The Next Wave of AI Is Practical
Artificial intelligence often dominates headlines through the lens of Silicon Valley. Startups showcase new models, tech giants debate regulation, and investors hunt for the next unicorn. But some of the most meaningful applications of AI aren’t happening in labs or venture-backed firms. They’re unfolding in industries that have traditionally lagged in technology adoption—manufacturing, logistics, agriculture, and even home improvement.
The lesson for business leaders is clear: you don’t need to be a tech company to think like one. In fact, embedding AI into physical industries may yield the greatest productivity gains of all.
Lesson 1: Embed AI in Core Workflows, Not at the Edges
One mistake non-tech companies make is treating AI as a “bolt-on” experiment. A chatbot here, a dashboard there. The results are usually underwhelming. AI delivers its real value when it’s woven into the operating fabric of a business.
In home improvement, that means using AI to handle the very processes that have long frustrated customers: scheduling, permitting, inventory planning, and workforce allocation. Instead of asking a customer to wait weeks for a call back, companies can now provide installation dates at the point of sale. AI systems can parse PDFs of product data, match inventory to local warehouses, and factor in permitting timelines. What once required days of human coordination is reduced to seconds.
This isn’t unique to home services. A 2025 McKinsey survey found that more than 75% of organizations now deploy AI in at least one business function, and they are actively redesigning workflows and strengthening governance to derive real value (mckinsey.com).
Lesson 2: Own Your Data Before You Scale AI
AI systems are only as strong as the data that feeds them. Many legacy companies face a hard truth: their data is fragmented, inconsistent, or trapped in paper-based processes. Before AI can unlock new capabilities, the foundation has to be rebuilt.
In home improvement, decades of handwritten work orders and siloed software meant that scaling was nearly impossible. The breakthrough came from consolidating everything into a single platform, eliminating paper, and ensuring every job—from sales to installation—flowed through one system. That discipline created a “single source of truth,” allowing AI to operate with clean, structured data.
This is the less glamorous work of AI adoption, but it’s what makes everything else possible. Leaders who skip the data foundation stage often find themselves unable to scale, no matter how advanced the tools they purchase.
Lesson 3: Augment People, Don’t Replace Them
Any conversation about automation raises the specter of job loss. In reality, the best AI deployments free people from repetitive tasks so they can focus on higher-value work.
Take scheduling. Traditionally, large teams of coordinators would spend hours calling customers, checking inventory, and booking installers. AI can now handle the mechanics, but employees are redeployed toward customer experience, problem-solving, and training. The payoff comes when workers spend less time on administrative busywork and more time delivering value to customers.
The same principle applies across sectors. Financial services firms, for example, are using AI to automate regulatory paperwork so advisors can spend more time with clients. Agriculture companies are turning to predictive models to guide planting decisions, while farmers focus on yield strategies. The lesson is universal: framing AI as augmentation rather than replacement makes adoption far smoother.
Lesson 4: Start Small, Stack Wins
One reason AI adoption stalls is that leaders aim too big too fast. The more effective approach is to identify well-documented, repeatable processes and apply AI incrementally.
For example, an AI agent may start by extracting structured data from product PDFs. Another may match installer availability with job complexity. On their own, each improvement saves minutes. Stacked together, they transform the customer journey.
Small, measurable wins create momentum. Each success builds confidence inside the organization and demonstrates ROI, turning skepticism into buy-in. This stacking effect is how legacy industries can move from isolated experiments to true transformation.
Lesson 5: Industry Context Shapes AI Strategy
Unlike software companies, physical industries face unique constraints. Home remodeling contends with local permitting, unpredictable site conditions, and supply chain shocks. Airlines face FAA regulations and weather. Healthcare must navigate HIPAA and patient safety.
These realities make generic AI solutions less effective. Leaders must adapt AI to the messy specifics of their industry. In some cases, that means building custom systems in-house rather than relying solely on off-the-shelf tools. The investment is higher, but so is the defensibility.
In fact, the home improvement sector is already embracing this shift. A June 2025 industry survey found that 65% of companies plan to increase AI investments in the next year, while 78% of contractors believe AI will significantly improve project management efficiency. Meanwhile, AI chatbots are already handling up to 60% of customer inquiries, proof that what sounds experimental is already delivering real results (wifitalents.com).
Lesson 6: Culture Is the Multiplier
Scaling AI depends as much on culture as it does on code. Teams need to believe in pushing past what seems realistic. I’ve seen this firsthand: in one five-year span, the home improvement company I founded, West Shore Home, grew revenue from $50 million to over $750 million by setting audacious goals and challenging employees to maximize their potential. AI was the lever, but culture was the multiplier.
This mindset applies anywhere. Manufacturers adopting robotics or logistics firms turning to predictive analytics succeed not only because of the tools they implement, but because leadership fosters a culture of experimentation and resilience.
AI’s Future Is in Everyday Industries
The story of AI will not be written solely in Palo Alto. Its most transformative chapters will unfold in warehouses, supply chains, and service industries that shape daily life. The industries that employ millions of people, still run on outdated systems, and shape daily life are the ones most ripe for reinvention.
Traditionally analog sectors like home services are also seeing tangible results. According to a June 2025 report from Kiplinger, citing a Housecall Pro survey, 40% of contractors are now actively using AI, and saving an average of four hours per week (kiplinger.com). That may not make splashy headlines in the tech press, but for businesses and customers alike, it’s transformative.
The lesson for leaders is simple: don’t wait for the next wave of innovation to hand you the future. Build it into your workflows, own your data, empower your people, and adapt to your industry’s realities.
Because the real frontier of AI isn’t the next app or algorithm. It’s in reimagining how we deliver the products and services that make the world work.
