How Businesses Across Industries Are Actually Using AI Today

For years, AI has been sold as a magic bullet: automate anything, disrupt everything, and watch productivity skyrocket. But 2025 felt different. After waves of costly pilots and inflated promises, business leaders need to see where AI is actually making a difference. Many generative AI initiatives show promise in narrow experiments but fall short when it comes to measurable outcomes in broader business operations. 

What’s becoming clear is that AI delivers value only when it’s tied to how work actually gets done. The examples below show AI embedded into workflows, decision layers, and physical systems understanding, not as a headline feature, but as infrastructure that supports real outcomes.

AI as an Operational Backbone

For many organizations, AI adoption fails not because the technology is weak, but because it adds friction. Teams don’t have time to learn new systems or manage complex configurations. and tools that interrupt existing workflows rarely survive.

“Working professionals are busy people, so they need AI that is intuitive and that can be seamlessly incorporated into existing workflows. There is little time or desire to wrestle with complex configurations or spend weeks training teams,” said Nate MacLeitch, founder and CEO of QuickBlox.

Therefore, QuickBlox embeds AI directly into the communication infrastructure businesses already use. Built on SDKs and APIs originally designed to connect CRM systems with live chat and video, the platform now allows companies to integrate AI assistants into any app or website.

“With AI at the forefront, we enable businesses to build and integrate AI assistants into any app or website. These chatbots… can automate data collection, provide administrative support and quick answer responses, and generally automate personalized customer experiences.” The result is AI that scales customer interactions without forcing teams to adopt new tools. A pattern increasingly common among AI systems that deliver real operational value.

AI in Commerce, Lending, and Risk: Where Automation Ends and Judgment Begins

In commerce and sales operations, AI is often treated as a background tool, useful for reporting or automation, but disconnected from everyday decisions. Leadsales takes a more direct approach. Within its CRM for WhatsApp, AI is built into daily workflows to help teams spot issues faster, test changes quickly, and identify opportunities as conversations unfold. 

Rather than isolating AI in dashboards, Leadsales integrates it into the same tools used by sales, product, and engineering teams. This allows AI to analyze patterns across customer interactions and operational data, generating insights that humans can validate and act on, improving how teams prioritize leads and decide where attention is most needed. 

“AI becomes valuable when it’s treated as part of the workflow, not something running in the background,” said Roberto Peñacastro, CEO and co-founder of Leadsales. “The real shift happens when teams are clear about what AI should own, where it should support people, and where human judgment needs to stay in control.” In Leadsales’ case, AI doesn’t replace sales judgment but sharpens it, helping teams act faster without surrendering control.

That boundary between automation and accountability becomes even more critical in regulated environments. In insurance, property teams deal with thousands of documents, edge cases, and deadlines, and manual review doesn’t scale without introducing risk. Get Covered applies AI to absorb the volume and avoid operational bottlenecks that slow compliance and increase risk, particularly around insurance verification at scale. 

The platform uses machine learning, natural language processing, and optical character recognition to scan and verify Certificates of Insurance submitted by residents. AI flags incomplete or potentially fraudulent policies automatically, allowing property teams to identify compliance issues across thousands of units without manual review. When resident data is missing or invalid, machine learning models help match residents to compliant insurance plans, reducing gaps before they become liabilities.

Crucially, Get Covered draws a line between automation and accountability. No critical compliance decisions are made by AI alone, as licensed experts conduct the final review for policy disputes, claims, or non-compliance flags. The company’s rule is simple: AI can prioritize and surface risk, but people must own the final decision, especially in regulated environments. 

That same tension between automation and judgment shows up differently in retail, and not because decisions are more regulated, but because they’re constant. Pricing, inventory, and merchandising decisions are made continuously across thousands of SKUs, locations, and channels. Hence, AI has to function consistently under changing conditions; without replacing human judgment or producing recommendations, teams can’t realistically act on it. 

While AI may generate accurate forecasts or pricing recommendations, those insights often fail to reach the systems that drive commercial decisions. Nisum works with retailers at that decision layer, embedding AI into pricing, forecasting, inventory, and merchandising platforms so predictive insights flow directly into day-to-day operations. This means AI is influencing decisions such as when prices are adjusted, how inventory is allocated, and which products or offers are prioritized, not through one-off recommendations, but as part of ongoing commercial decision-making.

As AI becomes embedded in workflows and trusted with risk prioritization, it inevitably moves upstream, shaping not just how decisions are made but which lending and finance options are even presented in the first place. That’s where Yaacov Martin, CEO of The Jifiti Group, sees many banks falling behind—not on efficiency, but on discoverability. 

“Banks usually think about AI in terms of cutting costs, improving operational efficiency, and making better decisions. But it’s actually a two-layered issue. Beyond internal optimization, there’s a layer most banks don’t prioritize at all: AI for loan discovery and origination via AI agents,” says Martin.

As AI moves closer to revenue and credit decisions, the real risk isn’t automation but invisibility.

How AI Is Supporting Design and Experience at Scale

In experience-driven businesses, design isn’t limited to how things look. It’s defined by how systems behave under real conditions. AI is increasingly being used to scale both creative exploration and day-to-day execution without removing human judgment.

Decorilla uses AI to relieve pressure at the early stages of the design process, where iteration and visualization can slow teams down. “AI has become a practical part of how we help clients move from ideas to finished spaces faster,” said Agnieszka Wilk, CEO at Decorilla. “It helps streamline room planning, visualize layouts, and explore design directions more efficiently. AI could never replace designers; instead, it supports them.” 

AI-assisted tools allow Decorilla designers to generate and refine layouts, experiment with room configurations and test stylistic directions quickly. “Our designers are central to every project and use AI to reduce manual work and focus more time on creative decisions, personalization, and delivering human-centric designs.”

While Decorilla applies AI at the creative front end, other teams use it to improve how complex systems evolve after launch, where experience is shaped by execution, not aesthetics.

For creative teams whose work depends on insight and narrative, AI is proving most useful when it accelerates thinking while not influencing the outcome. psyagency, a communications and brand positioning agency for scaleups and startups, leverages AI for research and analysis while keeping content creation and strategy human-led.

“We use AI primarily to speed up research, scale content production, and data analytics. The agency applies it to scan media landscapes, map audience interests, draft initial versions of content, and streamline internal workflows,” said Katya Shcherbatenko, co-founder of psyagency. “Final strategy, messaging, and editorial decisions remain human-led, with AI used as a supporting tool rather than a replacement for expertise.”

Ribbon is on a mission to not only help credit unions automate inheritance processes but to do so while driving empathy at scale. Too often, the rush to automate comes at the cost of human connection, which is still a fundamental component of digital experiences, particularly in sensitive, high-stakes situations. Ribbon’s platform enables credit unions (and other financial institutions) to embed workflows that facilitate an empathy-first experience. For instance, an inheritor uploading the death certificate of the deceased member prompts the platform to automatically send flowers—an important touch at a key moment in an overall grueling experience. These small details make all the difference in how AI’s role is perceived in the real world, in real human experiences.

AI in the Physical World: Turning Observation Into Decisions

Much of today’s AI discussion focuses heavily on software and digital products. But some of the most practical applications are happening in the physical world, where systems are too large or complex to understand through manual observation alone. Here AI works best by making real-world activity visible at scale so people can make better decisions.

Automotus applies AI and computer vision to help cities understand what’s happening at the curb, one of the most complex and contested pieces of urban infrastructure. The platform analyzes video data from existing cameras to identify parking activity, curb usage, and near-miss incidents, giving transportation and planning teams a clearer picture of how streets function. 

“Cities have always struggled to understand how their streets are actually used day to day,” said Ganesh Vanama, computer vision engineer at Automotus. “AI allows us to analyze curb activity and safety patterns at a scale that simply isn’t possible manually, giving planners and transportation teams the evidence they need to make better, safer decisions.”

Rather than relying on periodic audits, Automotus helps cities review patterns across time and location, such as where illegal parking creates safety risks. This allows cities to make more informed decisions about enforcement, safety, and curb design based on evidence, not anecdote. 

KYRO AI, a digital operations platform, was built to help teams manage complex fieldwork and admin coordination across projects and compliance tasks. It centralizes project management, time tracking, forms and reporting, document control, and team communication into a single mobile-friendly system used by field crews and office staff.

“Field teams don’t need more software; they need focus,” said Hari Vasudevan, founder and CEO of KYRO AI.  “We use AI inside everyday workflows to surface risks, flag anomalies, and streamline tasks across projects, timesheets, forms, and documents. The result is earlier issue detection and faster execution without piling on process.”

Logistics also faces a similar challenge, as planners must make decisions across fleets and routes they can’t observe in real time. Transmetrics applies AI to planning and analytics for logistics operations, working with small and mid-sized fleet owners navigating fluctuating demand and tight margins. 

According to Asparuh Koev, co-founder and CEO of Transmetrics, many operators already have valuable data but struggle to connect it effectively. “Small logistics operators are sitting on valuable operational signals like timestamps, telematics, asset location and order patterns, but most don’t know how to link up this data as efficiently as they could.”

Rather than pushing full automation, Transmetrics emphasizes focused, explainable use cases. “The pragmatic path for small fleet owners looking to sustain long-term growth is to clean their data, define a single KPI and run a focused pilot that produces a repeatable business result,” Koev said.  

While predictive models can reduce uncertainty, he cautions against removing human judgment entirely: “Expect human oversight for a long time; AI should amplify good planners, not try to imitate perfect ones.”

As the hype around generative AI continues to settle, the focus is shifting from what AI could do to what it actually supports. The most successful deployments look less like transformation initiatives and more like infrastructure that’s reliable and integrated into the systems that keep businesses moving.

Article Co-Authored by Lily Blake