AI/GenAI Learning Path for IT & Enterprise Architects

 

HTH...

A Tech Artist 🎨

The Types of AI Audience

 Continuing our conversation from the Part-1

Understanding the Spectrum of AI Enthusiasts: Builders, Consumers, and Fiddlers

Introduction

In the rapidly evolving landscape of artificial intelligence (AI), the term "AI enthusiast" encompasses a diverse group of individuals with varying levels of expertise and engagement. From those who architect complex AI systems to those who integrate AI tools into their daily workflows, and even those just beginning to explore this transformative field, AI enthusiasts can be categorized into three distinct groups: AI Builders/Producers, AI Consumers, and AI Fiddlers or AI Expert Beginners. 

With the demand for AI professionals projected to grow significantly—for instance, the U.S. Bureau of Labor Statistics predicts a 26% increase in computer and information research scientist roles by 2032 —understanding these categories is critical for navigating the AI ecosystem.

This article explores these categories, detailing the essential skills required for AI Builders, the practical applications for AI Consumers, and the exploratory activities of AI Fiddlers. By examining these roles, we aim to provide clarity on what it takes to thrive in the AI landscape and how individuals can chart their path forward.

AI Builders/Producers: The Architects of AI Innovation

AI Builders, also known as AI Producers, are the masterminds behind AI systems. They design, develop, and implement the algorithms and models that power applications ranging from autonomous vehicles to intelligent chatbots. These individuals possess a deep understanding of both the technical and theoretical foundations of AI, enabling them to create innovative solutions to complex problems. Their role is pivotal, as they drive the technological advancements that shape industries and societies.

To excel as an AI Builder, one must master a comprehensive set of skills that blend technical expertise with business acumen. The following table outlines the essential skills required, as identified in the context of AI development and corroborated by industry standards:

Skill	Description
The Math Behind AI	Proficiency in linear algebra, calculus, probability, and statistics, which form the backbone of machine learning algorithms.
Algorithms and Machine Learning	Knowledge of machine learning techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning, to select and optimize algorithms for specific tasks.
Software Architectures	Ability to design scalable and efficient software systems, leveraging cloud computing and distributed systems to handle large datasets and computational demands.
Data Science	Skills in data wrangling, analysis, and visualization to extract meaningful insights from data, the fuel for AI models.
Automation and Databases	Proficiency in automating processes and managing databases (e.g., SQL, NoSQL) to ensure efficient data collection and storage.
IT Strategy and Management	Understanding how to align AI initiatives with business goals and manage IT resources effectively for successful deployment.
Stakeholders and Program Management	Ability to communicate with stakeholders, manage project timelines, and ensure AI projects meet their objectives.
Basic Product Management	Skills in defining product requirements and ensuring AI solutions address user needs for impactful applications.
Foundational Cyber Security	Knowledge of security principles to protect AI systems from threats and ensure data privacy.
Basic Finance	Understanding financial concepts to assess the cost-effectiveness and return on investment of AI projects.
Platforms and Integration	Familiarity with AI platforms (e.g., TensorFlow, PyTorch) and integration techniques to incorporate AI solutions into existing systems.
Basics of Business and Management	General business acumen to understand market dynamics and organizational contexts for AI deployment.

These skills enable AI Builders to develop cutting-edge technologies while ensuring their solutions are practical, secure, and aligned with organizational objectives. Industry reports emphasize that AI engineering roles demand both technical proficiency and business-oriented competencies, as evidenced by job postings analyzed in 2025. For example, AI Builders might work on projects like developing neural networks for image recognition or deploying chatbots for customer service, requiring a blend of these skills to succeed.

AI Consumers: Driving Practical Applications

AI Consumers are individuals who leverage AI tools and technologies in their professional or personal activities without building the underlying systems. They use AI to enhance productivity, make data-driven decisions, and solve problems more efficiently across various industries. According to industry analyses, AI is transforming sectors like healthcare, finance, retail, and education, with the global AI market expected to reach $1,811.8 billion by 2030.

Examples of AI Consumers include:

• Healthcare Professionals: Using AI for diagnostic assistance, such as analyzing medical images, or optimizing patient care workflows.

• Financial Analysts: Employing AI for fraud detection, risk assessment, and algorithmic trading, with banks like J.P. Morgan Chase using proprietary AI algorithms to flag unusual transactions.

• Retail Professionals: Utilizing AI for personalized product recommendations and inventory management to enhance customer experiences.

• Educators: Integrating AI-powered tools for adaptive learning platforms that personalize student education.

AI Consumers do not require the deep technical expertise of Builders but should have a foundational understanding of AI’s capabilities, limitations, and ethical implications. This knowledge enables them to interpret AI-generated insights accurately and use tools responsibly. For instance, a marketer using AI for customer segmentation must understand how to evaluate the reliability of AI-driven insights to avoid biased outcomes.

By adopting AI, Consumers drive innovation and efficiency in their fields, contributing to the widespread adoption of AI technologies. Their role is crucial in translating AI advancements into practical, real-world applications.

AI Fiddlers or AI Expert Beginners: The Curious Explorers

AI Fiddlers, or AI Expert Beginners, are individuals exploring AI out of curiosity or as a hobby. They may be students, professionals from unrelated fields, or enthusiasts eager to learn about AI but lacking extensive experience or professional application. Their activities lay the foundation for potential growth into more advanced roles.

Typical activities of AI Fiddlers include:

• Enrolling in online courses or workshops on AI and machine learning, such as those offered by platforms like Coursera or DataCamp.

• Experimenting with AI tools and frameworks, such as TensorFlow, PyTorch, or low-code platforms like Google AutoML.

• Participating in AI communities, forums, or hackathons to collaborate and share knowledge.

• Reading books, articles, and research papers to build foundational knowledge in AI topics.

While AI Fiddlers may not yet contribute to production-level AI systems, their engagement is vital for the growth of the AI community. Many AI Builders and Consumers begin as Fiddlers, and their curiosity can lead to significant contributions over time. For example, a Fiddler experimenting with a neural network in a hackathon might later transition to a professional role as an AI Consumer or Builder.

Conclusion

The AI ecosystem is vibrant and diverse, encompassing Builders who create AI systems, Consumers who apply AI in their work, and Fiddlers who explore AI with curiosity. Each group plays a critical role in advancing AI’s impact on society. For aspiring AI Builders, mastering a blend of technical and business skills is essential to lead innovation. AI Consumers drive practical adoption by leveraging AI tools effectively, while AI Fiddlers contribute to the field’s growth through their learning and exploration.

As AI continues to shape industries and economies, recognizing where you fit in this spectrum can guide your journey. Whether you aim to build cutting-edge AI systems, apply AI to enhance your work, or simply explore its possibilities, there is a place for you in the AI landscape. The projected growth of AI-related jobs, with a 20.17% CAGR for AI engineer demand through 2029, underscores the opportunities available for all enthusiasts to contribute meaningfully.

Further Readings:

Expert Generalists 

HTH...

A Tech Artist 🎨

What are Absolute and Big "NOs" for a Consultant to be Successful in an Engagement? - 3Ps & a Bonus "P"

 

1. Preconceptions
2. Prejudice
3. Talking About the Perceived Benefits

 

And your best friend is the 4th P - Preparation (An intense one)

As IT consultants and enterprise architects, delivering value in complex engagements requires more than technical expertise—it demands a strategic mindset that avoids common pitfalls and prioritizes client needs. This blog post, outlines three critical mistakes to avoid: Preconceptions, Prejudice, and Talking About the Perceived Benefits. It also emphasizes the 4th P: Preparation. This article expands on these principles from an IT consulting and enterprise architecture perspective, integrating best practices from modern enterprise architecture, particularly API-centric approaches, to provide actionable insights and examples.

1. Preconceptions: The Danger of Assumptions

Definition and Impact

Preconceptions occur when consultants assume solutions without thoroughly analyzing the client’s environment, needs, or constraints. In IT consulting, this might mean presuming that a specific technology, such as cloud computing or microservices, is universally applicable. Such assumptions can lead to misaligned strategies, wasted resources, and dissatisfied clients. For example, recommending a complete cloud migration without assessing data sovereignty or legacy system dependencies can introduce unnecessary complexity and costs.

Enterprise Architecture Perspective

Enterprise architecture demands a tailored approach, as IT landscapes vary widely across organizations. A key best practice, as outlined in 16 Common Enterprise Architecture Best Practices by digitalML (Enterprise Architecture Best Practices), is adopting an API-centric architecture. This requires understanding how existing systems can be integrated or exposed via APIs rather than assuming they must be replaced. Preconceptions about replacing legacy systems can overlook their value and disrupt operations.

Example

Consider a mid-sized manufacturing company seeking to modernize its supply chain management system. A consultant with preconceptions might immediately propose a cloud-based ERP solution, citing scalability and cost-efficiency. However, a deeper analysis might reveal that the existing system, though outdated, is deeply integrated with critical processes and customized to meet unique needs. A complete overhaul could be costly and risky. Instead, the consultant could propose exposing key functionalities via APIs, enabling incremental modernization while preserving stability. This aligns with the best practice of data-driven decisions (Best Practice #5), using system usage and performance data to inform strategies.

How to Avoid

To mitigate preconceptions, consultants should conduct comprehensive discovery sessions, leveraging tools like SWOT analysis, stakeholder interviews, and technical assessments. Creating a holistic catalog of the client’s IT landscape (Best Practice #7) ensures decisions are based on a clear understanding of the current state. For instance, tools like the ignite Platform can map APIs and systems, providing a data-driven foundation for recommendations.

Preconception Pitfall	Consequence	Mitigation Strategy
Assuming cloud migration is always best	Ignores data sovereignty, legacy dependencies	Conduct technical assessments, use API integration
Presuming microservices suit all projects	Overlooks organizational maturity, complexity	Analyze DevOps readiness, business needs
Recommending new systems without analysis	Disrupts operations, increases costs	Create holistic system catalog, engage stakeholders

2. Prejudice: Overcoming Biases

Definition and Impact

Prejudice in consulting involves biases against certain technologies, methodologies, or the client’s team, which can lead to suboptimal decisions and strained relationships. In IT, this might manifest as dismissing legacy systems as obsolete or assuming the client’s IT staff lacks the skills to adopt new technologies. Such biases undermine objectivity and collaboration, critical for successful engagements.

Enterprise Architecture Perspective

In enterprise architecture, prejudice can lead to favoring certain architectural patterns or technologies without due diligence. For example, an architect might prefer proprietary solutions over open-source alternatives based on past experiences, ignoring cost, flexibility, or community support. The best practice of treating all APIs as products (Best Practice #10) encourages a neutral evaluation of all system components, ensuring even legacy systems are assessed for their business value.

Example

An enterprise architect working with a financial services firm might have a prejudice against COBOL-based mainframe systems, viewing them as outdated. They might recommend rewriting the system in a modern language like Java, overlooking the fact that the COBOL system is mission-critical, well-maintained, and supports processes embedded in the organization’s operations. A more effective approach would be to modernize incrementally, perhaps by containerizing the application or integrating it with newer systems via APIs. This aligns with aligning business capabilities to APIs (Best Practice #2), objectively mapping system contributions to organizational goals.

How to Mitigate

Consultants should strive for objectivity by continuously educating themselves on diverse technologies and methodologies. Structured decision-making frameworks, such as cost-benefit analysis or alignment with business goals, can reduce bias. Respecting the client’s existing assets and team expertise is also crucial. For example, collaborating with the client’s IT team to leverage their knowledge of legacy systems fosters trust and ensures practical solutions.

Prejudice Pitfall	Consequence	Mitigation Strategy
Dismissing legacy systems	Ignores reliability, business value	Evaluate systems objectively, consider API integration
Bias against client’s team	Hinders collaboration, erodes trust	Engage team early, leverage their expertise
Favoring proprietary solutions	Increases costs, limits flexibility	Use structured frameworks, explore open-source options

3. Talking About the Perceived Benefits: Aligning with Client Priorities

Definition and Impact

This pitfall involves emphasizing benefits that the consultant perceives as valuable rather than those that matter to the client. In IT consulting, this can lead to a disconnect between expectations and reality, resulting in dissatisfaction or project failure. For instance, promoting advanced features of a new system when the client prioritizes cost savings or compliance can misalign the project’s focus.

Enterprise Architecture Perspective

Enterprise architects often face the temptation to pursue idealized architectures that may not address the client’s immediate needs. The best practice of improving API discoverability and reuse (Best Practice #8) emphasizes understanding what the client values—whether it’s cost reduction, faster time-to-market, or enhanced customer experiences. By framing solutions in terms of these priorities, architects ensure alignment with client goals.

Example

When proposing a new customer relationship management (CRM) system, a consultant might highlight AI-driven analytics and advanced integrations, which are powerful features. However, if the client’s primary concern is improving basic sales tracking and reporting, these features might be seen as overkill and not justify the investment. Instead, the consultant should focus on how the CRM streamlines sales processes and enhances data accuracy, while outlining how additional features could provide long-term value. This aligns with automated, flexible governance (Best Practice #11), tailoring solutions to specific client needs.

How to Align

Effective communication involves understanding the client’s key performance indicators (KPIs) and success metrics. By framing solutions in terms of how they impact these metrics, consultants can ensure proposals resonate with the client’s objectives. Regular feedback loops and validation sessions help maintain alignment throughout the project.

Perceived Benefits Pitfall	Consequence	Mitigation Strategy
Promoting irrelevant features	Misaligns with client goals	Understand client KPIs, tailor proposals
Focusing on idealized architecture	Delays practical outcomes	Prioritize quick wins, align with strategy
Ignoring compliance needs	Risks regulatory issues	Validate solutions against client requirements

4. The 4th P: Preparation—The Foundation of Success

Importance

Intense preparation is the cornerstone of successful IT consulting and enterprise architecture engagements. It enables consultants to demonstrate expertise, build credibility, and deliver value from the outset. Without thorough preparation, the risks of falling into preconceptions, prejudice, or misaligned benefits increase significantly.

Components of Preparation

• Business Understanding: Gain insight into the client’s industry, market position, and strategic goals.

• Technical Due Diligence: Assess the current IT landscape, including infrastructure, applications, and data.

• Stakeholder Analysis: Identify key decision-makers, their interests, and potential influencers.

• Solution Research: Stay abreast of industry best practices, emerging technologies, and proven methodologies.

• Risk Anticipation: Foresee potential challenges and develop mitigation strategies.

Enterprise Architecture Best Practices

• Holistic Cataloging: Using a unified catalog (Best Practice #7) to map APIs, systems, and capabilities provides a comprehensive view, essential for informed decision-making.

• Stakeholder Engagement: Focusing on enabling a small group of API stakeholders (Best Practice #4) ensures the architecture meets key players’ needs, building a foundation for broader adoption.

• Standardization: Implementing domain and information models (Best Practice #14) standardizes data and processes, facilitating smoother integration and reducing miscommunication risks.

Example

Before starting a digital transformation project for a retail client, an enterprise architect conducts extensive preparation. This includes analyzing the client’s e-commerce platform, understanding their omnichannel strategy, reviewing sales data to identify pain points, and researching successful case studies from similar retailers. Using a tool like the ignite Platform, the architect creates a holistic catalog of the client’s IT landscape, identifying gaps, redundancies, and opportunities for reuse. This informs a roadmap that addresses specific needs, such as improving mobile checkout experiences or integrating with third-party logistics providers, while planning for long-term scalability.

Conclusion

Avoiding preconceptions, prejudice, and misaligned benefit communication is critical for IT consultants and enterprise architects. By embracing intense preparation and integrating best practices—such as adopting API-centric architectures, prioritizing data-driven decisions, and treating all systems as potential assets—consultants can deliver tailored, value-driven solutions. As the digital landscape evolves, particularly with the rise of API-driven ecosystems, these principles become even more essential for driving successful business outcomes and building lasting client relationships.

Further Readings:

16 Common Enterprise Architecture Best Practices

HTH...

A Tech Artist 🎨

The Enterprise Security Meta Model

When talking about security, people often don't quite understand the big picture. They often jump into the details and specifics without necessarily understanding or have a clear view of what overall context drives the need for their specific solutions. So often, it turns into a spaghetti that can of-course be served on the table, but no one know - Who asked for it, Why we are serving this, To whom beside how it comes together as a whole to make a perfect sense out of it. Neither anyone knows if it tastes good !

Here is a meta model as how things look like from a bird eye view perspective and should help you better connect the dots next time to make some sense of the security mess such as  - Cyber security, Infra Security, Applications Security, Data Security, AI Security and so forth. 

A more detailed view was shared earlier here. 

And remember...

Further Readings:

How to Organize Your Cybersecurity Program

Gartner Cybersecurity Business Value Benchmark

HTH...

A Tech Artist 🎨

AI/GenAI, Enthusiasm and the Missing Bits - The Expert Beginners - Part 1

Amidst the AI/GenAI revolution, a new breed of "gurus" has emerged, often with inflated claims and limited practical experience. While genuine expertise is essential, it's crucial to distinguish between true AI practitioners and those who simply capitalize on the hype. It's important to critically evaluate the claims and contributions of such individuals, as superficial understanding can lead to misguided implementations and missed opportunities. 

Often during my conversations these days with the IT people and leaders from different markets within the Asia-Pacific, everyone is learning AI, since not being able to spell that out correctly means you are legacy.

On the flip side, interestingly enough, less than 1% people actually understand what AI really is and perhaps 0.1% are able to talk and follow through with the details around:

• AI Strategy
• AI project life cycle 
• AI infrastructure 
• AI Algorithms 
• AI Governance
• AI Ethics

While over simplification is good way to communicate effectively with the non-technical audience, but then we are talking about IT folks and industry. Just getting familiar with the lingo likely give the false sense of mastery. 

There seems to be significant gaps in terms of how IT industry folks are getting trained on these topics (outside the old traditional DS community people).

If people tells you that they understand what GenAI is and how it works, but haven't heard of "Semantic Model, Ontology & Knowledge Graphs", very unlikely they know anything beyond the lingo.

Further Readings:

Expert Beginners

HTH...

A Tech Artist 🎨