
Why The AI Race Will Be Won By Companies That Improve Product Access
Why The AI Race Will Be Won By Companies That Improve Product Access
Most companies looking at generative AI right now are asking the wrong question. They want to know how it can improve their product, automate internal tasks, or create clever campaigns. But the companies that will win the AI race aren't necessarily the ones with the best product. They're the ones that make it easiest to get. No one expected Domino's to become a tech leader. But that's exactly what happened. In the mid-2000s, Domino's focused on digital transformation long before it was cool to do so. They created one of the first pizza tracker apps, allowed ordering through voice, smart TVs, smartwatches, Twitter, and even used emojis. It wasn't just about ordering pizza. They wanted to make ordering frictionless, and that paid off big for them.
How Domino's Won The AI Race Before It Even Started
If you had invested $1,000 in Domino's stock in 2010, that investment would be worth far more than the same investment in Amazon, Apple, Tesla, Netflix, or Google. Why would a pizza chain like Domino's outperform some of the most innovative tech giants in the market? Because they used technology to solve a very human problem: hunger plus impatience.
Their pizza wasn't dramatically better, but you could track your pizza, customize your order, and get it delivered without picking up the phone. Convenience was the real product. And that's the overlooked power of technology when it's used to remove friction.
Why The AI Race Will Be Won By Convenience, Not Complexity
As generative AI tools get more powerful, many companies are racing to build things that are smarter, faster, and more impressive. But if they overlook usability, they lose. Customers care about ease of ordering, customizing, and getting what they want without needing to watch a tutorial.
The next winners of the AI race will be companies that apply AI not just to operations, but to the experience of access.
Who Could Be The Next Domino's In The AI Race?
Some of the most compelling players aren't traditional tech companies. They're companies that serve everyday consumers and are using AI to make access easier.
Allstate is using generative AI to help customers understand their coverage faster, file claims more easily, and get answers to confusing documents in plain language. That kind of speed and clarity in a moment of stress makes a brand memorable.
Procter & Gamble is testing AI to predict when households might need to restock essential items, reducing the need for people to keep track of things or even remember them. If a parent doesn't have to think about running out of diapers or detergent because P&G's system already shipped it, that's loyalty.
Toyota is creating tools that allow employees without coding experience to prototype design concepts using generative AI. This internal accessibility can speed innovation and remove the tech bottleneck that often slows big organizations.
Walmart is testing drone deliveries and AI-assisted inventory systems to get products into hands faster and with fewer delays. They're also using generative AI to make customer service more responsive and personalized.
Canva took what Photoshop and PowerPoint made difficult and made it usable for people who don't see themselves as 'creatives.' With generative AI features now integrated into the platform, anyone can create professional-grade visuals, documents, and even presentations without needing formal design training.
5 Brands That Could Win The AI Race By Making One Bold Move
Domino's took a page from Uber's model to make pizza ordering to order and track. When companies recognize the value of looking outside their industry, they get out of status-quo thinking. What could well-known companies do with AI that customers don't expect, but would love? That is the question they should ask.
Starbucks might use AI to offer a 'caffeine concierge' which could be an adaptive system that suggests drinks based on your sleep data, weather, mood, or calendar load. On a hot, stressful Monday with back-to-back meetings? You'd get a mobile alert suggesting a cold brew with extra espresso, waiting with your name on it.
Southwest Airlines might use generative AI to let travelers rebook themselves instantly through a simple conversation-style interface when flights are delayed or canceled, without waiting on hold. Imagine a prompt that says, 'Would you prefer to leave tomorrow morning or get a hotel and take a red-eye tonight? Here are your options. Tap to confirm.'
CVS might use AI to transform the pharmacy pickup process into a silent, no-contact experience. Based on facial recognition or license plate scanning, your medication is dispensed to a locker as you pull up, with no app tap required and no waiting. Just like Amazon Go, but for healthcare.
Target might offer AI-powered 'household rhythm' planning. Instead of shopping lists, it might be more like event prep. If it detects that you have three kids, a birthday coming up, and you always forget party decorations, it sends you a complete cart to review. Add it all in one click, and it gets delivered, or it is ready for pickup with balloons already inflated.
Kohl's or Macy's could flip the fitting room experience by using generative AI to create real-time outfit videos on your 3D avatar instead of on flat images. You walk in, scan a QR code, and your virtual twin shows you what that outfit looks like walking, sitting, or twirling. That kind of real-time decision help could slash returns and actually make shopping fun again.
These aren't massive investments in technology. They're curiosity-driven ideas grounded in real pain points: waiting, forgetting, rebooking, overthinking, misfitting. The question should be 'What slows people down—and how can we remove that without asking them to learn anything new?'
The Secret To Winning The AI Race Is In The Path To The Product
One of the most powerful shifts happening with generative AI is how it changes the customer journey. Every business leader should be asking: what is it like for someone to get our product or service today? Where are the hurdles? Where are people confused, delayed, or forced to go find someone for help?
Generative AI gives us new tools to answer those questions, like the ability to:
This is about shortening the distance between interest and satisfaction. That's what creates loyalty and keeps people coming back.
How HR And Internal Teams Can Support The AI Race
HR departments can play a critical role by:
One simple exercise is to ask, "If someone had to use our product or service today, but couldn't talk to a human, how long would it take them to succeed?"
Curiosity Is Still The Key To Winning The AI Race
The companies that outperform others in the AI race will be the ones asking better questions, not just building better tech. Domino's asked how people wanted to order pizza. The answer shaped the company's entire strategy and fueled a level of growth no one saw coming. Right now, every company has an opportunity to do the same. The next winner of the AI race may not be a tech company. It might be a brand that figures out how to remove one more step, one more delay, one more frustration. And that's where the real opportunity lives.
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'We are close, for the very first time, to a meta-analysis for anemia management in dialysis through AI,' he said. Even device integration is advancing, with AI-enabled tools like phonangiography and robotic tomographic ultrasound improving vascular assessments. You won't need a technician — the device itself will standardize the analysis, he said. Despite his optimism, Covic issued a strong caveat: AI models are increasingly opaque, and 'we are not going to learn the algorithms,' he said. 'This process is close to beliefs in divinity.' Expanding Access Through Policy Shifting the focus to access, Rajnish Mehrotra, MD, MS, argued for policy solutions that leverage home dialysis with peritoneal dialysis (PD) to close the global dialysis gap. 'Nearly 2 million people die with kidney failure every year because they have limited access to kidney replacement therapy care,' said Mehrotra, Belding H. 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She also described a real-world protocol for integrating telemedicine, with frequent reviews of new patients' data during the first 15 days of treatment, tapering to weekly reviews for stable patients. We only invite the patient to come to the hospital if there is a clinical problem, she added. However, she acknowledged that not all patients benefit equally. Older individuals may struggle with connectivity or tech literacy, and the evidence for complication reduction remains mixed. 'The significant results were about disease-specific hospitalization and technical failure, but the evidence was low or very low,' said Manani. Still, she sees future promise in wearable devices and AI-enhanced alerts. 'Flexibility and innovation are essential for promoting home therapies,' she concluded. 'Remote monitoring could integrate the traditional follow-up, allowing safe, high-quality care.' The Road Ahead Key challenges in the integration of AI into kidney care remain. As Covic put it, 'Prediction is currently the most widely used application of AI and machine learning in dialysis,' but rigorous studies are needed. 'We should now move to this sort of randomized control trials,' he urged. The issues of trust and interpretability loom large with AI. 'It is really difficult, or even impossible, to know how they produce results,' Covic said of AI models. But the results, increasingly, are hard to ignore. 'Optimized machine learning models could enhance risk identification and drive preemptive interventions,' he concluded. No funding or relevant financial relationships were declared.