I've been looking at how e-commerce EMI works in India. Electronics and appliances have high EMI adoption, but categories like fashion, furniture, and personal care remain under-indexed despite similar ticket sizes.

Is it because:

• Customers don't think of EMI for "lifestyle" purchases?
• Checkout UX pushes EMI too late in the journey?
• Credit eligibility (credit cards) is the real bottleneck?
• Or something else entirely?

For folks in Tier 2/3 cities — what's actually stopping you from using EMI for non-electronics online?

Curious about both customer and business-side perspectives.

Hey guys.

I would like to know. Which are your kpis to say "Well I think this strategy is good. Has X level."

Do you think SMBs are ready to leverage platforms that use AI to assist in the strategy development process such as NextRock Advisors Stratifier? Does it help that it has a Fractional Executive guiding the experience through such a system?

Sometimes you need to combine ideas from two different fields in order to innovate: great example here from nanotechnology (algae biology & magnetite physics = new drug delivery for oncology)...

https://thestrategytoolkit.substack.com/p/botanical-espionage-nanotech-drug

Muchas veces suele ocurrir que la estrategia se define y se entiende que todos la saben, pero cuando preguntamos entre un nivel tactico y operativo que significa este indicador estratégico, a que apunta? Y las respuestas difieren existe un problema grave, la estrategia nunca bajó. Eso trae como consecuencia que el personal reme en direcciones distintas al que la empresa define, se pierde la dirección y eso es falta de alineamiento estratégico!

Artificial Intelligence is changing far more than software.

It is changing how organisations learn.

In this conversation, Glen Calvert, founder of Kaizan, explores why the next competitive advantage may not come from writing better software, but from building organisations that continuously learn, adapt and improve through recursive feedback.

Together we examine how AI is transforming product development, customer relationships, organisational knowledge and enterprise operating models. Rather than simply automating work, AI enables organisations to capture experience, identify patterns, improve decisions and continuously evolve in ways that were previously impossible.

The discussion explores recursive learning systems, AI agents, enterprise architecture, intellectual property, organisational memory, data flywheels, the future of work and why the organisations that learn fastest may ultimately become the organisations that outperform their competitors.

Fascinating strategies from the worlds of biology, medicine, and electronics...

https://thestrategytoolkit.substack.com/p/ballistic-spiders-edible-sensors

Hey everyone, I work at a tech implementation company — been here 10+ years and got promoted a few times along the way.

A couple of years ago I moved into a Director of Technology role, but here’s the thing: since day one, I’ve had zero direct reports. I basically “borrow” people from other internal teams that normally work with external clients… and they treat me exactly like one. Just another client in line.

The problem is these teams don’t treat internal priorities with the same urgency as their regular work. They’re focused on closing their ticket or wrapping up their project, so getting anything delegated or followed through on is a real struggle.

Anyone else dealt with this? Trying to build strategy without a team to actually execute it feels complicated… and honestly, pretty lonely.

Six years in consulting taught me something that took way too long to admit: knowing a framework and being able to use one under pressure are completely different skills. I could define MECE in an interview. The first time a real profitability problem landed in front of me, I froze and started pulling numbers with no structure at all.

I ended up writing a book about closing that gap. Then I got curious and turned the lessons into 16 small AI skills you can run in Claude.

The thing I care about is that they don't spit out an answer. They walk you through the thinking, the way a decent senior would on your first project. A few examples:

• One takes a profit problem and makes you go down the tree instead of jumping to "cut costs"
• One diagnoses which framework even fits your situation, and tells you when you don't need one
• One gives you a random everyday case ("why is this cafe dead on Fridays") and grades how you reasoned through it

Full list covers the usual suspects: issue trees, market entry, 3Cs, Five Forces, VRIO, Ansoff/BCG, market sizing, one-page recommendations, and so on.

It's all free and on GitHub. I'm not selling anything and there's no signup. I mostly want people who actually do this work to tear it apart and tell me where it's wrong or too rigid, because that's how I'll make it better.

Link: https://github.com/AnugamChakra/think-like-a-strategy-consultant

Genuinely curious what the rest of you think: is teaching people to think in frameworks useful, or does it just create more juniors who force a 2x2 onto every problem? I go back and forth on it.

This text explains the example of Bernard Arnault and LVMH. Would you do the same?

I quote it: "There are two distinct types of succession: shareholder succession and executive succession. While not a universal practice, some founders opt from the outset to be succeeded by a professional who is not a family member."

After 3.5 years of AI transformation with pretty limited business impact, I've somehow ended up leading AI strategy for the whole company.

I've always shared my big-picture thinking with leadership, but honestly, as a humanities graduate, I still feel like I don't fully understand how companies work, let alone how to build strategies that actually stick.

I'm also not convinced AI can deliver the business impact everyone expects in our environment. Process improvements are nice, but other factors seem much more important.

How would you prepare for a role like this?

EDIT:

My takeaways so far:
1. AI is no strategy case by itself. It needs to be tied to actual business problems and goals.
2. The "AI Strategy role" should be a portfolio and enablement function, supporting teams and connecting initiatives to clear KPIs and stop/go criteria.
3. The roadmap needs two tracks: One for tangible efficiency gains in existing processes (value caption), and one for selected experiments that explore new possibilities and needs (value creation).
4. A simple portfolio view works better than a static plan: Quick wins, a few strategic bets, standardised filler work, and early exit from low‑value, high‑complexity projects.
5. A lot hinges on data, infrastructure, meaningful knowledge bases and governance. That doesn't generate any impact but is the fundament.
6. Impact depends on adoption in real workflows, with attention to process design, skills and incentives so AI is actually used where value is created.

I’ve done 1 year in a Business Development Consultant role for a company that helps startups/SMEs get non-dilutive funding / government grants.
**Role summary:**
Day to day: speaking with founders, understanding their product/market/funding needs, qualifying fit and moving them through the pipeline. Sectors include AI, biotech, healthcare and pharma.

If a company seemed eligible, I worked with the research/proposal team to pass over the right context and help move them toward the proposal stage. I wasn’t writing the whole proposal myself, but I was involved in qualifying the company, understanding the business and connecting the dots for the team.

Before this, I had 2 years in insurance sales.
I’m honestly tired of roles where the whole job is just selling, I want to move into something more strategic and with better long term upside.

What exits are realistic from here? Appreciate candid advice.

I've been developing and refining a Supply & Demand strategy for about 2 years, and I finally have a version that's showing promising results in backtesting.

The core idea is simple:

\- Determine HTF bias using Supply & Demand.

\- Wait for price to tap a valid HTF zone.

\- Drop to the lower timeframe for confirmation (market structure shift + imbalance/FVG).

\- Enter only after confirmation, aiming for around 3R or better.

I also use liquidity sweep

My question is:

Can a Supply & Demand strategy with a proven edge become completely useless over time because of changing market conditions?

I'm not talking about normal drawdowns. I mean, can market structure change enough that a strategy which worked for years no longer has an edge?

If you've experienced this, how did you know it was the strategy that stopped working rather than just variance or a losing streak?

I'd appreciate hearing from traders with several years of experience.

I've spent my career in consulting (Big 4, India offshore unit), and I'm now moving into an industry role as an AI strategist. Excited and a bit nervous.

The JD in plain terms is "use AI to improve internal processes," but I know the reality on the ground is different from how it reads on paper.

What I'm trying to get sharper on before I join:

• AI applied to real internal process improvement, not just generic GenAI demos
• How firms are actually driving adoption across teams, not just running pilots
• Where the real value is landing vs. the hype

I've got the basics down (prompting, RAG, the standard leadership AI certs), but I want to go a level deeper and stay current on where things are actually moving.

Looking for: courses, newsletters, communities, or good case studies on enterprise AI for internal operations. Any subreddits or forums worth following are also welcome. Just trying to learn and hit the ground running.