How agentic AI will transform mobile apps and field operations

Many first-generation mobile applications were direct adaptations of their web equivalents. Poor user experiences, slow performance, and low adoption forced organizations to rethink their business objectives and implementation strategies. As development tools improved, organizations adopted a “mobile first” mindset and designed phone and tablet apps for specific user personas and job contexts.

Flash-forward to today, and we see a similar trend. As agentic AI capabilities are adopted, they will usher in new mobile AI experiences. Construction, manufacturing, healthcare, and other industries with significant field operations will benefit from mobile AI agents and the resulting operational agility. 

Are you ready for mobile AI agents?

Digital trailblazers should review their current mobile applications and sprawl of SaaS tools to determine which ones might benefit the most from AI capabilities and agentic AI experiences. Considerations include;

The overall business value of the application supporting primary field-operation workflows.

The number of field operators and touchpoints with back-office staff.

The data volumes and timeliness factors in field-operation decision-making.

Mission-critical workflows performed by more people, in real-time, and with higher data volumes are indicators of where AI can provide business value. A second opportunity is to consider the graywork and operations that lack digitized workflows and tools to connect field and back-office operations.

“If you are considering adding AI to your field operations workflows, you should first make sure you can connect field teams to the office and vice versa,” says Matthew Frye, FastField account director at Quickbase. “Fixing that connection and centralizing data in one operations platform is critical if you’re introducing AI, providing a solid foundation of shared information to empower smarter decision-making onsite.”

Technology leaders should also review their architecture and assess their technical debt and readiness for integrating AI capabilities. Recent research shows that 92% of manufacturers say outdated infrastructure critically hinders their generative AI initiatives, and fewer than half have conducted a full-scale infrastructure readiness assessment.

“Legacy systems, cybersecurity vulnerabilities, and the lack of structured governance can hinder progress,” says Prasoon Saxena, co-head of products industries of NTT DATA. “Organizations can unlock genAI’s transformative potential while protecting operations and security by focusing on infrastructure readiness, upskilling, and ethical AI practices.”

How mobile AI agents will redefine the user experience

Mobile apps for the field usually consist of forms, checklists, access to information, dashboards, and reports. They can inform field operations about work that needs to be done, answer implementation questions, and provide information to planning and scheduling teams working at the office.

“Mobile UX is entering a new era—one where we don’t navigate software, we collaborate with it,” says Christian Buckner, SVP of analytics and IoT, Altair. “Forms, buttons, and static menus were built for users to adapt to machines, and AI assistants flip that by adapting to us.”

Instead of menus and structured workflows, mobile AI apps will include prompt interfaces and personalized data visualizations. AI will forecast what the end-user needs to know based on their current job, and prompt interfaces will simplify both querying for information and providing job updates.  

“As assistants become the front door to software, field apps must stop mimicking desktops and start acting like teammates,” adds Buckner. “These assistants won’t just fetch data—they’ll help people think, summarize complex technical manuals, hypothesize root causes, suggest next-best actions, and even brainstorm fixes. In the field, that means fewer calls back to headquarters, less time searching for answers, and more informed decisions made on the spot.”

Use cases for agentic AI in field operations

Forrest Zeisler, co-founder & CTO of Jobber, says, “AI agents can’t cut down a tree or fix your plumbing, but they can answer a phone, prepare a quote, follow up on outstanding invoices, and do most admin work so field workers can focus on what they do best.”

While one use case for agentic AI aims to reduce administrative work, others will help field operations shift from reactive to more proactive types of work.

“Capabilities like real-time contextual guidance, predictive task sequencing, and language processing for multilingual support now make it possible for mobile apps to offer proactive and personalized support to workers on the ground,” says Paul Wnek, CEO and founder of ExpandAP. “These genAI-based applications aren’t just reactive—they’re anticipatory for workers to see the full picture of what’s happening in real-time to make the most informed decisions they can at a time. Mobile apps must now offer dynamic workflows that adjust based on conditions and user input in real-time, delivering a higher-value CX that boosts decision-making and productivity.”

Swapnil Jain, co-founder and CEO at Observe.AI, says one of the most powerful capabilities lies in predictive assistance driven by geo-contextual data, historical records, and sensor inputs. “Imagine a utility technician approaching a remote site and using AI to develop a predictive user experience. A genAI-powered mobile agent can proactively surface known issues in the area, recommend the safest route based on weather and terrain, pull up relevant maintenance history, and guide inspection procedures before the worker asks.”

Other use cases focus on the need for speed and the impact of making the wrong decisions. These agents require comprehensive knowledge bases and testing to ensure that they lead to valid and better decision-making.

“In high-stakes industries such as healthcare or financial services, where seconds matter, genAI raises safety standards and operational speed, leading to fewer mistakes and faster decisions,” says John S. Kim, CEO and co-founder of Sendbird. “But to avoid ungrounded or inaccurate responses, it’s important to pair genAI with domain-specific data and enterprise-grade transparency.”

Advanced capabilities with AI agents, wearables, and 5G

Some field operations perform a set of common tasks at different locations and must adapt to local conditions and requirements. For field operations that perform a wider variety of work types in highly differentiated conditions, AI agentic experiences partner with field engineers to provide real-time information and guidance. 

“We’re seeing a rise in agentic AI being used for troubleshooting, whereby the agent is guiding the user to a resolution and instantly surfacing relevant information and operational recommendations for the field worker to implement,” says Francois Laborie, EVP of strategic projects at Cognite. “We’ve also seen a rise in ambient agents, working quietly in the background, performing checks and evaluations, alerting the user when appropriate. This enables heavy industries to predict equipment failures, optimize maintenance schedules, and manage risk.”

Learning and knowledge management applications go from feasible to operational differentiators when field operators have higher-functioning tablets, connect to 5G networks, and utilize AR/VR and other wearable devices.

“GenAI-powered agents will elevate the capabilities of field technicians by providing multimodal guidance through text, voice, video tutorials, and AR overlays directly on equipment,” says Artem Kroupenev, VP of strategy at Augury. “By democratizing expertise and seamlessly transferring knowledge, these agents enable every technician to operate at expert-level proficiency. Despite early adoption risks like reliability and dependency, the benefits of dramatically reduced downtime, improved safety, and lower operational costs make these agents essential for competitive field operations.”

Risks in rolling out AI agents

While genAI, virtual assistants, and AI agents generate significant excitement, IT leaders must have a pragmatic approach when transforming mobile field applications into agentic future of work experiences. Areas like time tracking, communications, and job reporting with minimal industry-specific business needs are early use cases that will appear in vendor applications.

Even when SaaS platforms announce agentic experiences, data teams should evaluate whether data volume and quality on the platform are sufficient to support the AI models.

“Without complete, accurate data, agents can produce unreliable outputs, and their out-of-the-box capabilities rarely align with the demands of complex environments that require contextual models tailored to specific workflows and operational needs,” says Ramprakash Ramamoorthy, director of AI research at ManageEngine.

Additional review is needed when there are data privacy and regulatory concerns. IT leaders should update their data and AI governance frameworks to cover field operations use cases that require sharing sensitive data with third-party LLMs and AI agents.

“Feeding sensitive data into genAI applications can lead to leaks, licensing conflicts, IP exposure, and regulatory violations,” says Viswesh Ananthakrishnan, co-founder and VP of product at Aurascape. “The stakes are high for companies that handle sensitive customer data in industries such as healthcare, where they have the most to lose, and the results could have paramount negative CX repercussions if data is mishandled. Critical infrastructure like construction and energy facilities are further examples where the room for error is slim.”

Information security and infrastructure teams should also reassess vendors and review their internal infrastructure to support agentic capabilities, especially those requiring access to more sensitive information.

“Early adoption carries data security risks and infrastructure challenges,” says Luiz Domingos, CTO and head of large enterprise R&D at Mitel. “To minimize disruption, organizations must invest in cybersecurity, scalable systems, governance frameworks, and phased rollouts.”

Industry-specific agentic AI mobile experiences require more data, testing, and investment. Nicholas Muy, CISO of Scrut Automation, says, “Vertical AI agents unlock massive efficiency but need context and structure to unlock them, otherwise, you have agents that will get in the way and slow you down.”

To gain significant value with industry-specific capabilities, experts suggest blue-sky thinking new mobile AI experiences rather than just bolting AI on existing mobile applications.

“Agentic AI in industries like manufacturing, construction, healthcare, and energy will enhance decisions, boost safety, and streamline tasks, but early adopters must be cautious because AI errors and security gaps are real risks,” says Anand Subbaraj, CEO of Zuper. “Winners will rethink workflows, combining human expertise with AI autonomy to build smarter, safer, and stronger operations.”

The best place to start is to walk in the shoes of a field engineer. Observe their work and how they use mobile applications today. Buy them lunch and come prepared with questions. Ask about how AI can provide value, then learn how to present the concepts without drumming up fears of job loss. The most promising opportunities should simplify work for field engineers while allowing them to deliver more value to customers.