AI-Friendly Before AI-First: GCC Perspectives

In a recent conversation with my ex-boss, we explored some of the unique dynamics of Global Capability Centers (GCCs). One key takeaway: a company must be AI-friendly before it can become AI-first.

Alignment Starts with Initiatives, Not Just ROI

Organizations often begin their AI journey through initiatives rather than immediate ROI-driven motives. There might be AI talent within the team, but project priorities shape what gets implemented. As a consultant, I’ve seen that internal teams possess strong capabilities but are often bandwidth-constrained. They will evaluate, challenge, and probe external perspectives, yet their ability to execute depends on time and focus.

The Role of GCCs: Delivery vs. Innovation

GCCs can function in multiple ways—either as pure delivery centers catering to regional project needs or as innovation hubs driving transformational change. The distinction is crucial because execution models differ significantly.

Innovation vs. Execution: The Long Game of AI

Project success, project innovation, and project vision are all distinct. A project doesn’t end when it goes live; it evolves. AI and data-driven products thrive on iteration—data quality improvements, model refinements, customer feedback loops, and continuous enhancement. The real differentiator isn’t just building and shipping—it’s about creating lasting impact.

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Top 5 Practices to Master GenAI Product Development

• Focus on Solving the GenAI aspect - Prompts / Model Versions
• Focus on Scaling for multiple formats - Prompt Catalogs / Prompt Versions
• Focus on Low latency - Cache key data, Reuse data, RAG over docs, Graphs, Summarized data
• Focus on Accuracy across the board - Preprocess, Normalize, and Organize data effectvely based on use case, RAG over docs, Graphs, Summarized data
• Focus on Safe usage - Enforce Guardrails
• Entry of Agent - Once you have achieved it you can migrate to agentic approach but have more controls

If you need more AI Advisory, I am always available, You can learn from my course / schedule a call :)

GenAI and Cybersecurity – Frameworks and Best Practices 2024

Our course footprint :)

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Responsible AI Adoption, Profits vs Purpose

 

Klarna CEO stopped hiring employees last year because “AI can do most jobs” and is down -22% headcount to 3500.

The private Swedish buy-now-pay-later company is worth $14.6B, up from $6.7B in 2022 but down from $45.6B in 2021 and is trying to go public next year. pic.twitter.com/P0eFADQqUS

— Deedy (@deedydas) December 15, 2024

Productivity Improvements vs Job Pressure vs Job Cuts

This is the reason we need Responsible AI Adoption!!! 

AGI = Iterative Learning

AGI = Current AI methods + RHLF Experiments + Human Applied Fine Tuning + Custom Experiments + Ton of Guardrails + Domain Specifics Pattern Ingestion

Ilya Sutskever had a TED talk this year and predicted the two most important concepts that we haven't discussed much:

• AGI will have the ability to improve itself
• AGI will work on the next generation of AGI pic.twitter.com/CMvmgTc0oz

— Haider. (@slow_developer) October 19, 2024

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Data Science & Data

Every project is a learning experience. Data science is based on "Data". Working with no data, less data, or encrypted domain knowledge with minimal data has been challenge over the past 4 years. Yet, even when data is plentiful, there remains a balancing act between leveraging it effectively and mitigating trust issues, as collaboration can sometimes be overshadowed by the scramble for credit. Everyone wants to work on a model, not on data, the old google paper still comes into their eyes :). The current trend is to train large language models (LLMs) on uniform datasets, yet this approach glosses over an important truth: no dataset can capture the full spectrum of reality. Issues such as digital poverty, underrepresentation, and inherent biases are embedded within the data we collect. Without addressing these challenges, solutions can be superficial and short-lived. Moving fast with a lot of guardrails is essentially a band-aid, not a solution. Take a step back and balance data vs model. Build something that lasts forever not for paychecks!!!

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AI Skills

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Ten reasons why you don't need AI / ML Platform

• Data Disparity: Your datasets are dispersed across multiple silos without a unified view, hindering effective data analysis for AI/ML.
• Unclear Business Objectives: Without well-defined business problems and corresponding data mapping, your organization cannot identify valuable AI/ML use cases.
• Cross-Functional Misalignment: Lacking a collaborative ecosystem among product management, domain experts, and AI/ML specialists can prevent meaningful integration of AI/ML into business processes.
• Limited Data Operations: Your data volume is insufficient for significant AI/ML insights, and without preliminary model testing, the utility of AI/ML is questionable.
• Technology Stack Assessment Gap: Your data science team has not yet evaluated major cloud AI/ML and MLOps offerings, which is essential before committing to an AI/ML platform.
• Model Deployment Inexperience: The absence of experience with deploying machine learning models at scale on cloud platforms indicates that your organization might not yet be ready for an AI/ML platform.
• Cloud Integration Deficiency: Running on a major cloud provider without having experience deploying models integrated with cloud-based databases or CDPs suggests a lack of technical preparedness.
• Business-Tech Disconnect: Missing alignment and understanding between your business goals and technology capabilities, coupled with uncertainty about data privacy and compliance, poses significant risks.
• Strategic Incongruence: If AI/ML initiatives do not align with your company's product roadmap, then investing in an AI/ML platform may not support your business strategy.
• Adoption Ambiguity: Not having a defined path for how AI/ML will be leveraged for text, video, recommendations, forecasting, etc., leads to uncertainty in the adoption of an AI/ML platform.

In many companies, I observed these challenges. 

If you are a startup, or SMB looking to apply AI/ML in your solutions, We can connect and collaborate on your AI Strategy. My coordinates [sivaram2k10][at][gmail]

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AI skills at work

• Selling AI is a skill
• Building (Billing) with AI is a skill
• Keeping the end goal a moving target is a skill
• Build vs Buy vs Manage cost is a skill
• Hiring someone who can Build (Bill) effectively is a skill
• Differentiating AI demos vs AI reality is a skill

Choose wisely!!!!

How to get correct in the First Attempt with AI

Experience in AI = Ability to ask the right questions even if you don't have answers and provide AI awareness, complexity, ROI, and helping them manage costs vs Selling vision + charging $$$$ hefty for all types of costs build/buy/explore/expand. 

Build targeted products :)

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Failures in AI/ML/GenAI Adoption

Success in #AI/ML/GenAI projects has a lot of challenges. Some projects' data availability / some projects handling bias / Some projects balance features vs bugs / Knowing 80% features vs 20% future releases. This needs a lot of iteration and team mix to make it work. Success goes in LinkedIn posts. Failures end up haunting us searching for the next success.

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My Consulting Journey - AI - DL - GenAI Projects

As I wrap up my consulting tenure, I reflect on my success stories in the past 4 years. Here are some key projects that serve as my badges of success:

Bundle Recommendations Project #1 - Bundle recommendations for a specialty retailer of children’s apparel, from newborns to pre-teens (2020) Work/Impact - Transitioned from automated merchandiser-based recommendations to ML-based bundle recommendations. Achieved a 100% match with the ML approach. For a category level, we analyzed 6 months of transactions, comprising 1.5 million orders, and generated recommendations in 15 minutes.

Performance Optimization Project #2 (2021) - For a multinational mining company, optimized an existing app, more akin to a trading app, deployed between OLAP vs. OLTP. Applied a blend of DB/user and usage analysis/patterns/ML-based techniques to provide a list of recommendations to optimize.

GenAI + Vision Project #3 (2023-2024) - For a British multinational fast-moving consumer goods company, My key contribution is solution architecture based on Vision + GenAI for product detection and personalized recommendations, for its customers' products and brands.

Plants Classification Project #4 - Developing vision-based state-of-the-art classification models for the world's leading gardening charity. This work involved data curation, augmentation, and training, and ended as a paper :). Link

GenAI and CX improvement Project #5 - For a US-based leading specialty retailer of organizing solutions, custom spaces, and in-home services, leveraging GenAI + Vision to improve the customer journey. Pitched/deployed selected use cases. This is similar to what you see in Amazon/Swiggy GenAI Changes.

Forecasting Project #6 - Domain played a key role for me to contribute. For a leading South American beauty retailer, developing forecast models.

I had a mix of responsibilities as a Solution Architect, DB, and ML Engineer. I relied mostly on SA/DB/ML. In all projects, The team was a mix of platform, MLOps, and ML engineers. Sometimes the platform is a vendor cloud or an in-prem solution.

Hoping to undertake a few more similar projects in my next self-employed consulting roles.

If you are a startup, or SMB looking to apply AI/ML in your solutions, We can connect and collaborate on your AI Strategy. My coordinates [sivaram2k10][at][gmail]

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