Retailers need to break through the AI hype to see tangible results.
Written by Kartik Ganapati
There is no escaping the conversation surrounding artificial intelligence. For executives, it can feel like standing in the middle of a crowded street. There’s a lot of noise, but it’s not very clear. But for business owners, the way forward lies not in speculation or hype, but in results. AI is not a magic wand, nor is it an imminent threat waiting to replace the workforce. It’s a tool. Like any other industrial innovation, its value comes from how it is applied. We already experience AI every day. Netflix recommendations, spell checking, sentence completion prompts, voice assistants, fraud prevention, driver assistance, and more all enhance human decision-making without taking control away from humans.
Before applying AI, it is important to clarify what we mean by AI. One useful distinction is between narrow AI and general AI. Narrow AI is already delivering value to industrial automation, analytics, and customer operations by building targeted intelligence into systems to help make faster, better decisions. General AI, the so-called Holy Grail, will have the ability to understand, reason, learn, and act on a human level, passing the “Turing Test.” However, it remains largely a hypothesis, often driven by marketing buzz, and is the source of widespread claims.
Companies often hesitate to act for fear of doing the wrong thing or commit themselves to testing programs that never leave the lab. In reality, successful AI implementation requires something much simpler: focus. Start with important business challenges, such as reducing downtime, improving quality, and streamlining logistics. Apply AI to these challenges, measure the impact, and scale what works. Next, we translate theoretical promises into tangible results, moving from “we can” to “we did it.”
The truth is, the hype around AI will fade. What persists is the competitive advantage for companies that take a disciplined and pragmatic approach to AI. For business owners looking to beat the hype, these three principles provide a clear starting point.
- Get your data right. The performance of AI is determined by the data you feed it. Without clean, structured, and harmonized information, even the most advanced models will produce noisy and misleading results. Start by organizing your customer, product, and equipment data into a single source of truth. This approach provides visibility into profitability, operational performance, and opportunities for improvement, creating a foundation for confidently operating AI-enabled systems. This view also helps organizations analyze current performance, identify gaps, and prepare for more advanced AI applications.
- Start small and scale quickly. The most effective AI implementations start with focused, high-friction problems rather than broad, undefined efforts. For example, at Vontier, a spike in call volume prompted the team to apply classic Kaizen methodology to identify areas for continuous improvement across the organization. This analysis revealed which application and process changes were needed to reduce call volume. With this foundation in place, the company leveraged agent AI tools to handle incoming calls more efficiently. Some improvements were simple, like deploying AI agents to augment support staff. Other features, such as automatic triage and closed-loop backlog management, required more advanced AI models and careful orchestration. This first step demonstrated that small, disciplined pilots can yield tangible results and lay the foundation for broader adoption.
- Always keep abreast of human information. AI can predict outcomes, generate insights, and automate tasks, but it cannot replace human judgment. Leaders must treat AI as an assistant rather than a replacement, ensuring output is verified, errors are detected, and bias is reduced. Human oversight protects quality and accountability while allowing teams to direct their efforts to higher-value work, interpreting insights, making strategic decisions, and innovating in ways that machines can’t replicate.
Practical applications of AI span multiple areas. In product design, software development, and delivery, AI will bring near-immediate benefits with projected efficiency savings of $2.6 to $4.4 trillion. AI accelerates discovery, isolates core functional needs and transforms them into discrete engineering tasks, reduces cycle time, and improves roadmap fidelity.
The conclusion is clear. Today’s AI will never fully replace operations, but companies that implement it with discipline and purpose will outperform those that don’t. The winners will not be those who talk the loudest about AI, but those who get the data right, solve real problems, and keep humans at the center of decision-making.
Karthik Ganapathi is president of Invenco by GVR, a Vontier business. For more information, please visit invenco.com.
