI for sustainable value creation, customer satisfaction, and business growth.
Verifying and Validating AI Systems
Ensuring a robust approach to the verification and validation (V&V) of AI systems is critical to maintaining trustworthiness, reliability, validity, safety, security, privacy, fairness and ethical integrity. This V&V requires comprehensive testing against intended use cases and expectations, as well as exploring edge cases, unseen data, and potential malicious inputs to uncover vulnerabilities and ensure resilience in real-world scenarios.
Considering common reasons why AI systems typically fail, including issues like brittleness, embedded bias, and catastrophic forgetting… your Phenomenati vCAIO will recommend using a thorough approach to testing and validation, including: Model Cards or fact sheets compiled to provide standardized information about each model's functions and outputs, promoting transparency and informed decision-making within teams and with stakeholders. Repeatability Assessments conducted to verify consistent performance. Counterfactual Explanations (CFEs) employed to understand how changes in input could impact AI decisions, offering insights into the model's decision-making processes and enhancing interpretability. And Threat Modeling and Adversarial Testing which are integral to identifying and mitigating security threats, safeguarding your AI systems against potential attacks and vulnerabilities.
AI Ethics and Bias Mitigation
The integration of AI into business operations presents significant opportunities, but also introduces substantial risks, particularly related to ethical concerns and biased decision-making. Unaddressed biases in AI systems can lead to unfair practices, legal liabilities, and reputational damage, undermining stakeholder trust, brand reputation, and corporate integrity. Further, ethical lapses in AI can result in regulatory non-compliance and financial penalties. By identifying and advocating for specific AI Ethics and Bias Mitigations using specific AI Risk Scenarios managed in a Risk Register dedicated to the company's use of AI, our team helps organizations embed ethical principles into AI development and deployment processes, promoting fairness and inclusivity.
Legal and Regulatory Compliance
Phenomenati vCAIOs guide organizations through the complex landscape of AI regulations and standards. From existing laws on unfair/deceptive practices (ref. EU Digital Services Act), non-discrimination, product safety, and intellectual property... to AI's affects on current privacy laws such as GDPR and CCPA, ... to AI-specific laws such as the EU's AI Act or Canada's AI and Data Act (AIDA), to emergent national laws around the globe (e.g., Singapore, Australia, South Africa, India, South Korea, and Japan), to nascent industry-specific AI regulations... AI relevant legal obligations typically focus on transparency, accountability, and fairness in AI systems.
For instance, some U.S. states require companies to disclose (through Notice and Transparency) when AI systems are being utilized, especially in critical decision-making processes such as hiring or lending. Additionally, there are efforts to ensure AI systems are not discriminatory or biased, particularly in sectors like healthcare and law enforcement where the stakes are high.
Your Phenomenati vCAIO will work with your general counsel to ensure your AI initiatives identify and comply with all relevant legal obligations, minimizing regulatory risks.
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