Wednesday, October 1, 2025

Have you tested your strategy lately?


Ten timeless tests can help you kick the tires on your strategy, and kick up the level of strategic dialogue throughout your company.

 

Test 1: Will your strategy beat the market?

Test 2: Does your strategy tap a true source of advantage?

Test 3: Is your strategy granular about where to compete?

Test 4: Does your strategy put you ahead of trends?

Test 5: Does your strategy rest on privileged insights?

Test 6: Does your strategy embrace uncertainty?

Test 7: Does your strategy balance commitment and flexibility?

Test 8: Is your strategy contaminated by bias?

Test 9: Is there conviction to act on your strategy?

Test 10: Have you translated your strategy into an action plan?



HTH...

A Tech Artist ðŸŽ¨

Tuesday, September 2, 2025

So What Really Went Wrong with AI & GenAI Movement ? & How We Can Do Course Correction. (And an opp. to win $100)

 


As soon as MIT report came out few weeks ago suggesting 95% of GenAI pilots fail (The number is close to what Mckinsey suggested as 86% or something a long time ago) or IDC Lenovo report numbers suggesting it as 88%.

So in the world where everyone wants to hop onto the GenAI bandwagon, what really went wrong so far and is AI/GenAI actually a good solution for some specific problems? Let's examine:

Over the last 4 years (since the chat gpt movement in late 2021), I have spent good amount of time learning and practicing AI (perhaps 1000+ hours), beside talking to 1000+ people across industries (IT, Banking, Retail, Aviation etc.)  &  across the roles (Leadership, Mid Management, Employees and even HRs) as part of my market research.

My findings suggest (Of course you may share a different view):

1. Most leadership people have absolutely no clue about what AI can and can't do (They can't even tell the firm differences between AI & Automation beyond generic and over simplified examples). Because no one is keen to dive deep into the topic at hand and pretty much rely on media reports, what partners/OEMs tells them,  60 mins. online courses, what their peers or reportee project them as AI being silver bullet and what not.

Of course sooner or later, the FOMO kicks in very quickly beside getting a push from CEO or board to do something about AI without a clear direction.

2. So most organizations AI strategy is nothing but false narratives and FOMO on steroids. 

90% of employees in those organizations have no real access to AI/GenAI capabilities (because since there is no clear strategy & needs specific trainings available for them to leverage those beside costs comes in to the equation), shadow AI becomes prevalent and it often contributes to AI psychosis without people necessarily noticing it. 

This also put the organisation wide knowledge management at risk and no one is noticing it. Since everyone is told by their seniors - Did you chat gpt it to find the answers?

3. I haven't come across a single real world DS practitioner in my circle who would articulate AI/GenAI as silver bullet or magic. There are some good use cases that AI/GenAI can deliver, but the underlying foundation is build on clear - strategy, automation, cross teams collaboration and data with the firm use cases and roi beside an active innovation mindset. 

I love the way BCG folks articulated it with their basic framework for AI success pillars beside the HBR's Digital Maturity model.

And if you are still not convinced, I'll be giving a $100 to to show me how GenAI is completely transforming the landscape in any context when applied to complex problems. Just drop me a note (anetworkartist@gmail.com) to have a 30 mins. zoom meeting to surprise me. We will follow the BCG's GenAI Success Stairway to have a quick sanity check and determine the business impact.

Further Readings:














HTH...

A Tech Artist ðŸŽ¨

Thursday, July 10, 2025

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:


HTH...

A Tech Artist ðŸŽ¨

Monday, May 19, 2025

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 ðŸŽ¨