Artificial intelligence stopped being synonymous with chatbots a while ago.
What is happening now is a much more concrete shift: AI systems that do not just answer questions but actually interact with the physical world, making decisions, operating machines, and automating processes that used to depend entirely on human hands. This transition is not just technological — it is redefining entire sectors of the global economy, from manufacturing to digital security, programmatic advertising, and industrial logistics.
One of the most recent catalysts for all this attention has been the advancement of the JEPA project from AMI Labs, which breathed new life into discussions about AI reasoning applied to the real world. JEPA represents a different approach to how machines learn to model the environment around them, going beyond simple token prediction and moving toward something that looks more like contextual understanding of physical and dynamic situations. This has enormous implications for robotics, because a robot that understands context makes far better decisions than one following a fixed script.
And when a technical topic starts moving real capital, the financial market quickly starts looking at companies already riding this wave with fresh eyes — whether they are involved in robotics, industrial automation, or AI-based analytics tools. Capital flowing into this sector has been growing steadily, and analysts who once overlooked smaller automation-related companies are now revisiting investment theses with much greater attention to the technical fundamentals behind each business.
That is exactly where this article comes in. We are going to talk about three stocks selected from a screener focused on AI innovation leaders, each taking a different path but all with meaningful exposure to the AI and automation theme:
- Tennant (TNC) — autonomous cleaning machines and the challenge of scaling robotics in the physical world
- Cellebrite DI (CLBT) — AI-powered digital forensics and a subscription growth story that is turning heads
- Viant Technology (DSP) — digital advertising with AI at its core and a potential undervaluation case in the market
None of these is an obvious bet, and that is precisely why it is worth understanding what each one is building — and where the real risks behind the hype actually lie. 🤖📊
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Tennant (TNC): Robotics on the Factory Floor, Literally
Tennant Company is not exactly the first name that comes to mind when someone talks about artificial intelligence and robotics, but maybe it should come up more often. This is a company that has been established for many years in the cleaning equipment business, manufacturing both manual and autonomous machines, along with cleaning technologies focused on sustainability and service solutions used in environments like warehouses, hospitals, schools, offices, and large public spaces. They operate across the Americas, the Europe, Middle East and Africa region, and Asia-Pacific.
From an operations standpoint, Tennant generates approximately $1.2 billion by designing, manufacturing, and selling equipment and solutions used to maintain non-residential surfaces worldwide. With a market cap around $1.47 billion, the company positions itself as a mid-cap name but with strategic relevance within the physical automation space.
What makes Tennant interesting for investors paying attention to automation is its expansion into autonomous equipment — scrubbers and sweepers — at precisely the moment when funding and research are shifting toward AI that interacts with the physical world. The company is targeting a significant revenue opportunity in the robotics segment while using share buybacks and equipment-as-a-service models to build a larger share of recurring revenue. This brings Tennant’s business model closer to something resembling a continuous service, where the value is not just in selling the equipment but in the embedded intelligence that keeps evolving after the robot leaves the factory.
Of course, there are real challenges here. Scaling robotics in the physical world is much harder than training a language model on cloud servers. On the risk side, Tennant deals with somewhat weaker profitability, debt that is not fully covered by operating cash flow, and intense price competition in regions like Asia-Pacific. Industrial environments are unpredictable, and ensuring that a robot operates reliably across different surface types, lighting conditions, and foot traffic requires engineering that is far more robust than it might seem at first glance. The big question for anyone following the company is how these AI-guided products will scale, and whether management can turn that into durable margins and consistent cash generation. 🏭🤖
Cellebrite DI (CLBT): AI Applied to Digital Forensics
Cellebrite DI offers software and services that help public safety agencies and corporations legally access, process, and analyze digital evidence from devices, apps, and cloud sources. The range of investigations spans from serious crimes like child exploitation and homicide to financial crimes and corporate security matters. This positioning places Cellebrite in a very specific and defensible niche, because its customers are not everyday consumers — they are governments, law enforcement agencies, and regulatory bodies that depend on reliable and auditable tools.
In terms of operations, the company generates approximately $496.4 million in revenue tied to software and internet services focused on forensic and digital investigation platforms. With a market cap of roughly $3.9 billion, Cellebrite operates right at the intersection of rising digital crime, AI-driven analytics, and governments moving their investigations to the cloud. That is why investors are closely watching its subscription-based forensic platforms and tools like Genesis, Guardian, and Pathfinder.
What has caught investment analysts’ attention is that the company is already profitable, with high-quality earnings, strong subscription growth and annual recurring revenue, plus a FedRAMP High authorization that could support deeper adoption at the U.S. federal level. This kind of credential is highly valued by the market because it opens doors to long-term contracts with government agencies that demand rigorous security and compliance standards.
Cellebrite’s risks, however, also deserve attention. The company has heavy exposure to federal budgets, making it vulnerable to budget cycles that are not always predictable, especially during periods of fiscal tightening. On top of that, there is an expectation baked into its price-to-earnings multiple, which is considered premium, and intense competition as mobile operating systems become increasingly difficult to access. The big long-term question is how Cellebrite will convert its early AI traction into durable and fairly valued growth while managing all of these pressures at the same time. 🔍📱
Viant Technology (DSP): Advertising with Artificial Intelligence at Its Core
Viant Technology operates a cloud-based demand-side platform, or DSP, that helps advertisers buy and measure digital ads across multiple channels, including connected TV, streaming audio, digital out-of-home, mobile, and desktop. To do this, the company uses tools like ViantAI, Household ID, IRIS_ID, and its own data platform — all designed to connect audiences and campaign performance in an integrated way. In a market where consumer attention is increasingly fragmented across streaming, social media, podcasts, and interactive content, having a platform that can navigate this ecosystem intelligently is a meaningful competitive advantage.
From an operations perspective, Viant generates approximately $362.1 million, with all reported revenue coming from the United States, and has a market cap near $866.9 million. The company positions itself at the intersection of AI, privacy-focused advertising, and connected TV. ViantAI already accounts for the majority of ad spend on the platform, and new products aim to further automate campaign workflows. This aligns with management’s view that large language models are a commodity, while proprietary data is the true competitive moat of the business.
The undervaluation argument around Viant’s stock comes from estimates suggesting the company is heavily discounted relative to fair value. Viant has been posting revenue growth, improving margins, and a large pipeline of new advertiser spend, which attracted investor attention following quite significant earnings growth over the past year. The platform has also been investing in connectivity with emerging channels like connected TV and digital audio — high-growth segments where demand for media buying automation is ramping up quickly.
But it is important not to ignore the headwinds Viant faces. There is heavy competition from larger DSPs, along with client concentration, insider selling, and one-time items that affected recent results. Because of that, Viant’s case may call for a more careful analysis than a quick read based solely on headlines. Overall, Viant represents a more contrarian play within the automation and AI space, with real upside potential if the undervaluation thesis plays out over the coming quarters. 📊💡
What These Three Cases Have in Common
Looking at all three companies together, what becomes clear is that artificial intelligence and automation are reaching businesses through very different paths — and that is exactly what makes this moment so interesting for anyone following the tech market. Tennant shows that physical robotics with embedded AI is already happening in sectors considered traditional. Cellebrite demonstrates that AI applied to specific, high-value problems like forensic investigation can create highly defensible businesses with growing recurring revenue. And Viant illustrates how automating complex processes like digital media buying can be the core of a scalable business model in massive markets.
None of these cases is a sure thing, and any honest analysis needs to acknowledge that tech investments carry inherent uncertainties, especially when the sector is transforming at an accelerated pace. What changes is the type of risk: it is no longer the risk of betting on technologies that do not yet exist, but the risk of execution on top of technologies that are already working and need to scale. That distinction matters for anyone building an investment thesis based on fundamentals rather than just hype narratives.
It is worth noting that these three stocks are just a starting point. Screeners focused on AI innovation leaders tend to surface dozens of other companies with equally compelling narratives tied to artificial intelligence and robotics, many of them still flying under most investors’ radar. The central point is to carefully analyze the specific catalysts, financial characteristics, and business stories that truly matter for your profile, focusing on the highest-conviction ideas within this theme.
An Important Note on Investment Analysis
A reminder that is always worth repeating: any commentary about stocks should be treated as general analysis based on historical data and analyst projections, not as a direct buy or sell recommendation. Everyone has different goals, timelines, and financial situations, and what makes sense for one investor may not make any sense for another. Market analyses do not always incorporate companies’ most recent announcements or all the relevant qualitative information, so the best approach is always to seek multiple sources before making any decision.
The movement that the JEPA project and other recent advances represent is one of artificial intelligence learning to act in the world — not just talk about it. The companies best positioned to capture that value over the coming years probably will not just be the most well-known ones, but those that have built the strongest technical and commercial foundations to turn this wave into real results. 🚀
