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When Product Superiority Becomes a GTM Liability: MishiPay’s $10M to $250M Pivot

Mustafa Khanwala waited twenty minutes in a supermarket queue to buy a Coke. That 2015 frustration sparked a mobile-first checkout solution that would eventually process over $250 million in annual transactions—but only after he abandoned the core product thesis that nearly killed the company.

In a recent episode of Category Visionaries, Mustafa, CEO and Founder of MishiPay, detailed how his retail technology platform scaled to 2,000+ stores across four continents by solving a problem most B2B founders ignore: the gap between product innovation and market adoption readiness.

The Adoption Asymmetry Nobody Models

MishiPay’s original scan-and-go product was technically superior. Customers scanned barcodes on their phones, paid in-app, and walked out. No queue, no cashier interaction, no friction.

By March 2018, they had raised $2 million and landed their first major retail pilot. The product worked. Adoption didn’t.

“We realized that while Scan and Go is amazing, there’s a huge adoption curve with the user to really learn what to do with this and start using it,” Mustafa explained. The education investment required—both from retailers and from MishiPay—created a chicken-and-egg problem that limited scale.

For years, Mustafa held firm. Scan-and-go was objectively better than traditional checkout. Building anything less innovative felt like regression.

That conviction stalled growth for three critical years.

Customer Frequency as a Product Constraint

The breakthrough insight came from mapping education ROI against customer visit frequency—a variable most founders never incorporate into product strategy.

“If you look at the typical consumer, they go through their airport maybe once, twice a year,” Mustafa noted. In travel retail, the customer education investment never generates returns. Even if a shopper successfully learns scan-and-go during one airport visit, they won’t use it enough times to justify the retailer’s implementation cost or MishiPay’s customer acquisition spend.

“Whereas in the grocery, almost more than 70-80% of their customers would be coming very often, right? Multiple times a week, if not at least once a week. So there, the education, the investment you do in letting users know how to use the product has a very quick payback.”

This wasn’t about product-market fit in the traditional sense. The product solved a real problem. The issue was behavioral change payback period—a metric that varies by vertical based on customer visit patterns.

High-frequency environments (weekly grocery) justify education investment. Low-frequency environments (annual travel retail) cannot, regardless of how well the product works.

The 2022 Product Expansion That Unlocked Scale

In 2022, MishiPay expanded beyond scan-and-go into self-checkout kiosks, RFID checkout systems, mobile POS, and traditional cash registers with improved UX.

These solutions required minimal customer education because shoppers had already been trained by other retailers. “We’re leveraging the learning that has been done over the last decade,” Mustafa said. Users knew what to do at a self-checkout kiosk because they’d used them at other stores. Staff understood mobile POS because they’d experienced it at Apple Stores.

The results: $10 million to $250 million in annual transaction volume. Roughly 300 stores to 2,000+ stores. Triple-digit revenue growth with flat headcount at 50-60 people.

Looking back, Mustafa identifies this as the critical error: “Should we have started on some of our other products in 2019 instead of 2022? Probably.”

Three years of growth lost to product ideology.

Why “Worse” Products Win in Enterprise

Mustafa references the Henry Ford faster-horses quote, then admits: “I got too lost in that rather than thinking about what is actual problem you’re solving, if there is a path that is sort of midway between the best experience that you’re creating versus where they are today.”

The scan-and-go vision wasn’t wrong. The sequencing was. Self-checkout kiosks and mobile POS weren’t compromises—they were the adoption bridge that would eventually make scan-and-go viable at scale.

Retailers under e-commerce pressure won’t implement solutions that create additional risk. “When it comes to in-store retail, they’re already under attack in so many ways. If they feel like this will even create any risk they don’t want to go for that.”

Lower-innovation products with proven adoption patterns reduce implementation risk. Once those products generate transaction data and operational trust, retailers become more willing to test higher-innovation solutions like scan-and-go.

The lesson: in enterprise B2B, adoption velocity often matters more than product superiority. Bridge products that customers already understand can unlock markets that revolutionary products cannot access.

Operationalizing “Startup Agility at Enterprise Scale”

Every vendor claims flexibility. MishiPay operationalizes it through specific SLAs: two-week store go-lives, ten-minute UI changes, two-day promotion additions, two-week payment method integrations. All while maintaining 99.9999% uptime.

“We still maintain that agility of a startup, but with the stability and reliability required at the enterprise level,” Mustafa explained.

This capability stems from architectural decisions made early: mobile-first, cloud-native, API-driven. Traditional POS providers built on legacy infrastructure cannot match these timelines without stability trade-offs. MishiPay’s modern stack enables rapid customization because changes don’t require recompiling or redeploying entire systems.

For B2B founders, the lesson is specific: “agility” and “flexibility” are meaningless unless you can quantify them in hours or days for implementation changes, coupled with measurable uptime guarantees. If you can’t operationalize these claims into SLAs, they’re not differentiators.

Vertical Product Expansion Through Data Accumulation

MishiPay’s roadmap follows a deliberate org-chart climb: frontline staff first, then mid-level managers, then store managers, then headquarters.

They started with checkout experiences for store assistants. Next came operational tools for assistant managers. Now they’re launching AI-powered analytics for store managers to optimize pricing, promotions, and inventory.

“From here, the next step for us is to go up the chain to also help the store managers, the assistant managers, and eventually the headquarters with operations, management, decision making,” Mustafa shared.

Each layer requires accumulated data from below. Checkout systems generate transaction data. Operations tools aggregate that data across shifts and days. Analytics platforms identify patterns across stores. Eventually, headquarters gets multi-location optimization capabilities.

This expansion also shifts buyer personas from IT and Operations (who care about checkout speed) to Finance and Merchandising (who care about revenue optimization and margin improvement). Same underlying data, different value propositions.

Geographic GTM Sequencing: Trust-Building Mechanisms Vary

As MishiPay expanded globally, Mustafa discovered that sales motion sequencing differs fundamentally by region—not in relationship importance, but in when trust must be established.

Western markets: product merit → pilot → relationship building → expansion. Video calls handle initial meetings. Face-to-face happens after contracts close.

Middle Eastern markets: relationship building → pilot opportunity → product merit demonstration → deal. Face-to-face meetings must happen before contracts, not after.

“In Europe and the US, we’ve almost moved fully to video calls for those first few meetings. When you actually meet people face-to-face, that’s already after you’ve signed the deal,” Mustafa explained. “Whereas here, it’s the other way around. You build the relationship first, properly, before you get to even sign the deal.”

He shared context that explained the cultural origin: “Their biggest trade was pearl diving. Their whole job was to be able to look at someone in the eyes and decide if that person was going to scam them or were they a pirate.”

For founders expanding internationally, the implication is tactical: you cannot export a single sales motion globally. Western-style demo-first approaches fail in regions where trust precedes technical evaluation. Conversely, relationship-first approaches waste time in Western markets where buyers expect to evaluate product merit before investing in relationship building.

The AI Integration Strategy: Internal First, Then Customer-Facing

MishiPay’s AI adoption follows a specific sequencing: use it internally to prove value, then productize for customers.

Internally, they’ve maintained flat headcount at 50-60 people while achieving triple-digit revenue growth. “If MishiPay’s revenue has expanded over the last two years probably by triple digit percentage and the headcount has only remained pretty much stable around 50 to 60 people… because of being able to use more tools make the same number of people more efficient.”

Now they’re launching customer-facing AI analytics that helps store managers make operational decisions: “How to change the price? What promotions to add to the store? What products to discount? What products to increase the price of or increase the quantity pre-order?”

The approach is pragmatic. Test AI internally where mistakes are contained and learnings are private. Once you understand how to deliver value reliably, productize it for customers. This de-risks AI implementation and ensures you’re not experimenting on customer operations.

Implementation-Ready Takeaway

MishiPay’s journey from $10 million to $250 million in annual transactions reveals a counter-intuitive GTM principle: in enterprise markets, adoption readiness often constrains growth more than product capability.

The path to delivering revolutionary products may require building bridge solutions that feel like compromises. Those bridges reduce implementation risk, generate operational trust, and accumulate the data that makes future innovations viable.

Map your product’s behavioral change requirements against customer interaction frequency. If the education payback period exceeds typical usage patterns, you need a different entry point—regardless of how superior your end-state product is.

Sometimes the fastest path to your vision requires building products that get customers 70% of the way there with 10% of the friction. Once they’re comfortable at 70%, they’ll follow you to 100%.