Enterprises investing in Workplace Mobility Can Survive Pandemics

4 minutes, 21 seconds read

Published on Mar 31, 2020

Updated on May 11, 2020

Nearly one-third of the global population is under coronavirus lockdown. Large-scale quarantines and travel restrictions are posing challenges for businesses to continue their operations. While workforce protection remains the top priority for enterprises, prolonged isolation is an eye-opener to adopt workplace mobility.

As the world continues to fight the pandemic, flatten the curve and try to maintain normalcy by working from home — teams everywhere are trying to stay productive so that daily operations can continue to some degree. But this is not an easy task. By working remotely, there are a lot of challenges especially in communication and connectivity, not to mention challenges with remaining productive throughout the day.

There was a time when mobility at work was considered a perk. Today, almost everyone, at some point, agrees that flexibility and liberty to work from home is essential. The 2020 Enterprise Mobility Trends Report anticipates that 42% (18.7 billion) of the global workforce will embrace mobility by 2020.

The need for workplace mobility

Workplace mobility empowers people to work from anywhere, at any time and from any device. It directly impacts employee productivity as well as the speed to execute business processes. How?

Dan Ariely, in his book Predictably Irrational, categorizes human behaviour in lines with the market and social norms. Market norms apply a monetary value to every transaction — salaries or payments against skill/talent. Whereas, social norms rely on the exchange of gifts, kindness, favour, etc. and is far away from any monetary transaction.

While stringent work policies tend to inculcate market norms (skills are calculated against salaries), flexibility instils social norms (empathy and concern). People are willing to do more on their free-will.

“In this 24/7 work environment social norms have a great advantage: they tend to make employees passionate, hardworking, flexible, and concerned. In a market where employees’ loyalty to their employers is often wilting, social norms are one of the best ways to make workers loyal, as well as motivated.”
– suggests Ariely

How apps and AI-driven mobility solutions for employees can keep businesses operationally afloat?

By 2025, the number of unique mobile subscribers is projected to reach 5.9 billion. Market researchers also anticipate that there’ll be nearly 25 billion IoT devices, most of which will comprise business-related connected devices. However, it’s not just handy devices that are enabling mobility at work. Technologies are also empowering businesses to readily adopt mobility.

For instance, Google has introduced a deck of enterprise mobility solutions. It provides cloud support to collaboration apps and management tools. Apart from G Suite, Google has invested in android and chrome platforms to support workplace mobility.

Workplace mobility apps and features

Many organizations require time logs to ensure overtime and bonuses. Apps like SecurTime provide a cloud-based time-attendance workforce management solution with real-time tracking. It seamlessly integrates with payroll/HRMS and biometric systems without any dependency on hardware.

When people work remotely, creating a virtual collaborative environment can concern businesses. While email is the channel for all formal communication, it’s usual to lose track of conversations in emails and messengers. To organize work and priorities at the team level, Slack and Trello are popular apps.

Organizations with in-house software development teams often face hassles while planning, tracking, resolving bugs & issues and releasing products. Jira — an agile project management tool helps organizations to track every phase of product development and team progress irrespective of their physical location.

AI-driven enterprise mobility solutions

Mobile devices and cloud platforms are making it easier for teams to collaborate and deliver. Moreover, employees save substantial time on travelling, which gives them time to indulge in activities that foster creativity.

Gartner predicts that by 2021, 40% of new enterprise applications will include AI technologies. So far, the adoption of AI was seen in consumer-facing operations to enhance customer experiences. Now, organizations are also focusing on enhancing employee experiences. For example, leading organizations are using NLP-powered chatbots for handling employee-queries regarding leave, work from home intimation, business-travel, etc.

Technology can equip employees with information at hand. AI solutions like Zelros provide instant information to Insurance sales advisors regarding products, clients, etc.

AI-powered applications are becoming more human-centred and they can execute commands without touching/pressing a button. For example, with gesture recognition technology and voice user interface, simple tasks like sharing a file, reading a report, etc. can be done while driving, spending time with kids, evening walks, etc. removing dependencies that delay work.

The use of AI is evolving to automatically prioritize problems and send notifications to the concerned departments. SVM (Support Vector Machine) and CNN (Convolutional Neural Network) are machine learning algorithms for building classification models.

The bottom line

While one can prevent wars, natural calamities and pandemics are unavoidable. In the current context, the heat of the Corona outbreak is severely impacting industries including aviation, e-commerce, education, tourism, entertainment, hospitality, electronics, consumer and luxury goods. Businesses are thriving to remain operationally afloat.

Embracing mobility at work today can prepare organizations for tomorrow’s pandemic resilience.

Mantra Labs is helping enterprises invest in building their pandemic resilience by planning and scaling their mobility infrastructures, and enable greater use of mobility as a service. Talk to us today to know how we can help you, or reach out to us at hello@mantralabsglobal.com.

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled

Enterprises investing in Workplace Mobility Can Survive Pandemics

The need for workplace mobility