For some, AI enables always-on customer service through web-based chatbots. For others, AI is embedded in sensors for predictive maintenance across a wide range of devices and machinery. While it’s easy to get caught up in the AI buzz, many practical use cases also happen behind the scenes, moving the needle on how we operate as a society. Industrial B2B manufacturing is a prime example. A recent Deloitte survey of 600 manufacturing executives found that 80% plan to invest at least 20% or more of their “improvement budgets” into smart manufacturing. Inclusive of technologies like automation and AI, the push towards smart manufacturing emphasizes the importance of data as a competitive driver.  

Why Does Data Matter?

Imagine you’re a tool manufacturer working on your latest line of drills. Having these drills produced isn’t as simple as following a set list of criteria. Why? Because the criteria changes based on regional specifications, such as the drill’s battery size, the outlet configuration each drill is compatible with, or the screw size. This quickly creates more product variants and complicates an already crowded list of SKUs. It’s not even just the drill itself. There are aftermarket parts to consider, and the ever-expanding list of channels where the drill and its parts will be sold. Imagine that during that process, legislative or environmental developments occur, such as Digital Product Passports, altering how these drills must be produced and increasing scrutiny of traceable and transparent data. Suddenly, the intentionality of action and management increases, alongside an endless amount of product data to support copious versions of the drill. Without a single source of truth for all the necessary data, manufacturers end up producing a multitude of flawed, noncompliant products, while also distributing that information to external channels at a much slower rate. Not only does this decrease manufacturers' reliability, but it can also severely decrease time to market.

In an increasingly omnichannel landscape, product data management serves as the intelligent system that organizes, updates, and distributes the information that powers production. As manufacturers juggle expanding product catalogs, stringent regulatory demands, and rising expectations from digital-first consumers, the value of consolidated, accurate data becomes clear, making product information management (PIM) software indispensable. It also better positions manufacturers to optimize their data, leveraging it as a strategic advantage to scale across channels.

How AI Software Helps Manufacturers Win

So where does AI fit into the equation? Through PIM software, the backbone of quality and smart manufacturing. PIM transforms fragmented product data into a unified engine for growth, serving as a centralized hub that enables manufacturers to orchestrate data across multiple product lines and commerce channels more effectively. PIM provides manufacturers with a route to accelerate time-to-market and operate at the highest quality, serving as a key AI tool that fuels growth.

AI improves data quality by reducing errors and inconsistencies introduced by manual intervention. Manual errors may not seem like a huge deal, but they can easily disrupt time-to-market or channel accuracy.  Additionally, AI-driven PIM drastically improves data onboarding, aggregating all of a manufacturer’s given product data into one accurate, concise, and comprehensive catalog of information. By cutting down on errors and inconsistencies from manual intervention, along with seamless access, onboarding, and management of internal and external product data, manufactures find notable cost and time reductions. Keeping this in mind, AI quickly becomes a business and operational enhancer for manufacturers across the B2B landscape. A business driver that matches the speed of demand. AI-integrated PIM solutions also present generative AI tools that create and syndicate content on manufacturers' behalf, enabling them to differentiate and adapt product information for different markets, sales channels, or languages to reach new global audiences.

AI is becoming essential to improving not just the speed of data syndication, but the quality and commercial impact of product information over time. Once manufacturing, regionalization, and compliance work is complete, product data still has a long journey ahead—fueling digital channels, partner ecosystems, search engines, and service organizations. Relying on manual syndication slows time-to-market and limits a manufacturer’s ability to enrich, refine, and optimize content as markets evolve. AI-driven approaches help automate enrichment, continuously improve content performance, and ensure information is discoverable through both traditional SEO and emerging answer-engine optimization (AEO). Just as importantly, they enable manufacturers to better support aftermarket and service revenue by keeping replacement parts, accessories, and service data accurate, visible, and easy to transact across every touchpoint. In this way, AI transforms syndication from a one-time publishing task into an ongoing engine for growth and lifecycle value.

Intelligent software is the answer to smarter manufacturing

When looking at the pace of commerce, the market disruption from e-commerce, and the growing desire for smarter manufacturing, decision-makers need a tool that supports them in those journeys. AI-powered data management enables manufacturers to keep up and stand out with speed, accuracy, and scalability. It’s the quiet infrastructure at the heart of manufacturing making a real difference, and the manufacturers who embrace intelligent software now will lead and define the future we're heading toward.

Jay Roxe is a software engineer and product leader turned general manager and marketing executive. At Inriver, he leads marketing with a mandate that spans category strategy, positioning, and revenue impact, helping define how modern enterprises control and commercialize product data at scale. Across his career, Roxe has built, scaled, and transformed marketing organizations at global leaders such as Microsoft and GE, as well as high-growth B2B technology companies including athenahealth, Rapid7, and BitSight. He has led the creation of three distinct market categories and consistently built digital-first, product-driven marketing engines.