For Joshua David Farley, artificial intelligence is a leadership test. “What changed most everything wasn’t really the predictions itself. It was that leadership allowed the system to trigger action automatically,” he says. In traditional, asset-heavy industries, that shift from insight to execution marks the real transformation. Farley, an executive sales and revenue management leader and Chief Revenue Officer at BESTMOW, has spent more than 25 years guiding organizations through billion-dollar growth trajectories, complex integrations and KPI-driven turnarounds. His focus now sits at the intersection of legacy business models and AI-powered solutions, where operational discipline meets data-led decision-making.
Rewiring Legacy Operations for Intelligent Action
Across manufacturing, logistics and residential services, Farley has seen AI deployed to predict equipment failure, optimize maintenance timing and improve throughput ratios. He is quick to point out, however, that predictive capability alone does not change a business. The breakthrough occurs when companies trust the system enough to automate response.
“As humans, one of the great things about us is we do things with our gut as well as facts,” Farley says. AI enhances that equation. It provides visibility into patterns and risks that are not always obvious, encouraging leaders to question whether long-standing practices are truly the most efficient. At BESTMOW, an AI-powered autonomous lawn care technology company focused on transforming the residential services market, AI is embedded into a scalable operating model designed to expand from $50 million to $1 billion within five years.
Confronting the Fear That Stalls Progress
Resistance, however, remains a defining feature of AI adoption in traditional sectors. Farley encounters a familiar concern. “People are afraid it’s going to take their job. They don’t look at it as an enhancement. They look at it as it’s going to replace me.” The anxiety is not entirely irrational. AI can expose inefficiencies and reshape roles. It can also make mistakes. Farley recounts using generative tools to support a go-to-market plan in southern Florida, only to discover that the statistics provided were incorrect. “AI can make mistakes too. It’s not infallible.”
This dual reality demands a measured approach. Businesses that ignore AI risk being overtaken by more efficient competitors capable of delivering services at lower cost. Yet blind faith in automation is equally risky. Farley advocates for an informed middle ground: embrace AI as a tool, not as a replacement for judgment. Companies must evolve, much as marketing shifted from mailers and billboards to digital channels.
From Experimentation to Embedded Capability
For mid-sized organizations that have run pilots without achieving scale, Farley outlines three decisive moves. First, neutralise fear. Leaders must explicitly frame AI as an enhancement to the business rather than a headcount reduction exercise. Cultural buy-in precedes technical integration. Second, enforce disciplined implementation. “Just like any new CRM tool, employees have to use it,” he says. Adoption requires accountability, measurable usage metrics and leadership reinforcement. AI initiatives fail when they are optional.
Third, rigorously test and validate. Tools must be assessed against real operational outcomes, whether that means improved conversion rates, faster maintenance cycles or measurable EBITDA lift. “Make sure you’re using it smart,” Farley says. AI should be deployed where it drives efficiency, mitigates risk and frees human capital for higher-value work.
In sales, the impact can be immediate. AI-driven lead sourcing, automated scheduling and algorithmic prospect identification reduce time spent behind a desk. “Which would you rather your employees be doing? Sitting in front of customers or sitting behind a desk?” By reallocating effort towards relationship-building and strategy, organizations unlock both revenue growth and improved morale.
The Efficiency Imperative
Looking three to five years ahead, Farley believes leaders are underestimating the scale of efficiency gains AI will force upon traditional industries. The technology will not merely streamline tasks; it will reshape operating models. Time is money. When AI absorbs administrative burden and surfaces high-probability opportunities, companies gain speed and precision. Supply chains become more responsive. Sales teams operate with clearer insight into which activities actually drive revenue. Fact-based decision-making becomes the norm rather than the exception.
“AI can only interpret data based on how that data was entered into the system. It doesn’t have the gut,” says Farley, rejecting the narrative that AI will eliminate the need for experienced leadership. Senior leaders are hired for perspective forged through experience, for the ability to challenge assumptions and validate outputs. AI may recommend a course of action; it cannot own the consequence. The competitive edge, then, lies in integration. Businesses that treat AI as a strategic capability rather than a peripheral experiment will outpace peers who hesitate. Those that combine data-driven insight with seasoned judgment will define the next chapter of traditional industry.
Follow Joshua David Farley on LinkedIn or visit his website for more insights.
