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By:

Rashmi Kulkarni

23 March 2025 at 2:58:52 pm

Making a New Normal Feel Obvious

Normal is not what’s written. Normal is what repeats. The temple bell rings at the same time every day. Not everyone prays. Not everyone even walks in. Some people don’t care at all. And yet when that bell rings, the whole neighborhood syncs. Shops open, chores move, calls pause. The bell doesn’t convince anyone. It simply creates rhythm. That’s how “normal” is built inside a legacy MSME too. Not by speeches. By repetition. Quick recap: Week 1: You inherited an equilibrium. Week 2: People...

Making a New Normal Feel Obvious

Normal is not what’s written. Normal is what repeats. The temple bell rings at the same time every day. Not everyone prays. Not everyone even walks in. Some people don’t care at all. And yet when that bell rings, the whole neighborhood syncs. Shops open, chores move, calls pause. The bell doesn’t convince anyone. It simply creates rhythm. That’s how “normal” is built inside a legacy MSME too. Not by speeches. By repetition. Quick recap: Week 1: You inherited an equilibrium. Week 2: People resist loss, not improvement. Week 3: Status quo wins when your new way is harder. Week 4 is the next problem: even when your idea is good and even when it is easy, it can still fail because people don’t move together. One team starts. Another team waits. One person follows. Another person quietly returns to the old way. So, the old normal comes back … not because your idea was wrong, but because your new normal never became normal. Which Seat? • Inherited : people expect direction, but they only shift when they see what you consistently protect. • Hired : people wait for proof “Is this just a corporate habit you’ll drop in a month?” • Promoted : people watch whether you stay consistent under pressure. Now here’s the useful idea from Thomas Schelling: a “focal point”. Don’t worry about the term. In simple words, it means: you don’t need everyone convinced. You need one clear anchor that everyone can align around. In a legacy MSME, that anchor is rarely a policy document. It’s not a rollout email. It’s a ritual. Why Rituals? These firms run on informal rules, relationships, memory, and quick calls. That flexibility keeps work moving, but it also makes change socially risky. Even supportive people hesitate because they’re thinking: “If I follow this and others don’t, I’ll look foolish.” “If I share real numbers, will I become the target?” “If I push this new flow, will I upset a senior person?” “If I do it properly, will it slow me down?” When people feel that risk, they wait. And waiting is how the status quo survives. A focal ritual breaks the waiting. It sends one clean signal: “This is real. This is how we work now.” Focal Ritual It’s a short, fixed review that repeats with the same format. For example: a weekly scoreboard review (15 minutes) a daily dispatch huddle (10 minutes) a fixed purchase-approval window (cutoff + queue) The meeting isn’t the magic. The repetition is. When it repeats without drama, it becomes believable. When it becomes believable, people start syncing to it, even the ones who were unsure. Common Mistake New leaders enter with energy and pressure: “show impact”. So they try to fix reporting, planning, quality, procurement, digitization … everything. The result is predictable. People don’t know what is truly “must follow”. So everything becomes “optional”. They do a little of each, and nothing holds. If you want change to stick, pick one focal ritual and make it sacred. Not forever. Just long enough for the bell to become the bell. Field Test Step 1 : Pick one pain area that creates daily chaos: delayed dispatch, pending purchase approvals, rework, overdue collections. Step 2 : Set the ritual: Fixed time, fixed duration (15 minutes). One scoreboard (one page, one screen). Same three questions every time: – What moved since last time? – What is stuck and why? – What decision is needed today? One owner who closes the loop (decisions + due dates). Step 3 : Protect it for 8 weeks. Don’t cancel because you’re busy. Don’t skip because a VIP came. Don’t “postpone once” because someone complained. I’ve seen a simple weekly dispatch scoreboard die this exact way. Week one was sharp. By week three, it got pushed “just this once” because someone had a client visit. Week four, it moved again for “urgent work”. After that, nobody took it seriously. The old follow-ups returned, and the leader was back to chasing people daily. The first casual cancellation tells the system: “This was a phase”. And the old normal returns fast. One Warning Don’t turn the ritual into policing. If it becomes humiliation, people will hide information. If it becomes shouting, people will stop speaking. If it becomes a lecture, people will mentally leave. Keep it calm. Keep it consistent. Keep it useful. A bell doesn’t shout. It just rings. (The author is Co-founder at PPS Consulting and a business operations advisor. She helps businesses across sectors and geographies improve execution through global best practices. She could be reached at rashmi@ppsconsulting.biz)

The Hidden Prerequisites for AI Leverage

Multipliers don’t create direction. They amplify what already exists

Over the last two weeks, we’ve done something most AI conversations avoid. We’ve slowed down. First we acknowledged the truth founders don’t usually say aloud: AI isn’t a cure. It’s a diagnostic.


In Week 2, Rashmi took us inside real operations and showed where AI breaks first: SOP gaps, unclear inputs, broken handoffs.


This week, I want to address the question that quietly follows both pieces: If AI is not the engine of change, then what actually creates leverage? The answer is uncomfortable, especially in a year obsessed with tools. Leverage doesn’t come from intelligence. It comes from conditions.


Why tools don’t create leverage on their own

Founders often ask me, “Which AI tool should we standardise on?” But that question skips a more important one: “What must already be true in our business for any tool to help?” Because AI doesn’t generate value in isolation. It multiplies whatever system it is plugged into.


If the system is stable, AI accelerates outcomes. If the system is fragile, AI accelerates noise. This is why some businesses see dramatic gains from very simple AI use cases, while others struggle even after large investments. The difference isn’t ambition or intelligence. It’s readiness.


Condition 1: Stable processes, not heroic execution

In many SMEs, outcomes depend on who handled the work, not how the work is designed. The best performer becomes the process. Everyone else “manages somehow.” Humans are surprisingly good at compensating for this. They remember exceptions. They improvise. They fix things quietly. AI cannot.


For AI to add leverage, work must be stable enough that two different people can do it the same way and get roughly the same result. Not perfect … just predictable. This doesn’t require a consulting-grade process manual. It requires one honest answer to a simple question:


“If someone new joined tomorrow, would they know the one right way this is done?” If the answer is no, AI won’t help yet. It will only surface the inconsistency faster.


Condition 2: Clear decision rights, not more information

Most SMEs don’t suffer from lack of data or ideas. They suffer from lack of closure. Decisions float. Approvals are implicit. Founders become the final checkpoint. AI is excellent at generating options. But options without decision rights create paralysis, not speed.


For AI to create leverage, three things must be clear: 1) who decides, 2) on what basis, and 3) when the decision is considered final Without this, AI increases the volume of suggestions, reports, and analysis—but execution still stalls.


Leverage comes not from knowing more, but from deciding faster once the information is good enough.


Condition 3: Data discipline, not “more data”

Most businesses don’t have a data shortage. They have a trust problem. Ask three people the same question about price, delivery date, inventory and you’ll get three answers. Each answer has a story. Each story feels valid. AI doesn’t resolve this. It confidently responds based on whatever inputs it sees.


So, the question is not, “Is our data perfect?” It’s, “Which data must be right for this decision to work?”


AI leverage begins when a business agrees on:

  • a small set of critical fields

  • a single source of truth

  • and clear ownership for keeping those fields accurate


This discipline is boring. It’s also non-negotiable. Without it, AI becomes a well-spoken guesser and founders return to verifying everything themselves.


Condition 4: Rhythm and review loops, not constant monitoring

One reason founders feel exhausted is that the business has no visible rhythm. Everything is urgent. Nothing is truly reviewed. Issues surface only when they become painful. AI doesn’t fix this. In fact, it can make it worse by creating the illusion that “everything is being tracked”.


Leverage comes when a business has:

  • a weekly cadence for reviewing key work

  • clear checkpoints where output is evaluated

  • and predictable moments where problems are surfaced early


When these rhythms exist, AI becomes useful support … summarising, flagging, highlighting. Without them, AI just adds another stream of activity to monitor.


The reframe that matters AI is not an engine that pulls your business forward. It is a multiplier. Multipliers don’t create direction. They amplify what already exists. This is why two companies using the same tools can experience completely different outcomes. One has conditions in place. The other doesn’t. And this is also why rushing to “scale AI” often backfires.


You can’t multiply chaos into clarity. A grounded starting point If you’re wondering where to begin, don’t start with AI strategy. Start with one workflow. Just one. Stabilise it. Clarify ownership. Define inputs. Install a review rhythm. Then and only then introduce AI into that slice of work. When AI works there, you’ll know why. And when it doesn’t, you’ll know what to fix. That’s leverage.


(The writer works with founders and second-generation leaders to design operating systems where growth strengthens people, not exhausts them.)

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