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Berkeley vs AI ML etc.: Which One Is Right for Senior IT Professionals?
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TL;DR
If your primary objective is to earn a certificate from a prestigious institution like IIM Bangalore, this may not be the right program for you.
However, if your goal is to understand AI deeply, build real systems, and lead AI implementation inside your organisation, “AI ML etc. program” is designed specifically for that purpose.
Our View on Competition
We don’t see competitors as rivals.
In an emerging space like AI education, competition plays a critical role in:
- educating the market,
- shaping product-market fit,
- and raising overall standards.
Institutions like UC Berkeley have played an important role in educating leaders about AI over the years. Many executives were first introduced to AI concepts through such programs.
However, AI has evolved dramatically in the last few years.
When evaluating an AI program today, the most important question is no longer who is teaching, but which era of AI is being taught.
Berkeley Executive AI Program vs AI ML etc.
Berkeley Executive Education – Artificial Intelligence: Business Strategies
https://em-executive.berkeley.edu/artificial-intelligence-business-strategies
AI + GenAI Program from AI ML etc.
https://www.aimletc.com/online-instructor-led-ai-llm-coaching-for-it-technical-professionals/
| Aspect | Berkeley Executive AI Program | AI ML etc. |
|---|---|---|
| AI era covered | Classical ML + early NLP | Modern AI (LLMs, GenAI, Agentic AI) |
| Focus | AI awareness and strategy | AI systems and real-world application |
| Coverage of LLMs | Not covered in depth | Core focus |
| RAG & Agentic AI | Not covered | Core focus |
| Hands-on work | Case discussions | 25+ labs |
| Real-world applications | Conceptual examples | 35+ real applications |
The Core Issue: Berkeley Teaches Pre-LLM AI
Berkeley’s program is built around foundational AI concepts:
- traditional machine learning
- early NLP techniques
- analytics-driven decision framing
- high-level AI strategy and governance
This curriculum made sense before the rise of:
- Large Language Models
- Agentic AI systems
- RAG-based enterprise AI architectures
Today, these systems are not optional.
They are what businesses are actively deploying.
Learning AI without understanding these concepts means learning an incomplete version of reality.
Key Questions to Ask Yourself Before You Choose
Before enrolling in any executive AI program, pause and reflect on these questions
- Does this program reflect how AI is actually being used today, or how it was taught a few years ago?
- If an institute is known for management, does that guarantee excellence in cutting-edge tech like AI, GenAI, AI Security, and Agentic AI?
- Are instructors actively building and contributing to AI systems today?
- Do faculties have recognition or certification from Google/ Microsoft/ Amazon/ Meta, like Google Developer Expert or Microsoft’s Most Valuable Professional or AWS Hero or similar?
- AI is a highly dynamic field, do you think their instructors are upskilling themselves monthly, if not weekly?
- Does the course include actual hands-on labs and projects, or just lectures and theory?
- Have they showcased testimonials, feedback, or outcomes from previous learners?
- Have they disclosed the names, roles, or backgrounds of past participants to help you gauge peer relevance?
- At this stage in your career, do you really have 1 full year or months to spare, or would a shorter, focused, high-impact course be more practical?
- What’s at stake for the faculty if the course doesn’t deliver results? Are they truly invested in your success, or is it just another job compulsion for them?
Your answers to these questions matter far more than the brand name on the certificate.
Why This Matters Even for Business Leaders
Some people assume business leaders only need strategy, not technical understanding.
That assumption is now outdated.
Modern AI decisions involve questions like:
- Can this use case be solved with an LLM or does it require a custom system?
- How do agents change workflows and organisational design?
- What are the cost, latency, and risk trade-offs of GenAI systems?
- What should be built in-house vs bought?
Without understanding LLMs and Agentic AI, even business leaders are forced to rely on second-hand interpretations.
That is risky.
If your goal is to lead AI initiatives confidently, not just talk about them, this program is designed for your context.
For all other details like Course Curriculum, Past learners, Testimonials, FAQs and more
Disclaimer: This comparison is based on publicly available information from respective course websites as of Jan 09, 2026. We encourage interested learners to visit official sites and verify the latest details before making a decision.
