DETROIT, July 09, 2025 (GLOBE NEWSWIRE) — Lotlinx, the auto industry’s leading VIN-specific data company for dealership inventory management, today announced that its advanced generative AI inventory and pricing management solution has been selected as winner of the “LLM Innovation Award” in the 8^th annual AI Breakthrough Awards program conducted by AI Breakthrough, a leading market intelligence organization that recognizes the top companies, technologies and products in the global Artificial Intelligence (AI) market today.

As the auto retail industry faces increasing challenges in inventory management, pricing optimization, and market adaptability—particularly in light of automotive tariffs that directly impact vehicle costs and dealership profitability—dealers are seeking new ways to navigate complex pricing environments. Tariffs and economic pressures are driving up the price of imported vehicles and parts, squeezing profit margins, shifting consumer demand, and requiring real-time recalibration of inventory strategies.

While many dealerships strive to enhance profitability through data-driven decision-making, traditional inventory and pricing management solutions often rely on static reports and historical data, leaving dealers reactive rather than proactive. These outdated tools fail to capture and analyze the dynamic factors affecting vehicle pricing, such as tariffs, economic conditions, competitor activity, and regional demand fluctuations. As a result, dealers risk overpricing or underpricing vehicles, leading to lost revenue opportunities, inventory stagnation, and eroded margins.

Lotlinx’s advanced Vertical AI solution addresses these challenges by leveraging Large Language Models (LLMs) and Agentic AI to analyze millions of data points per vehicle in real time, delivering region-specific, data-backed recommendations tailored to the dealer’s unique market conditions.

At its core is the Agentic AI-powered virtual assistant, designed as a Virtual Internet Sales Manager that understands complex inventory and pricing scenarios and provides intelligent, automated guidance. After analyzing vehicle performance within the local market, the assistant suggests proactive actions, including strategic pricing adjustments, competitive positioning, follow-up reminders, and demand-based inventory alerts. The intelligent system continuously monitors sales velocity, market conditions, and pricing trends down to the zip code level.

By seamlessly integrating into dealership workflows, the solution ensures that data-backed insights are immediately actionable, eliminating guesswork and enabling dealers to proactively optimize inventory and pricing strategies. In addition, the solution also delivers real-time interpretation and automated recommendations for active, strategic decision-making.

“We’re thrilled to accept this award from AI Breakthrough. The strength of our AI technology is that it gives control back to dealers through an automated, proactive approach that helps them maintain profitability in an era where external economic forces add layers of complexity to pricing and inventory strategies,” said Len Short, Executive Chairman of Lotlinx. “By equipping dealers with a powerful, AI-driven inventory and pricing management system, we are modernizing the auto retail industry with predictive decision-making capabilities that drive efficiency, profitability, and strategic agility in an increasingly volatile market.”

The AI Breakthrough Awards shine a spotlight on the boldest innovators and most impactful technologies leading the charge in AI across a comprehensive set of categories, including Generative AI, Computer Vision, AIOps, Agentic AI, Robotics, Natural Language Processing, industry-specific AI applications and many more. This year’s program attracted more than 5,000 nominations from over 20 different countries throughout the world, underscoring the explosive growth and global importance of AI as a defining technology of the 21st century.

“Lotlinx’s solution provides forward-looking, AI-driven insights that help dealers adapt to the always changing economic and regulatory landscape. Traditional inventory and pricing solutions don’t capture and analyze dynamic factors like tariffs, economic conditions, competitor activity, and fluctuating regional demand, leaving dealers to struggle with pricing vehicles competitively, inventory strategy, and adjusting to rapid market changes,” said Steve Johansson, managing director, AI Breakthrough. “This technology ensures that dealerships are no longer constrained by outdated, reactive management strategies but instead gain access to an intelligent, automated partner that enhances decision-making, boosts profitability, and streamlines operations. We’re pleased to recognize Lotlinx with the ‘LLM Innovation Award!’”

About Lotlinx

Founded in 2012 and based out of Peterborough, New Hampshire, Lotlinx is the automotive industry leader in VIN-specific data solutions for inventory risk management. The Lotlinx platform provides automobile dealers and manufacturers with enhanced operational control over their retail business. Leveraging state-of-the-art real-time data and machine learning technology, Lotlinx provides a precision retailing solution that enables dealers to automatically adapt to market dynamics, mitigating inventory risk through VIN-specific strategies. To learn more about Lotlinx, please visit www.lotlinx.com.

About AI Breakthrough

Part of Tech Breakthrough, a leading market intelligence and recognition platform for global technology innovation and leadership, the AI Breakthrough Awards program is devoted to honoring excellence in Artificial Intelligence technologies, services, companies, and products. The AI Breakthrough Awards provide public recognition for the achievements of AI companies and products in categories including Generative AI, Machine Learning, AI Platforms, Robotics, Business Intelligence, AI Hardware, Computer Vision and more. For more information visit AIBreakthroughAwards.com.

Tech Breakthrough LLC does not endorse any vendor, product or service depicted in our recognition programs, and does not advise technology users to select only those vendors with award designations. Tech Breakthrough LLC recognition consists of the opinions of the Tech Breakthrough LLC organization and should not be construed as statements of fact. Tech Breakthrough LLC disclaims all warranties, expressed or implied, with respect to this recognition program, including any warranties of merchantability or fitness for a particular purpose.

Among the 6 in 10 managers who use artificial intelligence tools at work, nearly all — 94% — use them to make decisions about their direct reports, according to a June 30 report from Resume Builder.

When making personnel decisions, managers use AI to determine raises (78%), promotions (77%), layoffs (66%) and terminations (64%). More than 7 in 10 of the leaders who said they use AI to help manage their teams expressed confidence in the technology making fair and unbiased decisions about employees.

However, only 32% of those using AI to manage said they’ve received formal training on how to do so ethically, and 43% said they’ve received informal guidance. About a quarter said they haven’t received any training.

Of the managers turning to AI, 46% said they were told to evaluate whether AI could replace a direct report’s position. Among those, 57% said they decided AI could replace the position, and 43% decided to replace the human position with AI.

“It’s essential not to lose the ‘people’ in people management. While AI can support data-driven insights, it lacks context, empathy and judgment,” said Stacie Haller, chief career advisor at Resume Builder.

“AI outcomes reflect the data it’s given, which can be flawed, biased or manipulated,” Haller said. “Organizations have a responsibility to implement AI ethically to avoid legal liability, protect their culture and maintain trust among employees.”

In the survey of more than 1,300 U.S. managers with direct reports, more than 1 in 5 using AI to lead said they frequently let AI make final decisions without human input. Even so, nearly all managers said they’re willing to step in if they disagree with an AI-based recommendation.

Those who integrate AI at work also say they use it for training materials (97%), employee development plans (94%), performance assessments (91%) and performance improvement plans (88%). 

Using AI for employment decisions could introduce bias into the algorithm, depending on how the AI model is trained and previous human decisions. At the same time, AI tools could potentially aid diversity, equity and inclusion efforts if hiring managers objectively analyze their people data to find patterns of exclusion or lack of promotion. 

For instance, GoDaddy uses promotion flagging to identify potential eligible employees who should be reviewed for promotion consideration, said GoDaddy’s vice president of diversity, inclusion and belonging. Instead of relying on subjective data, HR pros can mitigate bias through structured processes.

Organizations have long viewed artificial intelligence as a way to achieve productivity gains. But recent research about AI adoption at U.S. manufacturing firms reveals a more nuanced reality: AI introduction frequently leads to a measurable but temporary decline in performance followed by stronger growth output, revenue, and employment.

This phenomenon, which follows a “J-curve” trajectory, helps explain why the economic impact of AI has been underwhelming at times despite its transformative potential.

“AI isn’t plug-and-play,” said University of Toronto professor Kristina McElheran, a digital fellow at the MIT Initiative on the Digital Economy and one of the lead authors of the new paper “The Rise of Industrial AI in America: Microfoundations of the Productivity J-Curve(s).” “It requires systemic change, and that process introduces friction, particularly for established firms.” 

University of Colorado Boulder professor Mu-Jeung Yang; Zachary Kroff, formerly with the U.S. Census Bureau and currently an analytics specialist at Analysis Group; and Stanford University professor Erik Brynjolfsson, PhD ’91, co-authored the report.

Working with data from two U.S. Census Bureau surveys covering tens of thousands of manufacturing companies in 2017 and 2021, the researchers found that the AI adoption J-curve varied among businesses that had adopted AI technologies with industrial applications. Short-term losses were greater in older, more established companies. Evidence on young firms showed that losses can be mitigated by certain business strategies. And despite early losses, early AI adopters showed stronger growth over time. 

Here’s a look at what the study indicates about the adoption and application of AI, and the types of firms that outperform others in using new technology. 

1. AI adoption initially reduces productivity.

The study shows that AI adoption tends to hinder productivity in the short term, with firms experiencing a measurable decline in productivity after they begin using AI technologies.  

Even after controlling for size, age, capital stock, IT infrastructure, and other factors, the researchers found that organizations that adopted AI for business functions saw a drop in productivity of 1.33 percentage points. When correcting for selection bias — organizations that expect higher returns are more likely to be early AI adopters — the short-run negative impact was significantly larger, at around 60 percentage points, the researchers write.

This decline isn’t only a matter of growing pains; it points to a deeper misalignment between new digital tools and legacy operational processes, the researchers found. AI systems used for predictive maintenance, quality control, or demand forecasting often also require investments in data infrastructure, staff training, and workflow redesign. Without those complementary pieces in place, even the most advanced technologies can underdeliver or create new bottlenecks. 

“Once firms work through the adjustment costs, they tend to experience stronger growth,” McElheran said. “But that initial dip — the downward slope of the J-curve — is very real.”

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2. Short-term losses precede long-term gains.

Despite companies’ early losses, the study found a clear pattern of recovery and eventual improvement. Over a longer period of time — there was a four-year gap in the study data — manufacturing firms that adopted AI tended to outperform their non-adopting peers in both productivity and market share. This recovery followed an initial period of adjustment during which companies fine-tuned processes, scaled digital tools, and capitalized on the data generated by AI systems. 

That upswing wasn’t distributed evenly, though. The firms seeing the strongest gains tended to be those that were already digitally mature before adopting AI. 

“Firms that have already done the digital transformation or were digital from the get-go have a much easier ride because past data can be a good predictor of future outcomes,” McElheran said. Size helps too. “Once you solve those adjustment costs, if you can scale the benefits across more output, more markets, and more customers, you’re going to get on the upswing of the J-curve a lot faster,” she said.

Better integration of the technology and strategic reallocation of resources is important to this recovery as firms gradually shift toward more AI-compatible operations, often investing in automation technologies like industrial robots, the researchers found.

3. Older firms see greater short-term losses.

Short-term losses aren’t felt equally across all firms, the study found. The negative impact of AI adoption was most pronounced among established firms. Such organizations typically have long-standing routines, layered hierarchies, and legacy systems that can be difficult to unwind. 

These firms often have trouble adapting, partly due to institutional inertia and the complexity of their operations. “We find that older firms, in particular, struggle to maintain vital production management practices such as monitoring key performance indicators and production targets,” the researchers write. 

“Old firms actually saw declines in the use of structured management practices after adopting AI,” McElheran said. “And that alone accounted for nearly one-third of their productivity losses.” 

In contrast, younger, more flexible companies appear better equipped to integrate AI technologies quickly and with less disruption. They may also have less to unlearn, making the transition to AI-enabled workflows more seamless. 

“Taken together, our findings highlight AI’s dual role as a transformative technology and catalyst for short-run organizational disruption, echoing patterns familiar to scholars of technological change,” the researchers write. They note that the results also show the importance of complementary practices and strategies that mitigate adjustment causes and boost long-term returns to “flatten the J-curve dip and realize AI’s longer-term productivity at scale.”