But what you do need is an all-round business plan that includes how to implement AI to achieve your goals
Over the past few years, the buzz around artificial intelligence (AI) has become hard to ignore, especially in the automotive industry. It’s no surprise. Recent reports have shown that businesses utilizing AI in their operations are reporting a 15-30 per cent increase in revenue.
Though the fundamentals of AI are over 70 years old, the recent release of large language models like ChatGPT has created an inflection point. This has transformed advanced uses of AI from conceptual ideas into key components of strategic planning sessions — from boardrooms to factory floors.
However, after spending the last year speaking about AI at industry conferences, it has become evident that many companies in the auto part industry are still grappling with how to fully harness the potential of AI-powered technology. Many indicate they are developing an “AI strategy,” but there is no such thing as an AI strategy. What companies need to be assessing are forward-thinking business strategies that AI supports.
Solving real business challenges
The automotive industry is a prime example of a sector that is often “data-rich but information-poor.” Challenges around managing large inventories, complex supply networks and vehicle fitment data have historically constrained companies, as analyzing such vast amounts of data often exceeds the capacity of even large organizations.
However, with the right tools matched to the right strategy, AI can thrive in this environment — especially when it comes to extracting key insights. Below are just a few key industry challenges where AI is bound to make a huge impact:
Inventory management
Auto parts businesses often face regular instances of poor inventory management due to a lack of data. By studying real-time information, market tendencies and seasonal changes, AI can enhance inventory control by processing and analyzing more data, improving accuracy, and reducing errors typical of manual forecasting methods.
Dynamic pricing strategies
Real-time overseeing of market trends and competitor prices is what AI does best. Therefore, companies can make instant adjustments that will keep them competitive as well as maximize profits. Properly integrated with other systems this allows businesses to overcome delays and errors inherent in conventional processes enabling them to capitalize on market opportunities.
Enhanced customer experience
AI can analyze real-time customer data to offer personalized recommendations and support. This proactive approach boosts satisfaction and loyalty by delivering tailored experiences that meet modern consumer expectations. By incorporating AI into customer experience strategies, businesses can swiftly adapt to changing preferences, outperforming traditional segmentation methods.
AI not only makes processing data easier and faster but also makes significant progress in detecting inefficiencies and preventing their costly consequences. For example, Predii uses the power of artificial intelligence to analyze service data for prediction of what might go wrong with a vehicle’s component. At my company, Tromml, we utilize AI to analyze large amounts of sales data in real time, uncovering insights that often go unnoticed through manual analysis.
However, with the right tools matched to the right strategy, AI can thrive in this environment — especially when it comes to extracting key insights.
AI needs a purpose
Tying AI to clear business objectives isn’t just critical for prioritization; it’s essential for the technology to function effectively. Many modern AI systems, particularly those that leverage machine or deep learning, rely on feedback loops to improve over time. It’s much like training an employee. Without ongoing refinement, AI can become outdated, delivering insights that no longer serve your business. A good AI system continuously learns, adapts and provides actionable insights aligned with your goals.
If an AI system is delivering irrelevant or outdated insights, it’s a signal to reassess why it’s not working. Often, this is due to factors like incomplete or poor-quality data or a lack of diversity in data sets. This is why collaborating with your vendor or technical team is key; rather than abandoning the system, work together to refine data inputs and objectives, ensuring the AI evolves to better meet your business needs.
In the end, AI’s true potential will only be unlocked when the right tools are thoughtfully integrated into your business strategy, where you can understand both its limits and its capabilities. This isn’t about chasing the latest tech trend; it’s about using AI to solve real challenges, streamline operations, and foster sustainable growth.
As you engage others around artificial intelligence at work, consider how best it might help us achieve particular targets instead of perceiving it as an end-all solution itself.
Critical thinking among employees should be encouraged so they may identify areas where maximum value addition may be achieved through the deployment of this technology. Every project undertaken under this banner must show clearly what impact it will have on overall performance within your enterprise.
By approaching AI with a strategic mindset, you can not only avoid costly missteps but also position your company as a thought leader, driving innovation and staying ahead in an increasingly competitive landscape.
Lauren McCullough is the founder and CEO of Tromml, an AI-powered business intelligence platform specifically designed for the auto parts industry. She is actively involved with industry organizations including MEMA, Auto Care Association, SEMA and YANG.
This article originally appeared in the September issue of Jobber News
Leave a Reply