Microsoft Launches AI Agents to Help Workers Use AI!

Rohit Mahajan

Can AI help us find better ways to use AI? The answer is apparently “yes!”

Microsoft recently announced that it has launched a range of AI agents designed to provide workers with artificial intelligence assistance during meetings by creating agendas, taking notes, and more. The new “Facilitator agent” is among several AI agents the company said are coming to Teams and other Microsoft 365 apps.

In a press release discussing the launch of their new agentic AI “Copilots,” Microsoft 365 and Copilot vice president, Nicole Herskowitz, revealed that the new collaborative agents are designed to enhance work across Microsoft Teams, SharePoint, and Viva Engage. She said the agents help groups coordinate, communicate, and execute more clearly and efficiently.

Herskowitz noted that each AI agent is tailored to the context of the group and offers unique ways for groups to collaborate in channels, meetings, and communities in Teams, libraries, and SharePoint sites. She noted that the “Facilitator” agent in particular comes in handy when planning meetings in Teams to discuss a project, since it prepares agendas for the meeting. The AI agent also takes notes during the meeting, keeps the discussion on track, notes decisions, and converts them into owned actions with follow-ups.”

The Copilot VP added that Teams will support an open ecosystem of partner-built agents. The Model Context Protocol (MCP) will also help those agents collaborate seamlessly with native Teams agents by sharing context and invoking each other’s tools within the same workflow. She also confirmed that, as a company, Microsoft will continue to invest in more capabilities that enable agents in channels to communicate with other agents.

Herskowitz revealed that the new AI agents are now available to all Microsoft 365 Copilot users in public preview. Facilitator for Teams meetings is also available. She believes the organizations can confidently adopt the agents because their experiences are built on the same security, compliance, and privacy standards under Microsoft 365.

Microsoft argued that the newly designed Workflows experience in Teams helps users by making automation easier and more accessible. The firm will allow users to use newly AI-powered workflow templates to automate tasks with Copilot or their channel’s agent with no coding experience required.

As the launch of these new AI agents proves, AI tools can act as translators, coaches, and collaborators, helping people bridge the gap between complex technology and everyday use. By explaining concepts in plain language, guiding effective use, personalizing support, and encouraging critical thinking, AI empowers humans to not only use these tools more confidently but also to understand their strengths and limits. The real value is in turning AI from a mysterious “black box” into a practical, trusted partner that enhances productivity, creativity, and decision-making in both work and life.

BigRio Understands the Transformative Impact of Agentic AI

At BigRio, we agree that Microsoft is spot on with the idea of leveraging AI to help humans better use and adapt to the use of AI. We have seen firsthand how you can transform the way your customers engage, automate your operations, and elevate the customer experience with AI Agents. Our tailored Agentic AI and AI-voice agent solutions are crafted specifically for the healthcare, BFSI, media, e-commerce, and education industries, providing personalized interactions, enhanced efficiency, and measurable outcomes.

Whether you’re looking to automate scheduling, improving call center responsiveness, or simply understanding better and leveraging Agentic AI and Generative AI Tools, BigRio can help you implement an AI strategy that drives better outcomes.

Please join us for our next informative Webinar,  Designing, Deploying, and Scaling Voice Agents in the Healthcare Ecosystem, to be held on Wednesday,  November 5, 2025. Click here to register.

Why AI Literacy Matters for All Healthcare Staff: Clinical and Non-Clinical

Rohit Mahajan

A recent study published by the World Economic Forum (WEF) says that to overcome challenges hindering meaningful adoption of AI and to realize AI’s full potential in healthcare, one of the six pivotal shifts for public and private leaders proposed by the report is equipping health leaders and clinicians with the necessary AI literacy to make informed strategic decisions that align with their vision.

A lack of fundamental knowledge about AI among leaders and decision-makers sparks societal concerns and hinders progress with AI. A comprehensive upskilling of leaders helps them grasp AI’s strategic purposes and foundational digital principles more effectively. Thus, enabling informed decision-making, effectively interrogating AI solutions, and building trust across the healthcare ecosystem. An enhanced AI literacy can also help health workers and patients trust and adopt AI tools, showing that the technology is here to complement, not replace, healthcare professionals.

The rapid transformation of healthcare and the life sciences by AI, encompasses clinical decision-making, drug discovery, and patient engagement, requires more than just technology; it necessitates an AI-literate workforce across executive leadership, technical teams, clinicians, researchers, and business functions.

In the following section, I discuss why introducing AI literacy among the clinical and non-clinical workforce matters:

 AI Literacy for Clinical Workforce

Clinical workforce, including doctors, nurses, and technicians, requires AI literacy to ensure patient safety, deliver high-quality care, and adapt to the evolving medical technology. Key reasons include:

  • Enhancing diagnostic accuracy: AI algorithms can analyze vast medical imaging datasets, helping clinicians detect subtle patterns that may be imperceptible to the human eye. AI-literate clinicians can interpret and utilize these AI-generated insights more effectively to enhance diagnostic precision and efficiency.
  • Strengthening clinical decision-making: AI-powered clinical decision support systems provide evidence-based recommendations and real-time alerts for patients at risk. An understanding of how these systems arrive at their conclusions allows clinicians to make informed decisions and better personalize treatment plans.
  • Reducing burnout: AI tools automate time-consuming administrative tasks, such as documentation, transcription, and EHR management. AI-driven automations free up clinicians to focus on direct patient care, potentially reducing burnout and improving job satisfaction.
  • Maintaining ethical standards: Clinical staff must recognize the potential for bias in AI algorithms, which can perpetuate or even amplify existing health disparities. AI literacy enables them to assess tools critically, protect patient privacy, and ensure fair, equitable care for all populations.
  • Ensuring safety and accountability: Over-reliance on AI without understanding its limitations can lead to diagnostic errors and patient harm. An AI-literate workforce is better equipped to oversee AI applications, check for system failures or “hallucinations,” and ensure accountability for their use.

 AI literacy for non-clinical workforce

Non-clinical workforce, including administrative assistants, IT personnel, and managers, can use AI to automate routine processes, manage resources efficiently, and securely handle sensitive data in the following manners:

  • Streamlining operations: AI-driven systems can efficiently manage scheduling, process claims, and handle billing, thereby reducing manual workloads and minimizing errors. Non-clinical workers must understand how these systems work to implement and effectively oversee them.
  • Enhancing financial performance: Utilize AI for tasks such as revenue cycle management and fraud detection. A literate team can leverage AI analytics to identify inefficiencies, control costs, and maximize revenue, ultimately contributing to the organization’s financial health.
  • Enhancing data security: AI systems manage and process vast amounts of sensitive patient information. Non-clinical staff with AI literacy can understand the security risks and are equipped to protect data through access controls, encryption, and compliance with regulations such as HIPAA, SoC2, FHIR, and more.
  • Optimizing resource allocation: AI can help forecast patient demand and staffing needs by analyzing historical data and trends. Administrative and managerial staff should be AI literate to effectively use these predictive tools for scheduling and resource management, thereby preventing both under- and over-staffing.
  • Enabling interdisciplinary collaboration: The successful implementation of AI requires collaboration between technical developers and health system staff. AI-literate non-clinical employees can better communicate organizational needs and clinical insights to technical teams, ensuring that AI solutions are user-friendly and clinically relevant.

Why AI Literacy Matters in Healthcare

For AI to fulfill its promise as a transformative partner in healthcare, clinical and non-clinical professionals must go beyond simply using the technology without really understanding it. Actual adoption requires a workforce that recognizes both the strengths and the limitations of AI, and knows how to apply it responsibly, ethically, and effectively.

Building AI literacy across healthcare organizations is not just a technical imperative; it’s a cultural one. When executives, clinicians, researchers, technical teams, and business leaders share a common understanding of AI and the business goals of using it, the results go beyond improved workflows. They foster innovation, enhance patient experiences, and ensure that AI is embedded in a way that is trustworthy and sustainable.

An AI-literate workforce enables:

  • A shared foundation of understanding across roles and disciplines.
  • Responsible integration of AI into areas such as genomics, clinical workflows, and business operations.
  • Empowered technical teams who can leverage modern AI coding tools for rapid innovation.
  • A culture of experimentation through collaborative forums like hackathons and challenges.
  • Continuous knowledge-sharing that keeps pace with AI’s rapid evolution.

AI literacy is not about teaching everyone to code; it is about equipping healthcare professionals to ask the right questions, make informed decisions, and embrace AI as a partner in improving outcomes. In this way, literacy becomes the foundation for trust, adoption, and transformation.

Voice Agents That Work: Insights and Takeaways from the Latest Webinar

The rapid evolution of AI has fundamentally changed the dynamics of customer interactions across industries. In the recent “Voice Agents That Work” webinar hosted by Big Rio and Demo Consulting, industry leaders explored the present and future of agentic AI—particularly voice agents—as an inflection point for business transformation. The session brought together technical minds, healthcare strategists, and practitioners eager to understand not just how voice agents work, but how they deliver real impact in high-stakes environments like healthcare, finance, retail, education, and beyond.

Why Voice Agents and Why Now?

Opening the discussion, Rohit Mahajan, CEO of Big Rio, set the stage by highlighting the exponential leaps in agentic AI—AI agents that, unlike bots, are autonomous, context-aware, and capable of decision-making. As conversational AI transitions from rigid, menu-driven IVR and basic chatbots to nuanced, empathetic, learning-driven agents, we stand at an inflection point: “This is no longer a pilot technology,” Mahajan emphasized, “with our platform partner, we are handling fifty million calls per year, at scale, across clients.”

The industry landscape is changing almost weekly, but one message is clear: voice agents are moving from novelty to necessity, particularly in healthcare, where ambient listening and real-world integration are driving measurable ROI.

Key Concepts: Agentic AI and Voice Agents

The webinar demystified the core differences between traditional bots and agentic AI. Key characteristics of modern voice agents include:

Context-awareness: Unlike bots, these agents retain both short- and long-term memory, enabling coherent multi-turn conversations and the capacity to learn from every interaction.

Autonomy: Voice agents use large language models (LLMs) and can independently interact with external systems (e.g., EHRs, CRMs) to execute tasks without human assistance.

Natural conversation: Harnessing advanced speech-to-text (STT) and text-to-speech (TTS) technology, these agents deliver real-time, dynamic, and empathetic voice interactions that approach human fluency.

Continuous learning: Real-world data—millions of calls—feeds back into system prompts, allowing for ongoing refinement.

A thought-provoking quote from Nvidia’s Jensen Huang, cited during the session, captures the shift: “The hottest new programming language is now human,” highlighting the move from English-based commands to fully natural speech interfaces.

Real-World Use Cases Across Industries

The session showcased live demos illustrating the versatility of voice agents and the breadth of use cases already in production.

Healthcare

Appointment scheduling and reminders: Agents interface with hospital scheduling systems, perform outbound calls, send reminders, and confirm bookings—a high-value, in-production use case that has demonstrably improved patient engagement.

Prescription management: Agents integrated with Epic MyChart can refill patient prescriptions including for minor dependents—entirely through natural language, without any human operator intervention.

Pre-authorizations: Autonomous agents call payer portals, gather data, and manage insurance interactions, saving clinicians and admin staff significant time.

Outside Healthcare

Higher education: 24/7 multilingual voice assistants help students with registrations, deadlines, and events—accessible channels “from anywhere, including for international students.”

Financial advisory: Hyper-personalized, real-time wealth management guidance is made possible through agents that analyze financial history and external data, improving trust and client satisfaction.

Human Resources: Talent acquisition AI handles resume screening, candidate interviews, and scheduling, reducing bias and freeing up HR for strategic work.

E-commerce: Shopify-integrated help agents can check order statuses, manage refunds, and provide policy guidance tailored to each vendor’s unique database.

Fan engagement in sports: Agents like those in La Liga help deliver live match data, stats, and schedules, transforming fan interaction from passive to highly personalized real-time experiences.

Implementation: From Discovery to Pilot and Production

A recurring theme was the rapid pace at which organizations can move from ideation to deployment with today’s frameworks. Building and scaling voice agents is no longer an arduous, multi-year effort—platforms enable pilots to go live in as little as 4–12 weeks, once a use case is selected and integrations are defined.

Key Steps Include

Discovery phase: Analyzing workflows, pain points, and integration needs; generating a summary report and pilot roadmap.

Pilot project: Selecting one high-impact use case, rapid deployment, and tight feedback loops on performance metrics.

Scalable rollout: Moving from pilot to production, often across multiple workflows and channels—helped by frameworks condensing traditional “builds” into modular, pre-packaged components.

This shift enables enterprises to “rebuild workflows with agent enablement,” delivering much more dramatic gains than simply bolting an agent onto legacy processes. The session cited productivity improvements of up to 60–90% in task resolution time and up to 80% automation of Level 1 incidents, compared to only modest benefits from traditional chatbots.

Industry Trends and Analyst Insights

Several key statistics underscored the session:

  • By end of 2025, 25% of enterprises are projected to have deployed AI agents, rising to 50% by 2027.
  • 51% of companies already have some production AI agents, with mid-size companies leading adoption due to greater nimbleness.
  • 84% of organizations plan to further increase investment in voice AI over the next year.
  • Barriers: Top concerns include performance quality and latency (noted by 32% of respondents), yet platform advances are closing these gaps quickly.

Participants’ Questions: Guardrails, Integration, and ROI

The Q&A session revealed deep engagement and practical concerns from attendees:

Guardrails on AI behavior: Questions focused on keeping agents “on task” and compliant. The response highlighted detailed prompt engineering and Retrieval Augmented Generation (RAG) as methods to tightly constrain agent outputs. “Effectiveness on the guardrails for these agents is pretty high,” presenters noted.

Reporting and validation: Every call is recorded and can be rated, with feedback loops built for model improvement. Reporting tools are evolving, with continuous monitoring as a standard.

Integration: Integration with core platforms (Epic for EHR, Salesforce, HubSpot, telephony) is a key value driver and can be done out-of-the-box or with modest custom work within weeks.

Cost: Agentic AI is now well below $0.15 per minute for usage, and total implementation costs are far lower than just a few years ago. The major value comes from tight system integration and workflow reengineering.

Use Case Selection: To maximize ROI, organizations are advised to “pick the workflow that moves the needle most” on efficiency, customer experience, or cost reduction.

Conclusion: Don’t Bolt On—Reinvent with Agents

The ultimate message: agentic voice AI isn’t just an incremental tool or a bot with a new face—it’s a paradigm shift in how organizations interact with clients, patients, and customers. Reinventing processes, not just “bolting on” AI, is the true path to transformation and ROI.

The time to experiment, pilot, and scale is now. As one participant remarked, the consulting and best practices offered by experienced teams make this journey repeatable and measurable—turning this cutting-edge technology into a mainstream enterprise advantage.

For those interested, feedback and follow-up will guide future sessions and increasingly sophisticated deployments into production.

The Top 5 Real-World Problems Agentic Voice Agents Can Solve in Healthcare

Agentic AI and, more specifically, AI Voice agents are having a transformative impact on healthcare. From bringing real-time reasoning to clinical and operational workflows to reducing physician burnout, AI Agents are changing healthcare for the better and improving patient experiences.

Why now?

  • Conversational models are becoming more affordable and widely available (e.g., OpenAI cut real-time API costs by up to 87.5% in late 2024).
  • Enterprises are rapidly deploying them – 25% of enterprises will use AI agents by end of 2025, growing to 50% by 2027 (Deloitte).
  • In healthcare, voice AI is moving from “nice-to-have” to foundational for digital transformation (Deepgram State of Voice AI, 2025).

There are five real-world problems faced by the healthcare industry that AI agents can – or already are – solving.

Administrative Overload and Staff Burnout

Healthcare professionals, particularly physicians, often face significant stress and burnout due to the heavy administrative burden, spending a substantial portion of their time on tasks such as paperwork, documentation, scheduling, and billing. AI agents can automate and streamline many of these tasks, freeing up valuable time for medical staff to focus on direct patient care and reducing the cognitive load associated with repetitive administrative duties. For instance, AI-powered systems can automatically transcribe patient conversations into EHRs in real time, generate discharge summaries and clinical notes, handle appointment scheduling,      reminders, and billing queries, and even assist with insurance pre-authorizations and claims processing.

Diagnostic Inaccuracies and Delays

Traditional diagnostic methods can be time-consuming and prone to human error or variations in expertise. AI agents, particularly those trained on vast datasets of medical images and patient information, can enhance diagnostic accuracy and speed. For example, AI systems can analyze X-rays, MRIs, and CT scans to detect subtle abnormalities that might be missed by human observers, leading to earlier disease detection and more effective treatments. They can also act as powerful tools for identifying diseases like cancer and diabetic retinopathy and also deliver AI-driven recommendations to care teams.

Voice agents, combined with GenAI and multimodal models, enable real-time feedback loops—helping physicians act faster and with greater accuracy.

Gaps in Care Coordination and Patient Engagement

Traditional care models often suffer from fragmented communication and a lack of consistent follow-up, which can negatively impact patient outcomes. AI agents can enhance care coordination by acting as digital companions that provide continuous patient monitoring, engage in personalized communication (like medication reminders) and symptom tracking, and alert care teams to potential issues or deviations from treatment plans, and also offer multilingual, natural language engagement.      This helps bridge the gap between in-person appointments and empowers patients to be more proactive in managing their health.

Inefficient Hospital Operations and Resource Allocation

Hospitals frequently struggle with operational inefficiencies, such as overcrowding, suboptimal resource allocation (like managing ICU beds or surgical suites), and supply chain management challenges. AI agents can help address these issues by analyzing real-time data to predict patient flow, optimize staff scheduling based on patient needs and credentials, manage inventory of medical supplies and automate equipment monitoring,      assist with predictive maintenance for equipment, reduce call center load by handling navigation and FAQs, and provide real-time multilingual support for patient navigation.      This leads to smoother operations, reduced wait times, and a more efficient use of resources.

Challenges in Drug Discovery and Personalized Medicine

The process of discovering and developing new drugs is typically lengthy and costly. AI agents can accelerate this process by analyzing vast datasets of chemical structures and clinical trial information to identify potential drug candidates and predict their effectiveness. Furthermore, by analyzing patient genetic and lifestyle data, AI agents can support personalized medicine by helping physicians tailor treatment plans, predict how individuals will respond to medications (pharmacogenomics), and even recommend alternative ways to use existing drugs. AI agents also support clinical trial recruitment, protocol optimization, and compliance monitoring.

Pharma companies are piloting autonomous voice agents for prior authorization, drug substitution, and patient adherence monitoring—saving both time and cost in treatment delivery

Bottom Line: Voice Agents Are Shaping the Future of Healthcare

Next-generation voice assistants are set to revolutionize healthcare by providing intuitive, real-time, and emotionally aware interactions. From virtual nurses and mental health support to chronic disease management and emergency response, these AI-powered assistants are enhancing patient experiences and improving clinical efficiency.

  • Voice agents can streamline patient interactions, reduce call center load, and provide 24/7 engagement in healthcare and pharma.
  • The underlying advances in natural language understanding and generative AI have made voice agents more intelligent and less reliant on scripted flows.
  • Enterprises are rapidly deploying voice agents across functions like customer support, scheduling, and complex operations to drive operational efficiency and enhance user experience.
  • Voice assistants are no longer standalone technologies; they are integrated with computer vision, biometric data, and IoT devices to provide a holistic experience.
  • In smart hospitals, for example, a voice assistant could monitor a patient’s vitals, provide medication reminders, and alert doctors if any anomalies are detected.

Healthcare is under pressure to do more with less. Agentic voice agents offer a proven way to deliver better care, lower costs, and more satisfied patients—while helping clinicians reclaim their time.

What Makes a Voice Agent Truly Agentic?

Agentic AI and AI Voice Agents seem to be the next big thing in Generative AI for business. Let’s go beyond the buzz and explain exactly what agentic AI is – and isn’t.

Agentic AI refers to artificial intelligence systems that can act autonomously to achieve specific goals, making decisions and taking actions without constant human supervision. Unlike traditional AI or software that follows pre-defined rules or requires step-by-step guidance, agentic AI can reason, adapt, and even learn from its environment to optimize its performance and achieve complex tasks.

Here are some more details:

Instead of waiting for instructions or operating within a fixed command-response structure, an agentic AI:

  • Understands goals, not just queries.
  • Takes initiative based on context.
  • Acts independently to fulfill objectives.
  • Learns and improves over time.

While traditional AI is about prediction and automation, agentic AI is about intelligent, interactive execution.

What Is an Agentic Voice Agent?

An agentic voice agent is a voice-enabled digital assistant that goes beyond scripted voice interaction. It combines speech recognition, natural language understanding (NLU), planning, and autonomous action to perform complex tasks—often across multiple steps or systems—without constant human prompting.

A truly agentic voice agent can:

  • Take a request and translate it into a full task chain
  • (e.g., not just “book me a flight,” but researching flight options, booking, confirming via email, and updating your calendar).
  • Adapt its interaction based on new context or feedback
  • (e.g., understanding that your plans changed and canceling a prior booking without being explicitly told).
  • Operate proactively
  • (e.g., alerting you to better options, flagging issues, or reminding you to follow up).

Rather than being a passive intermediary between you and a dataset, an agentic voice agent behaves like a delegated team member—thinking, planning, and executing.

Agentic vs. Traditional AI Assistants (Chatbots)

The difference between traditional assistants (like Alexa, Siri, or most IVR systems) and agentic AI is stark.

Feature Traditional Chatbot/Voice Assistant Agentic Voice Agent
Initiative Waits for command Can act proactively
Context Awareness Limited memory/context Maintains session and task continuity
Goal Understanding Keyword-driven Objective-oriented
Task Handling One-shot commands Multi-step, multi-system execution
Learning Static or scripted Continuously adaptive

Put simply, traditional assistants respond, while agentic agents respond and act. In real-world terms, a chatbot might tell you your bank balance. An agentic voice agent might alert you to an unusual transaction, suggest transferring funds, and carry out the transfer upon your verbal approval.

How Agentic AI Applies to Voice Assistants

Voice interfaces are inherently human-centered, but for years, they’ve been held back by narrow capabilities. Adding agentic AI to voice technology changes the game by transforming these assistants from glorified search engines into interactive collaborators.

Here’s how that works in practice:

  1. Semantic Understanding: Agentic voice agents use LLMs and context tracking to interpret complex, conversational input.
  2. Task Decomposition: Once a goal is understood, the agent breaks it into sub-tasks (e.g., “Find best local HVAC repair → check reviews → book appointment → send confirmation”).
  3. System Integration: It interacts with APIs, calendars, CRMs, booking systems, and more to execute actions.
  4. Error Recovery & Clarification: If the user’s intent is ambiguous, the agent can ask clarifying questions or adapt on the fly.
  5. Proactivity: It monitors external triggers—like changes in inventory or new medical lab results—and can prompt the user when action is needed.

In short, the addition of agentic reasoning to natural voice interaction makes these systems more fluid, intuitive, and useful—approaching the way a real human assistant might behave.

What Industries Are Benefiting from Agentic Voice Agents?

Several sectors are already beginning to unlock the power of agentic voice agents:

Healthcare

Voice agents can intake patient data, triage symptoms, book appointments, issue reminders, and follow up post-visit. They reduce the burden on administrative staff and enable continuous patient engagement.

  • Agentic benefit: Voice agents can proactively flag a missed prescription refill or suggest a telehealth follow-up based on lab results.

Finance

Agentic voice agents help users manage finances, detect anomalies, plan budgets, or even execute trades.

  • Agentic benefit: Instead of asking for your balance, a voice agent can detect unusual spending and recommend fraud protection steps.

Customer Support

Voice agents handle more complex queries than static IVRs, offering end-to-end resolution without transferring to a human.

  • Agentic benefit: It doesn’t just say “Let me connect you to billing”—it resolves your billing issue directly, and sends a follow-up summary.

E-Commerce

Voice agents act as smart shoppers—managing orders, returns, and preferences. They can even recommend products based on past purchases.

  • Agentic benefit: “Order me more detergent” turns into “I’ve reordered the same brand, but there’s a 10% off deal on a better one—should I get that instead?”

Travel and Hospitality

From itinerary management to last-minute rebooking, voice agents reduce friction in customer service.

  • Agentic benefit: If your flight is delayed, the voice agent can suggest new routes, update your hotel, and notify your contacts—all without being asked.

Final Thoughts: From Assistant to Agent

The difference between a traditional voice assistant and an agentic one is like the difference between a calculator and a personal concierge. One performs basic functions on demand. The other anticipates your needs, navigates complex systems, and acts with intelligent autonomy.

As generative AI and agent-based design converge, we’re entering a new era in voice technology. One where agents don’t just respond to you—they act for you.

That’s what makes a voice agent truly agentic.

– # # # –

Please join us for our next informative Webinar: Designing Voice Agents That Work – Real-World Use Cases, Tools, and Strategies to be held on August 27. Click here to register.

Apple’s Wearable AI Detects Pregnancy With Over 90% Accuracy

Rohit Mahajan

Wearables and the Internet of Things (IoT) have been integral to the way that Generative AI is transforming healthcare. Apple’s latest contribution to this revolution comes in the form of a model that can detect pregnancy with 92% accuracy by analyzing behavioral data from iPhones and Apple Watches. 

The findings are detailed in a study titled “Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions.” This study introduces the Wearable Behavior Model (WBM), which relies on higher-level health metrics—like sleep quality, mobility, and heart rate variability—rather than just raw sensor data, which can often be noisy and hard to interpret.

The WBM was trained using over 2.5 billion hours of wearable data and outperformed older models that relied on low-level sensor inputs. Researchers built a pregnancy dataset from 430 pregnancies, using Apple Health, HealthKit, and photoplethysmography (PPG) data. They labelled the nine months leading to childbirth and one month after as “positive” weeks—representing active pregnancy or postpartum recovery—while other weeks were marked “negative.”

The research comes from the Apple Heart and Movement Study, which collected over 15 billion data points from more than 162,000 participants. The data came through the everyday use of the Apple Watch and iPhone. For the pregnancy research, the model analyzed information from 430 reported pregnancies and more than 25,000 non-pregnant participants. The AI looked at more than heart rate and temperature. It also examined movement patterns, sleep habits, and exercise routines.

One of the key advancements of WBM is its use of expert-designed algorithms to convert raw sensor readings into meaningful behavioral metrics. These metrics are not only clearer but also better aligned with real health states. 

The model allows for more precise tracking and prediction of health changes over time, marking a significant step forward in wearable-based health monitoring and AI-powered diagnostics.

Pregnancy was just one of several health conditions the WBM model learned to identify. The researchers also tested the model on other health issues with strong results. It predicted diabetes with 82% accuracy, infection with 76% accuracy, and injury with 69% accuracy. These findings suggest that AI-powered wearables may soon do much more than count steps or track sleep. They could help detect serious health changes before symptoms even appear.

Even with these promising results, trust remains a major barrier in women’s health technology. Privacy concerns are growing, especially when it comes to sensitive data like menstrual cycles or pregnancy. In 2023, the Federal Trade Commission fined the popular app Premom for sharing user data without consent.  

A recent FTC study confirmed growing skepticism. Women are less likely to trust apps that collect reproductive health information, especially when the companies do not make their data practices clear. Even if the Apple Watch can detect early signs of pregnancy, would users want it to? This is a key question, one that echoes privacy and ethical concerns across the board as generative AI becomes more ubiquitous in healthcare. Because of such concerns, Apple has not announced any plans to turn the research findings into a consumer feature. But this research shows where Apple’s focus may be headed. With support from public health officials calling for widespread use of wearables, Apple could play a key role in shaping the future of personalized healthcare.

Share your feedback on this article by leaving a comment below or contacting us.

If you would like more information about AI and Healthcare, please listen to our Big Unlock Podcasts, read our Case Studies, or sign up for our Newsletter. 

Please join us for our next informative Webinar: Designing Voice Agents That Work – Real-World Use Cases, Tools, and Strategies to be held on August 27. Click here to register.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my first book, Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint. My soon-to-be-released second book – Generative AI: Unlocking the Next Chapter in Healthcare will focus on GAI and the impact of Agent AI on healthcare.

Reimagining Healthcare Operations with Voice Agents and Agentic AI: Key Takeaways from the June 2025 Webinar

Voice agents and Agentic AI are no longer futuristic technologies—they’re transforming healthcare operations today. In a recent webinar hosted by BigRio and Damo Consulting, industry leaders Ritu M. Uberoy, Rohit Mahajan, and moderator Helen Kotchoubey shed light on how these technologies are redefining patient engagement, clinical workflows, and enterprise operations across healthcare and pharma.

Brief Overview

The session kicked off with an introduction to Agentic AI and Voice AI agents, technologies that use large language models (LLMs) to create intelligent, context-aware AI agents capable of handling complex tasks with minimal human input. The speakers discussed real-world use cases, ranging from appointment scheduling and prescription refills to patient triage and clinical trial recruitment.

The highlight? A live demo of voice agents conducting natural, responsive conversations—including one that scheduled a patient appointment and another that refilled a prescription using patient records.

What participants said:

Great Webinar! I loved the attention to detail, clear explanations, and slides that were explained well. I just received my first AI cert, and this helped me understand more of what I learned.

I really liked how the webinar shared real-time examples of voice AGI in action-like assisting doctors with patient history in hospitals, or helping pharma reps quickly access drug data. (Excellent)

Great presentation, easy to understand, especially for those like me with limited knowledge on these technologies.

I am amazed where the technology is heading. There is so much progress that I would have not known.

I liked the real world use cases and summary of what voice ai agents can do “today” vs where we were 16 months ago.

I appreciated how Ritu started with the fundamentals-breaking down the components and foundations behind how Agentic works. The BIGRIO approach was especially insightful.

Top Q&A Takeaways

Here are the most insightful audience questions and responses from the webinar:

  1. What Makes Agentic Voice AI Different from Traditional IVRs or Siri?Agentic AI voice agents go far beyond rule-based systems or assistants like Siri. Unlike IVRs, they:
  • Understand natural language, intent, and emotion
  • Remember context from earlier in the conversation
  • Perform autonomous tasks, including API calls to EMRs or CRMsThis enables them to manage multi-step interactions—like scheduling an appointment or requesting a prescription refill—without human handholding.
  1. How Do These Voice Agents Handle Accents, Noise, or Unclear SpeechWhile modern speech recognition models have improved dramatically—thanks to training on diverse datasets—agents still face limitations. When speech is indecipherable or noisy, the system automatically escalates to a human agent. This “fallback” mechanism ensures continuity and patient safety.
  1. Is Patient Data Secure?Yes. Security and compliance are built into the architecture:
  • Platforms must be HIPAA and SOC 2 compliant
  • Data remains within geographical boundaries (e.g., US-based data must stay within the US)
  • Business Associate Agreements (BAAs) are standard for healthcare clients
    Governance and cloud security best practices are non-negotiable.
  1. How Long Does It Take to Deploy a Voice Agent PilotSurprisingly fast. BigRio and Damo shared a framework where pilots can be:
  • Designed and launched in 8–12 weeks
  • Supported by pre-built tools, workflows, and templates
  • Using a discovery-phase approach, they identify high-impact use cases and rapidly prototype working agents.
  1. Do Organizations Need to Build In-House or Use SaaSIt depends on digital maturity. Many healthcare providers prefer working with a managed service provider to handle data integration, system interoperability, and ongoing agent training. BigRio provides full-stack support—from defining the use case to deploying enterprise-grade solutions.
  1. What Are the Limitations of Agentic AI TodayDespite rapid advancement, current limitations include:
  • Handling ambiguous or highly emotional situations
  • Real-time memory constraints
  • Challenges with edge cases that don’t follow standard scripts

But thanks to real-time learning loops and adaptive memory, these limitations are narrowing down fast.

Why Voice Agents Matter Now

The demand for 24/7 engagement, operational efficiency, and personalized care is accelerating adoption. Voice agents:

  • Scale easily across languages and geographies
  • Offer empathetic, always-available support
  • Dramatically reduce call center volume
  • Drive measurable ROI—66% conversion rates in real-world pilots

As Helen Kotchoubey aptly put it: “This is not just a UI shift. It’s a shift in capability.”

Ready to Explore Voice Agents?

If you’re evaluating use cases for AI in your organization, consider starting with a focused pilot—such as appointment scheduling, prescription refills, or clinical trial outreach.

Contact BigRio or Damo to learn more about how they can help you design and deploy voice agent solutions at scale.

Stay tuned for future webinars.

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AI Agents Provide Powerful Assistance for Clinical Decision Making in Cancer Treatment

Rohit Mahajan

We have written a lot on these pages about how Agentic AI is transforming healthcare from patient compliance and diagnostic standpoint. New research is now showing the role that AI Agents can play in helping doctor with clinical decisions, in this case specifically regarding treatment plans for cancer patients.

Researchers at the Else Kröner Fresenius Center (EKFZ) for Digital Health at TUD Dresden University of Technology, in collaboration with partners from Germany, the UK, and the USA, have developed and validated an autonomous AI Agent capable of supporting clinical decision-making in oncology. The findings were published in the journal Nature Cancer. In the future, AI agents could assist healthcare professionals in navigating complex medical data and supporting informed, personalized treatment decisions for cancer patients.

Clinical decision-making in oncology is challenging and requires the analysis of various data types – from medical imaging and genetic information to patient records and treatment guidelines. To effectively support medical practice, AI models must be capable of processing multimodal data and have reasoning and problem-solving capabilities that resemble those of humans.

o build an autonomous AI agent for precision medicine, the researchers enhanced the large language model GPT-4 with several digital tools – including radiology report generation from MRI and CT scans, medical image analysis, prediction of genetic alterations directly from histopathology slides, and search functions across platforms such as PubMed, Google and OncoKB. To ensure that decisions were grounded in current medical knowledge, the model was given access to around 6,800 documents compiled from official oncology guidelines and clinical resources.

The autonomous AI agents successfully tested on realistic, simulated patient cases. According to the researchers, the AI agent reached correct clinical conclusions in 91% of cases and accurately cited relevant oncology guidelines in over 75% of its responses. Importantly, the use of specialized tools and medical information retrieval significantly improved the model’s performance. As a result, so-called “hallucinations” – seemingly plausible but incorrect statements – were significantly reduced. This improvement is particularly important in the sensitive area of healthcare.

 

“AI tools are designed to support medical professionals, freeing up valuable time for patient care,” says Dyke Ferber, first author of the publication. “They could help in daily decision-making processes and support doctors to stay updated on the latest treatment recommendations, contributing to the identification of optimal personalized care for cancer patients.”

You can access the full study by clicking on this link.

How BigRio Promotes Innovation in Agent AI 

At BigRio, we share Dr Ferber’s belief in the transformative impact Agentic AI is having on healthcare. Yet, we also recognize the challenges ahead and the need for education and training in the responsible implementation and use of these agents and copilots/ tools. To this end, we have launched a Gen AI Center of Excellence and hold ongoing Gen AI Workshops. 

Our Gen AI Center of Excellence focuses on working with industry leaders and innovators to create custom tools that safely augment human intellect, allowing people to know more, do more, and create more than ever before.

BigRio’s Gen AI Workshops are designed to help healthcare professionals meet the challenges of Gen AI and Agent AI implementation. We provide customized onsite, in-person healthcare-focused learning and ideation workshops to accelerate your Gen AI journey regardless of your current state of readiness. 

We believe that Generative AI will be most powerful when it is used to enhance, not substitute, human knowledge and creativity. BigRio has long been a facilitator and incubator in leveraging AI to improve healthcare delivery and all industries. We have recently been focusing our efforts on supporting startups and developing our own solutions that use GAI and Agent AI to boost productivity and transform core functions and customer service while innovating at top speed.

Agentic AI Will Shift Medicine From “Sick Care” to “Well Care!”

Rohit Mahajan

For decades, even modern healthcare has been for the most part “transactional,” we get sick we see a doctor. AI, and in particular Agentic AI is transforming healthcare into a system that is less transactional and more continuous. In other words, Agentic AI may bring about a fundamental paradigm shift in medicine from “sick care” to “well care.”

Dr Adil Khan, CEO, Tulu Health explains.

“Our [AI] agents don’t just assist—they build trust, guide patients, and enable smoother transitions into actual care. Hospitals now see more patients follow through with appointments, procedures, and treatment plans, often above industry conversion benchmarks. Most importantly, they’re witnessing clear ROI within weeks. It’s not just a tech upgrade; it’s a tangible transformation in how care is initiated and sustained.”

Dr. Khan made those comments in a recent interview with Express Healthcare.

In the same sit down he went on to say, “Agentic AI will shift healthcare from being transactional to continuous. Today, you seek care only when you’re sick. In the near future, intelligent agents will proactively remind you to take medication, interpret diagnostics, flag warning signs, and follow up after discharge—all autonomously.

He said he sees this being particularly true in a country like India, where doctor-patient ratios are stretched. “[Here in India], this will be a game-changer. AI agents won’t replace doctors—they’ll extend their capacity. We believe India is uniquely positioned to lead this shift at a population scale, showcasing how AI can enable inclusive, scalable, and always-on care.”

Tulu Health is a healthtech and fintech startup founded by experts from AIIMS, IIT, and Stanford University. In April of 2025 it launched an AI Agent Platform that provides modular AI agents tailored for both healthcare providers and patients, delivered through widely used platforms like WhatsApp and hospital websites.

How BigRio Promotes Innovation in Agent AI

At BigRio, we share Dr. Khan’s belief in the transformative impact Agentic AI is having on healthcare. Yet, we also recognize the challenges ahead and the need for education and training in the responsible implementation and use of these agents and copilots/ tools. To this end, we have launched a Gen AI Center of Excellence and hold ongoing Gen AI Workshops.

Our Gen AI Center of Excellence focuses on working with industry leaders and innovators to create custom tools that safely augment human intellect, allowing people to know more, do more, and create more than ever before.

BigRio’s Gen AI Workshops are designed to help healthcare professionals meet the challenges of Gen AI and Agent AI implementation. We provide customized onsite, in-person healthcare-focused learning and ideation workshops to accelerate your Gen AI journey regardless of your current state of readiness.

We believe that Generative AI will be most powerful when it is used to enhance, not substitute, human knowledge and creativity. BigRio has long been a facilitator and incubator in leveraging AI to improve healthcare delivery and all industries. We have recently been focusing our efforts on supporting startups and developing our own solutions that use GAI and Agent AI to boost productivity and transform core functions and customer service while innovating at top speed.

Join us for our next informative Webinar: Agentic AI and Voice Agents in Healthcare and Pharma to be held on June 25. Click here to register.

ElevenLabs Introduces Advanced Features for Building Voice Agents

Rohit Mahajan

ElevenLabs a pioneer in “text to speech” technology and Generative AI has announced a significant advancement in conversational AI technology with the introduction of a new multimodal system for AI “voice agents.” According to a company press releases, this cutting-edge development enables AI voice agents to process both voice and text inputs concurrently, enhancing the fluidity and effectiveness of user interactions.

“By enabling agents to process both text and voice, we empower users to choose the input method best suited to the information they need to convey. This hybrid approach allows for smoother, more robust conversations. Users can speak naturally and then, when precision is paramount or typing is more convenient, seamlessly switch to text input within the same interaction,” the release said.

The press release went on to explain the advantages of “multimodal interaction,” which include: 

  • Increased Interaction Accuracy: Users can enter complex information via text, reducing transcription errors.
  • Enhanced User Experience: The flexibility of input methods makes interactions feel more natural and less restrictive.
  • Improved Task Completion Rates: Minimizes errors and user frustration, leading to more successful outcomes.
  • Natural Conversational Flow: Allows for smooth transitions between input types, mirroring human interaction patterns.

Finally, concluding, “We believe that text+voice multimodality will significantly enhance the capabilities and user experience of Conversational AI. We look forward to seeing how our users leverage this powerful new feature.”

How BigRio Promotes Innovation in Agentic AI Development

At BigRio, we share the vision of innovators like ElevenLabs in the transformative power of voice agents. In fact, we now offer Voice Agent Development and Implementation as one of our core services. Like these other trend-setters we recognize how voice technology is reshaping healthcare and other industries by enabling faster, more natural interactions between customers and the organizations they patronize. At BigRio, we develop advanced, AI-powered voice agents that help any type of company boost efficiency, improve accessibility, and deliver more personalized customer experiences—while staying fully compliant with privacy concerns and regulatory standards.

Whether you’re looking to automate scheduling, improve call center responsiveness, or support remote monitoring, BigRio can help you implement a voice strategy that drives better outcomes.

We also continue to offer online Gen AI Workshops and Webinars that are now focused on Agentic AI and the impact of Voice Agents. Please join us for our next informative Webinar: Agentic AI and Voice Agents in Healthcare and Pharma to be held on June 25. Click here to register.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my first book, Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint. My soon-to-be-released second book will focus on GAI and the impact of Agentic AI on healthcare.

Rohit Mahajan is a Managing Partner with BigRio. He has particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.