How can you tell that the AI Era is here? One way is by noticing that large enterprises are increasingly focused on mass producing AI models.
It’s no longer enough to have a decent set of working AI models to power Spotify’s suggestion engine or Accenture’s Big Data analytics.
To keep up with—and surpass—the Joneses, Spotify and Accenture will need dedicated systems that work every day to create, evaluate and iterate their AI models.
These systems are called AI factories. Somewhat like a factory that creates physical widgets, an AI factory churns out new and updated AI models. This continual AI production process helps enterprises react quickly to market demands and competition.
Make no mistake: The development of AI factories represents a turning point in the evolution of AI-powered business.
No. 2 with a Bullet
This theory is supported by some of IT’s top thinkers. They include Tom Davenport, a professor, speaker and author; and Randy Bean, a corporate advisor.
Davenport and Bean co-wrote an article that appeared earlier this month In the Sloan Management Review: Five trends in AI and data science for 2026. In their article, the authors place AI factories in the Number 2 spot. AI factories, they say, will be adopted by users and “all-in” AI adopters that include consumer products makers, banks and software companies.
As Davenport and Bean explain, an AI factory combines technology platforms, methods, data and previously developed algorithms to make building AI systems easy and fast. The authors’ all-important message: Watch this space.
How AI Factories Work
To fully understand the concept of an AI factory, it can help to think of the traditional smoke-belching, brick-and-mortar factories it’s named for.
Of course, there are some differences. A physical factory takes in raw materials, uses machines to process them, and produces physical products.
By contrast, an AI factory takes in data (such as text, audio, images and logs), runs that data through massive compute engines, and outputs AI models for recommendations, predictions, automation and generative content.
Another difference: Unlike the static products that emerge from traditional factories, the products of AI factories are virtual. They learn and grow as new data, infrastructure and techniques become available. In this way, AI factories help their organizations keep up with rapid changes and market shifts.
For instance, a new AI model produced by an enterprise’s AI factory can be continuously retrained as new data becomes available. While each new iteration deployed in the field busily suggests which Netflix movie to watch next, a newer version is constantly being developed in the background. When the new suggestion engine is ready, Netflix can seamlessly slide it into place.
Why Your Clients Probably Need an AI Factory
It’s good to understand the abstract benefits of an AI factory. But your clients will also want to know how building one can translate into business results.
Here’s the bottom line. An AI factory can:
- Dramatically reduce the cost of business intelligence. Once an AI factory is built and a given AI model is trained, that model can run continuously, serving millions of decisions, predictions, etc., for a fraction of its initial cost. In other words, the cost per additional decision rapidly collapses toward zero.
- Help organizations maintain a decisive competitive advantage. This happens on two levels. First, maintaining a constant production stream of AI models and iterations helps your clients meet market demands as quickly as possible. And second, having that ability to react faster to customer needs and economic conditions can help create and sustain an advantage over competitors.
- Turn data into capital. Many organizations are ill-equipped to analyze and monetize all the data they collect. All that piled-up data can seem like an albatross around their neck. But by building an AI factory, the organization can harness that otherwise squandered data and put it to work.
Further, companies that don’t build an AI factory could find themselves at a competitive disadvantage. Davenport and Bean, in their Sloan Management Review article, say companies that lack an AI factory will find building AI at scale both expensive and time-consuming.
Stumbling Blocks? A Couple
Building an AI factory isn’t always easy. Enterprises can run into serious roadblocks.
For one, siloed, inconsistent or low-trust data can make for a messy AI production process. As programmers say, “garbage in, garbage out.” In other words, if the data is messy, the analysis will be, too.
Another thing that can wreak havoc on the virtual factory floor are talent bottlenecks. There are only so many data scientists to go around, and they’re in high demand. Finding the right employees is a key component here—even in an age of super-smart robots.
Another trap your clients need to watch out for are bureaucratic hold-ups. Legal, compliance and trust issues can cause AI projects to grind to a halt.
The AI Factory Future
Like everything else in the fast-moving AI world, AI factories are changing. In the near future, AI factories will likely focus on the immediacy of real-time, always-on learning.
As AI factories shift to nearly continuous adaptation, enterprises will use their AI model updates to keep pace with rapidly changing market conditions and customer demands.
Another likely future is inferencing at the edge. For “edge,” think vehicles, devices and brick-and-mortar factories. Organizations that move inferencing closer to where data is created can lower system latency (that is, increase speed) and reduce cloud costs.
Another factor that could make a big impact on AI factories is new software and hardware integrations. A recent Supermicro webinar on AI factories and related technology showed how enterprises can benefit from integrating software platforms such as Supermicro’s SuperCloud Composer (SCC) and Power Asset Orchestrator (PAO).
Supermicro says this potent combination allows operators to gain total visibility into AI Factories. It can also optimize everything from GPU telemetry to real-time grid pricing.
Overall, it’s safe to assume that when these and other updates are deployed, AI factories will quickly become part of the common AI infrastructure. In so doing, they’ll touch nearly every aspect of our daily lives.
Do More:
- Watch a video replay of the recent Supermicro webinar: Powering AI factories: unifying smart grid management and real-time visibility
- Read the Tom Davenport and Randy Bean article: Five trends in AI and data science for 2026 (Sloan Management Review)